Gao, Yueyang;
Fhionnlaoich, Niamh Mac;
Besenhard, Max;
Pankajakshan, Arun;
Galvanin, Federico;
Guldin, Stefan;
(2025)
Predicting Aggregation Behavior of Nanoparticles in Liquid Crystals via Automated Data-Driven Workflows.
Advanced Functional Materials
, Article 2501657. 10.1002/adfm.202501657.
(In press).
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Abstract
Gold nanoparticles (AuNPs) have gained prominence as versatile nanoscale building blocks in chemical and biomedical research. Liquid crystals (LCs) offer a promising composite matrix for fundamental research and in a variety of applications. However, optimizing the solubility of AuNPs within the LC matrix remains challenging due to the interplay of multiple experimental variables, necessitating extensive combinatorial trials. In this study, an automated AuNP synthesis platform combined with a Design of Experiment (DoE) framework was employed to streamline the optimization process. A random forest model, trained on a relatively small dataset, successfully predicted nanoparticle aggregate classifications with high accuracy. Aggregate behavior was further analyzed using UV–vis spectroscopy with automated data processing for feature reduction. These steps enhanced the closed-loop optimization workflow by iteratively constructing a generalized additive model for predicting spectral characteristics. AuNPs optimized for solubility were deployed in subsequent experiments for temperature-induced hierarchical assembly driven by the phase transition of the thermotropic LC. Computer vision methods were used to quantify the reversibility of LC-AuNP composites during self-assembly, utilizing entropy values derived from a pattern recognition algorithm. This study highlights the benefits of integrating cross-disciplinary approaches to refine analytical workflows, advancing the discovery of nanomaterial systems with programmable and reconfigurable features.
| Type: | Article |
|---|---|
| Title: | Predicting Aggregation Behavior of Nanoparticles in Liquid Crystals via Automated Data-Driven Workflows |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1002/adfm.202501657 |
| Publisher version: | https://doi.org/10.1002/adfm.202501657 |
| Language: | English |
| Additional information: | © The Author(s), 2025. This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. https://creativecommons.org/licenses/by/4.0/ |
| Keywords: | automated workflow, computer vision analysis, gold nanoparticles, machine learning, reversible self-assembly |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Chemical Engineering |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10206763 |
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