首页|Researchers from University of Minho Report on Findings in Machine Learning (Qui noline-based Hydrazones for Biocide Detection: Machine Learning-aided Design of New Tbt Chemosensors)
Researchers from University of Minho Report on Findings in Machine Learning (Qui noline-based Hydrazones for Biocide Detection: Machine Learning-aided Design of New Tbt Chemosensors)
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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 originating from Braga, Portuga l, by NewsRx correspondents, research stated, "Antifouling compounds are used as paint components to mitigate biofouling on ships and submerged structures. One of the most known and used antifouling compounds is tributyltin (TBT)." Financial support for this research came from North Portugal Regional Operationa l Pro-gramme (ON.2 - O Novo Norte) , under the National Strategic Reference Fram ework (NSRF) , through the European Regional Development Fund (ERDF). Our news journalists obtained a quote from the research from the University of M inho, "However, TBT is toxic to aquatic living beings, causing problems such as reduction of growth and imposex. The development of a TBT chemosensor could be o f utter relevance in the building of an in-situ TBT monitoring device. Therefore , this work reports the synthesis of five new quinoline-based hydrazones (HZ) an d two new quinoline-based thiosemicarbazones (TSC), with synthesis yields from 1 7 to 83 %. The compounds were tested in the presence of TBT, and so me compounds of the group showed colorimetric or fluorimetric changes. The inter action between these compounds and TBT was tested by spectrophotometric or spect rofluorimetric titrations, which allowed to calculate the limit of detection (LO D) for each interaction. The fluorimetric interaction between HZ 4a and TBT was shown to be the most sensitive chemosensory method, with a LOD value of 1.7 mu M . A Ridge classifier model was developed to correlate the ability for TBT detect ion and the modification of the structure of each molecule. The validity of the proposed model was tested by assessing the TBT-sensing ability of the two novel TSC 5a and 5b, which were synthesized after the development of the model. These two compounds also showed colorimetric changes in the presence of TBT, with LODs of 13.8 and 3.1 mu M, respectively, in good accordance with the model's predict ions."
BragaPortugalEuropeCyborgsEmergi ng TechnologiesHydrazinesHydrazonesMachine LearningUniversity of Minho
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