Transcript

LED Induced Fluorescence using microscale visualization

methods

Jorge André Garcia Arromba

Thesis to obtain the Master of Science Degree in

Mechanical Engineering

Supervisor: Prof. António Luís Nobre Moreira

Examination Committee

Chairperson: Prof. Viriato Sérgio de Almeida Semião

Supervisor: Prof. António Luís Nobre Moreira

Member of the Committee: Prof. José Maria Campos da Silva André

October 2014

i

ii

Acknowledgements

First of all, I would like to thank my supervisor Professor Luís Moreira for the opportunity of working

with him and his availability, patience and guidance throughout the elaboration of this work.

Financial support through project PTDC/EME-MFE/109933/2009 from Fundação para a Ciência e a

Tecnologia, FCT, is gratefully acknowledged. Laboratory facilities were built in the framework of project

RECI/EMS-SIS/0147/2012 and therefore also acknowledged.

I would like to express my gratitude towards Vânia Silvério, for all the help, discussions, guidance,

lectures and laughs. Your support was essential for the outcome of this work, and thank you for

believing in me and for the encouragement until the very end.

To all my closest friends, and to my fellow colleagues in the Laboratory, for the support, fellowship and

understanding that helped me to go through this 6 months and take this to a conclusion, thank you.

At last, but not at least, I thank all the unconditional love and support from my family. To my brother,

my parents, I am infinitely grateful to you. Love you all.

iii

iv

Resumo

Este trabalho consiste na optimização e aplicação de duas técnicas de fluorescência, utilizando um ou

dois corantes, para medição não intrusiva da temperatura em escoamento à microescala. Foram

efetuadas medições num sistema microfluídico, iluminado em volume com uma fonte de luz LED, a

partir das quais se definiram e otimizaram os parâmetros que determinam a precisão do método.

Posteriormente a técnica foi aplicada a dois casos de teste, ao escoamento num microcanal de

paredes aquecidas e a uma mistura térmica de dois escoamentos numa zona de mistura em T.

Foram utilizados dois corantes, Rodamina B e Rodamina 110, uma vez que a intensidade de

fluorescência emitida por cada um deles varia de forma diferente com a temperatura. Enquanto o sinal

da Rodamina B apresenta uma dependência acentuada da temperatura, o da Rodamina 110 é

aproximadamente independente (sensibilidade < 0.011 %. ℃−1). A técnica de um corante (RhB)

apresentou-se mais vantajosa, com sensibilidade de 1.68 %. ℃−1, sendo posteriormente aplicada ao

caso prático num microcanal, onde foi encontrada concordância na variação de temperatura medida

através de um termopar na sua parede. Os erros associados à medição do sinal de intensidade de

fluorescência são inferiores a 3.8% enquanto os associados às medidas de temperatura são inferiores a

0.71%.

Medidas das distribuições bidimensionais de temperatura no escoamento de soluções aquosas de

corantes com corantes em concentrações residuais num canal de dimensões micrométricas

comprovaram a capacidade da técnica ser aplicada com a fonte de iluminação LED e com elevadas

resoluções espacial (1.54 𝜇𝑚) e temporal (~5 𝑚𝑠).

Palavras-chave: Técnica de Fluorescência, Iluminação LED, Medição temperatura escoamento,

Microfluidos, Perfis de temperatura 2D.

v

Abstract

A non-intrusive LED Induced Fluorescence Thermometry measurement technique is developed and

applied using microscale visualization techniques. Whole field temperature measurements in a

volume-illuminated microfluidic setup were performed with a good spatial and temporal resolutions,

being applied to practical that serve as training benchmark tests, often imposed to CPU chips, and to

the thermal mixing of two fluid streams in a T-shaped micro-mixer.

Two different techniques are addressed: Normalized Induced Fluorescence Thermometry (N-LED-IFT)

and Normalized Ratiometric Induced Fluorescence Thermometry (NR-LED-IFT), using one and two

dyes, respectively. Parameters influencing the results and the feasibility of these techniques at the

microscale using a Leica illumination system LED SFL100 530 𝑛𝑚 were also addressed.

Rhodamine B and Rhodamine 110 are used as temperature sensitive and insensitive dyes, respectively.

The single-dye technique (N-LED-IFT) proved most advantageous, obtaining a sensitivity of

1.68 %. ℃−1. This technique was then used for training benchmark testing, where good agreement with

temperature variations on wall temperature measured using a thermocouple was found. The N-LED-IFT

results present errors lower than 3.8 % in fluorescence intensity and lower than 0.71 % in temperature

measurements. Radial and longitudinal temperature profiles in the microchannel were also observed.

The capability of this technique to be applied to low and high velocity microscale flows using a LED

illumination source was proved and 2D fluid temperature profiles where obtained with high spatial

(1.54 𝑚) and temporal (~ 5 𝑚𝑠) resolutions.

Keywords: LED Induced Fluorescence Thermometry, LED illumination, Flow temperature measurement,

Microfluidics, 2D fluid temperature profiles.

vi

Nomenclature

𝐴 – Cross section area [𝑚2]

𝐴1→2 – External area between sections 1 and 2

[𝑚2]

𝑏 – Number of bits

C – Dye concentration in the solution [𝑘𝑔/𝑚3]

𝐶𝑝 – Specific heat of the fluid [𝐽. 𝑘𝑔−1. 𝐾−1]

𝐷ℎ – Hydraulic diameter [𝑚]

𝐹 – Light fraction [−]

ℎ – Heat transfer coefficient [𝑊. 𝐾−1. 𝑚−2]

𝐼 – Fluorescent intensity emitted per unit of

volume [𝐴. 𝑈. ]

𝐼0 – Light incident flux [𝑊/𝑚2]

𝐿 – Percentage of energy loss from the

resistance directly to environment [−]

𝐿𝐻𝑒 – Hydrodynamic entrance length [𝑚]

𝐿𝑇𝑒 – Thermal entrance length [𝑚]

�̇� – Mass flow [𝑘𝑔. 𝑠−1]

𝑝 – Perimeter [𝑚]

𝑃 – Output electrical power from power

generator [𝑊]

Particles A – Temperature dependent dye

particles

Particles B – Temperature independent dye

particles

𝑃𝑟 – Prandtl number [−]

𝑄 – Volumetric flow [𝑚𝑙. ℎ−1]

𝑞′′ – Heat flux [𝑊. 𝑚−2]

𝑞1→2′′ – Net heat flux dissipated from section 1

to section 2 [𝑊. 𝑚−2]

𝑅𝑒 – Reynolds number [−]

SNR – Signal-to-Noise Ratio [dB]

𝑇 – Temperature [𝐾 𝑜𝑟 ℃]

𝑈 – Flow velocity, eq. 22 and 23 [𝑚. 𝑠−1]

𝑈 – Voltage [𝑉]

𝑉 – Signal output from camera [𝑉]

𝑉 – Solution volume, Equation 24 [𝑚3]

Greek symbols

βC – Collection efficiency

∆𝑇 – Temperature difference [𝐾 𝑜𝑟 ℃]

휀 – Absorption coefficient [𝑚2/𝑘𝑔]

𝛬 – Microchannel characteristic dimension [𝑚]

𝜆0 – Wavelength of light in vacuum [𝑚]

𝜇 – Mean intensity of the image set [𝐴. 𝑈. ]

𝜐 – Fluid kinematic velocity [𝑚2. 𝑠−1]

𝜌 – Fluid density [𝑘𝑔. 𝑚−3]

𝜎 – Standard deviation of the image set,

Equation 16 [𝐴. 𝑈. ]

𝛷 – Quantum yield [−]

vii

Acronyms

CCD – Charge Coupled Device

CPU – Central Processing Unit

DOF – Depth Of Field

FRET – Fluorescent Resonance Energy Transfer

FS – Fluorescence Signal

IC – Integrated Circuits

LaVision HSS – LaVision HighSpeedStar high

speed imaging camera

LED – Light emitting diode

LED-IFT – LED Induced Fluorescence

Thermometry

LIF – Laser Induced Fluorescence

NA – Numerical Aperture

N-LIFT – Normalized Laser Induced

Fluorescence Thermometry

N-LED-IFT – Normalized LED Induced

Fluorescence Thermometry

NR-LIFT – Normalized Ratiometric Laser

Induced Fluorescence Thermometry

NR-LED-IFT – Normalized Ratiometric LED

Induced Fluorescence Thermometry

Phantom – Vision Research Phantom v4.0 high

speed imaging camera

RhB – Rhodamine B, temperature dependent

dye

Rh110 – Rhodamine 110, temperature

independent dye

TLCs – Thermochromic Liquid Crystals

VLSI – Very Large Scale Integration systems

Subscripts

𝐴 – Related to Particles A

𝐵 – Related to Particles B

𝑖 – Inner dimension

𝑜 – Outer dimension

Superscripts

𝛼 – Image that captures particles A

fluorescence emission

𝛽 – Image that captures particles B

fluorescence emission

viii

Table of Contents

Acknowledgements ....................................................................................................................................................................... ii

Resumo ............................................................................................................................................................................................. iv

Abstract .............................................................................................................................................................................................. v

Nomenclature ................................................................................................................................................................................. vi

List of Tables ................................................................................................................................................................................... ix

List of Figures .................................................................................................................................................................................. x

1. Introduction ................................................................................................................................................................................. 1

2. Objectives and Dissertation Outline .................................................................................................................................. 4

3. Principles of the LIF for thermometry measurements ................................................................................................ 5

4. Experimental Campaign....................................................................................................................................................... 13

4.1 Flow Configurations ....................................................................................................................................................... 13

Microchannel flow ............................................................................................................................................................. 13

T-shaped micro-mixer ..................................................................................................................................................... 18

4.2 Equipment .......................................................................................................................................................................... 20

Microscope ........................................................................................................................................................................... 20

High speed cameras ......................................................................................................................................................... 21

Illumination system ........................................................................................................................................................... 25

Pumping systems ............................................................................................................................................................... 26

4.3 Experimental method .................................................................................................................................................... 27

Fluorescent dyes ................................................................................................................................................................ 27

Calibration of the fluorescence intensity.................................................................................................................. 29

Uncertainty estimates ...................................................................................................................................................... 31

5. Results and Discussion ......................................................................................................................................................... 32

5.1. Test Parameters .............................................................................................................................................................. 32

Dye Concentration ............................................................................................................................................................ 34

Influence of Background Noise .................................................................................................................................... 35

Auto-absorption and Beer-Lambert Law ................................................................................................................. 37

Calibration Curves – NR-LED-IFT vs N-LED-IFT ..................................................................................................... 38

N-LED-IFT .............................................................................................................................................................................. 39

5.2. Application of the LED-IFT technique .................................................................................................................... 42

Training Benchmark .......................................................................................................................................................... 42

T-shaped micro-mixer ..................................................................................................................................................... 45

6. Concluding Remarks and Future Work ......................................................................................................................... 47

References...................................................................................................................................................................................... 49

Appendix ........................................................................................................................................................................................ 52

Processing algorithm ............................................................................................................................................................ 52

ix

List of Tables

Table 1 – Glass microchannel installation characteristics. .......................................................................................... 15

Table 2 – Main characteristics of the high speed cameras used. ............................................................................. 24

Table 3 – Correspondence between the pixel and image size and depth of field in both high speed

cameras for different optical arrangements. .................................................................................................................... 25

Table 4 – Characteristics of aqueous solutions of Rhodamine B and Rhodamine 110 in de-ionized water

at 20 ℃ ............................................................................................................................................................................................ 27

Table 5 – Aqueous solutions used in experiments. ....................................................................................................... 28

x

List of Figures

Figure 1 – Example of Perrin-Jablonski Diagram with absorption and radiative dissipation methods

examples. S0, S1 and S2 – singlet electronic states; T1, T2 – triplet electronic states; IC – internal

conversion; ISC – intersystem crossing (adapted from [35]). ....................................................................................... 6

Figure 2 – Schematics of the microchannel experimental setup. ............................................................................ 14

Figure 3 – Detail of thermocouples location in the microchannel experimental setup. ................................. 15

Figure 4 – Example of temperature response after an imposed heat flux increase. ........................................ 17

Figure 5 – Schematics of the T-shaped micro-mixer experimental setup. ........................................................... 18

Figure 6 – T-shaped micro-mixer scheme. ....................................................................................................................... 19

Figure 7 – Secondary flow loop used to control the temperature of the hot flow. Reservoir,

potentiometer and two electric resistances. .................................................................................................................... 19

Figure 8 – Leica DM IL inverted microscope. Image extracted from [43]. ............................................................ 20

Figure 9 – LaVision HighSpeedStar high speed camera with the x0.55 CCD adapter. ..................................... 21

Figure 10 – Phantom v4.2 high speed camera and custom made support attached to the inverted

microscope. ................................................................................................................................................................................... 22

Figure 11 – Rhodamine B filter characteristics. Transmission percentage as function of wavelength.

(Chroma Technology Corp., Scan range from 480.0 𝑛𝑚 to 680 𝑛𝑚, ET-TRITC Filter Set for 530 LED_Leica

DMR_Un-Mounted). .................................................................................................................................................................... 23

Figure 12 – Rhodamine 110 filter characteristics: Transmission percentage as function of wavelength.

(Melles Griot 03FIL004 Laser Filter 514.5 𝑛𝑚 25 DIA, 03FIL00405100762). .......................................................... 23

Figure 13 – HighSpeedStar calibration target from LaVision represented in a) and in b) the reticle from

Peak Optics used to establish the correspondence of pixel to image size for both cameras...................... 24

Figure 14 – Leica LED SFL100 illumination system. ....................................................................................................... 26

Figure 15 – Pumping systems: a) NE-300 syringe pump and b) Harvard 22 syringe pump. ........................ 26

Figure 16 – Visual aspect of three solutions used. From left to right: RhB with a concentration of 20

mg.L-1; RhB with a concentration of 20 mg.L-1 and Rh110 with a concentration of 15 mg.L-1; Rh110 with

a concentration of 50 mg.L-1. ...................................................................................................................................... 28

Figure 17 – Schematics of the thermally insulated pool. ............................................................................................ 29

Figure 18 – Schematics of the calibration setup............................................................................................................. 30

Figure 19 – Fluorescent intensity signal of a) RhB collected with HighSpeedStar high speed

visualization camera and b) Rh110 collected with Phantom V4.2 high speed visualization camera in

microchannel experiments ...................................................................................................................................................... 32

Figure 20 – MATLAB® equivalent images to those of Figure 18: a) RhB and b) Rh110. ................................. 33

Figure 21 – Fluorescence intensity response obtained for the same control point for seven aqueous

RhB solutions with different concentrations. ................................................................................................................... 34

Figure 22 – Influence of solution concentration in fluorescence signal sensitivity. ......................................... 35

Figure 23 – Fluorescent signal from both dyes, for the same control point, with and without room

illumination. ................................................................................................................................................................................... 36

Figure 24 – Rhodamine B fluorescent signal with and without background image removal. ..................... 36

Figure 25 – Fluorescent signal on HSS image for both particles, separately and with both in solution. . 37

Figure 26 – RhB and Rh110 fluorescence signal (FS) response to temperature. ............................................... 38

xi

Figure 27 – LED-IFT techniques comparison: NR-LED-IFT in solid squares and N-LED-IFT in hollow

triangles. ......................................................................................................................................................................................... 39

Figure 28 – RhB fluorescence signal collected in the Pool Calibration system. ................................................. 40

Figure 29 – Normalized intensity for 6 different control points............................................................................... 41

Figure 30 – Calibration curve for the N-LED-IFT technique with a 4th order best fit polynomial. .............. 41

Figure 31 – Microchannel setup. ........................................................................................................................................... 43

Figure 32 – Flow temperature represented in purple, wall temperature in blue and power output from

power source in black. .............................................................................................................................................................. 44

Figure 33 – Detail on the responses from the N-LED-IFT technique and from the thermocouple

measurements for a power increase. .................................................................................................................................. 45

Figure 34 – Mixing plane of two laminar stream flows with different velocities: a) 𝑄𝑐𝑜𝑙𝑑 = 200 𝑚𝑙. ℎ − 1,

𝑄ℎ𝑜𝑡 = 200 𝑚𝑙. ℎ − 1; b) 𝑄𝑐𝑜𝑙𝑑 = 300 𝑚𝑙. ℎ − 1, 𝑄ℎ𝑜𝑡 = 300 𝑚𝑙. ℎ − 1; c) 𝑄𝑐𝑜𝑙𝑑 = 500 𝑚𝑙. ℎ − 1,

𝑄ℎ𝑜𝑡 = 50 𝑚𝑙. ℎ − 1. Cold fluid coming from left to right and hot fluid inlet is on the top. ........................ 46

Figure 35 – Mixing plane of two stream flows for the same volumetric flow, 𝑄𝑐𝑜𝑙𝑑 = 1000 𝑚𝑙. ℎ −

1 and 𝑄ℎ𝑜𝑡 = 1000 𝑚𝑙. ℎ − 1. Consecutive images captured with a 200 𝐻𝑧 acquisition rate. Cold fluid

coming from the left to the right and hot fluid inlet on the top. ............................................................................ 46

1

1. Introduction

Faster and smaller electronic parts are consistently being developed at the same time the number of

transistors per chip is increasing and consequently the heat flux dissipated, which can exceed

100 W.cm-2. In order to remove such high heat rates, air cooling or single-phase liquid cooling in plain

channels in contact with the chip are becoming insufficient [1]. The need to explore the use of

microchannel heat sinks to achieve high cooling rates emerged, and Tuckerman & Pease in 1981 [2]

developed and tested the first VLSI system. After that, a large number of researchers have addressed

the microscale heat transfer issues. Comprehensive literature reviews on the fluid flow, transport and

heat transfer in microchannel heat sinks for both, single and two-phase regimes can be found in [3, 4].

However, measurements of the temperature distribution in microscale flow systems still represents one

of the most challenging aspects of experimentation due to very small temperature gradients

associated with the high heat transfer rates and short heat dissipation timescales [5].

In line with this, temperature measurements in microscale field of research gained some highlight and

preponderance due to the lack of knowledge and the need to control small scale transport and heat

transfer phenomena in different areas of interest, such as biochemistry [6] and electronics [7, 8].

Widely-used methods for measurement of fluid temperature at macroscale cannot be directly applied

to the microscale. Well known contacting measurement devices like high-precision thermocouple

probes are not the ideal solution [9]. Besides being intrusive, these probes have poor spatial and

temporal resolution since most of them have a characteristic size comparable to the cross section of

the microchannels. Embedded thermocouples along the microchannel base [10] or inside its walls [11,

12] have also been used, however the spatial resolution remains low.

Microfluidic devices fabricated with integrated resistance temperature detectors (RTDs) are a viable

alternative, which allows surface temperature monitoring and, although presenting a better spatial

resolution, do not provide information on the local fluid temperature [13]. Infrared thermography can

also be used but is limited to its sole application in surfaces, requiring an accurate value of the

emissivity of the medium [14, 15] and, thus, increasing its complexity. On the other hand,

thermochromic liquid crystals (TLCs) [16] can be used in solution to measure fluid flow temperature

with a maximum spatial resolution of approximately 1 m, while encapsulated TLCs can vary from 10

to 150 𝑚 [9]. However, limitations associated with the range of temperature still exist, from ~1 ℃ for

narrow-band TLCs and from 5 to 20 ℃ for wide-band TLCs [17]. For example, Richards et al. in 1998

[18] measured temperatures between 30 and 35 ℃ with a temporal resolution of 30 𝜇𝑠 and a spatial

2

resolution of 100 𝑚, and in the work of Lin and Kandlikar [19] stainless steel microtubes were used

addressing temperature variations from 35 to 45 ℃ and from 42 to 47 ℃.

But in experimental microfluidics, optical measurement techniques acquired increased relevance in the

last decade (due to progresses in IC technology), allowing higher resolutions and acquisition frame

rates. Software for image processing has also been improved. In this context, Laser Induced

Fluorescence Thermometry (LIFT) emerged, among other options, as a non-intrusive technique able to

make whole-field temperature measurements inside a volume of fluid by capturing the fluorescence

light emitted by particles in solution and making use of high spatial and temporal resolution

illumination and recording systems.

The LIFT technique has been first used by Omenetto et al. in 1972 [20] to measure the temperature of

reacting species or dyes in flames, followed by and Chan & Daily in 1980 [21] in the same research

area. The technique then evolved capitalizing on fluorescent dye properties: when excited at certain

wavelengths by an illumination source, the dyes emit a fluorescent light in a different wavelength

band, higher than the excitation one.

In the early stages of the Laser Induced Fluorescence, mostly in macroscale flows (late 90’s), a

temperature dependent dye was dissolved in the flowing fluid in order to apply the technique [22–24].

Detailed studies on different fluorescent dyes and their compatibility can be found in [25, 26].

However, the quality of such measurements is still hindered by the difficulties in guaranteeing a

homogeneous illumination intensity distribution and recording imperfections. A new two-color LIFT

technique was then proposed by Sakakibara & Adrian in 1999 [27], who added a temperature-

insensitive dye to be used as a reference to compensate for the fluctuations at illumination.

Experiments performed in 2004 by Lavieille et al. [28] on heated jets showed that the droplet size

influences the results from the two-dye technique. The authors suggested a new approach that

consists in considering the ratio of fluorescence light emitted by the temperature-dependent dye at

different spectral frequencies, also called the single dye, two color LIFT method. Bruchhausen et al. in

2004 [29] were the first to apply the single dye, two color LIFT using a pulsed Nd:YAG laser, achieving

shorter timescales than those from thermal transport at the microscale.

The first temperature measurements via volume illumination in microscale were reported by Ross et al.

in 2001 [30] with a claimed accuracy of ±1.5 ℃. A standard fluorescence microscope and a CCD

camera were used to measure the temperature distribution in the flow inside microchannels, with

spatial and temporal resolutions of 1 𝜇𝑚 and 33 ms, respectively. Yoon & Kim in 2006 [31] introduced

an ultra-thin laser sheet (10 µm thickness) as an illumination source in microchannel experiments but

3

their results accuracy could not be retrieved since no temperature measurements were performed;

only measurements of concentration distribution of the suspended particles were made. Natrajan and

Christensen [5] highlighted, in 2008, the importance of the illumination intensity and the need to

illuminate over timescales much shorter than those of the microscale thermal transport. In this context,

the authors used a pulsed Nd:YAG laser and applied the two-color LIFT technique achieving

uncertainties of ±0.55 ℃ and ±0.45 ℃ for dyes diluted in ethanol and water, respectively. Chamarty et

al. in 2010 [9] designed a setup to perform experiments using two fluorescent dyes. A single camera

captured two sequential images (each image corresponding to the signal emitted by each dye in

solution) by using a filter wheel with two filters, one for each dye, acknowledging the fact that this

setup could only be appropriate to study steady flows as there is necessarily a time delay between the

first and second images. Uncertainties of ±1.25 ℃ and of ±2.68 ℃ were found for traditional single-

dye and two-dye LIFT, respectively. Sakakibara & Adrian in 2004 [32] claimed an uncertainty of ±0.2 ℃

using the two-dye LIFT. Better results were attributed to the use of a 14-bit digital CCD camera and by

using a new convolution technique that allowed them to remove more efficiently some blurring

present on fluorescent images.

Moreover, the standard light source of LIF used at macroscale is a continuous, usually Argon laser,

which does not provide sufficient illumination intensity over the much shorter thermal transport at the

microscale and, therefore, does not allow obtaining accurate instantaneous measurements of

temperature. Pulsed lasers, such as the Nd:YAG laser, can provide higher peak power than the

continuous-wave lasers at the same time that the short pulse time is useful for good temporal

resolution [5]. More recently, laser diodes emitting in the ultraviolet region of the spectrum provided

compact solutions for fluorescence emission from blue to near-infrared [33]. However, though

compact, they can provide low output power and are expensive. Recently, high-power LEDs emitting in

the ultraviolet became commercially available and, though both LED and laser diodes (LDs) are small in

size, LEDs have longer operating lifetimes, are stable, have reasonable input power requirements and

are of low cost and accessible [34]. The use of LEDs, emitting in the visible region, are then considered

as a viable and more affordable alternative light source.

Fluorescence techniques have been applied for more than 20 years, being fluid temperature

measurements essential to enhance heat transfer phenomena studies. Going from the utilization of

one, in its early stages, to the usage of three fluorescent dyes more recently, the normalized

techniques using one or two fluorescent dyes are assessed in the present study to measure fluid

temperature at microscale as well as the usage of a fluorescent LED, as an alternative illumination

source to traditional lasers.

4

2. Objectives and Dissertation Outline

Temperature measurements are essential to study heat transport phenomenon, which is the major

field of research in the Multiscale Transport Phenomena Laboratory, part of the Centre for Innovation,

Technology and Policy Research, IN+, at the Mechanical Engineering Department of Instituto Superior

Técnico. To complement ongoing research, LED-IFT, a non-intrusive technique that can be applied to

microfluids is implemented, allowing whole field temperature measurements in a volume-illuminated

microfluidic setup with good spatial and temporal resolutions.

In this context, and in the wake of the conclusions of the literature review in chapter 1, the present

thesis considers the implementation and experimental characterization of a Light Induced

Fluorescence Thermometry technique at microscale. In particular, the study assesses the feasibility of

applying the two-dye LED-IFT approach at the microscale using a Leica illumination system LED SFL100

530 𝑛𝑚. The development of image processing algorithms along with the experimental setup

idealization and concretization are essential parts of this thesis.

As in previous researches, both at micro and macroscale, this study considers the use of Rhodamine B

and Rhodamine 110 as the temperature sensitive and insensitive dyes, respectively. The performance

of this two-dye approach (NR-LED-IFT) for microfluidic temperature measurement is compared with

that of the single-dye approach (N-LED-IFT). The most adequate is then tested in two practical

examples: a training benchmark test applied to a microchannel with heated walls, often used for CPU

chips, and visualization of the thermal mixing of two flows in a T-shaped micromixer. These tests allow

to evaluate quantitatively the results and the spatial and temporal resolutions of the technique, along

with its application to some fields of research where this technique can be crucial for scientific

enhancements, such as in heat transfer experiments with different flow regimes turbulent performed at

microscale.

5

3. Principles of the LIF for thermometry measurements

Laser or LED Induced Fluorescence Thermometry are techniques in which dye molecules subjected to a

temperature field are excited by an illumination source (Laser or LED) and their fluorescence signal,

dependent on molecule temperature, dye properties and solution PH is converted into temperature

with the help of a normalization curve.

Fluorescence is a radiative decay process that takes place by spontaneous photon emission from single

excited state to ground state of atoms or molecules. In the present case, it is intended to excite dye

molecules with a LED illumination system.

When a fluorescent dye molecule is excited, the energy coming from exciting photons can be stored in

three different states: electronic, vibrational and rotational.

The electronic state is the most energetic and is equivalent to electrons potential energy, being the

transition between two of these states associated with radiation emitted in the visible region. Due to

oscillations of atoms or groups of atoms in the molecule, there is the vibrational state in which energy

can be related to a photon energy in the infrared zone. At last, the rotation of two or more atoms

around a shared centre of mass leads to different rotational states in the molecule, which represent the

lowest energy level when compared to the other two. Rotational dissipations are related to radiation

on the microwave range.

For the molecule to return to its energy ground state, there are essentially two methods: non-radiative

(via intra and intermolecular energy dissipation) and radiative methods (through photon emission).

Non-radiative methods can be divided in two main effects: intra and intermolecular effects.

Intramolecular effects are evident mainly through molecular vibration, depending on the temperature

(energy level occupation follows a Boltzmann distribution). These energy trades occur until thermal

equilibrium is reached, a phenomenon that is also called vibrational relaxation. As for intermolecular

effects, energy dissipation takes place when a molecule interacts with the surroundings, reducing

emitted fluorescence intensity, also known as fluorescence quenching. Energy trades occur by

Fluorescent Resonance Energy Transfer (FRET), needless of direct contact, or through collisional

quenching where direct contact takes place. Here, the amount of energy to excite the electrons in the

molecule is converted into rotational and vibrational states such that, in the end of the process,

interacting molecules are in their ground state of energy.

For radiative methods, the fluorescence occurs when electrons go from singlet excited state to ground

state (Figure 1), and phosphorescence occurs when electrons move from the triplet excited state to

6

ground state. Singlet and triplet excited states difference resides in the conservation or change of the

excited electron spin, respectively, being the singlet excited state more energetic when compared to

the triplet excited state according to one of Hund’s rules.

Figure 1 – Example of Perrin-Jablonski Diagram with absorption and radiative dissipation methods examples. S0, S1

and S2 – singlet electronic states; T1, T2 – triplet electronic states; IC – internal conversion; ISC – intersystem

crossing (adapted from [35]).

The wavelength of energy absorption is smaller than that of energy emission due to a process known

as Stokes shift. Such phenomenon is explained by a vibrational relaxation that takes place in a very

small time scale (around 10-12 s), before the radiative processes takes place.

The fluorescent intensity emitted per unit of volume, 𝐼 [𝑊. 𝑚−3] [27] is dependent on the light incident

flux 𝐼0 [𝑊. 𝑚−2], the dye concentration 𝐶 [𝑘𝑔. 𝑚−3], the quantum yield 𝛷 [−] (ratio of photons emitted

and absorbed by the molecule, depending on the molecule temperature) and the absorption

coefficient 휀 [𝑚2/𝑘𝑔] (which has low temperature dependence when compared to that of quantum

yield) according to Equation 1

𝐼 = 𝐼0𝐶Φ휀 Equation 1

For low dye concentrations, Chamarty et al. [9] modified Equation 1 into Equation 2

𝐼 = βCΦ𝐼0휀𝑏𝐶 Equation 2

where βC is the collection efficiency [−] and 𝑏 is the absorption path length [−].

7

For some fluorescent dyes, such as Rhodamine B, quantum yield shows significant dependence with

temperature (around 2% K-1), which cannot be neglected, while the absorption coefficient remains

practically constant, with a variation smaller than 0.05% K-1 [27].

The incident light flux 𝐼0 depends on several factors, such as the convergence/divergence of the focal

plane and the light refraction across the medium. That said, it is necessary to measure the illumination

intensity in real time, which brings the need to use fluorescent particles whose quantum yield is

temperature independent. The ideal match up would be to use a pair of particles, one highly sensitive

to temperature (e.g. Rhodamine B) and another insensitive to temperature (e.g. Rhodamine 110) –both

with a similar absorption spectrum, in order to use the same excitation source, with separated emission

spectra to allow emitted light separation through optical methods.

In the following paragraphs the method is addressed, which considers the notation:

Particles A – particles whose fluorescence intensity emitted is highly dependent on

temperature;

Particles B – particles whose fluorescence emissions are nearly temperature independent;

Image α – image of the fluorescence emitted by particles A;

Image β – image of the fluorescence emitted by particles B;

Being the emission spectra of both A and B particles independent, the ratio 𝐼𝐴 𝐼𝐵⁄ will not depend on

the light incident flux, 𝐼0, and will be given by the ratio 𝛷𝐴/𝛷𝐵.

𝐼𝐴

𝐼𝐵

=𝐶𝐴Φ𝐴휀𝐴

𝐶𝐵Φ𝐵휀𝐵

Equation 3

However, in practice, both emission spectra cannot be completely separated as the emission spectra of

most organic dyes are broad and spectral filters are inherently imperfect.

Defining 𝐹𝐴α and 𝐹𝐴

β as the light fractions emitted from particle A in images α and β, and 𝐹𝐵

α and 𝐹𝐵β as

the light fractions emitted from particle B in images α and β, it yields

𝐹𝐴α =

𝑉𝐶𝐵=0α

𝐼0′ 𝐶𝐴

′ Φ𝐴′ 휀𝐴

Equation 4

8

𝐹𝐴β

=𝑉𝐶𝐵=0

β

𝐼0′ 𝐶𝐴

′ Φ𝐴′ 휀𝐴

Equation 5

𝐹𝐵α =

𝑉𝐶𝐴=0α

𝐼0′ 𝐶𝐵

′ Φ𝐵′ 휀𝐵

Equation 6

𝐹𝐵β

=𝑉𝐶𝐴=0

β

𝐼0′ 𝐶𝐵

′ Φ𝐵′ 휀𝐵

Equation 7

Note that primes represent property values during the experiments when the concentration of one of

the dyes is equal to zero (𝐶𝐴 = 0 or 𝐶𝐵 = 0). The output signal, 𝑉, as a function of each intensity

fraction from both dyes is given by

𝑉α = 𝐹𝐴α𝐼𝐴 + 𝐹𝐵

α𝐼𝐵 = 𝐼0(𝐹𝐴α𝐶𝐴𝛷𝐴휀𝐴 + 𝐹𝐵

α𝐶𝐵𝛷𝐵휀𝐵) Equation 8

𝑉β = 𝐹𝐴β

𝐼𝐴 + 𝐹𝐵β

𝐼𝐵 = 𝐼0(𝐹𝐴β

𝐶𝐴𝛷𝐴휀𝐴 + 𝐹𝐵β

𝐶𝐵𝛷𝐵휀𝐵) Equation 9

Those fractions can be determined by measuring the fluorescence intensity on both images when one

dye concentration is set to zero. The image intensity ratio as a function of dye concentration and of

the temperature, as demonstrated in [27], is

The relationship between temperature and Φ𝐴 and Φ𝐵 depends on the dyes used. Dye properties such

as polarity or viscosity influence the dye quantum yield in such a way that the quantum yield has to be

determined experimentally [25]. Apart from the quantum yield, dye concentration also affects the

𝑉α

𝑉β=

𝐼𝐴

𝐼𝐵

=𝐶𝐴𝐶𝐵

′ Φ𝐴Φ𝐵′ 𝑉𝐶𝐵=0

α + 𝐶𝐵𝐶𝐴′ Φ𝐵Φ𝐴

′ 𝑉𝐶𝐴=0α

𝐶𝐴𝐶𝐵′ Φ𝐴Φ𝐵

′ 𝑉𝐶𝐵=0β

+ 𝐶𝐵𝐶𝐴′ Φ𝐵Φ𝐴

′ 𝑉𝐶𝐴=0β

Equation 10

9

sensitivity of the intensity ratio to temperature. Writing 𝐶𝐴/𝐵 = 𝐶𝐴/𝐶𝐵 and deriving Equation 10 in order

to temperature, it results

𝜕𝑇 (

𝑉α

𝑉β) =

𝐶𝐴′ Φ𝐴

′ 𝐶𝐴/𝐵 𝐶𝐵′ Φ𝐵

′ (𝑉𝐶𝐵=0α 𝑉𝐶𝐴=0

β− 𝑉𝐶𝐴=0

α 𝑉𝐶𝐵=0β

) (Φ𝐵∂ΦA

𝜕𝑇− Φ𝐴

∂ΦB

𝜕𝑇)

(𝑉𝐶𝐵=0β

𝐶𝐴/𝐵 𝐶𝐵′ Φ𝐵

′ Φ𝐴 + 𝑉𝐶𝐴=0β

𝐶𝐴′ Φ𝐴

′ 𝛷𝐵)2 Equation 11

If Φ𝐴 and Φ𝐵 have similar values, high sensitivity to temperature can be obtained when both quantum

yield sensitivities to temperature are as much far apart as possible, i.e., when ∂ΦA

𝜕𝑇≫

∂ΦB

𝜕𝑇 or

∂ΦA

𝜕𝑇≪

∂ΦB

𝜕𝑇.

By normalizing the intensity ratio 𝑉α 𝑉β⁄ , this technique becomes needless of extra calibrations for

different setups. A reference value of the fluorescence intensity at a given temperature is then used for

normalization.

Two methods to evaluate normalized images are the Normalized Image for Ratiometric LED Induced

Fluorescence Thermometry (NR-LED-IFT), in which dyes A and B are used, and the Normalized Image for

LED Induced Fluorescence Thermometry (N-LED-IFT), where only dye A (dye whose fluorescence

intensity emitted is highly dependent on temperature) is used.

NR-LED-IFT method is based on Equation 12 below and, as it requires the processing of four images in

order to obtain a single value of temperature, it turns out to be a heavy method with high

computational costs.

Looking closely, if the intensity ratio of both dyes at ambient temperature, I0,A/I0,B, is known (which is

a constant), the previous expression can be simplified, requiring only the acquisition of two

simultaneous images (α and β).

On the other hand, N-LED-IFT method makes use of Equation 12 for the NR-LED-IFT method,

simplified for the use of one dye only. Being dye B the one that is approximately temperature

independent, one would expect that images at ambient and at whatever temperature intended to be

measured, would be the same. Hence, the ratio, and consequently, the intensity information will only

depend on temperature dependent dye images.

INR−LED−IFT =IA/IB

I0,A/I0,B

Equation 12

10

However, there are some constraints related to the use of this method, such as the fact that it is only

valid if both images used are identical and if there are no changes in the optical path length during the

experiment.

As stated before, within radiative methods, fluorescent light emission represents only one way to

dissipate energy, among other factors that can alter its response. Fluorescence efficiency strongly

depends on the molecular structure of the fluorescent dye and the bigger the molecule the more

degrees of freedom (rotational and vibrational), leading to a continuous wide absorption spectrum

instead of discrete absorption lines.

The accuracy of the method depends on several processes that influence, or even destroy, dye

molecules modifying somehow their fluorescent response. The main ones are:

Photo bleaching – a phenomenon more likely to happen when the illumination source is highly

energetic. It is caused by multiple electronic excitations, inducing molecule instability and consequent

irreversible dissociation.

Chemical reactions – Reactions, either between dyes or the dyes and the solvent, which may

lead to the formation of dimers or other structures whose fluorescent properties alter or even become

non-existing.

Solvent relaxation – Expansion of the electron cloud caused by electronic excitation, which

induces changes in molecule polarity and rearranges solvent molecules with the surroundings causing

energy transfer. Thus, photons emitted will be less energetic, and the light will be shifted to larger

wavelengths, which means that the polarity and viscosity of the solvent represents an important role

on fluorescent characteristics.

Auto-absorption and reemission effects due to Beer-Lambert law – when emission and

absorption spectra of different dyes in solution overlap, a fraction of emitted light by one dye is

reabsorbed by the other when crossing the medium. This fraction of emitted light is responsible for

fluorescence emissions at higher wavelengths. From a modified Beer-Lambert law [36], one can infer

that the ratio between two signals, emitted from fluorescent particles at different wavelengths, shows a

bigger dependence of the optical path length and of the dye concentration when reemission is

IN−LED−IFT =IA

I0,A

Equation 13

11

considered. To minimize this reabsorption effect, dye concentration can be reduced but this could

result in a compromise between signal intensity and the decrease of reabsorption effects.

Besides these factors, which can modify or destroy dye molecules, the optical setup also influences the

quality of LED-IFT results. One of such factors regards the influence of defocus on the measurements.

In this application, as in many others, it is necessary to bring a specimen into focus before any

measurement can be made. Therefore, light properties that allow deviations from the focal plane with

or without observable loss of sharpness, also known as depth of field (DOF), must be taken into

account. This parameter, 𝛿𝑧𝐷𝑂𝐹 , represents the thickness of focused image and depends mainly on the

objective numerical aperture (NA), which represents the light collecting power, the refractive index of

the immersion medium between the specimen and the objective lens, n, the wavelength of light in a

vacuum being imaged by the optical system, λ0, the total magnification of the system, M, and the

smallest distance that can be resolved by a detector located in the microscope image plane, e,

according to Equation 14 [37], seen on [38]

𝛿𝑧𝐷𝑂𝐹 =𝑛 𝜆0

𝑁𝐴2+

𝑛 𝑒

𝑁𝐴 𝑀 Equation 14

The depth of field is especially important if the particular case of LED-IFT is considered, where data

collected consists of a fluorescent signal from illuminated particles in the entire volume. The thicker

the DOF, the larger number of particles contribute to the collected signal, resulting in degradation of

data quality, compromising the spatial resolution (in illumination direction). Therefore, it is

recommended to reduce this quantity as much as possible and, according to Equation 14, this can be

achieved with a higher NA of the objective (the quality and numerical aperture of a lens can degrade

with its use, especially if UV-light is transmitted).

Also, the suitability of the camera to be used in LED-IFT determines the quality of collected data. The

signal-to-noise ratio (SNR), pixel size, fill factor, sensitivity, quantum efficiency and the type of

response are therefore extremely important to evaluate.

The SNR gives an indication of image quality and can be evaluated from two independent images, 𝐼1

and 𝐼2, taken from a homogeneous, uniformly illuminated area with the same mean intensity value, μ.

The variance of the difference between both images, 𝜎2, reflects non-uniformities on photon shot

noise (inherent noise due to quantum nature of light that cannot be removed), dark current (increases

12

with increasing camera temperature, originated by thermal agitation of electrons) and readout noise

(higher acquisition rates can increase the noise present in images recorded).

𝜎2 =1

2𝑣𝑎𝑟(𝐼1 − 𝐼2) Equation 15

Signal-to-noise ratio can thus be found from

𝑆𝑁𝑅 = 20 log (𝜇

𝜎) [𝑑𝐵] Equation 16

A value higher than 20 𝑑𝐵 [39] is a good indicator of camera suitability.

Regarding the pixel size, although small pixel size offers better sampling density, results are subjected

to higher noise, so a compromise between resolution and SNR is necessary when evaluating the

dimensions of each pixel. Another source of imaging noise is quantization noise, which is created in

data conversion to integer numbers, due to round off errors. The SNR is function of the number of

bits, 𝑏, that each camera uses in the process and can be quantified according to Equation 17. For 𝑏

equal or larger than 8, the amount of quantization noise can be considered negligible [39].

Blooming and dead pixels also need to be taken into account. The first takes place if a potential well is

saturated with charge but can be adjusted by regulating exposure time. Dead pixels do not respond to

incoming light which leads to information loss, meaning the less dead pixels a chip has the better for

the image quality.

𝑆𝑁𝑅 = 6𝑏 + 11 [𝑑𝐵] Equation 17

13

4. Experimental Campaign

The present section provides details of the laboratory experiments and of the equipment used

throughout the work, in the improvement of the technique and in its application. The improvement of

the technique envisages identification and optimization of the optical parameters involved in

temperature measurements, such as the illumination and light collecting systems, as well as the

experimental procedures necessary to guarantee a good accuracy, such as the preparation of the

solute dyes and calibration. Both techniques, the N-LED-IFT and the NR-LED-IFT, were considered,

making use of two fluorescent dyes (Rhodamine B and Rhodamine 110) in a water flow inside a

microchannel with heated walls.

Applications of the technique were then performed in two simple flow configurations, allowing to

demonstrate the applicability of the technique to microflows with realistic accuracy. The first consists

of a flow inside a microchannel with heated walls, the second is the mixing of two water streams at

different temperatures at a T-junction. The experiments were performed in a closed controlled ambient

in the laboratory with constant temperature of 22 ℃ and all image normalizations were performed

with a reference image at a temperature of 23 ℃, randomly selected.

In both flows the experiments make use of a standard micro-PIV setup equipped with a LED

illumination source to which a second camera has been adapted in order to allow radiation collection

in two different wavelength bands, emitted by each dye. The flow configurations are described in the

paragraphs below, followed by a detailed description of the main parts of the equipment and of the

experimental procedures.

4.1 Flow Configurations

Microchannel flow

The design of micro cooling technologies for high density power systems is one of the research areas

addressed at the Multiscale Transport Phenomena Laboratory, part of the Centre for Innovation,

Technology and Policy Research (IN+) of the Mechanical Engineering Department (IST). One of the

biggest challenges is the measurement of temperatures at microscale with good temporal and spatial

resolutions. In this context, flow inside a microchannel with heated walls has been used in the

fundamental studies, to optimize the LED-induced fluorescence system parameters and later, in a

benchmark test, to demonstrate the applicability of the present non-intrusive technique to small

scales.

14

Figure 2 – Schematics of the microchannel experimental setup.

The experimental facility consists of a glass microchannel with square cross section [40] (𝛬𝑖 =

318 𝜇𝑚, 𝛬0 = 632 𝜇𝑚) connecting two aluminium reservoirs with copper entrance and exit paths. The

reservoirs have an orifice on the top for air to escape, assuring that air is not mixed in the liquid flow

inside the microchannel during experiments.

The microchannel is externally coated with a thin transparent film of indium oxide deposited on the

outer wall as described by Silvério et al. [40], which allows the heat input through Joule effect at the

same time it provides optical access to the flow.

A Gen 150-5 power supply from TDK-Lambda (accuracy of ±0.05 V) regulated with GenesysControl

software is used for this purpose. Applying a known voltage on the desired heating area, depending

on where the electric contacts are placed, the heat flux can be regulated. Despite adjustable, the

distance between electric contacts on the microchannel wall was of 23.96 𝑚𝑚, kept constant

throughout all experiments.

The wall and liquid temperatures can be measured with precision fine wire type K thermocouples

(Omega Engineering) with 25 𝜇𝑚 tip diameter placed on the microchannel wall, upstream and

downstream of the visualization point and two type K thermocouples in the aluminium reservoirs, as

shown in Figure 2 and in detail in Figure 3. An 8-channel isolated thermocouple DAQ module, DT9828,

15

from Data Translation®, with a claimed accuracy of 0.09 ℃ and noise of 0.1 ℃ for thermocouples, was

used to convert thermocouples signal in temperature through Quick DAQ 2013 v3.0.0.2 software.

Figure 3 – Detail of thermocouples location in the microchannel experimental setup.

The liquid flow is supplied and controlled to the microchannel by a NE-300 Just Infusion syringe pump,

being all experiments performed at a constant volumetric flow rate of 15 𝑚𝑙. ℎ−1. Table 1 lists the main

specifications used for calculations for this experimental setup.

Table 1 – Glass microchannel installation characteristics.

Distance between electric contacts [𝑚𝑚] 23.96

Distance from downstream electric contact to section 1 (see Figure 31) [𝑚𝑚]

7.50

Inner microchannel characteristic dimension, 𝛬𝑖 [𝜇𝑚] 318

Outer microchannel characteristic dimension, 𝛬𝑜 [𝜇𝑚] 632

Air heat transfer coefficient, ℎ𝑎𝑖𝑟 [𝑊. 𝐾−1. 𝑚−2] 50

Microchannel glass emissivity, 휀 [−] 0.95

Stefan-Boltzmann constant, 𝜎 [𝑊. 𝑚−2. 𝐾−4] 5.6704 × 10−8

Water specific heat capacity, 𝐶𝑝 [𝑘𝐽. 𝑘𝑔−1. 𝐾−1] 4.18

Water specific capacity and Prandtl numbers used in the calculations were averaged for a temperature

range from 20 to 100 ℃.

Inlet aluminum

well

Outlet

aluminum well

Thermocouples

Electric terminal

16

In all the experiments involving technique parameters tests and in the calibration ones, it is necessary

to guarantee that the measurement point in the flow taken to test the technique is in a location

without gradients, either in space and time, assuring steady-state flow conditions. The hydrodynamic

and the thermal entrance lengths are, then, necessary to be determined prior to the experiments.

Hydrodynamic entrance length

The hydrodynamic entrance length of a flow represents the length in which the velocity profile

changes with position along the flow direction. From this distance on, a fully developed velocity profile

is verified and remains constant as far as no parameter in the flow is changed. The correlation

presented in Equation 18 has been proposed by Han [41] in 1960, to estimate the entrance length 𝐿𝐻𝑒

in micro square ducts with hydraulic diameters 𝐷ℎ smaller than 500 𝜇𝑚 over a range of Reynolds

numbers 𝑅𝑒 from 0.5 to 100.

𝐿𝐻𝑒

𝐷ℎ

=0.63

0.035 𝑅𝑒 + 1+ 0.0752 𝑅𝑒 Equation 18

For rectangular ducts,

𝐷ℎ =4 𝐴

𝑝 [𝑚] Equation 19

In the particular case of squared section ducts,

𝐷ℎ = 𝛬𝑖 [𝑚] Equation 20

where 𝛬𝑖 is the inner characteristic dimension of the channel.

The Reynolds number can be expressed as

𝑅𝑒 =𝑈 𝐷ℎ

𝜐 Equation 21

where 𝑈 is the flow average velocity [𝑚. 𝑠−1] which is obtained from the volumetric flow, 𝑄, using

Equation 22 and 𝜐 is the fluid kinematic velocity [𝑚2. 𝑠−1].

17

𝑄 = 𝐴 𝑈 [𝑚3. 𝑠−1] Equation 22

Thermal entrance length

From the heating zone inlet, heat is transported by the fluid gradually up to a point when the shape of

the temperature profile loses its identity and changes to a thermally fully developed region. The

distance from the heated region in which this phenomenon occurs is called thermal entrance length,

𝐿𝑇𝑒 [42], and can be estimated from

𝐿𝑇𝑒 = 0.01 𝑅𝑒 𝑃𝑟 𝐷ℎ [𝑚] Equation 23

for square ducts, where 𝑃𝑟 =𝑐𝑝𝜇

𝑘 is the Prandtl number and represents the ratio of momentum to

thermal diffusivity.

Figure 4 gives an example of microchannel wall temperature signal in time after a voltage is imposed

to the indium oxide layer terminals and shows that the flow reaches thermal steady state after 5

seconds.

Figure 4 – Example of temperature response after an imposed heat flux increase.

18

For the volumetric flow rate of 15 𝑚𝐿. ℎ−1 the maximum hydrodynamic entrance length, 𝐿𝑇ℎ, is

0.457 𝑚𝑚 and the maximum thermal entrance length, 𝐿𝑇𝑒 , is 0.295 𝑚𝑚 (𝜐 = 9.82 × 10−7𝑚2. 𝑠−1 and

𝑃𝑟 = 6.86). Thus, both velocity and thermal boundary layers are fully developed where visualization

and temperature measurements are taking place.

T-shaped micro-mixer

This is a simple flow configuration, which consists in the mixing between two liquid flows at different

temperatures in a T-shaped mixer, for the application of the present technique to measure 2D

temperature profiles at different flow regimes, but also of practical relevance to study mass and

thermal mixing at microscale.

Figure 5 – Schematics of the T-shaped micro-mixer experimental setup.

The experimental setup is schematically displayed in Figure 5 and consists of three silicon tubes, with

an internal diameter of 1 𝑚𝑚, pinched between two glass slides (detailed in Figure 6). An aqueous

solution with RhB at ambient temperature is supplied a NE-300 Just Infusion to the first inlet port, while

to the second inlet port is also supplied an aqueous RhB solution but at a higher temperature, pumped

Syringe pump

NE-300

K-type thermocouple

Inlet

C

Microscope

objective

HighSpeedStar 4G

CCD camera

DMIL LED

Microscope

Filter Cube

LED SFL100

Illumination

Reservoir

T-shaped

micro-mixer

Syringe pump

Harvard 22

Inlet

Pre-heater

Reservoir

Gear pump

19

by a Harvard 22 syringe pump, exiting the mixture through the third one. The mixing plane between

the hot and cold fluid streams for four different flow rates was visualized using a 4x/0.10 Leica

Microsystems objective lens, which is described in the following section.

Figure 6 – T-shaped micro-mixer scheme.

Hot fluid temperature is controlled in a secondary flow loop consisting of the custom made, thermally

isolated, acrylic reservoir shown in Figure 7, where two immersion resistances (1000 𝑊, model AI 03,

230 𝑉, 50 𝐻𝑧) controlled by an EGO Original 55.13022.060 thermostat (temperature range 30 – 110 ℃)

heat the water before it flows through silicon tubes encircling the primary flow loop.

Figure 7 – Secondary flow loop used to control the temperature of the hot flow. Reservoir, potentiometer and two

electric resistances.

𝑄𝐴𝑚𝑏

𝑄𝐻𝑜𝑡

𝑄𝐸𝑥𝑖𝑡

20

4.2 Equipment

The central part of the experimental facilities includes the LED illumination source, the CCD camera(s),

the microscope visualization system and the liquid pumping system(s). Temperature measurements are

performed to control and/or validate the optical temperature measurements in all experiments. For

that, precision fine wire type K thermocouples (Omega Engineering) with 25 𝜇𝑚 tip diameter are

placed either in contact with the solution and/or with the microchannel wall. An 8-channel isolated

thermocouple DAQ module, DT9828, from Data Translation®, with a claimed accuracy of 0.09 °C and

0.1 °C noise was used to convert the electrical signal of the thermocouples signal into temperature

through Quick DAQ 2013 v3.0.0.2 software.

Microscope

The microscope is the inverted fluorescence microscope Leica DM IL LED (Figure 8) with possibility to

interchange the objective lenses. Three lenses were then used: HI-PLAN 4x/0.10, HI-PLAN I 10x/0.22,

and N-PLAN EPI 20 x/0.40 from Leica and their suitability for the study was tested. The microscope can

also interchange sets of filter cubes. A LED SFL100 530 𝑛𝑚 size “s” set of filters was used.

Figure 8 – Leica DM IL inverted microscope. Image extracted from [43].

21

High speed cameras

Two high speed cameras were used to capture the fluorescence intensity signal, one for each dye.

HighSpeedStar camera from LaVision collected fluorescence information from RhB, while for Rh110

Phantom V4.2 from Vision Research was used.

The HighSpeedStar from LaVision, shown in Figure 9, is a high speed camera able to capture images at

a maximum rate of 3600 Hz with a resolution of 1024x1024 𝑝𝑖𝑥𝑒𝑙2 , which is connected to DMIL

microscope through a CCD adapter x0.55. A proprietary Software from LaVision, Davis8, is used to

acquire, save and export the images, as well as to trigger the whole setup through LaVision HighSpeed

controller.

Figure 9 – LaVision HighSpeedStar high speed camera with the x0.55 CCD adapter.

CCD adapter

Microscope

optics

HighSpeedStar

camera

22

To record the fluorescence signal emitted by the Rhodamine 110 solution, a Phantom V4.2 high speed

camera from Vision Research is used. This camera is attached to the custom made support shown in

Figure 10, purposely manufactured for this setup, to allow positioning the camera transversally to the

flow.

Prior to every experiment, a current session reference (calibration with black reference) with Phantom

Camera Control software has to be performed.

Figure 10 – Phantom v4.2 high speed camera and custom made support attached to the inverted microscope.

Custom

made

support

Melles Griot

16x/0.32

lens Melles Griot laser filter N-PLAN

20x/0.40

lens

Cosmicar CCD adapter

LED SFL100 530

𝑛𝑚 set of filters

23

Figure 11 – Rhodamine B filter characteristics. Transmission percentage as function of wavelength. (Chroma

Technology Corp., Scan range from 480.0 𝑛𝑚 to 680 𝑛𝑚, ET-TRITC Filter Set for 530 LED_Leica DMR_Un-Mounted).

To access the flow, a 16x/0.32 Melles Griot microscope objective lens is connected to a Cosmicar x2 TV

extender CCD adapter, then coupled to the camera. Between the lens and the flow a Melles Griot laser

filter 514.5 𝑛𝑚, with the transmission spectra shown in Figure 12, is precisely placed. Image acquisition,

saving and export are performed through Phantom Camera Control V.9.0.640.0-C software.

Figure 12 – Rhodamine 110 filter characteristics: Transmission percentage as function of wavelength. (Melles Griot

03FIL004 Laser Filter 514.5 𝑛𝑚 25 DIA, 03FIL00405100762).

Since both cameras have square pixels, sample density in x and y directions is the same. Both cameras

main characteristics are summarized in Table 2, according to manufacturer indications.

𝑊𝑎𝑣𝑒𝑙𝑒𝑛𝑔𝑡ℎ (𝑛𝑚)

%𝑇

24

Table 2 – Main characteristics of the high speed cameras used.

High Speed Camera

Characteristic

LaVision HighSpeedStar Vision Research Phantom V4.2

Maximum Resolution 1024x1024 pixel2 512x512 pixel2

Bits per sample 12 bits 8 bits

Exposure time 0.5 𝜇𝑠 80 𝜇𝑠

Acquisition rate 3600 Hz 2048 Hz

SNR (Equation 17) As both cameras have 𝑏 ≥ 8, quantization noise is negligible

To guarantee the accuracy of the optical arrangements, it is necessary to establish a relation between

the different optical arrangements and the size of the image being visualized. Making use of

calibration targets with well-defined mark spacing and precision dot patterning are represented in

Figure 13 a) and b), the HighSpeedStar camera calibration and the correspondence of pixel to image

size for both cameras can be determined, respectively. The spacing between dots in the target

represented in a) is of 20 𝜇𝑚 and the small lines in the reticle used represented in b) have a 100 𝜇𝑚

spacing.

a) b)

Figure 13 – HighSpeedStar calibration target from LaVision represented in a) and in b) the reticle from Peak Optics

used to establish the correspondence of pixel to image size for both cameras.

Results are presented in Table 3, with the respective depth of field, DOF, obtained by Equation 14.

25

Table 3 – Correspondence between the pixel and image size and depth of field in both high speed cameras for

different optical arrangements.

High Speed Camera

Arrangement

LaVision HighSpeedStar Vision Research Phantom V4.2

Pixel correspondent

size (𝜇𝑚) full

image size (𝑚𝑚)

DOF (𝜇𝑚)

Pixel correspondent

size (𝜇𝑚) full

image size (𝑚𝑚)

DOF (𝜇𝑚)

HI-PLAN 4x/0.10 with

x0.55 CCD adapter 7.69 7.87 97.95 – –

HI-PLAN I 10x/0.22 with

x0.55 CCD adapter 3.12 3.19 15.60 –

N-PLAN EPI 20 x/0.40 with

x0.55 CCD adapter 1.54 1.58 4.29 – –

Melles Griot 16x/0.32 with

x2 CCD adapter – – 2.09 1.07 6.36

Illumination system

The aqueous solutions containing fluorescent dyes are illuminated with the Leica illumination system

LED SFL100 530 𝑛𝑚 shown in Figure 14. This is a light source compatible with all microscopes

equipped for fluorescence, with low power consumption and represents an economic alternative to

traditional fluorescence microscopy setup. Despite light intensity can be adjustable, it was kept

constant during all experiments.

26

Figure 14 – Leica LED SFL100 illumination system.

Pumping systems

The flow(s) is/are pumped by a NE-300 syringe pump (Figure 15 a)) and/or a Harvard 22 syringe pump

(Figure 15 b)), both with an accuracy within ±1% over length of syringe, exclusive of syringe variations

and a reproducibility of ±0.1%. Solutions are prepared in flasks and then transferred to 60 𝑚𝐿 Norm-

Ject syringes. The syringe is connected to the installation through silicon piping and aluminium fittings.

a) b)

Figure 15 – Pumping systems: a) NE-300 syringe pump and b) Harvard 22 syringe pump.

27

4.3 Experimental method

One of the objectives of the present work is to compare the single dye method with the traditional

two-dye technique for microfluidic temperature measurements, as in [9]. Two dyes were then used,

RhB and Rh110, as temperature-sensitive and insensitive dyes, respectively.

Since LED-IFT uses intensity to measure temperature, a calibration is needed prior to applying the

technique, which takes into account for the losses of fluorescence intensity along the optical pathway

from the measurement volume to the CCD sensors.

The following paragraphs give the main characteristics of the fluorescence dyes and solutions followed

by a detailed description of the calibration setup, image processing and uncertainty estimation.

Fluorescent dyes

The accuracy of the Laser Induced Fluorescence relies on a particularly set of properties that particles

in solution must have. A summary of the most important optical characteristics of both dyes such as

absorption and emission wavelengths as well as quantum efficiency are summarized in Table 4.

Table 4 – Characteristics of aqueous solutions of Rhodamine B and Rhodamine 110 in de-ionized water at 20 ℃

(Adapted from [27] and [25])

Dye λabsorption [𝒏𝒎] λemission [𝒏𝒎] 𝚽 [–]

RhB 554 575 0.31

Rh110 496 520 0.8

Both dyes were purchased from Sigma-Aldrich and the aqueous solutions prepared (Table 5) using a

Mettler Toledo scale with accuracy and reproducibility of 0.1 mg. Figure 16 shows the visual aspect of

three of the solutions used in the experiments. Solutions containing RhB present a reddish tint, while

solutions with Rh110 shows a light green tone.

28

Figure 16 – Visual aspect of three solutions used. From left to right: RhB with a concentration of 20 mg.L-1; RhB

with a concentration of 20 mg.L-1 and Rh110 with a concentration of 15 mg.L-1; Rh110 with a concentration of

50 mg.L-1.

Mixtures of these solutions are prepared in order to obtain the desired solution concentrations

containing both dyes, following the relation:

𝐶𝑖𝑉𝑖 = 𝐶𝑓𝑉𝑓 Equation 24

where 𝐶 represents dye concentration and 𝑉 the volume, initial (𝑖) and final (𝑓). Two aqueous solutions

containing both dyes were prepared (see Table 5).

Table 5 – Aqueous solutions used in experiments.

RhB Concentration

[𝒎𝒈. 𝑳−𝟏] Rh110 Concentration

[𝒎𝒈. 𝑳−𝟏] RhB + Rh110 Concentrations

[𝒎𝒈. 𝑳−𝟏]

50

20 + 15

25 + 15

1.4

5

10

15

20

25

32.6

29

Calibration of the fluorescence intensity

The calibration consists in collecting the fluorescence intensity at known temperatures, which will allow

to convert the fluorescence signal into temperature (calibration curve) in further experiments.

As stated before in chapter 3, performing a normalization of the fluorescent signal retrieved from the

dyes makes the method needless of further calibration for different experimental setups.

The calibration process requires precision for temperature control and measure, so a specific setup is

needed. As shown in Figure 17, it consists on a thermally insulated reservoir on top of a microscope

slide (76 × 26 𝑚𝑚2 × 1 ± 0.05 𝑚𝑚 thick) with a deposited indium oxide layer on the slide bottom in

order to vary the temperature of the dye solution by Joule effect and, at the same time, allowing

optical access from the bottom (the inverted DM IL LED microscope was used).

Figure 17 – Schematics of the thermally insulated pool.

The temperature is monitored with two precision fine wire type K thermocouples (25 𝜇𝑚 tip diameter)

placed in the solution (Figure 18). Both thermocouple tips are placed on the focal plane to guarantee

that the temperature measured corresponds to the temperature of the dye solution emitting the

fluorescence light.

Pool

Glass slide

InOx thin film

30

Figure 18 – Schematics of the calibration setup.

31

The optical system makes use of the microscopic system described before: an inverted microscope DM

IL LED from Leica Microsystems equipped with the Leica HI PLAN I 10x/0.22 objective lens, and a 0.55x

amplifier tube connects the microscope to the HighSpeedStar 4G camera to collect the fluorescence

intensity signal from Rhodamine B, after being excited by the illumination system LED SFL100 530 𝑛𝑚

through size “S” set of filters from Leica Microsystems. Intensity in each 16-bit gray scale image was

acquired and exported to a .txt format using Davis 8 software from LaVision.

To simultaneously collect the signal emitted by Rhodamine 110, a 16x/0.32 Melles Griot objective lens

coupled to a Melles Griot laser filter 514.5 𝑛𝑚 are attached to a Phantom v4.2 high speed camera.

Phantom Camera Control Version 9.0.640.0-C software collects and saves the 8-bit grayscale images in

.bmp format.

The calibration process consists in filling the insulated pool with one or two dyes (depending on the

method) and applying a controlled voltage to the transparent thin film of Indium Oxide to heat the

solution up to the desired temperature. When fluid temperature stabilizes, temperature and

fluorescence intensity information are simultaneously collected.

Uncertainty estimates

The error expression adopted for this work was found in [44] and is described below. It has in

consideration the accuracy and the precision in measurements made, and runs

𝐸𝑟𝑟𝑜𝑟 = ±√𝐵2 + 2 × 𝜎2 Equation 25

where 𝐵 is the bias accuracy of the measurement device, 𝜎 is the standard deviation of the parameter

measurements, corresponding to the precision of measurements, and the constant 2 is a commonly

used constant to represent a 95% confidence interval with 4 degrees of freedom.

32

5. Results and Discussion

The experimental campaign starts with the calibration of the optical systems and tests to the different

parameters of the LED-IFT technique in the microchannel setup. Then the pool calibration setup

follows and the technique is applied to the two case studies: a training benchmark imposed to a

microchannel flow and flow inside a ‘T’-shaped micromixer.

This chapter is divided in two main sections. The first addresses the parameter optimization and

calibration of the LED-induced fluorescence system for temperature measurements; the second

considers the applications of the optimized configuration.

5.1. Test Parameters

The experiments for the technique parameters were performed in the fully developed region of a water

flow inside a microchannel with a constant heat flux at the wall. Rhodamine B and Rhodamine 110

dyes were used as temperature dependent and temperature independent dyes, respectively.

Figure 19 a) and b) shows two examples of the fluorescent response obtained in the glass

microchannel heat exchanger setup with the HSS camera, using LaVision DaVis software and the

Phantom V4.2 camera, respectively.

a) b)

Figure 19 – Fluorescent intensity signal of a) RhB collected with HighSpeedStar high speed

visualization camera and b) Rh110 collected with Phantom V4.2 high speed visualization camera in

microchannel experiments

33

Since HSS images have a tilt angle with respect to the channel, a small routine was implemented in

order to choose four equidistant control points in the image, from the centre of the channel, to

monitor the intensity across the experiments. The results are presented for only one control point, as

the results for the remaining control points are found to be similar (within deviations smaller than 10%

of the measured value) and, therefore, repeatable and reproducible. For the Pool Calibration System,

six randomly selected control points were chosen near the thermocouple tip to monitor the

fluorescence response, in order to obtain the calibration curve. Spatial and quantitative measurements

are intended for the T-shaped micro-mixer experiment, so all image pixels were considered.

The corresponding processed images in MATLAB® are presented in Figure 20 a) and b). A radial

fluorescent intensity gradient from the centre to the borders of the image can also be noticed in both

images, which are attributed to non-uniformities of the illumination source (LED), whose intensity is

believed to have a Gaussian distribution.

a) b)

Figure 20 – MATLAB® equivalent images to those of Figure 18: a) RhB and b) Rh110.

34

Dye Concentration

Seven different concentrations of Rhodamine B were prepared and measurements taken for eight

different temperatures, obtained by applying eight different voltages to the Indium Oxide thin layer.

The results are depicted in Figure 21 and show the expected decrease of the fluorescence intensity as

the dye concentration decreases and temperature increases.

Figure 21 – Fluorescence intensity response obtained for the same control point for seven aqueous RhB solutions

with different concentrations.

The 𝑆𝑒𝑛𝑠𝑖𝑡𝑖𝑣𝑖𝑡𝑦 [%. ℃−1] of the technique is given by the gradient of the fluorescence intensity 𝑑𝐼 𝑑𝑇⁄

and can be written as a function of image intensity, 𝐼0, as

𝑆𝑒𝑛𝑠𝑖𝑡𝑖𝑣𝑖𝑡𝑦 =1

𝐼0

(𝑑𝐼

𝑑𝑇) × 100 Equation 26

As Rhodamine B concentration increases, the fluorescence signal also increases and with it the

temperature sensitivity. Figure 22 shows that the sensitivity increases almost linearly from 0.83 to a

maximum of 1.68 %. ℃−1 as dye concentration increases from 1.4 to 25 mg.L-1, after which it starts to

decrease.

35

Figure 22 – Influence of solution concentration in fluorescence signal sensitivity.

Therefore, a solution containing RhB with a concentration of 25 𝑚𝑔. 𝐿−1 was chosen for all the

subsequent experiments. The fluorescence signal of Rhodamine 110 was recorded with the Phantom

V4.2 high speed visualization camera: with a concentration of 15 𝑚𝑔. 𝐿−1, a sensitivity of 0.011% was

found, reflecting the independence of this dye fluorescence signal to temperature.

Influence of Background Noise

Another relevant parameter often mentioned in several studies as influencing the fluorescence signal is

the background noise present in the images caused by, either room illumination and/or static noise

induced by both camera and surroundings. Two set of experiments were performed to quantify this

influence: the first consists in comparing images collected with and without room illumination; the

second consists in subtracting an image obtained with the LED illumination source turned off to the

images collected with both, room and the LED illuminations on.

It is worth noting at this point that the second procedure will not be used with the Phantom camera

since its acquisition software always requires a current session reference before each measurement.

0 5 10 15 20 25 30 350.0

0.5

1.0

1.5

2.0

Sen

sitiv

ity [%

.ºC

-1]

Concentration [mg.L-1]

36

Figure 23 – Fluorescent signal from both dyes, for the same control point, with and without room illumination.

The results of the first set of experiments obtained with both, the HSS and the Phantom cameras, are

depicted in Figure 23, and show no significant dependence of the room illumination and, therefore, of

the background light. The results of the second set of experiments are summarized in Figure 24 and

show that subtracting the image obtained with the LED illumination source turned off only offsets the

original curve, and do not add any improvement.

Figure 24 – Rhodamine B fluorescent signal with and without background image removal.

20 30 40 50 60 70 80 900

200

400

600

800

1000

1200

1400

1600

Inte

nsity [A

.U.]

Temperature [°C]

RhB with room illumination

Rh110 with room illumination

RhB without room illumination

Rh110 without room illumination

37

Auto-absorption and Beer-Lambert Law

The last parameter to be tested before applying the LED-IFT technique is the influence of the auto-

absorption and reemission, as described in chapter 3. For that, experiments were performed with each

dye separately and the corresponding fluorescence signals were simultaneously captured with both

cameras. It is worth noting that, as already mentioned in the fluorescent dyes section, Rhodamine B

emits at a higher wavelength than the absorption wavelength of Rh110 and is, therefore, filtered by

the Rh110 filter, so there is no cross talk between the emission of RhB and the signal collected by the

Phantom V4.2 camera.

Figure 25 shows the fluorescence signal emitted by both Rh110 and RhB aqueous solutions obtained

at the same temperature conditions and captured with the HSS camera, first separately and after with

both dyes in solution, represented in black. At ambient temperature it is noticed an increase in

fluorescent signal intensity with both dyes in solution, when compared with only RhB fluorescent

signal, decreasing for higher solution temperatures. It is demonstrated then the auto-absorption and

reemission effects caused by the partial overlap between Rh110 emission and RhB absorption spectra

that can lead to image saturation problems, decreasing temperature sensitivity in the fluorescence

response obtained.

Figure 25 – Fluorescent signal on HSS image for both particles, separately and with both in solution.

20 30 40 50 60 70 80 900

200

400

600

800

1000

1200

1400

1600

Inte

nsity [A

.U.]

Temperature [°C]

Rh110 on HighSpeedStar

RhB on HighSpeedStar

RhB+Rh110 on HighSpeedStar

38

Calibration Curves – NR-LED-IFT vs N-LED-IFT

As mentioned before, there are essentially two ways to apply the LED Induced Fluorescence

Thermometry technique: the Normalized Image for Ratiometric LED-IFT (NR-LED-IFT) and the

Normalized LED-IFT (N-LED-IFT).

The pool calibration setup using both cameras was used in this set of experiments. As described

before, the heat flux applied to the microchannel flow can be controlled by varying the voltage applied

to the terminals of the indium oxide layer. The experiments are performed by increasing the voltage by

small steps (3V), only after reaching thermal steady-state conditions.

The intensity of the signals retrieved from both the HSS and Phantom cameras are shown in Figure 26.

The intensity difference between the control points within the same image, as explained in the

beginning of this section, is due to illumination non-uniformities caused by the Gaussian distribution

of the light source. The signal emitted by the temperature dependent dye (RhB) decreases as the

temperature increases from 1154 Arbitrary Units (𝐴. 𝑈.) at room temperature up to 253 𝐴. 𝑈. for 78.5℃,

which corresponds to a 78% change in fluorescence intensity. The signal emitted by the temperature

independent dye (Rh110) remains nearly constant, achieving a 2% maximum variation in fluorescence

intensity response along the temperature range tested, as expected.

Figure 26 – RhB and Rh110 fluorescence signal (FS) response to temperature.

20 30 40 50 60 70 80 900

200

400

600

800

1000

1200

1400

1600

Inte

nsity [A

.U.]

Temperature [°C]

HSS FS CP1

HSS FS CP2

HSS FS CP3

HSS FS CP4

Phantom FS CP1

Phantom FS CP2

Phantom FS CP3

Phantom FS CP4

39

The normalized curve (inverse of the calibration curve, which shows solution Temperature as a function

of Normalized Intensity) applying the NR-LED-IFT technique, is obtained and represented in solid

squares on Figure 27. On the same figure is also represented the N-LED-IFT curve, which is the

fluorescence intensity emitted by the RhB (hollow triangles). The difference between the curves of both

techniques is rather small (1.2% maximum deviation between curves), being NR-LED-IFT

computationally more demanding since it involves operations with four different images, increasing

the numerical error of the results (round-off errors).

Figure 27 – LED-IFT techniques comparison: NR-LED-IFT in solid squares and N-LED-IFT in hollow triangles.

N-LED-IFT

From the previous section, the application of the N-LED-IFT technique presents similar results to

NR-LED-IFT and, at the same time, smaller computational error associated since it requires fewer

operations. Thus, N-LED-IFT was selected to perform all subsequent experiments.

The same pool calibration setup was used, now only with the HSS camera. Similar procedure as before

is followed, although now with smaller voltage steps (1 𝑉) so a higher sample size is obtained.

Figure 28 shows the fluorescence signal obtained for one control point with the individual intensity

(green) and temperature (red) error bars, determined according to Equation 25. For the temperature

acquisition, the bias of the acquisition board was 0.09 ℃ with 0.1 ℃ noise for thermocouples, claimed

by the manufacturer, and the highest standard deviation verified across all measurements was 0.1 % of

20 30 40 50 60 70 80 900.0

0.2

0.4

0.6

0.8

1.0

1.2

Norm

aliz

ed Inte

nsity [ -

]

Temperature [°C]

NR-LED-IFT

N-LED-LIFT

40

the temperature measured. As for the intensity information, the standard deviation for each set of 50

images was determined. The error obtained for intensity measurements is of 1.5 % for solution at

ambient temperature and increases up to 3.8 % for 92 ℃, as for temperature the error goes from 0.19

to 0.23 ℃, with temperature increasing from 27 ℃ to 92 ℃, corresponding to a maximum relative error

of 0.71% for the lower temperature.

Figure 28 – RhB fluorescence signal collected in the Pool Calibration system.

Normalized fluorescence signal from RhB dye particles for 6 different control points is represented in

Figure 29. As it can be seen, despite the non-uniformities in the illumination system, all six normalized

curves overlap, validating the illumination independence of the method.

The calibration curve is obtained by inverting the normalized curve as presented in Figure 30. A forth

order polynomial that best fits the results with a correlation factor near the unity is also presented so a

continuous range of temperatures for a continuous range of normalized intensities is obtained. A table

with the polynomial characteristics is also shown in the same figure, including the standard deviations

for its coefficients.

30 40 50 60 70 80 90200

400

600

800

1000

1200

Inte

nsi

ty [A

.U.]

Temperature [°C]

Experimental data

Temperature error

Intensity error

41

Figure 29 – Normalized intensity for 6 different control points.

It is worth noting that the polynomial shown is a best-fit for the experimental results, thus special

attention must be paid when applying it with normalized intensity values outside the calibration data

range. Because robustness is needed, the polynomial inflection points are determined so the range of

normalized intensities is limited accordingly, and no major extrapolation errors were observed.

Figure 30 – Calibration curve for the N-LED-IFT technique with a 4th order best fit polynomial.

20 30 40 50 60 70 80 900.0

0.2

0.4

0.6

0.8

1.0

1.2

Norm

aliz

ed Inte

nsi

ty [ -

]

Temperature [°C]

CP 1

CP 2

CP 3

CP 4

CP 5

CP 6

0.0 0.2 0.4 0.6 0.8 1.0 1.20

20

40

60

80

100

Te

mp

era

ture

[°C

]

Normalized Intensity [ - ]

Calibration Experimental Data

Polynomial Fit

Model Polynomial

Adj. R-Square 0.99904

Value Standard Error

Temperature Intercept 149.52582 3.28655

Temperature B1 -381.13887 28.37796

Temperature B2 603.42819 84.99966

Temperature B3 -533.38187 105.58829

Temperature B4 189.61135 46.31142

𝑇 [℃] = 189.6 × 𝑁𝐼4 − 533.4 × 𝑁𝐼3 + 603.4 × 𝑁𝐼2

− 381.1 × 𝑁𝐼 + 149.5

42

5.2. Application of the LED-IFT technique

The results in the previous section showed that the N-LED-IFT technique is the most advantageous.

This chapter addresses the results obtained with this technique applied in two proof-of-concept tests

of different nature, the Training Benchmark applied to the microchannel and the T-shaped micro-

mixer, aiming at discussing the accuracy of the technique to measure temperature distributions in

microflows.

Training Benchmark

The experimental setup used in this application is that described in section 4.1 and shown in Figure 2.

The flow conditions consisted in applying different voltages to the indium oxide layer deposited in the

microchannel outer wall and measuring the fluorescence response, which is later compared with a

simplified theoretical model. This is the benchmark test usually applied to computer chips to track CPU

utilization and performance.

The configuration of the flow is shown in Figure 31, where can be seen the location of two

thermocouples, in 1 and 2, which allow measuring the temperature in the microchannel wall in two

different sections. In the following, subscript 2 refer to the temperature measured in the microchannel

wall in section 2 and subscript 𝑣 represents the fluid temperature measured by the N-LED-IFT

technique in the visualization section, v.

The outer wall of the microchannel is heated by Joule effect with the current supplied by the Gen 150-

5 power supply, with a power output, 𝑃, that can be determined from the product of voltage, 𝑉, by the

current intensity, 𝐼, both acquired from GenesysControl software:

𝑃 = 𝑉𝐼 Equation 27

43

Figure 31 – Microchannel setup.

44

Figure 32 depicts the value of the fluid temperature measured by the fluorescence technique in

purple, 𝑇𝑣, plotted together with the time varying wall temperature measured in section 2, located

downstream from the visualization section. It is also represented in the same figure, in black squares,

the electrical power output from the power source, obtained according to Equation 27. The results

show that the fluid temperature measured with the N-LED-IFT technique behaviour is in accordance

with that of the wall temperature measured by the thermocouple, also in agreement with the power

variations.

Figure 32 – Flow temperature represented in purple, wall temperature in blue and power output from power

source in black.

Looking closely to the heating done around the 43 seconds of the experiment, represented in Figure

33, it can be seen in more detail the temperature responses from the fluorescent technique, of the

flow, and from the thermocouple placed on the microchannel wall, being observed temperature

sensitivities of 30.8 %. 𝑠−1 and of 21.9 %. 𝑠−1, respectively. There can also be noticed a time delay

between the increase in temperature of the fluid and of the microchannel wall, around 0.42 𝑠, which is

in agreement with the time that the fluid takes to go from visualization section to section 2.

0 100 200 300 400

20

40

60

80

Tv T

2 P

Output

Te

mp

era

ture

[°C

]

Time [s]

0

1

2

Pow

er

(W)

45

Figure 33 – Detail on the responses from the N-LED-IFT technique and from the thermocouple measurements for

a power increase.

T-shaped micro-mixer

One of the great advantages of the LED-IFT technique is to be able to obtain 2D fluid temperature

profiles, which are difficult to obtain through traditional temperature measurement techniques as

thermocouples. The suitability of N-LED-IFT method for temperature measurements in microchannel

applications is tested through quantitative visualization of the temperature field in a mixing plane

obtained by driving a hot and a cold fluid stream together in a T-shaped micro mixer.

In Figure 34 the mixing plane for three different volumetric flows is depicted. A lighter (hotter) fluid jet

is observed to flow across the main cold flow stream. Despite small temperature differences between

the two fluid streams, entering at 35 and 24 ℃, contact zones between them are well defined. By

varying the volumetric flow rate from 𝑄𝑐𝑜𝑙𝑑 = 200 𝑚𝐿. ℎ−1 and 𝑄ℎ𝑜𝑡 = 200 𝑚𝐿. ℎ−1 (Figure 34 a), to

𝑄𝑐𝑜𝑙𝑑 = 300 𝑚𝐿. ℎ−1 and 𝑄ℎ𝑜𝑡 = 300 𝑚𝐿. ℎ−1 (Figure 34 b) is evident the movement from the mixing of

both flows to a region further away from the hot fluid inlet, with a higher curve angle when

𝑄𝑐𝑜𝑙𝑑 = 500 𝑚𝐿. ℎ−1 𝑄ℎ𝑜𝑡 = 50 𝑚𝐿. ℎ−1 (Figure 34 c).

46

a) b) c)

Figure 34 – Mixing plane of two laminar stream flows with different velocities: a) 𝑄𝑐𝑜𝑙𝑑 = 200 𝑚𝑙. ℎ−1, 𝑄ℎ𝑜𝑡 =

200 𝑚𝑙. ℎ−1; b) 𝑄𝑐𝑜𝑙𝑑 = 300 𝑚𝑙. ℎ−1, 𝑄ℎ𝑜𝑡 = 300 𝑚𝑙. ℎ−1; c) 𝑄𝑐𝑜𝑙𝑑 = 500 𝑚𝑙. ℎ−1, 𝑄ℎ𝑜𝑡 = 50 𝑚𝑙. ℎ−1. Cold fluid

coming from left to right and hot fluid inlet is on the top.

Increasing the volumetric flow rate, Reynolds number increases, which after some point is reflected in

flow properties modifications. Results are shown in Figure 35. Images were captured for

𝑄𝑐𝑜𝑙𝑑 = 1000 𝑚𝑙. ℎ−1 and 𝑄ℎ𝑜𝑡 = 1000 𝑚𝑙. ℎ−1 with a 5 𝑚𝑠 difference between each image. It can be

noticed that the high spatial resolutions obtained with this temperature measurement technique are

able to retrieve detailed temperature information in the resulting vortexes, and capture the evolution

of heat transfer phenomena in flows with this characteristics in such short timescales.

a) b) c)

Figure 35 – Mixing plane of two stream flows for the same volumetric flow, 𝑄𝑐𝑜𝑙𝑑 = 1000 𝑚𝑙. ℎ−1 and 𝑄ℎ𝑜𝑡 =

1000 𝑚𝑙. ℎ−1. Consecutive images captured with a 200 𝐻𝑧 acquisition rate. Cold fluid coming from the left to the

right and hot fluid inlet on the top.

47

6. Concluding Remarks and Future Work

In this study two LED-IFT techniques were described, tuned and applied to microscale flows in a

microchannel and in a T-shaped junction using a LED SFL100 530 𝑛𝑚 as illumination source.

Rhodamine B and Rhodamine 110 were used as temperature sensitive and temperature insensitive

dyes, respectively. Several parameters impacting the technique implementation were addressed and a

comparison between N-LED-IFT and NR-LED-IFT was performed. Results indicated no straight

advantage in using an extra dye, requiring more computer time/memory capability and adding

computational errors in the process, so N-LED-IFT was chosen to proceed with experiments.

Flow temperatures measured with the N-LED-IFT technique showed similar behaviour with the

microchannel wall temperature measured by the thermocouple. It was verified a time delay between

temperature responses that is in agreement with the time that the fluid takes from visualization to

thermocouple’s section. The capability of this technique to be applied to low and high velocity

microscale flows using a LED illumination source was demonstrated. 2D fluid temperature profiles

where obtained with high spatial (1.54m) and temporal (5 𝑚𝑠) resolutions, being the last one possible

to be increased with higher acquisition rates supported by the highspeed camera. LED illumination

being more stable, less expensive and energy consuming, and at the same allowing a wider range of

wavelengths to appropriately match the maximum wavelength for the fluorescence of the dye,

presents an alternative to lasers in fluorescence based techniques.

Precision and accuracy are directly related with the equipment used for image acquisition and

temperature measurement for the calibration curve. A thorough calibration process was performed

and found crucial to obtain quality results. The results presented showed errors lower than 3.8 % in

fluorescence intensity and lower than 0.71 % in temperature measurements. Call of attention upon the

fact that reference temperatures for the calibration and for the measurements normalization images

must be the same.

Although LED-IFT related errors can be reduced by averaging results over larger areas, consequently

reducing the effect of possible noise present in data acquired, for non-stationary conditions averaging

over large areas will lead to information loss as seen after some preliminary processing. All results

presented in this study are therefore obtained from single-pixel information.

As recommendation for future work, it would be interesting to use another calibration system and/or

illumination source to infer about the influence of those in experimental results, also validating the

48

results presented in this work. Other influencing parameters, such as photo bleaching could become

more relevant and should also be addressed.

In order to validate the results from any LED-IFT technique it is necessary to make use of

Computational Fluid Dynamics (CFD) tools to compare the results obtained from the technique. Also,

studies involving heat transfer in microscale turbulent flows with 2D temperature visualization can

provide better understanding to such complex phenomena.

49

References

[1] S. G. Kandlikar and W. J. Grande, “Evaluation of Single Phase Flow in Microchannels for High Heat Flux

Chip Cooling—Thermohydraulic Performance Enhancement and Fabrication Technology,” Heat Transf.

Eng., vol. 25, no. 8, pp. 5–16, Dec. 2004.

[2] D. B. Tuckerman and R. F. W. Pease, “High-performance heat sinking for VLSI,” IEEE Electron Device

Lett., vol. 2, no. 5, pp. 126–129, May 1981.

[3] C. B. Sobhan and S. V. Garimella, “A Comparative Analysis of Studies on Heat Transfer and Fluid Flow

in Microchannels,” Microscale Thermophys. Eng., vol. 5, no. 4, pp. 293–311, Oct. 2001.

[4] S. Garimella and C. Sobhan, “Transport in microchannels - A critical review,” Annu. Rev. heat Transf., pp.

1–50, 2003.

[5] V. K. Natrajan and K. T. Christensen, “Two-color laser-induced fluorescent thermometry for

microfluidic systems,” Meas. Sci. Technol., vol. 20, no. 1, p. 015401, Jan. 2008.

[6] L. N. Sanford and C. T. Wittwer, “Monitoring temperature with fluorescence during real-time PCR and

melting analysis,” Anal. Biochem., vol. 434, no. 1, pp. 26–33, Mar. 2013.

[7] S. V. Garimella, “Advances in mesoscale thermal management technologies for microelectronics,”

Microelectronics J., vol. 37, no. 11, pp. 1165–1185, Nov. 2006.

[8] S. G. Kandlikar and A. V. Bapat, “Evaluation of jet impingement, spray and microchannel chip cooling

options for high heat flux removal,” Heat Transf. Eng., vol. 28, no. 11, pp. 911–923, Nov. 2007.

[9] P. Chamarthy, S. V. Garimella, and S. T. Wereley, “Measurement of the temperature non-uniformity in

a microchannel heat sink using microscale laser-induced fluorescence,” Int. J. Heat Mass Transf., vol. 53,

no. 15–16, pp. 3275–3283, Jul. 2010.

[10] P.-S. Lee, S. V. Garimella, and D. Liu, “Investigation of heat transfer in rectangular microchannels,” Int. J.

Heat Mass Transf., vol. 48, no. 9, pp. 1688–1704, Apr. 2005.

[11] S. P. Jang, S. J. Kim, and K. W. Paik, “Experimental investigation of thermal characteristics for a

microchannel heat sink subject to an impinging jet, using a micro-thermal sensor array,” Sensors

Actuators A Phys., vol. 105, no. 2, pp. 211–224, Jul. 2003.

[12] T. Chen and S. V. Garimella, “Measurements and high-speed visualizations of flow boiling of a dielectric

fluid in a silicon microchannel heat sink,” Int. J. Multiph. Flow, vol. 32, no. 8, pp. 957–971, Aug. 2006.

[13] J.-H. Tsai and L. Lin, “Transient thermal bubble formation on polysilicon micro-resisters,” J. Heat

Transfer, vol. 124, no. 2, p. 375, 2002.

[14] T. Astarita, G. Cardone, G. M. Carlomagno, C. Meola, M. Melloni, G. Kirchho, C. Maxwell, J. Stefan, L.

Boltzmann, and M. Planck, “A survey on infrared thermography for convective heat transfer

measurements,” Opt. Laser Technol., vol. 32, no. 2000, pp. 593–610, 2001.

[15] G. Hetsroni, a. Mosyak, E. Pogrebnyak, and R. Rozenblit, “Infrared temperature measurements in

micro-channels and micro-fluid systems,” Int. J. Therm. Sci., vol. 50, no. 6, pp. 853–868, Jun. 2011.

[16] M. Wae-hayee, P. Tekasakul, S. Eiamsa-ard, and C. Nuntadusit, “Effect of cross-flow velocity on flow

and heat transfer characteristics of impinging jet with low jet-to-plate distance,” J. Mech. Sci. Technol.,

vol. 28, no. 7, pp. 2909–2917, Jul. 2014.

50

[17] V. U. Kakade, G. D. Lock, M. Wilson, J. M. Owen, and J. E. Mayhew, “Accurate heat transfer

measurements using thermochromic liquid crystal. Part 1: Calibration and characteristics of crystals,”

Int. J. Heat Fluid Flow, vol. 30, no. 5, pp. 939–949, Oct. 2009.

[18] C. D. Richards, R. F. Richards, and S. Boltzman, “Transient temperature measurements in a

convectively cooled droplet,” Exp. Fluids, vol. 25, 1998.

[19] T.-Y. Lin and S. G. Kandlikar, “Heat Transfer Investigation of Air Flow in Microtubes-Part II: Scale and

Axial Conduction Effects.,” J. Heat Transfer, vol. 135, no. 3, pp. 317041–317046, Mar. 2013.

[20] N. Omenetto, P. Benetti, and G. Rossi, “Flame temperature measurements by means of atomic

fluorescence spectrometry,” Spectrochim. Acta Part B At. Spectrosc., vol. 27, no. 10, pp. 453–461, Oct.

1972.

[21] C. Chan and J. W. Daily, “Measurement of temperature in flames using laser induced fluorescence

spectroscopy of OH,” Appl. Opt., vol. 19, no. 12, pp. 1963–1968, 1980.

[22] T. Nakajima, M. Utsunomiya, and Y. Ikeda, “Simultaneous Measurement of Velocity and Temperature of

Water Using LDV and Fluorescence Technique,” in Applications of Laser Techniques to Fluid Mechanics

SE - 3, R. J. Adrian, D. F. G. Durão, F. Durst, M. Maeda, and J. Whitelaw, Eds. Springer Berlin

Heidelberg, 1991, pp. 34–53.

[23] J. Sakakibara, K. Hishida, and M. Maeda, “Experiments in Fluids Measurements of thermally stratified

pipe flow using image-processing techniques,” Exp. Fluids, vol. 96, pp. 82–96, 1993.

[24] J. Sakakibara, K. Hishida, and M. Maeda, “Vortex structure and heat transfer in the stagnation region of

an impinging plane jet (simultaneous measurements of velocity and temperature fields by digital particle

image velocimetry and laser-induced fluorescence),” Int. J. Heat Mass Transf., vol. 40, no. 13, pp. 3163–

3176, Sep. 1997.

[25] T. L. Arbeloa, M. J. T. Estévez, F. L. Arbeloa, I. U. Aguirresacona, and I. L. Arbeloa, “Luminescence

properties of rhodamines in water/ethanol mixtures,” J. Lumin., vol. 48 & 49, pp. 400–404, 1991.

[26] J. Coppeta and C. Rogers, “Dual emission laser induced fluorescence for direct planar scalar behavior

measurements,” Exp. Fluids, vol. 25, no. 1, pp. 1–15, Jun. 1998.

[27] J. Sakakibara and R. J. Adrian, “Whole field measurement of temperature in water using two-color laser

induced fluorescence,” Exp. Fluids, vol. 26, no. 4941, 1999.

[28] P. Lavieille, A. Delconte, D. Blondel, M. Lebouché, and F. Lemoine, “Non-intrusive temperature

measurements using three-color laser-induced fluorescence,” Exp. Fluids, vol. 36, no. 5, pp. 706–716,

May 2004.

[29] M. Bruchhausen, F. Guillard, and F. Lemoine, “Instantaneous measurement of two-dimensional

temperature distributions by means of two-color planar laser induced fluorescence (PLIF),” Exp. Fluids,

vol. 38, no. 1, pp. 123–131, Dec. 2004.

[30] D. Ross, M. Gaitan, and L. E. Locascio, “Temperature measurement in microfluidic systems using a

temperature-dependent fluorescent dye.,” Anal. Chem., vol. 73, no. 17, pp. 4117–23, Sep. 2001.

[31] S. Y. Yoon and K. C. Kim, “Signal intensity enhancement of μ-LIF by using ultra-thin laser sheet

illumination and aqueous mixture with ethanol/methanol for micro-channel applications,” Opt. Lasers

Eng., vol. 44, no. 3–4, pp. 224–239, Mar. 2006.

[32] J. Sakakibara and R. J. Adrian, “Measurement of temperature field of a Rayleigh-Bénard convection using

two-color laser-induced fluorescence,” Exp. Fluids, vol. 37, no. 3, pp. 331–340, Jul. 2004.

51

[33] I. S. Burns and C. F. Kaminski, “Diode Laser Induced Fluorescence for Gas-Phase Diagnostics,”

Zeitschrift für Phys. Chemie, vol. 225, no. 11–12, pp. 1343–1366, Dec. 2011.

[34] A. Rodat-Boutonnet, P. Naccache, A. Morin, J. Fabre, B. Feurer, and F. Couderc, “A comparative study

of LED-induced fluorescence and laser-induced fluorescence in SDS-CGE: application to the analysis of

antibodies.,” Electrophoresis, vol. 33, no. 12, pp. 1709–14, Jul. 2012.

[35] J. V. do N. Palmeira, “Desenvolvimento e optimização de um sensor de fibra óptica para determinação

da concentração de oxigénio,” Universidade de Lisboa, Faculdade de Ciências e Tecnologia, 2009.

[36] J. Vogt, “Development of novel Particle Image Thermometry methods for highly resolved

measurements of temperature and velocity fields in fluids,” TU Darmstadt, 2014.

[37] Inoué, Shinya, Spring, and Kenneth, Video Microscopy: The Fundamentals, 2nd ed. New York: Springer

Berlin Heidelberg, 1997, p. 742.

[38] C. D. Meinhart, S. T. Wereley, and M. H. B. Gray, “Volume illumination for two-dimensional particle

image velocimetry,” Meas. Sci. Technol., vol. 11, no. 6, p. 809, 2000.

[39] S. L. Ellenberger, Influence of Defocus on Measurements in Microscope Images, Volume 51 . ASCI, 2000,

2000, p. 160.

[40] V. Silvério, A. L. N. Moreira, V. Semião, C. A. N. de Carvalho, G. Lavareda, P. Parreira, and A. Amaral,

“Heat transfer investigations in micro-channel flows,” in 15th Int Symp on Applications of Laser Techniques

to Fluid Mechanics, 2010.

[41] L. S. Han, “Hydrodynamic Entrance Lengths for Incompressible Laminar Flow in Rectangular Ducts,” J.

Appl. Mech., vol. 27, no. 3, pp. 403–409, Sep. 1960.

[42] W. Kays, M. Crawford, H. Iacovides, F. P. Incropera, D. P. DeWitt, T. L. Bergman, and A. S. Lavine,

Convective heat and mass transfer, vol. 6th. 1993, p. 137.

[43] “Leica DM IL inverted microscope.” [Online]. Available:

http://cfnewsads.thomasnet.com/images/large/553/553860.jpg. [Accessed: 01-Sep-2014].

[44] R. B. Abernethy, R. P. Benedict, and R. B. Dowdell, “ASME Measurement Uncertainty,” J. Fluids Eng., vol.

107, no. 2, pp. 161–164, Jun. 1985.

52

Appendix

Processing algorithm

A custom made MATLAB® program reads and processes the fluorescence intensity information as well

as the temperature information as described:

% Import data from experiments

If experiment == Dynamic %non-stationary experiments in which fluorescence is monitored over time

Asks for data directory;

Asks for experiment parameters;

Imports data (.txt or .bmp);

Saves data on a structure;

Else

Asks for how many measurements within same experiment and its parameters;

Asks for data directories;

For j = 1 : number of directories

For i = 1 : number of files in each directory

Imports data (.txt or .bmp);

{Intensity on each position} = read i data;

End

Average intensity on each position = mean (intensity on each position);

Standard deviation of Intensity on each position = std (Intensity on each position);

End

Saves data on a structure;

End

If experiment == Test Parameter % usage of control points to monitor parameters influence

Asks for name of the structure containing data;

Asks for the coordinates of opposite edges of the microchannel section;

Calculate central, equally spaced control points;

Creates intensity vectors for each control point;

If plot == Intensity vs Temperature

53

Creates an Intensity vs Temperature plot for the different control points;

Else if plot == Normalized Intensity vs Temperature

Asks for the structure and the name of the reference image;

Performs intensity vectors normalization; % N-LED-IFT or NR-LED-IFT

Creates a Normalized Intensity vs Temperature plot for the different control points;

End

Else if experiment == Calibration

Asks for name of the structure containing the normalized data for the calibration curve;

Asks for the coordinates of the desired control points;

Creates normalized intensity vectors for each control point;

Creates a Normalized Intensity vs Temperature plot for the different control points;

Else if experiment == Dynamic

% Temperature data

For n = 1 : number of voltage steps

Temperature ( n ) = mean (temperature collected for each voltage step);

Standard deviation of Temperature ( n ) = std (temperature collected for each voltage

step);

End

% Imaging data

Asks for the structure and the name of the reference image;

Performs intensity matrices normalization; % N-LED-IFT or NR-LED-IFT

Converts normalized intensity matrices in temperature ones;

Saves temperature matrices in a structure;

End


Recommended