NanoSIMS has been the most widely used analytical technique for measuring the elemental and isotopic ratios of micrometer to sub‐micrometer particles, e.g., pre‐solar grains, soil particles, and fog‐helium aerosol particles. Automated sample stage movement combined with particle recognition algorithm is commonly used to improve analytical efficiency. However, due to the effect of sample topography or variation in ion yield rates, the global thresholding method used in previous studies has a low recognition rate. In order to improve the recognition rate, here we have developed a high‐efficiency sub‐micron particle recognition method. This method searches for the threshold that minimizes the intra‐class variance of the local domain (the radius of the local is set to 10 pixels). The central pixel of this circular ROI is then tested against the threshold found for this region. Presolar grains from the Qingzhen (EH3) meteorite and soil organic particles from Oxisol were analyzed. The analytical efficiency was improved by 120‐200% compared with the global thresholding algorithms. This method can be widely applied in automated studies of large numbers of micrometer to sub‐micrometer particles.

中文翻译：

---

使用局部阈值技术对亚微米颗粒进行NanoSIMS测量

NanoSIMS是最广泛使用的分析技术，用于测量微米级与亚微米级颗粒的元素和同位素比，例如太阳前颗粒，土壤颗粒和雾氦气溶胶颗粒。自动化的样品台移动结合粒子识别算法通常用于提高分析效率。但是，由于样品形貌的影响或离子产率的变化，以前的研究中使用的全局阈值法识别率较低。为了提高识别率，我们在这里开发了一种高效的亚微米颗粒识别方法。此方法搜索使局部域的类内差异最小化的阈值（局部的半径设置为10个像素）。然后对照该区域的阈值测试该圆形ROI的中心像素。分析了来自清镇（EH3）陨石的太阳前颗粒和来自Oxisol的土壤有机颗粒。与全局阈值算法相比，分析效率提高了120-200％。该方法可广泛用于大量微米至亚微米颗粒的自动化研究。