CORE CAPABILITY
Product
Insights
Revealing process-structure and structure-property relationships for optimal product performance.
CORE CAPABILITY
Product
Insights
Revealing process-structure and structure-property relationships for optimal product performance.
CORE CAPABILITY
Product
Insights
Revealing process-structure and structure-property relationships for optimal product performance.
USE CASE
From measurements to mechanistic process knowledge
Publication
What we did
We combined time-resolved UV–Vis spectroscopy, analytical ultracentrifugation, STEM–EDXS and hydrochemical calculations with a two-dimensional population balance model. By connecting particle size, composition and optical response, the model reconstructed the multi-stage AgAu formation pathway and its kinetics.
Key Outcomes
01
Reveal the formation pathway. Identify AgCl precipitation, nucleation, silver-shell growth, and final alloying.
02
Expose hidden process dynamics. Quantify dynamics in Au, Ag and AgCl concentration, hardly measurable.
03
Enable knowledge-based process design. Build predictive property–process relationships for scale-up and control.
Model validation through optical response
The calibrated model follows the measured LSPR peak shift across all investigated alloy compositions and captures the final optical state.
One composition, three hidden species
Individually normalized Au⁰, Ag⁰, and AgCl trajectories reveal fast gold depletion, longer silver availability and gradual AgCl redissolution.
01 MEASURE
Capture experimental data during nanoalloy formation.
02 CALIBRATE
Calibrate a two-dimensional population balance model to measured data.
03 UNDERSTAND
Recover hidden process info and use them to guide process decisions.
Publication
Mechanistic insights into silver-gold nanoalloy formation by two-dimensional population balance modeling
Authors
N. E. Traoré, T. Schikarski, A. Körner, P. Cardenas Lopez, L. Hartmann, B. Fritsch, J. Walter, A. Hutzler, L. Pflug, W. Peukert
Journal
Chemical Engineering Journal
Year
2024
DOI
10.1016/j.cej.2024.149429
View publication →
USE CASE
From measurements to mechanistic process knowledge
Publication
What we did
We combined time-resolved UV–Vis spectroscopy, analytical ultracentrifugation, STEM–EDXS and hydrochemical calculations with a two-dimensional population balance model. By connecting particle size, composition and optical response, the model reconstructed the multi-stage AgAu formation pathway and its kinetics.
Key Outcomes
01
Reveal the formation pathway. Identify AgCl precipitation, nucleation, silver-shell growth, and final alloying.
02
Expose hidden process dynamics. Quantify dynamics in Au, Ag and AgCl concentration, hardly measurable.
03
Enable knowledge-based process design. Build predictive property–process relationships for scale-up and control.
Model validation through optical response
The calibrated model follows the measured LSPR peak shift across all investigated alloy compositions and captures the final optical state.
One composition, three hidden species
Individually normalized Au⁰, Ag⁰, and AgCl trajectories reveal fast gold depletion, longer silver availability and gradual AgCl redissolution.
01 MEASURE
Capture experimental data during nanoalloy formation.
02 CALIBRATE
Calibrate a two-dimensional population balance model to measured data.
03 UNDERSTAND
Recover hidden process info and use them to guide process decisions.
Publication
Mechanistic insights into silver-gold nanoalloy formation by two-dimensional population balance modeling
Authors
N. E. Traoré, T. Schikarski, A. Körner, P. Cardenas Lopez, L. Hartmann, B. Fritsch, J. Walter, A. Hutzler, L. Pflug, W. Peukert
Journal
Chemical Engineering Journal
Year
2024
DOI
10.1016/j.cej.2024.149429
View publication →
USE CASE
From measurements to mechanistic process knowledge
Publication
What we did
We combined time-resolved UV–Vis spectroscopy, analytical ultracentrifugation, STEM–EDXS and hydrochemical calculations with a two-dimensional population balance model. By connecting particle size, composition and optical response, the model reconstructed the multi-stage AgAu formation pathway and its kinetics.
Key Outcomes
01
Reveal the formation pathway. Identify AgCl precipitation, nucleation, silver-shell growth, and final alloying.
02
Expose hidden process dynamics. Quantify dynamics in Au, Ag and AgCl concentration, hardly measurable.
03
Enable knowledge-based process design. Build predictive property–process relationships for scale-up and control.
Model validation through optical response
The calibrated model follows the measured LSPR peak shift across all investigated alloy compositions and captures the final optical state.
One composition, three hidden species
Individually normalized Au⁰, Ag⁰, and AgCl trajectories reveal fast gold depletion, longer silver availability and gradual AgCl redissolution.
01 MEASURE
Capture experimental data during nanoalloy formation.
02 CALIBRATE
Calibrate a two-dimensional population balance model to measured data.
03 UNDERSTAND
Recover hidden process info and use them to guide process decisions.
Publication
Mechanistic insights into silver-gold nanoalloy formation by two-dimensional population balance modeling
Authors
N. E. Traoré, T. Schikarski, A. Körner, P. Cardenas Lopez, L. Hartmann, B. Fritsch, J. Walter, A. Hutzler, L. Pflug, W. Peukert
Journal
Chemical Engineering Journal
Year
2024
DOI
10.1016/j.cej.2024.149429
View publication →