Structural features promoting adsorption of contaminants of emerging concern onto TiO2 P25: experimental and computational approaches

[1]  Chul-Woong Cho,et al.  Development of prediction models for adsorption properties of chitin and chitosan for micropollutants , 2021 .

[2]  A. Fernández-Alba,et al.  Fate, modeling, and human health risk of organic contaminants present in tomato plants irrigated with reclaimed water under real-world field conditions. , 2021, The Science of the total environment.

[3]  Joana P. Fernandes,et al.  Pharmaceutical Compounds in Aquatic Environments—Occurrence, Fate and Bioremediation Prospective , 2021, Toxics.

[4]  Alexander Nti Kani,et al.  A review on functionalized adsorbents based on peanut husk for the sequestration of pollutants in wastewater: Modification methods and adsorption study , 2021 .

[5]  N. Bolan,et al.  Modification of naturally abundant resources for remediation of potentially toxic elements: A review. , 2021, Journal of hazardous materials.

[6]  Zhongfang Chen,et al.  Predicting the adsorption of organic pollutants on boron nitride nanosheets via in silico techniques: DFT computations and QSAR modeling , 2021, Environmental Science: Nano.

[7]  Xiaotian Xu,et al.  Prediction of organic compounds adsorbed by polyethylene and chlorinated polyethylene microplastics in freshwater using QSAR. , 2021, Environmental research.

[8]  F. Lai,et al.  Occurrence and removal of chemicals of emerging concern in wastewater treatment plants and their impact on receiving water systems. , 2020, The Science of the total environment.

[9]  H. Kušić,et al.  Degradation of polar and non-polar pharmaceutical pollutants in water by solar assisted photocatalysis using hydrothermal TiO2-SnS2 , 2020 .

[10]  S. Subbiah,et al.  Comparative assessment of raw and acid-activated preparations of novel Pongamia pinnata shells for adsorption of hexavalent chromium from simulated wastewater , 2020, Environmental Science and Pollution Research.

[11]  T. Bolanča,et al.  Structural features of contaminants of emerging concern behind empirical parameters of mechanistic models describing their photooxidative degradation , 2020, Journal of Water Process Engineering.

[12]  K. Roy,et al.  Exploring QSPR modeling for adsorption of hazardous synthetic organic chemicals (SOCs) by SWCNTs. , 2019, Chemosphere.

[13]  William R. Dichtel,et al.  QSARs to predict adsorption affinity of organic micropollutants for activated carbon and β-cyclodextrin polymer adsorbents. , 2019, Water research.

[14]  T. Mlsna,et al.  Pharmaceuticals of Emerging Concern in Aquatic Systems: Chemistry, Occurrence, Effects, and Removal Methods. , 2019, Chemical reviews.

[15]  T. Bolanča,et al.  Toxicity of aromatic pollutants and photooxidative intermediates in water: A QSAR study. , 2019, Ecotoxicology and environmental safety.

[16]  D. Dionysiou,et al.  Key structural features promoting radical driven degradation of emerging contaminants in water. , 2019, Environment international.

[17]  M. R. Alavi Moghaddam,et al.  Application of response surface methodology in physicochemical removal of dyes from wastewater: A critical review. , 2018, The Science of the total environment.

[18]  Seth R. Newton,et al.  Comparison of emerging contaminants in receiving waters downstream of a conventional wastewater treatment plant and a forest-water reuse system , 2018, Environmental Science and Pollution Research.

[19]  A. Goonetilleke,et al.  Treatment Technologies for Emerging Contaminants in water: A review , 2017 .

[20]  M. Farzadkia,et al.  Contaminants of emerging concern: a review of new approach in AOP technologies , 2017, Environmental Monitoring and Assessment.

[21]  M. Fanetti,et al.  TiO2-SnS2 nanocomposites: solar-active photocatalytic materials for water treatment , 2017, Environmental Science and Pollution Research.

[22]  C. Metcalfe,et al.  Estimating removals of contaminants of emerging concern from wastewater treatment plants: The critical role of wastewater hydrodynamics. , 2017, Chemosphere.

[23]  T. Bolanča,et al.  Prediction of biodegradability of aromatics in water using QSAR modeling. , 2017, Ecotoxicology and environmental safety.

[24]  I. Konstantinou,et al.  Degradation of venlafaxine using TiO2/UV process: Kinetic studies, RSM optimization, identification of transformation products and toxicity evaluation. , 2017, Journal of hazardous materials.

[25]  D. Bikiaris,et al.  Photocatalytical removal of fluorouracil using TiO2-P25 and N/S doped TiO2 catalysts: A kinetic and mechanistic study. , 2017, The Science of the total environment.

[26]  Neera Singh,et al.  Kinetic and isotherm error optimization studies for adsorption of atrazine and imidacloprid on bark of Eucalyptus tereticornis L. , 2016, Journal of environmental science and health. Part. B, Pesticides, food contaminants, and agricultural wastes.

[27]  M. Rahimi‐Nasrabadi,et al.  Predicting adsorption of aromatic compounds by carbon nanotubes based on quantitative structure property relationship principles , 2015 .

[28]  John L. Zhou,et al.  Adsorptive removal of antibiotics from water and wastewater: Progress and challenges. , 2015, The Science of the total environment.

[29]  D. Dionysiou,et al.  Prediction of key structural features responsible for aromaticity of single-benzene ring pollutants and their photooxidative intermediates , 2015 .

[30]  T. Bolanča,et al.  Modeling Photo-oxidative Degradation of Aromatics in Water. Optimization Study Using Response Surface and Structural Relationship Approaches , 2015 .

[31]  Paola Gramatica,et al.  QSARINS‐chem: Insubria datasets and new QSAR/QSPR models for environmental pollutants in QSARINS , 2014, J. Comput. Chem..

[32]  Pengfei Xuan,et al.  Development of a 3D QSPR model for adsorption of aromatic compounds by carbon nanotubes: comparison of multiple linear regression, artificial neural network and support vector machine , 2013 .

[33]  Paola Gramatica,et al.  QSARINS: A new software for the development, analysis, and validation of QSAR MLR models , 2013, J. Comput. Chem..

[34]  P. Pichat Photocatalysis and water purification : from fundamentals to recent applications , 2013 .

[35]  D. Ollis Photocatalytic Treatment of Water: Irradiance Influences , 2013 .

[36]  Ting Shao,et al.  Predictive model development for adsorption of aromatic contaminants by multi-walled carbon nanotubes. , 2013, Environmental science & technology.

[37]  Kunal Roy,et al.  Electrotopological state atom (E-state) index in drug design, QSAR, property prediction and toxicity assessment. , 2012, Current computer-aided drug design.

[38]  D. Bahnemann,et al.  Removal of microorganisms and their chemical metabolites from water using semiconductor photocatalysis. , 2012, Journal of hazardous materials.

[39]  Nikolaos Koukouzas,et al.  Removal of heavy metals from wastewater using CFB-coal fly ash zeolitic materials. , 2010, Journal of hazardous materials.

[40]  G. Vidal,et al.  OPTIMIZATION BY FACTORIAL DESIGN OF COPPER(II) AND TOXICITY REMOVAL USING A PHOTOCATALYTIC PROCESS WITH TIO2 AS SEMICONDUCTOR , 2009 .

[41]  Paola Gramatica,et al.  Evaluation and QSAR modeling on multiple endpoints of estrogen activity based on different bioassays. , 2008, Chemosphere.

[42]  S. Esplugas,et al.  Photocatalytic degradation of non-steroidal anti-inflammatory drugs with TiO2 and simulated solar irradiation. , 2008, Water research.

[43]  P. Belelli,et al.  Theoretical modeling of photocatalytic active species on illuminated TiO2 , 2007 .

[44]  Gerta Rücker,et al.  y-Randomization and Its Variants in QSPR/QSAR , 2007, J. Chem. Inf. Model..

[45]  Paola Gramatica,et al.  Principles of QSAR models validation: internal and external , 2007 .

[46]  A. Marczewski,et al.  Effect of adsorbate structure on adsorption from solutions , 2002 .

[47]  Anderson Coser Gaudio,et al.  BuildQSAR: A New Computer Program for QSAR Analysis , 2000 .

[48]  Hein Putter,et al.  The bootstrap: a tutorial , 2000 .

[49]  I. Arslan Heterogeneous photocatalytic treatment of simulated dyehouse effluents using novel TiO2-photocatalysts , 2000 .

[50]  B. Bourges,et al.  Quantitative Structure−Property Relationship (QSPR) for the Adsorption of Organic Compounds onto Activated Carbon Cloth: Comparison between Multiple Linear Regression and Neural Network , 1999 .

[51]  I. Suffet,et al.  Quantitative structure-activity relationship using molecular connectivity for the activated carbon adsorption of organic chemicals in water , 1994 .

[52]  P. Gramatica Principles of QSAR Modeling: Comments and Suggestions From Personal Experience , 2020 .

[53]  Paola Gramatica,et al.  CHEMOMETRIC METHODS AND THEORETICAL MOLECULAR DESCRIPTORS IN PREDICTIVE QSAR MODELING OF THE ENVIRONMENTAL BEHAVIOR OF ORGANIC POLLUTANTS , 2010 .

[54]  M. Muir Physical Chemistry , 1888, Nature.