FastNano Liquid: An Automated Platform for Small‐Angle X‐ray Scattering‐Based Materials Discovery

Despite recent progress in machine learning for materials research, a persistent bottleneck is the generation of large, high‐quality datasets to train models. Here, we present a laboratory‐based small‐ and wide‐angle X‐ray scattering (SAXS/WAXS) platform designed for combined synthesis and characterization of (nano)materials. The platform is constructed around an automated SAXS/WAXS instrument coupled with several reactor systems in three different workflows: batch, screening, and continuous flow. The batch workflow allows mL‐ to L‐scale syntheses to be monitored online and in situ under controlled temperature, pH, and reactant injection. The screening workflow uses a liquid‐handling robot to perform syntheses in standard reservoirs and microwell plates, which are passed to the SAXS instrument for high‐throughput at‐line ex situ analysis. The continuous flow workflow enables reactions to be monitored inline and in situ within microfluidic or millifluidic systems by SAXS/WAXS, UV–vis, and/or Raman spectroscopy. Additionally, a spray deposition and laser annealing workflow is presented to enable the preparation and at‐line ex situ characterization of functional thin films from nanomaterial suspensions. We characterize the general performance of the platform and then present a case study for each workflow, illustrating its potential for accelerating materials discovery with future autonomous and machine learning‐based methods.

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Publication Details

Journal
Advanced Engineering Materials
Published
2026-09-14
DOI
https://doi.org/10.1002/adem.71267
Primary Topic
Machine Learning in Materials Science
Type
article
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article

FastNano Liquid: An Automated Platform for Small‐Angle X‐ray Scattering‐Based Materials Discovery

Mark A. Levenstein, Damien Faivre, Corinne Chevallard, Amandine Séné et al.
Advanced Engineering Materials
Machine Learning in Materials Science
article

FastNano Liquid: An Automated Platform for Small‐Angle X‐ray Scattering‐Based Materials Discovery

Mark A. Levenstein, Damien Faivre, Corinne Chevallard, Amandine Séné, Thierry Gacoin, Fabienne Testard, David Benhaiem, Simon Delacroix, Jérémie-Luc Sanchez, David Carrière, Olivier Taché, Michael Lesa, Émeline Cournède, Pierre‐Baptiste Flandrin, Foyzul Hasan, Arnaud Lahouati, Jens Krarup
article en

Abstract

Despite recent progress in machine learning for materials research, a persistent bottleneck is the generation of large, high‐quality datasets to train models. Here, we present a laboratory‐based small‐ and wide‐angle X‐ray scattering (SAXS/WAXS) platform designed for combined synthesis and characterization of (nano)materials. The platform is constructed around an automated SAXS/WAXS instrument coupled with several reactor systems in three different workflows: batch, screening, and continuous flow. The batch workflow allows mL‐ to L‐scale syntheses to be monitored online and in situ under controlled temperature, pH, and reactant injection. The screening workflow uses a liquid‐handling robot to perform syntheses in standard reservoirs and microwell plates, which are passed to the SAXS instrument for high‐throughput at‐line ex situ analysis. The continuous flow workflow enables reactions to be monitored inline and in situ within microfluidic or millifluidic systems by SAXS/WAXS, UV–vis, and/or Raman spectroscopy. Additionally, a spray deposition and laser annealing workflow is presented to enable the preparation and at‐line ex situ characterization of functional thin films from nanomaterial suspensions. We characterize the general performance of the platform and then present a case study for each workflow, illustrating its potential for accelerating materials discovery with future autonomous and machine learning‐based methods.

Advanced Engineering Materials
Latvia University of Life Sciences and Technologies (LV), Centre National de la Recherche Scientifique (FR), École Polytechnique (FR), Commissariat à l'Énergie Atomique et aux Énergies Alternatives (FR), Université Paris-Saclay (FR), Temple College (US), CEA Paris-Saclay (FR), University of Latvia (LV)
Openalex Percentile: Top 24%
Machine Learning in Materials Science
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