首页|Reports on Machine Learning Findings from Sichuan University Provide New Insights (Portable Pyrolysis-point Discharge Optical Spectrometer for In Situ Plastic Polymer Identification By Coupling With Machine Learning)
Reports on Machine Learning Findings from Sichuan University Provide New Insights (Portable Pyrolysis-point Discharge Optical Spectrometer for In Situ Plastic Polymer Identification By Coupling With Machine Learning)
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NETL
NSTL
Amer Chemical Soc
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on Machine Learning. According to news originating from Chengdu, People’s Republic of China, by NewsRx correspondents, research stated, “Rapid and in situ identification of specific polymers is a challenging and crucial step in plastic recycling. However, conventional techniques continue to exhibit significant limitations in the rapid and field classification of plastic products, especially with the wide range of commercially available color polymers because of their large size, high energy consumption, and slow and complicated analysis procedures.” Funders for this research include National Natural Science Foundation of China (NSFC), National Natural Science Foundation of China (NSFC). Our news journalists obtained a quote from the research from Sichuan University, “In this work, a simple analytical system integrating a miniature and low power consumption (22.3 W) pyrolyzer (Pyr) and a low temperature, atmospheric pressure point discharge optical emission spectrometer (mu PD-OES) was fabricated for rapidly identifying polymer types. Plastic debris is decomposed in the portable pyrolyzer to yield volatile products, which are then swept into the mu PD-OES instrument for monitoring the optical emission patterns of the thermal pyrolysis products. With machine learning, five extensively used raw polymers and their consumer plastics were classified with an accuracy of >= 97.8%. Furthermore, the proposed method was applied to the identification of the aged polymers and plastic samples collected from a garbage recycling station, indicating its great potential for identification of environmentally weathered plastics.”
ChengduPeople’s Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine LearningSichuan University
NETL
NSTL
Amer Chemical Soc
