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Peer-reviewedCC BYAdvanced Engineering Materials

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

5 more authors

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.

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BibTeX
@article{flandrin2026fastnano,
  title = {{FastNano Liquid: An Automated Platform for Small‐Angle X‐ray Scattering‐Based Materials Discovery}},
  author = {Pierre‐baptiste Flandrin and Émeline Cournède and Jens Krarup and Michael Lesa and Foyzul Hasan and Amandine Séné and Arnaud Lahouati and David Benhaiem and Jérémie‐luc Sanchez and Simon Delacroix and Thierry Gacoin and Fabienne Testard and Damien Faivre and David Carriere and Corinne Chevallard and Olivier Taché and Mark Alan Levenstein},
  journal = {Advanced Engineering Materials},
  year = {2026},
  month = sep,
  doi = {10.1002/adem.71267},
  url = {https://hal.science/hal-05782101v1},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - FastNano Liquid: An Automated Platform for Small‐Angle X‐ray Scattering‐Based Materials Discovery
AU  - Pierre‐baptiste Flandrin
AU  - Émeline Cournède
AU  - Jens Krarup
AU  - Michael Lesa
AU  - Foyzul Hasan
AU  - Amandine Séné
AU  - Arnaud Lahouati
AU  - David Benhaiem
AU  - Jérémie‐luc Sanchez
AU  - Simon Delacroix
AU  - Thierry Gacoin
AU  - Fabienne Testard
AU  - Damien Faivre
AU  - David Carriere
AU  - Corinne Chevallard
AU  - Olivier Taché
AU  - Mark Alan Levenstein
PY  - 2026
DA  - 2026/09/14
JO  - Advanced Engineering Materials
DO  - 10.1002/adem.71267
UR  - https://hal.science/hal-05782101v1
AB  - 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.
SN  - 1438-1656
SN  - 1527-2648
ER  -