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Environmental impact of PFAS: Filling data gaps using theoretical quantum chemistry and QSPR modeling

Environment international, 2024-03, Vol.185, p.108568-108568, Article 108568

2024

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Relatively high-Seebeck thermoelectric cells containing ionic liquids supplemented by cobalt redox couple

Journal of molecular liquids, 2020-10, Vol.316, p.113871, Article 113871

2020

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How the Structure of Per- and Polyfluoroalkyl Substances (PFAS) Influences Their Binding Potency to the Peroxisome Proliferator-Activated and Thyroid Hormone Receptors-An In Silico Screening Study

Molecules (Basel, Switzerland), 2023-01, Vol.28 (2), p.479

2023

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Representation of the Structure-A Key Point of Building QSAR/QSPR Models for Ionic Liquids

Materials, 2020-05, Vol.13 (11), p.2500

2020

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The Effects of Two Kinds of Dietary Interventions on Serum Metabolic Profiles in Haemodialysis Patients

Biomolecules (Basel, Switzerland), 2023-05, Vol.13 (5), p.854

2023

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HBM4EU Chromates Study: Urinary Metabolomics Study of Workers Exposed to Hexavalent Chromium

Metabolites, 2022-04, Vol.12 (4), p.362

2022

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A New Metric for Long-Range Transport Potential of Chemicals

Environmental science & technology, 2014-03, Vol.48 (6), p.3245-3252

2014

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Direct QSPR: the most efficient way of predicting organic carbon/water partition coefficient (log KOC) for polyhalogenated POPs

Structural chemistry, 2014-06, Vol.25 (3), p.997-1004

2014

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Predicting bioconcentration factors (BCFs) for per- and polyfluoroalkyl substances (PFAS)

Chemosphere (Oxford), 2024-09, Vol.364, p.143146, Article 143146

2024

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Prediction of dielectric constant of ionic liquids

Journal of molecular liquids, 2018-06, Vol.260, p.57-64

2018

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Towards higher scientific validity and regulatory acceptance of predictive models for PFAS

Green chemistry : an international journal and green chemistry resource : GC, 2023-02, Vol.25 (4), p.1261-1275

2023

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How the structure of ionic liquid affects its toxicity to Vibrio fischeri?

Chemosphere (Oxford), 2016-09, Vol.159, p.199-207

2016

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Expanding the applicability domain of QSPRs for predicting water solubility and vapor pressure of PFAS

Chemosphere (Oxford), 2023-11, Vol.340, p.139965-139965, Article 139965

2023

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Which structural features stand behind micelization of ionic liquids? Quantitative Structure-Property Relationship studies

Journal of colloid and interface science, 2017-02, Vol.487, p.475-483

2017

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AquaBoxIL – a computational tool for determining the environmental distribution profile of ionic liquids

Green chemistry : an international journal and green chemistry resource : GC, 2018, Vol.20 (14), p.3359-3370

2018

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Multi-Objective Genetic Algorithm (MOGA) As a Feature Selecting Strategy in the Development of Ionic Liquids’ Quantitative Toxicity–Toxicity Relationship Models

Journal of chemical information and modeling, 2018-12, Vol.58 (12), p.2467-2476

2018

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Filling environmental data gaps with QSPR for ionic liquids: Modeling n-octanol/water coefficient

Journal of hazardous materials, 2016-02, Vol.303, p.137-144

2016

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Hybrid Machine Learning and Experimental Studies of Antiviral Potential of Ionic Liquids against P100, MS2, and Phi6

Journal of chemical information and modeling, 2024-03, Vol.64 (6), p.1996-2007

2024

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Towards designing environmentally safe ionic liquids: the influence of the cation structure

Green chemistry : an international journal and green chemistry resource : GC, 2014-01, Vol.16 (11), p.4749-4757

2014

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AquaBoxIL - a computational tool for determining the environmental distribution profile of ionic liquidsElectronic supplementary information (ESI) available. See DOI: 10.1039/c8gc01582a

2018-07

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Virtual screening in the design of ionic liquids as environmentally safe bactericidesElectronic supplementary information (ESI) available. See DOI: 10.1039/c8gc03400a

2019-04

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Geometry optimization method versus predictive ability in QSPR modeling for ionic liquids

Journal of computer-aided molecular design, 2016-02, Vol.30 (2), p.165-176

2016

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Virtual screening in the design of ionic liquids as environmentally safe bactericides

Green chemistry : an international journal and green chemistry resource : GC, 2019, Vol.21 (8), p.1965-1973

2019

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Assessment of the application of selected metal-organic frameworks as advanced sorbents in passive extraction used in the monitoring of contaminants of emerging concern in surface waters

The Science of the total environment, 2024-06, Vol.927, p.172215-172215, Article 172215

2024

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Comment on “Filling environmental data gaps with QSPR for ionic liquids: Modeling n-octanol/water coefficient”

Journal of hazardous materials, 2017-05, Vol.329, p.351-352

2017

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Environmental impact of PFAS: Filling data gaps using theoretical quantum chemistry and QSPR modeling

Relatively high-Seebeck thermoelectric cells containing ionic liquids supplemented by cobalt redox couple

How the Structure of Per- and Polyfluoroalkyl Substances (PFAS) Influences Their Binding Potency to the Peroxisome Proliferator-Activated and Thyroid Hormone Receptors-An In Silico Screening Study

Representation of the Structure-A Key Point of Building QSAR/QSPR Models for Ionic Liquids

The Effects of Two Kinds of Dietary Interventions on Serum Metabolic Profiles in Haemodialysis Patients

HBM4EU Chromates Study: Urinary Metabolomics Study of Workers Exposed to Hexavalent Chromium

A New Metric for Long-Range Transport Potential of Chemicals

Direct QSPR: the most efficient way of predicting organic carbon/water partition coefficient (log KOC) for polyhalogenated POPs

Predicting bioconcentration factors (BCFs) for per- and polyfluoroalkyl substances (PFAS)

Prediction of dielectric constant of ionic liquids

Towards higher scientific validity and regulatory acceptance of predictive models for PFAS

How the structure of ionic liquid affects its toxicity to Vibrio fischeri?

Expanding the applicability domain of QSPRs for predicting water solubility and vapor pressure of PFAS

Which structural features stand behind micelization of ionic liquids? Quantitative Structure-Property Relationship studies

AquaBoxIL – a computational tool for determining the environmental distribution profile of ionic liquids

Multi-Objective Genetic Algorithm (MOGA) As a Feature Selecting Strategy in the Development of Ionic Liquids’ Quantitative Toxicity–Toxicity Relationship Models

Filling environmental data gaps with QSPR for ionic liquids: Modeling n-octanol/water coefficient

Hybrid Machine Learning and Experimental Studies of Antiviral Potential of Ionic Liquids against P100, MS2, and Phi6

Towards designing environmentally safe ionic liquids: the influence of the cation structure

AquaBoxIL - a computational tool for determining the environmental distribution profile of ionic liquidsElectronic supplementary information (ESI) available. See DOI: 10.1039/c8gc01582a

Virtual screening in the design of ionic liquids as environmentally safe bactericidesElectronic supplementary information (ESI) available. See DOI: 10.1039/c8gc03400a

Geometry optimization method versus predictive ability in QSPR modeling for ionic liquids

Virtual screening in the design of ionic liquids as environmentally safe bactericides

Assessment of the application of selected metal-organic frameworks as advanced sorbents in passive extraction used in the monitoring of contaminants of emerging concern in surface waters

Comment on “Filling environmental data gaps with QSPR for ionic liquids: Modeling n-octanol/water coefficient”

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