首页|Researchers from Ningbo University Report New Studies and Findings in the Area o f Machine Learning (Broad Application Prospects Near-infrared Carbonized Polymer Dots Combined With Machine Learning for the Detection of Cu2+in Seawater and .. .)
Researchers from Ningbo University Report New Studies and Findings in the Area o f Machine Learning (Broad Application Prospects Near-infrared Carbonized Polymer Dots Combined With Machine Learning for the Detection of Cu2+in Seawater and .. .)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Research findings on Machine Learning are discussed in a new report. According to news reporting from Ningbo, People's Republic of China, by NewsRx journalists, research stated, "A highly selective and sensitive fluorescence sensor, using the decision tree (DT) machine learning , was successfully fabricated for the quantitative detection of Cu2+, which is b ased on near-infrared carbonized polymer dots (r-CPDs). Using o-phenylenediamine and 1,3,5-benzene tricarboxylic acid as raw materials first to synthesize the r -CPDs (quantum yield of 32.9%), which pyrrole NH ring can specifica lly form metal chelates with Cu2+ hiding the electron leap result in the fluores cence burst, by a one-step hydrothermal synthesis method." Funders for this research include Public Welfare Research Project of Ningbo, Nin gbo Science and Technology Bureau, Pioneer and Leading Goose R & D Program of Zhejiang. The news correspondents obtained a quote from the research from Ningbo Universit y, "Furthermore, the fluorescence sensor based on the r-CPDs was fabricated for ultra-sensitively detect Cu2+ in the range of 0.5-80.0 nM (R2 =0.9986) in aqueou s environments and aquatic products with relative standard deviation (RSD) below 4.4% and a limit of detection (LOD) of 0.24 nM. Combined with the machine learning algorithm model, the r-CPDs fluorescence color changes accompa nied by different Cu2+ concentrations were classified. A self-developed smartpho ne application equipped with 3D printing technology to prepare portable cartridg es successfully applied to rapid real-time detection of trace Cu2+ in various pr actical samples. The experimental results show that the method has not only conv enient for calculating but also accurate."
NingboPeople's Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine LearningNingbo University
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