Assessing the ecotoxicity of ionic liquids on Vibrio fischeri using electrostatic potential descriptors

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60460709%3A41330%2F20%3A81825" target="_blank" >RIV/60460709:41330/20:81825 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S0304389420307500?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0304389420307500?via%3Dihub</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.jhazmat.2020.122761" target="_blank" >10.1016/j.jhazmat.2020.122761</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Assessing the ecotoxicity of ionic liquids on Vibrio fischeri using electrostatic potential descriptors

Popis výsledku v původním jazyce

Ionic liquids (ILs) have attracted increasing attention both in the scientific community and the industry in the past two decades. Their risk of being inevitable released to ecosystem lights up the urgent research on their toxicity to the environment. To reduce the time and capital consumption on testing tremendous ILs ecotoxicity experimentally, it is essential to construct predictive models for estimating their toxicity. The objective of this study is to provide a new approach for evaluating the ecotoxicity of ILs. A comprehensive ecotoxicity dataset for Vibrio fischeri involving 142 ILs, was collected and investigated. The electrostatic potential surface areas (S-EP) of separate cations and anions of ILs were firstly applied to develop predictive models for ecotoxicity on Vibrio fischeri. In addition, an intelligent algorithm named extreme learning machine (ELM) was employed to establish the predictive model. The squared correlation coefficients (R-2), the average absolute error (AAE%) and the roo

Název v anglickém jazyce

Assessing the ecotoxicity of ionic liquids on Vibrio fischeri using electrostatic potential descriptors

Popis výsledku anglicky

Ionic liquids (ILs) have attracted increasing attention both in the scientific community and the industry in the past two decades. Their risk of being inevitable released to ecosystem lights up the urgent research on their toxicity to the environment. To reduce the time and capital consumption on testing tremendous ILs ecotoxicity experimentally, it is essential to construct predictive models for estimating their toxicity. The objective of this study is to provide a new approach for evaluating the ecotoxicity of ILs. A comprehensive ecotoxicity dataset for Vibrio fischeri involving 142 ILs, was collected and investigated. The electrostatic potential surface areas (S-EP) of separate cations and anions of ILs were firstly applied to develop predictive models for ecotoxicity on Vibrio fischeri. In addition, an intelligent algorithm named extreme learning machine (ELM) was employed to establish the predictive model. The squared correlation coefficients (R-2), the average absolute error (AAE%) and the roo

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10511 - Environmental sciences (social aspects to be 5.7)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2020

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Journal of Hazardous Materials

ISSN

0304-3894

e-ISSN

1873-3336

Svazek periodika

397

Číslo periodika v rámci svazku

122761

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

9

Strana od-do

1-9

Kód UT WoS článku

000541927100037

EID výsledku v databázi Scopus

2-s2.0-85084241119