CORE CAPABILITY
Targeted
Design
Designing processes for targeted product properties by model based process optimization.
CORE CAPABILITY
Targeted
Design
Designing processes for targeted product properties by model based process optimization.
CORE CAPABILITY
Targeted
Design
Designing processes for targeted product properties by model based process optimization.
USE CASE 01
From target color to particle design
Publication
What we did
We translated a desired transmission color into particle morphology and concentration using optical models and mathematical optimisation. Instead of screening formulations experimentally, we identified promising silver, gold and silica–gold core–shell designs first, then validated selected blueprints in synthesis.
Key Outcomes
01
Start from the specification. Convert a target color into particle size, shell thickness and concentration.
02
Reduce experimental screening. Focus synthesis work on model-ranked candidates instead of trial-and-error.
03
Make trade-offs visible. Compare prediction and experiment to identify robust designs and limits.
The model identifies particle geometries close to the blue target. Darker tiles indicate a lower predicted color difference at optimised concentration. Tiled representation derived from the published map; nonlinear colour scale. Based on Bleyer et al. (2025), Fig. 4b.
Material system
TARGET
PREDICTED
MEASURED
Ag particles
AU particles
Si@Au particles
Three predefined colors realised using silver particles, gold particles and silica–gold core–shell particles. Comparison between taget color, predicted color based on the physical model, and measured color of the optimized structure. Based on Bleyer et al. (2025), Fig. 4.
Publication
Predictive design of plasmonic color
Authors
Gudrun Bleyer, Andrian Uihlein, Umair Sultan, Maret Ickler, Ruri Yamashita, Lukas Pflug, Satoshi Watanabe, Nicolas Vogel
Journal
Journal of Colloid and Interface Science
Year
2025
DOI
10.1016/j.jcis.2025.137408
View publication →
USE CASE 01
From target color to particle design
Publication
What we did
We translated a desired transmission color into particle morphology and concentration using optical models and mathematical optimisation. Instead of screening formulations experimentally, we identified promising silver, gold and silica–gold core–shell designs first, then validated selected blueprints in synthesis.
Key Outcomes
01
Start from the specification. Convert a target color into particle size, shell thickness and concentration.
02
Reduce experimental screening. Focus synthesis work on model-ranked candidates instead of trial-and-error.
03
Make trade-offs visible. Compare prediction and experiment to identify robust designs and limits.
The model identifies particle geometries close to the blue target. Darker tiles indicate a lower predicted color difference at optimised concentration. Tiled representation derived from the published map; nonlinear colour scale. Based on Bleyer et al. (2025), Fig. 4b.
Material system
TARGET
PREDICTED
MEASURED
Ag particles
AU particles
Si@Au particles
Three predefined colors realised using silver particles, gold particles and silica–gold core–shell particles. Comparison between taget color, predicted color based on the physical model, and measured color of the optimized structure. Based on Bleyer et al. (2025), Fig. 4.
Publication
Predictive design of plasmonic color
Authors
Gudrun Bleyer, Andrian Uihlein, Umair Sultan, Maret Ickler, Ruri Yamashita, Lukas Pflug, Satoshi Watanabe, Nicolas Vogel
Journal
Journal of Colloid and Interface Science
Year
2025
DOI
10.1016/j.jcis.2025.137408
View publication →
USE CASE 01
From target color to particle design
Publication
What we did
We translated a desired transmission color into particle morphology and concentration using optical models and mathematical optimisation. Instead of screening formulations experimentally, we identified promising silver, gold and silica–gold core–shell designs first, then validated selected blueprints in synthesis.
Key Outcomes
01
Start from the specification. Convert a target color into particle size, shell thickness and concentration.
02
Reduce experimental screening. Focus synthesis work on model-ranked candidates instead of trial-and-error.
03
Make trade-offs visible. Compare prediction and experiment to identify robust designs and limits.
The model identifies particle geometries close to the blue target. Darker tiles indicate a lower predicted color difference at optimised concentration. Tiled representation derived from the published map; nonlinear colour scale. Based on Bleyer et al. (2025), Fig. 4b.
Material system
TARGET
PREDICTED
MEASURED
Ag particles
AU particles
Si@Au particles
Three predefined colors realised using silver particles, gold particles and silica–gold core–shell particles. Comparison between taget color, predicted color based on the physical model, and measured color of the optimized structure. Based on Bleyer et al. (2025), Fig. 4.
Publication
Predictive design of plasmonic color
Authors
Gudrun Bleyer, Andrian Uihlein, Umair Sultan, Maret Ickler, Ruri Yamashita, Lukas Pflug, Satoshi Watanabe, Nicolas Vogel
Journal
Journal of Colloid and Interface Science
Year
2025
DOI
10.1016/j.jcis.2025.137408
View publication →
USE CASE 02
From target particle size to optimized process control
Publication
What we did
We coupled reaction kinetics, nucleation and particle growth in a differentiable eMoM process model. The workflow optimizes the time-dependent precursor inflow so production can meet a prescribed mean particle size while narrowing the final size distribution. Equipment limits, dosing windows and smoothness constraints can be evaluated before a recipe reaches the plant.
Key Outcomes
01
Design the recipe backwards. Translate a target particle size and distribution width into a time-dependent dosing strategy.
02
Improve product consistency. Concentrate the particle-size distribution around the desired product specification.
03
Respect production constraints. Evaluate dosing limits, batch time and control smoothness before implementation.
USE CASE 02
From target particle size to optimized process control
Publication
What we did
We coupled reaction kinetics, nucleation and particle growth in a differentiable eMoM process model. The workflow optimizes the time-dependent precursor inflow so production can meet a prescribed mean particle size while narrowing the final size distribution. Equipment limits, dosing windows and smoothness constraints can be evaluated before a recipe reaches the plant.
Key Outcomes
01
Design the recipe backwards. Translate a target particle size and distribution width into a time-dependent dosing strategy.
02
Improve product consistency. Concentrate the particle-size distribution around the desired product specification.
03
Respect production constraints. Evaluate dosing limits, batch time and control smoothness before implementation.
USE CASE 02
From target particle size to optimized process control
Publication
What we did
We coupled reaction kinetics, nucleation and particle growth in a differentiable eMoM process model. The workflow optimizes the time-dependent precursor inflow so production can meet a prescribed mean particle size while narrowing the final size distribution. Equipment limits, dosing windows and smoothness constraints can be evaluated before a recipe reaches the plant.
Key Outcomes
01
Design the recipe backwards. Translate a target particle size and distribution width into a time-dependent dosing strategy.
02
Improve product consistency. Concentrate the particle-size distribution around the desired product specification.
03
Respect production constraints. Evaluate dosing limits, batch time and control smoothness before implementation.
Publication
Model based process optimization for nanoparticle precipitation
Authors
Andrea Gilch, Adeel Muneer, Jana Dienstbier, Lukas Pflug
Journal
Reaction Chemistry & Engineering, Vol. 11, p. 1108
Year
2026
DOI
10.1039/D5RE00333D
View publication →
Publication
Model based process optimization for nanoparticle precipitation
Authors
Andrea Gilch, Adeel Muneer, Jana Dienstbier, Lukas Pflug
Journal
Reaction Chemistry & Engineering, Vol. 11, p. 1108
Year
2026
DOI
10.1039/D5RE00333D
View publication →