We present X-AutoMap, a modular framework for autonomous X-ray fluorescence (XRF) mapping that enables chemically informed targeting of regions of interest through a correlative feature detection strategy. The system integrates classical computer vision and rule-based logic to identify features based on spatial relationships across multiple elemental maps, rather than relying solely on intensity or morphology. Tight integration with the Bluesky control infrastructure at the NSLS-II Hard X-ray Nanoprobe (HXN) beamline enables real-time, closed-loop scan orchestration. Applied to a chemically heterogeneous urban PM2.5 sample, X-AutoMap reduced high-resolution acquisition time from over 44 hours to approximately 10 hours by targeting compositionally significant features identified from coarse scans. High-resolution results revealed diverse particle types, including fully mixed, partially overlapping, and spatially distinct multi-element structures, demonstrating the ability of the framework to isolate chemically relevant features with minimal user intervention. The framework supports interactive and autonomous modes, operates within hardware constraints via grid-based scanning, and is robust across varying sample conditions. Future extensions will incorporate machine learning and probabilistic sampling to further improve detection sensitivity and scan efficiency. X-AutoMap is currently in active use at HXN and provides a flexible foundation for scalable, intelligent imaging workflows at synchrotron beamlines.

翻译：我们提出了X-AutoMap，一种用于自主X射线荧光（XRF）映射的模块化框架，通过关联特征检测策略实现基于化学信息的感兴趣区域定位。该系统集成了经典计算机视觉和基于规则的逻辑，通过多元素图谱间的空间关系识别特征，而非仅依赖强度或形态学信息。与NSLS-II硬X射线纳米探针（HXN）光束线的Bluesky控制基础设施紧密集成，实现了实时闭环扫描编排。应用于化学异质的城市PM2.5样本时，X-AutoMap通过针对粗扫确定的成分显著特征进行高分辨率采集，将耗时从超过44小时缩短至约10小时。高分辨率结果揭示了多样化的颗粒类型，包括完全混合、部分重叠和空间分离的多元素结构，证明了该框架能以最小用户干预分离化学相关特征的能力。该框架支持交互和自主模式，通过基于网格的扫描在硬件限制内运行，并在不同样品条件下保持鲁棒性。未来扩展将结合机器学习和概率采样，以进一步提升检测灵敏度和扫描效率。X-AutoMap目前已在HXN投入使用，为同步辐射光束线的可扩展智能成像工作流程提供了灵活基础。