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Particle design and formulation for hierarchical materials

Wolfgang PeukertInstitute of Particle Technology

Cluster of Excellence – Engineering of Advanced Materialswww.lfg.fau.de, www.eam.fau.de

150 years BASFSmart energy for a sustainable future

Outline

Concepts of product design and materials assembly

Towards rigorous mathematical optimization

Particle Formation

• Modelling of gas phase synthesis

• Optimization of time – temperature profiles

• Modelling of quantum dot synthesis

• FIMOR: a fully implicit method for Ostwaldt ripening

Aspects of formulation science and technology

Hierarchies: self-assembly and thin films

Conclusions

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Structure – property functions across all levels:Molecular, particulate, particle ensemble, final product

Product design

Process – structure functionsProcess design and process variables determine structure formation ofmolecules, particles, particle ensembles and final product.

Process chain, e.g. for semiconductor formation/application

SynthesisPhase A

Phase transferPhase B Formulation Thin film

High surface area &low pressure drop & catalytic

Transparent & smooth & very strong

Free flowing & easily dispersible& controlled defect state

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Property functions

Property function = f (dispersity, composition)Dispersity: Size, shape, structure, surface

Functional properties of interest:• Mechanical - Young‘s modulus, strength …• Thermodynamic – solubility …• Opto-electronic – permittivity, band gap… • Particle interactions – vdW, charge …

Dry coating

Shape & surface optimization ofpolymer powders for 3D printing

Schmidt et al, Powder Technology 2014 Mehringer et al, Nanoscale 2015

Bandgap of quantum dots (PbS)

Segets et al, ACS Nano 2012

Si@Ge alloys

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Natural Engineering Process Science Fundamentals Technology

0.1 nm 1 nm 10 nm 0.1 µm 1 µm 10 µm 0.1 mm 1 mm 1 cm 0.1 m 1 m

From building blocks to functional devices

Building blocks

200 n

Super-structures

Application

Functional devices

DemonstratorsPrinted transistorsSolar cellsMetamaterialsNew catalystsLightweight components

Semiconducting film

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Enabling technologies

Unifying concepts of product design

Modelling &Simulation

Top-downBottom-up

IntermolecularInterparticulatePhase behaviour

Transport phenomenaSelf-organization

Off-lineOn-lineIn-line

MultiscaleQuantum mechanicsDiscrete elementsContinuum

Particleformation Interactions Structure

formationCharacter-

ization

Hierarchical structure in mesocrystals

200nm 20nm 5nm

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Property

Structure

ProcessOptimized!

Validated models (process, structure) integrated in a unified optimization framework

Numerical optimization based on predictive models

Can we optimize properties and processes based on predictive modeling?

Not optimized!PropertyStructureProcessDefine processvariables

Define properties

process optimization structural optimization

e.g. cooling rate,RTD-temperature profile

LSTM

Klupp Taylor et al, Advanced Materials 2011

Optical properties

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Optimization paradigms

Repeat until done: trial and error approachno improvement guaranteed, no estimates

Stopping criteria: systematic search, no gradientstypically many iterations, no estimates

com

plex

ity

T&E

SciCo

In cooperation with G. Leugering (Erlangen)

FilterSample

Ar, H2, SiH4

GeH4

T = 500°C - 1000°C

Ar quench

N2 quenchPump

T = 900° - 1200°C

Gas phase synthesis of Si@Ge

Objective: Ultrapure semiconductors (Si, Ge)Two-stage hot wall reactor set-up

Patchy particlesSi@Ge

„Monodisperse“SiNPs, σ > 1.04

Körmer et al J. Aerosol Sci 2010, Crystal Growth & Design 2012Mehringer et al, J. Aerosol Sci. 2014, Nanoscale 2015

Particle formation dynamics

Modelling approach for Si NP formation from silane decomposition:

Simplified global gas phase kinetics of decomposition of silane

Homogeneous nucleation of silicon

Growth by global surface reaction of silane with Si NPs

Condensation of free monomers

No need to include agglomeration and sintering due to low particle concentration

( ) ( ) ( ) ( )( ) ( ) ( )x,nDx,nBx

xnxGxfBtxn

aggaggK −+∂

⋅∂−⋅=

∂∂

Population balance approach (simplest case - Si NP formation):

Körmer et al. J. Aerosol Sci. 2010, Gröschel et al, Chem. Eng. Sci. 2012

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Comparison of measured and simulated PSDs

Experimental validation

Optimization step 1:Optimized parameter set for unknown gas phase reaction kinetics for one base case(F1 and F2)

Körmer et al. J. Aerosol Sci. 2010, Gröschel et al, Chem. Eng. Sci. 2012

Size range: 10 – 50 nmT: 900 – 1100 °CIn-situ doping andfunctionalization

Narrow size distributionby separation ofnucleation and growthsimilar to colloid synthesis

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Optimization result (exemplary)

time

[s]

particle diameter [nm]

13

Optimal control aiming at of PSD

Narrow PSD Wide PSD

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Quantum dots: particles with quantum confinement (x < 10 nm)

Many synthetic protocols for tailoring of• size, shape and surface properties by• hot injection method but• mostly empirical approaches

Properties: electronic, optical, catalytic, biological …

Objectives:Develop colloidal process engineering including• understanding of formation mechanisms• stabilization and chemical modification• purification and classification• process modelling• continuous scalable production• modelling of formation dynamics• integration into devices

Towards knowledge-based design and production

Quantum dots

Hot injection method

Source: Talapin group

ZnO QD formation & characterization

0 2 4 6 8 10 120

0.5

1

1.5

2

2.5

particle size [nm]volu

me

dens

ity d

istri

butio

n [n

m-1

]

final PSD: TEMfinal PSD: model10 min20 min50 min100 min240 minfinal PSD: DLS

260 280 300 320 340 360 3800

0.2

0.4

0.6

0.8

wavelength [nm]

abso

rban

ce [-

]

10 min20 min50 min100 min240 min890 min correct construction

of PSDin situUV/Vis

Zn4O(Ac)6 + 6LiOH 4ZnO + 6Li+ + 6Ac-+ 3H2O

Segets et al., J. Phys. Chem. C, 2009

Segets et al., ACS Nano 2009

Evaluation of bimodal absorbance spectra by mixing small and large particles

correct constructionof PSD

small size fractionlarge size fraction

Simulation of Ostwald ripening: challenges due to stiff system behavior

Population Balance Equations (PBE) of Ostwald ripening are challenging

𝑅𝑅 𝑥𝑥, 𝑡𝑡, 𝑐𝑐 = 𝑓𝑓𝑐𝑐 𝑡𝑡𝑐𝑐𝐿𝐿∞

= 𝑓𝑓 𝑒𝑒𝑥𝑥𝑒𝑒4 � 𝛾𝛾 � 𝑉𝑉𝑚𝑚

𝜐𝜐 � 𝑥𝑥 � 𝑘𝑘𝐵𝐵 � 𝑇𝑇≈ 𝑓𝑓 1 +

4 � 𝛾𝛾 � 𝑉𝑉𝑚𝑚𝜈𝜈 � 𝑥𝑥 � 𝑘𝑘𝐵𝐵 � 𝑇𝑇

large gradients around the equilibrium particle size ripening rate and thus the solid concentration exceeds several orders of

magnitude when particle size changes by a few pm discretized system behaviour is extremely stiff

Fully Implicit Method for simulating Ostwald Ripening(developed by M. Gröschel and G. Leugering from Applied Mathematics, FAU)

0 2 4 6 8 102

4

6

8

Process time / h

x 1,3 /

nm

Exp. dataTaylorFIMOR

20 °C

40 °C

0 0.5 1 1.5 2

10-2

100

process time / h

ripen

ing

rate

/ pm

s-1

ΙR(x5,0)Ι

ΙR(x95,0)Ι

ΙR(x50,0)Ι

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From ripening to solubility

0 2 4 6 8 10 122

3

4

5

6

7

8

process time / h

x 1,3 /

nm

T

aggregation50 °C40 °C35 °C30 °C25 °C20 °C15 °C10 °C

0 2 4 62

3

4

5

process time / h

mea

n pa

rticl

e si

ze /

nm

ExperimentFIMOR

10 °C35 °C

25 °C10 °C

Ripening kinetics allows to determine solubility and surface energy

𝑐𝑐𝐿𝐿 𝑥𝑥,𝑇𝑇 = 𝑐𝑐𝐿𝐿∞(𝑇𝑇) � 𝑒𝑒𝑥𝑥𝑒𝑒4 � 𝛾𝛾(𝑇𝑇) � 𝑉𝑉𝑚𝑚𝑥𝑥 � 𝑘𝑘𝐵𝐵 � 𝑇𝑇

.

Kelvin equation

EA = 119 kJ/mol

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Continuous NP synthesis

Objectives Continuous, highly reproducible particle production Transfer from batch reactions Investigation of growth mechanism & kinetics Optimization of process parameters Development of new reactors & processes

Process conditions & challenges Continuous micro-reaction setup Several feed lines Wide temperature and pressure range Flow rates from 100 ml/h to 4 l/h, In-situ optical characterization Advanced process control

1nm

ZnS

10nm

PbS CIS

nucleation rate

Inline optics

UV-VisHRSPL

Continuous production

Here: BaSO4 (mixing controlled)ZnO/Cu (catalyst precursor)CoFe2O4 (battery material)FeOOH (pigment)

Modular microreaction technology

fast reaction / nucleation

slow ripening (or growth) in residence time reactor

in-situ absorbance spectroscopy

combination ofFIMOR – PBE approach

temperature control

ZnO

LiOH

ZnAc2

Modular microreaction setup

0

1

cum

. RTD

/ -

100 1020

1

2

3

diff.

RTD

/ m

in-1

time / min

19.7 ml/min9.8 ml/min4.9 ml/min2.0 ml/min1.0 ml/min0.5 ml/min

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Process optimization

Optimizer FIMOR

T, tR

Cost functional

PSDJ

Application of FIMOR within optimization frameworks• FIMOR: Evolution of PSD as function of T, c0, tR

→ Optimization parameters e.g.: T, tR: 2-D optimization (n = 2)• Objective: Meet mean particle size with sharp PSD in short ripening ti

• Cost functional: weighting factors w depending on application

Matching target mean particle size

Sharp PSD

Short ripening time

w are weights

still reducedoptimization:no simultaneousderivatives

ongoing:all-at-once approach,more optimizationvariables

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2D Process optimization

Variables: Temperature & residence time

Batch• Three different target sizes• Initial values equal for each run• RTD not yet considered• Computation time 15 - 25 min

0 5 10 1510

20

30

40

50

Residence time / hrs

Tem

pera

ture

/ °C

Initial valuesxtarget = 3.0 nmxtarget = 4.5 nmxtarget = 5.5 nmFinal values

Continuous microreactor

Interactions and structure formation

See the fundamental hierarchy:

electronic structure of atoms, molecules, particles which define the

interactions between the objects, these determine the

structure which in turn defines

macroscopic properties

Porosity, pressure losselectrical & thermal conductivity,

light absorption & scattering, catalytic activity …..

O

OHOH

NH

N N

BrBr

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Scalable synthesis of building blocks

Stabilization and surface engineering to• ensure colloidal stability• tailor rheology• control self-organization

Thin film formation in functional device

Formulation en route towards devices

Faber et al., Nanoscale 2011

Colloidal stability

explanation only with ζ-potential is not possible

stable unstable

ZnO QDs

Suspension is not stable when ελ = 400 nm > 0 Scattering by agglomerates Monitoring with UV/Vis spectroscopy and AUC

After synthesis: stable1 washing cycle: stable2 washing cycles: stable3 washing cycles: not stable

Stabilization against agglomeration

Balancing interaction potentials (electrostatic & steric repulsion, vdW attraction)

Reindl et al., J. Coll. Int. Sci., 2008

washing

• Removal of protecting acetate ionsfrom particle surface during washing

• Stability map by screening of variousexperimental datapoints

Marczak et al., Adv. Pow. Techn., 2010Segets, Marczak et al., ACS Nano, 2011 Electrostatic repulsion / vdW attraction

ster

icco

ntrib

utio

n

O

OHOH

NH

N N

BrBr

O

HO OH

HN

NBr

NP surfaces in the focus

Solid-liquid interface largely unknown (binding motifs, ligand exchange …) Case study: Functionalization of ZnO with different catechols

Wei Lin et al., Chemistry of Materials, 2015

0 2 4 6 8 10 120.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

2.6ML

2ML

∆G=-19.50 kJ mol-1

ZnO-catechol Langmuir fit

Surf

ace

cove

rage

(θ)

Free CAT concentration (mM)

R2=0.957Θ=K[L]/(1+K[L])K=2618247 Μ-1

physisorptionchemisorption

1ML

Surface coverage from ICP, NMR, AUC

K / M-1 3697 ± 620

∆H / kJ mol-1 -16.35 ± 0.89∆G / kJ mol-1 -20.32 ± 0.43∆S / J K-1 mol-1 +13.31 ± 3.24

For comparison:

ΔG = -50 kJ ∙ mol-1 for Au-SΔG = -30 kJ ∙ mol-1 for CdSe-S

Binding strength ITC, (here: ZnO / catechol)

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Self-organization of particles

Particles with „molecular“ complexityComplex superstructures@new propertiesPhase behaviourMechanisms and kinetics of formation

Glotzer et al, Nature 2007

Library of building blocks andtheir organization principles

1 µm

300 nm100 nm

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Zn(CH3COO)2 *2H2O

initiation in block copolymer melt,sonication in water

Complexation of Zn(CH3COO)2 within the polymer matrix at 150 °C in the melt.

ZnO superstructures in polymer melt

Jeffamine block copolymer

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Consecutive oriented, multiple aggregation process

ZnO ellopsoid formation mechanism

Klaumünzer et al, Cryst.Comm.Eng. 2014

Temporal evolution of UV/VIS spectra

5 min

10 min

15 min

20 min

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Define• Identification of

desirable optical properties

Design• Forward simulation• Shape and topology

optimization

Build• “Toolkit“ for

nanostructured particle synthesis

Test• Single and

multiple particle optical characterization

Ext

inct

ion

Wavelength

Product design of optical materials

Design of transparent UV absorbers

Nanoscale 2012

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One perspective: Optical properties …

…. strongly depend on size, shape, core-shell properties, topology and materials

Colloidal crystals (N. Vogel) Patchy particles (R. Klupp Taylor)

Shape optimization (G. Leugering): Making particles invisible

Functional thin films

Relevance of layered systems, e.g.

• Electrodes, e.g. fuel cells, electrolysis, batteries• Printable electronics, e.g. displays, LEDs• Membranes• Solar absorber & solar cells• Supercaps• Thermoelectrical devices• Heat management• Catalyst layers• …..

TEM Tomography, E. Spiecker, Erlangen

SAM@pressure Ordered C60-SAM monolayerSAM@FET

Key questions: Understanding and tailoring structure-property and process-structure functions Layered systems and their interactions (adhesion, transfer across interfaces) Continuous role-to-roll production

Particleformation

Gas phase synthesisHydrothermal synthesisPrecipitation…..

Process chain for printed thin films

200 nm

200 nm

200 nm

Functionalisation& Spectroscopy

fs spectroscopyESR, PL, IS, HRTEM

Sn doped In2O3 K

Ene

rgy

Undoped In2O3

StabilizationLayer formation

Chemical modificationSpin-/dip-coatingµ-contact printing….

Printing

Roll-to-roll-processesTape castingInk jet printing …

Process chain for printed thin films

Device fabrication

FETLED, Solar cellFuel cells, electrolysis

DryingDensification

Structure formationLaser annealing…

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between the building blocks,

across time and length scales,

between the involved disciplines,

between academia and industry,

… between you and us!

Many thanks !

My coworkersDr. B. Braunschweig, Dr. M. Distaso, M. Haderlein, M. Klaumünzer,

C. Mehringer, C. Meltzer, Dr. D. Segets (LFG)

For excellent cooperationColleagues in the Cluster of Excellence (EAM)

Profs. T. Clark, R. Klupp Taylor, G. Leugering, E. Spiecker

For fundingDFG - Priority Program „Dynamic Modelling of Solids Processes“

Research Training Group „Disperse Systems for Electronics“German Excellence Initiative

BASF