2026-10-13 ブルックヘブン国立研究所（BNL）
＜関連情報＞
- https://www.bnl.gov/newsroom/news.php?a=223089
- https://advanced.onlinelibrary.wiley.com/doi/10.1002/aidi.202600006
微粒子状物質のナノスケール化学種分析のための自律型X線蛍光マッピング Autonomous X-Ray Fluorescence Mapping for Nanoscale Chemical Speciation of Fine Particulate Matter
Carlos Deleon, Dmitri Gavrilov, Peggy O’Day, Ajith Pattammattel
Advanced Intelligent Discovery Published: 10 June 2026
DOI:https://doi.org/10.1002/aidi.202600006
Abstract
Fine particulate matter (PM) pollution contributes to millions of premature deaths annually, yet the physicochemical mechanisms governing toxicity remain poorly understood. This reactivity is encoded in the nanoscale chemical speciation and mixing state of individual particles, which are obscured in bulk measurements. Although synchrotron-based X-ray fluorescence (XRF) microscopy provides the necessary sensitivity and spatial resolution, manually locating and scanning statistically meaningful particle populations is prohibitively time-consuming and prone to selection bias. Here, we present X-AutoMap, a modular framework for autonomous XRF experimentation that couples real-time chemical analysis with smart scan orchestration. By formalizing expert heuristics, such as elemental correlation rules distinguishing geogenic (Fe–Si–Ca) from anthropogenic (Fe–Cr–Mn) sources, into an interpretable decision logic, X-AutoMap transforms the microscope into an intelligent agent capable of autonomously identifying chemically relevant features. Validated at the NSLS-II Hard X-ray Nanoprobe, X-AutoMap reduced acquisition time from over 44 h to approximately 10 h while autonomously analyzing ≈500 particles. This work establishes a foundation for smart, high-throughput nanoscale chemical speciation of heterogeneous materials.
