Junjuri Rajendhar, Gummadi Arun Prakash, Gundawar Manoj Kumar
Advanced Centre of Research in High Energy Materials, School of Physics, University of Hyderabad, Hyderabad, Telangana, India.
Leibniz Institute of Photonic Technology, Member of Leibniz Health Technologies, Member of the Leibniz Centre for Photonics in Infection Research (LPI), Jena, Germany.
Appl Spectrosc. 2024 Oct;78(10):1089-1097. doi: 10.1177/00037028241268348. Epub 2024 Aug 9.
We report the standoff/remote identification of post-consumer plastic waste by utilizing a low-cost and compact standoff laser-induced breakdown spectroscopy (ST-LIBS) detection system. A single plano-convex lens is used for collecting the optical emissions from the plasma at a standoff distance of 6.5 m. A compact non-gated Czerny-Turner charge-coupled device (CCD) spectrometer (CT-CCD) is utilized to analyze the optical response. The single lens and CT-CCD combination not only reduces the cost of the detection system by tenfold, but also decreases the collection system size and weight compared to heavy telescopic-based intensified CCD systems. All the samples investigated in this study were collected from a local recycling plant. All the measurements were performed with only a single laser shot which enables rapid identification while probing a large number of samples in real time. Furthermore, principal component analysis has shown excellent separation among the samples and an artificial neural network analysis has revealed that plastic waste can be identified within ∼10 ms only (testing time) with accuracies up to ∼99%. Finally, these results have the potential to build a compact and low-cost ST-LIBS detection system for the rapid identification of plastic waste for real-time waste management applications.
我们报告了利用一种低成本且紧凑的远距离激光诱导击穿光谱（ST-LIBS）检测系统对消费后塑料废物进行远距离/远程识别。使用单个平凸透镜在6.5米的远距离收集来自等离子体的光发射。利用一种紧凑的非门控切尔尼-特纳电荷耦合器件（CCD）光谱仪（CT-CCD）来分析光响应。与基于重型望远镜的增强型CCD系统相比，单透镜和CT-CCD的组合不仅将检测系统的成本降低了十倍，还减小了收集系统的尺寸和重量。本研究中调查的所有样品均从当地回收厂收集。所有测量仅用单次激光脉冲进行，这使得在实时探测大量样品的同时能够快速识别。此外，主成分分析显示样品之间有出色的分离，人工神经网络分析表明仅在约10毫秒（测试时间）内就能识别塑料废物，准确率高达约99%。最后，这些结果有可能构建一个紧凑且低成本的ST-LIBS检测系统，用于实时废物管理应用中塑料废物的快速识别。
