首页|Hangzhou Dianzi University Reports Findings in Machine Learning (Nonionic surfac tant Tween 80-facilitated bacterial transport in porous media: A nonmonotonic co ncentration-dependent performance,mechanism,and machine learning prediction)
Hangzhou Dianzi University Reports Findings in Machine Learning (Nonionic surfac tant Tween 80-facilitated bacterial transport in porous media: A nonmonotonic co ncentration-dependent performance,mechanism,and machine learning prediction)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Machine Learning is th e subject of a report. According to news originating from Zhejiang,People's Rep ublic of China,by NewsRx correspondents,research stated,"The surfactant-enhan ced bioremediation (SEBR) of organic-contaminated soil is a promising soil remed iation technology,in which surfactants not only mobilize pollutants,but also a lter the mobility of bacteria. However,the bacterial response and underlying me chanisms remain unclear." Our news journalists obtained a quote from the research from Hangzhou Dianzi Uni versity,"In this study,the effects and mechanisms of action of a selected noni onic surfactant (Tween 80) on Pseudomonas aeruginosa transport in soil and quartz sand were investigated. The results showed that ba cterial migration in both quartz sand and soil was significantly enhanced with i ncreasing Tween 80 concentration,and the greatest migration occurred at a criti cal micelle concentration (CMC) of 4 for quartz sand and 30 for soil,with incre ases of 185.2% and 27.3%,respectively. The experimen tal results and theoretical analysis indicated that Tween 80-facilitated bacteri al migration could be mainly attributed to competition for soil/sand surface sor ption sites between Tween 80 and bacteria. The prior sorption of Tween 80 onto s and/soil could diminish the available sorption sites for P. aeruginosa,resulting in significant decreases in deposition parameters (70.8% and 33.3% decrease in K in sand and soil systems,respectively),t hereby increasing bacterial transport. In the bacterial post-sorption scenario,the subsequent injection of Tween 80 washed out 69.8% of the bacte ria retained in the quartz sand owing to the competition of Tween 80 with pre-so rbed bacteria,as compared with almost no bacteria being eluted by NaCl solution . Several machine learning models have been employed to predict Tween 80-facilia ted bacterial transport. The results showed that back-propagation neural network (BPNN)-based machine learning could predict the transport of P. aeruginosa through quartz sand with Tween 80 in-sample (2 CMC) and out-of-sample (10 CMC) with errors of 0.79% and 3.77%,respectively."
ZhejiangPeople's Republic of ChinaAs iaCyborgsEmerging TechnologiesMachine LearningQuartzSilicon Dioxide
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