Modeling the time evolution of the structure factor during polymeric spinodal decomposition using dynamic mode decomposition

The development of the microstructure during polymeric spinodal decomposition can be monitored in real time using small-angle scattering. Information about the microstructure can be deduced from measurements of the structure factor-a quantity directly proportional to the scattered intensity. While the time evolution of the structure factor can be measured relatively easily, modeling it has proved to be much more difficult. We believe the latter could be impeding our ability to control spinodal decomposition. Using synthetic data corresponding to two different polymer blends, we investigate the use of dynamic mode decomposition to model the time evolution of the structure factor during polymeric spinodal decomposition. Based on the accuracy and range of the predictions we obtained using dynamic mode decomposition, we believe our results are promising for the use of dynamic mode decomposition in an experimental setting, which could improve our ability to control spinodal decomposition in automated experiments.

Authors

Institutions

Publication Details

Journal
The Journal of Chemical Physics
Published
2025-09-09
DOI
https://doi.org/10.1063/5.0286708
Primary Topic
Model Reduction and Neural Networks
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Modeling the time evolution of the structure factor during polymeric spinodal decomposition using dynamic mode decomposition

Nigel Clarke, Matthew Jones
The Journal of Chemical Physics
Model Reduction and Neural Networks
article

Modeling the time evolution of the structure factor during polymeric spinodal decomposition using dynamic mode decomposition

Nigel Clarke, Matthew Jones
article en

Abstract

The development of the microstructure during polymeric spinodal decomposition can be monitored in real time using small-angle scattering. Information about the microstructure can be deduced from measurements of the structure factor-a quantity directly proportional to the scattered intensity. While the time evolution of the structure factor can be measured relatively easily, modeling it has proved to be much more difficult. We believe the latter could be impeding our ability to control spinodal decomposition. Using synthetic data corresponding to two different polymer blends, we investigate the use of dynamic mode decomposition to model the time evolution of the structure factor during polymeric spinodal decomposition. Based on the accuracy and range of the predictions we obtained using dynamic mode decomposition, we believe our results are promising for the use of dynamic mode decomposition in an experimental setting, which could improve our ability to control spinodal decomposition in automated experiments.

The Journal of Chemical PhysicsVol. 163(10)
University of Sheffield (GB)
Openalex Percentile: Top 81%
Model Reduction and Neural Networks
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.