{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T09:07:21Z","timestamp":1789117641514,"version":"build-2803163510"},"reference-count":20,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T00:00:00Z","timestamp":1789084800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100007053","name":"Korea Institute of Energy Technology Evaluation and Planning","doi-asserted-by":"publisher","award":["RS-2023-00236697"],"award-info":[{"award-number":["RS-2023-00236697"]}],"id":[{"id":"10.13039\/501100007053","id-type":"DOI","asserted-by":"publisher"}]},{"award":["RS-2023-00236697"],"award-info":[{"award-number":["RS-2023-00236697"]}],"id":[{"id":"https:\/\/ror.org\/02zq38y32","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Rapid identification of radioactive cesium (Cs) aerosols is important for nuclear severe-accident monitoring. However, conventional analytical methods often require extended measurement times or complex instrumentation. Here, we present a sensitive and compact sensing approach that integrates laser-induced plasma RGB imaging with a convolutional neural network (CNN). A detection configuration was established in which laser irradiation generated plasma from Cs-containing aerosols within a flowing gas stream, and the resulting emission was directly captured using a CMOS camera. Rather than resolving individual emission lines, the CNN learned subtle Cs-dependent variations in the spatial and RGB intensity distributions of the plasma images. To simplify training under limited-data conditions, the model was designed to address a binary classification task, distinguishing Cs-negative conditions (normal) from Cs-positive conditions (abnormal). Predictions from 100 consecutive laser shots were aggregated to provide a sensing decision within 5 s. Using a criterion requiring Cs-positive classification in at least 99% of independent measurements, the operational limit of detection (LOD) was determined to be 0.03 \u03bcg\/m3, approximately one order of magnitude lower than values reported for the closest comparable laser-based cesium aerosol measurements. These results demonstrate that plasma RGB imaging combined with a CNN algorithm can provide a compact, highly sensitive, and real-time platform for cesium aerosol monitoring.<\/jats:p>","DOI":"10.3390\/s26185767","type":"journal-article","created":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T08:17:42Z","timestamp":1789114662000},"page":"5767","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["High-Sensitivity Cesium Aerosol Sensing for Nuclear Severe-Accident Monitoring by Integrating Laser-Induced Plasma RGB Imaging with Convolutional Neural Network (CNN) Analysis"],"prefix":"10.3390","volume":"26","author":[{"given":"Sung-Uk","family":"Choi","sequence":"first","affiliation":[{"name":"Laser Technologies Group, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"},{"role":"software","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"funding-acquisition","vocabulary":"credit"},{"role":"project-administration","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-5529-3296","authenticated-orcid":false,"given":"Chang Uk","family":"Koo","sequence":"additional","affiliation":[{"name":"Pohang Accelerator Laboratory, Pohang University of Science and Technology, Pohang 37673, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"software","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,11]]},"reference":[{"key":"ref_1","unstructured":"Soffer, L., Burson, S.B., Ferrell, C.M., Lee, R.Y., and Ridgely, J.N. 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