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.
Authors
- Mark A. Levenstein (ORCID: https://orcid.org/0000-0002-2309-3743)
- Damien Faivre (ORCID: https://orcid.org/0000-0001-6191-3389)
- Corinne Chevallard (ORCID: https://orcid.org/0000-0001-6859-8190)
- Amandine Séné
- Thierry Gacoin (ORCID: https://orcid.org/0000-0001-6774-3181)
- Fabienne Testard (ORCID: https://orcid.org/0000-0002-5603-9516)
- David Benhaiem
- Simon Delacroix (ORCID: https://orcid.org/0000-0002-0632-8627)
- Jérémie-Luc Sanchez (ORCID: https://orcid.org/0000-0003-0666-2745)
- David Carrière (ORCID: https://orcid.org/0000-0001-8432-5344)
- Olivier Taché (ORCID: https://orcid.org/0000-0001-6912-2610)
- Michael Lesa
- Émeline Cournède
- Pierre‐Baptiste Flandrin (ORCID: https://orcid.org/0009-0002-8115-5655)
- Foyzul Hasan (ORCID: https://orcid.org/0009-0001-8867-4178)
- Arnaud Lahouati (ORCID: https://orcid.org/0009-0006-1190-404X)
- Jens Krarup (ORCID: https://orcid.org/0009-0007-0173-1226)
Institutions
- 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)
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
- Field-Weighted Citation Impact
- 0.00