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Remediation of Endosulfan by Biotic and Abiotic Methods

Journal of Environmental Protection 2013年第5期4卷 418-425页

作者： Puthenveedu S. P. Harikumar Kottekottil Jesitha mannodi sreechithra Centre for Water Resources Development and Management Kozhikode India Department of Chemical Engineering Government Engineering College Kozhikode India

Endosulfan is a persistent, toxic broad-spectrum organochlorine insecticide and acaricide used on food and non-food crops. To overcome the problem of hydrophobicity of endosulfan, surfactants play a major role in soil remediation. A concentration of 1 g/L tween 80 released 83.89% ± 1.22% of endosulfan from the soil in 24 hours and a total of only 13.96% ± 0.14% was found remaining in the soil. Phytoremediation is an emerging technology that promises effective and inexpensive cleanup of contaminated hazardous waste sites. The potential of aquatic plant species Salvinia molesta and the terrestrial plant species, Spinach (Spinacia oleracea) and Tomato (Solanum lycopersicum), to remove persistent organochlorine pesticide endosulfan from contaminated water and soil respectively were investigated. Remediation of endosulfan in water using different aquatic plants showed that Salvinia molesta is more efficient in endosulfan removal as the percentage removal of endosulfan obtained was 97.94% ± 0.33% in 21 days (with an initial concentration of 123 μg/L endosulfan). Among the selected terrestrial plant species, Spinach and Tomato, percentage removal of endosulfan was found to be higher with tomato. On Day 21, complete removal of pesticide (with an initial concentration of 140 μg/Kg endosulfan) occurred in the soil in which phytoremediation was done with tomato while spinach took about 28 days for complete removal of endosulfan. Microbial degradation offers an effective approach to remove toxicants from the environment. Microbial degradation of endosulfan was studied and Pseudomonas species was able to completely degrade endosulfan in 16 days. The technologies developed can be utilized for the complete removal of endosulfan from a contaminated area. The surfactant enhanced recovery and phytoremediation will help to decontaminate the polluted water and soil. The recovered endosulfan in the surfactants and plants can be subjected to biodegradation by bacterial species using a bi

关键词： Endosulfan Surfactant Tween 80 Phytoremediation Pseudomonas

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Machine learning in materials informatics:recent applications and prospects

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npj Computational Materials 2017年第1期3卷 1-13页

作者： Rampi Ramprasad Rohit Batra Ghanshyam Pilania Arun mannodi-Kanakkithodi Chiho Kim Department of Materials Science&Engineering and Institute of Materials Science University of Connecticut97 North Eagleville Rd.Unit 3136StorrsCT 06269-3136USA Fritz-Haber-Institut der Max-Planck-Gesellschaft Faradayweg 4-614195 BerlinGermany Materials Science and Technology Division Los Alamos National LaboratoryLos AlamosNM 87545USA Center for Nanoscale Materials Lamont National Laboratory9700 S.Cass Ave.LemontIL 60439USA

Propelled partly by the Materials Genome Initiative,and partly by the algorithmic developments and the resounding successes of data-driven efforts in other domains,informatics strategies are beginning to take shape within materials *** approaches lead to surrogate machine learning models that enable rapid predictions based purely on past data rather than by direct experimentation or by computations/simulations in which fundamental equations are explicitly ***-centric informatics methods are becoming useful to determine material properties that are hard to measure or compute using traditional methods—due to the cost,time or effort involved—but for which reliable data either already exists or can be generated for at least a subset of the critical *** are typically interpolative,involving fingerprinting a material numerically first,and then following a mapping(established via a learning algorithm)between the fingerprint and the property of ***,also referred to as“descriptors”,may be of many types and scales,as dictated by the application domain and *** may also be extrapolative—extending into new materials spaces—provided prediction uncertainties are properly taken into *** article attempts to provide an overview of some of the recent successful data-driven“materials informatics”strategies undertaken in the last decade,with particular emphasis on the fingerprint or descriptor *** review also identifies some challenges the community is facing and those that should be overcome in the near future.

关键词： overcome subset properly

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Machine-learned impurity level prediction for semiconductors:the example of Cd-based chalcogenides

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npj Computational Materials 2020年第1期6卷 1346-1359页

作者： Arun mannodi-Kanakkithodi Michael Y.Toriyama Fatih G.Sen Michael J.Davis Robert F.Klie Maria K.Y.Chan Center for Nanoscale Materials Argonne National LaboratoryArgonneIL 60439USA Chemical Sciences and Engineering Division Argonne National LaboratoryArgonneIL 60439USA Department of Physics University of Illinois at ChicagoChicagoIL 60607USA

The ability to predict the likelihood of impurity incorporation and their electronic energy levels in semiconductors is crucial for controlling its conductivity,and thus the semiconductor’s performance in solar cells,photodiodes,and *** difficulty and expense of experimental and computational determination of impurity levels makes a data-driven machine learning approach *** this work,we show that a density functional theory-generated dataset of impurities in Cd-based chalcogenides CdTe,CdSe,and CdS can lead to accurate and generalizable predictive models of defect properties.

关键词： semiconductors prediction impurity

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Author Correction:Machine-learned impurity level prediction for semiconductors:the example of Cd-based chalcogenides

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npj Computational Materials 2020年第1期6卷 558-560页

作者： Arun mannodi-Kanakkithodi Michael Y.Toriyama Fatih G.Sen Michael J.Davis Robert F.Klie Maria K.Y.Chan Center for Nanoscale Materials Argonne National Laboratory Argonne USA Chemical Sciences and Engineering Division Argonne National Laboratory Argonne USA Department of Physics University of Illinois at Chicago Chicago USA

The authors became aware of a mistake in the original version of this ***,some of the band gap values plotted and reported in Fig.1c and Table SI-1 were *** error originated because two different types of k-point meshes were used in DFT computations performed on CdTe,CdSe and CdS:one which is gamma-centered and one which is not gamma-centered.

关键词： prediction originated centered

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