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<p>Solid sampling at the sub-milligram and milligram scale remains a major bottleneck for high-throughput chemistry or material sciences, as existing approaches rely on manual handling or slow deterministic microsampling that do not readily scale. Here we present STORMS, an automated STOchastic Robotic MicroSampling system that enables fast, reliable and parallel sampling of solid materials at sub-milligram and milligram masses. Rather than attempting deterministic mass control, STORMS leverages stochastic sampling combined with robotic automation, and glass encapsulation to achieve statistically robust and reproducible sample collection. We show that this approach delivers high precision and throughput across a range of solid materials, while supporting straightforward parallelization thanks to encapsulation and minimal operator intervention. Benchmarking against conventional sampling workflows demonstrates substantial gains in speed and reproducibility. By decoupling sampling reliability from deterministic mass control, STORMS establishes stochastic microsampling as a general and scalable strategy for solid analysis at the sub-milligram and milligram scale.</p><p></p>

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High-throughput solid microsampling through stochastic robotic automation

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月31日

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  • Adam Lisowski,
  • Henryk Żołnowski,
  • Keyan Villat,
  • Alec Schmidt,
  • Edy Mariano,
  • Jean-Charles Cousty,
  • Jérôme Guérin,
  • Ashley Stark,
  • Frédéric Vasserot,
  • Achim Ammon,
  • David Gueller,
  • Florian de Nanteuil,
  • Jean-Jacques Schwartz,
  • Mathias Cherbuin,
  • Marc Kunze,
  • Giuseppe Costanzo,
  • Pascal Miéville

摘要

Solid sampling at the sub-milligram and milligram scale remains a major bottleneck for high-throughput chemistry or material sciences, as existing approaches rely on manual handling or slow deterministic microsampling that do not readily scale. Here we present STORMS, an automated STOchastic Robotic MicroSampling system that enables fast, reliable and parallel sampling of solid materials at sub-milligram and milligram masses. Rather than attempting deterministic mass control, STORMS leverages stochastic sampling combined with robotic automation, and glass encapsulation to achieve statistically robust and reproducible sample collection. We show that this approach delivers high precision and throughput across a range of solid materials, while supporting straightforward parallelization thanks to encapsulation and minimal operator intervention. Benchmarking against conventional sampling workflows demonstrates substantial gains in speed and reproducibility. By decoupling sampling reliability from deterministic mass control, STORMS establishes stochastic microsampling as a general and scalable strategy for solid analysis at the sub-milligram and milligram scale.