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

Application of interpretable machine learning models to improve the prediction performance of ionic liquids toxicity

With the wide application prospect of ionic liquids (ILs) as solvent in the future industry, in order to promote green and sustainable chemical engineering, the toxicity problem of common concern has been systematically modeled. Machine learning has promoted the development of chemical property prediction model with its powerful data processing ability. Two typical ensemble learning models, Random Forest (RF) and eXtreme Gradient Boosting (XGBoost), were used to model the toxicity of ILs to Vibrio fischeri in this work. The model's hyperparameters were fine-tuned using Bayesian optimization, and its robustness was enhanced through the 5-fold cross validation. The results of the model comparison showed that the XGBoost model exhibited good generalization ability. In addition, the SHapley Additive exPlanations (SHAP) method was used to explain the model in more detail and the XGBoost model was used to supplement the toxicity value matrix of 1590 ILs.

Role of Fungi in Biodegradation of Imidazolium Ionic Liquids by Activated Sewage Sludge

Ionic liquids (ILs), due to their specific properties, can play the role of persistent water contaminants. Fungi manifest the ability to decompose hardy degradable compounds, showing potential in the biodegradation of ILs, which has been studied extensively on sewage sludge; however, attention was drawn mainly to bacterial and not fungal species. The aim of the research was to determine the significance of fungi in ILs' biodegradation to extend the knowledge and possibly point out ways of increasing their role in this process. The research included: the isolation and genetic identification of fungal strains potentially capable of [OMIM][Cl], [BMIM][Cl], [OMIM][Tf₂N], and [BMIM][Tf₂N] degradation, adjustment of the ILs concentration for biodegradability test by MICs determination and choosing strains with the highest biological robustness; inoculum adaptation tests, and finally primary biodegradation by OECD 301F test. The study, conducted for 2 mM [OMIM][Cl] as a tested substance and consortium of microorganisms as inoculum, resulted in an average 64.93% biodegradation rate within a 28-day testing period. For the individual fungal strain (Candida tropicalis), the maximum of only 4.89% biodegradation rate was reached in 10 days, then inhibited. Insight into the role of fungi in the biodegradation of ILs was obtained, enabling the creation of a complex overview of ILs toxicity and the possibilities of its biological use. However, only an inoculum consisting of a consortium of microorganisms enriched with a selected strain of fungi was able to decompose the IL, in contrast to that consisting only of an individual fungal strain.

Deep Probabilistic Learning Model for Prediction of Ionic Liquids Toxicity

Identification of ionic liquids with low toxicity is paramount for applications in various domains. Traditional approaches used for determining the toxicity of ionic liquids are often expensive, and can be labor intensive and time consuming. In order to mitigate these limitations, researchers have resorted to using computational models. This work presents a probabilistic model built from deep kernel learning with the aim of predicting the toxicity of ionic liquids in the leukemia rat cell line (IPC-81). Only open source tools, namely, RDKit and Mol2vec, are required to generate predictors for this model; as such, its predictions are solely based on chemical structure of the ionic liquids and no manual extraction of features is needed. The model recorded an RMSE of 0.228 and R2 of 0.943. These results indicate that the model is both reliable and accurate. Furthermore, this model provides an accompanying uncertainty level for every prediction it makes. This is important because discrepancies in experimental measurements that generated the dataset used herein are inevitable, and ought to be modeled. A user-friendly web server was developed as well, enabling researchers and practitioners ti make predictions using this model.

Ecotoxicity of binary mixtures of ILs and inorganic salts of electrochemical interest

The applicability of ionic liquids (ILs) has increased over the last years, and even new opportunities are becoming a reality, i.e. mixtures of pure IL and inorganic salt as electrolytes for smart electrochemical devices, yet the effects on the environment are almost unknown. In this work, the ecotoxicity of two pure protic ILs (Ethylammonium nitrate and Ethylimidazolium nitrate) and two pure aprotic ILs (butylmethylpyrrolidinium bis(trifluoromethylsulfonyl)imide and butyldimethylimidazolium bis(trifluoromethylsulfonyl)imide) and that of their binary mixtures with inorganic salts with common cation was tested towards changes in the bioluminescence of the bacteria Aliivibrio fischeri, using the Microtox® standard toxicity test. EC₅₀ of these mixtures was determined over three standard periods of time and compared with the corresponding values to pure ILs. Results indicate that the aprotic ILs are more toxic than protic and that aromatic are more toxic than non-aromatic. The addition of inorganic mono (LiNO₃), di (Ca(NO₃)₂·4H₂O, Mg(NO₃)₂·6H₂O) and trivalent (Al(NO₃)₃·9H₂O) salts in binary mixtures with EAN was analysed first. The latter was found to induce an important increase in toxicity. Finally, mixtures of IL-inorganic lithium salt (LiNO₃, for the protic ILs and LiTFSI for the aprotic ILs) toxicity was also studied, which showed toxicity levels strongly dependent on the IL of the mixture.

Comprehensive Interrogation on Acetylcholinesterase Inhibition by Ionic Liquids Using Machine Learning and Molecular Modeling

Quantitative structure-activity relationship (QSAR) modeling can be used to predict the toxicity of ionic liquids (ILs), but most QSAR models have been constructed by arbitrarily selecting one machine learning method and ignored the overall interactions between ILs and biological systems, such as proteins. In order to obtain more reliable and interpretable QSAR models and reveal the related molecular mechanism, we performed a systematic analysis of acetylcholinesterase (AChE) inhibition by 153 ILs using machine learning and molecular modeling. Our results showed that more reliable and stable QSAR models (R² > 0.85 for both cross-validation and external validation) were obtained by combining the results from multiple machine learning approaches. In addition, molecular docking results revealed that the cations and organic anions of ILs bound to specific amino acid residues of AChE through noncovalent interactions such as π interactions and hydrogen bonds. The calculation results of binding free energy showed that an electrostatic interaction (ΔE_ele < -285 kJ/mol) was the main driving force for the binding of ILs to AChE. The overall findings from this investigation demonstrate that a systematic approach is much more convincing. Future research in this direction will help design the next generation of biosafe ILs.

Review of the toxic effects of ionic liquids

Interest in ionic liquids (ILs), called green or designer solvents, has been increasing because of their excellent properties such as thermal stability and low vapor pressure; thus, they can replace harmful organic chemicals and help several industrial fields e.g., energy-storage materials production and biomaterial pretreatment. However, the claim that ILs are green solvents should be carefully considered from an environmental perspective. ILs, given their minimal vapor pressure, may not directly cause atmospheric pollution. However, they have the potential to cause adverse effects if leaked into the environment, for instance if they are spilled due to human mistakes or technical errors. To estimate the risks of ILs, numerous ILs have had their toxicity assessed toward several micro- and macro-organisms over the past few decades. Since the toxic effects of ILs depend on the method of estimating toxicity, it is necessary to briefly summarize and comprehensively discuss the biological effects of ILs according to their structure and toxicity testing levels. This can help simplify our understanding of the toxicity of ILs. Therefore, in this review, we discuss the key findings of toxicological information of ILs, collect some toxicity data of ILs to different species, and explain the influence of IL structure on their toxic properties. In the discussion, we estimated two different sensitivity values of toxicity testing levels depending on the experiment condition, which are theoretical magnitudes of the inherent sensitivity of toxicity testing levels in various conditions and their changes in biological response according to the change in IL structure. Finally, some perspectives, future research directions, and limitations to toxicological research of ILs, presented so far, are discussed.

Cumulative impact assessment of hazardous ionic liquids towards aquatic species using risk assessment methods

In the present research work, a comprehensive tool for cumulative ecotoxicological impact assessment of ionic liquids (ILs) to aquatic life has been constructed. Using the probabilistic tool, impact of individual ILs to a group of aquatic species is assessed by chemical toxicity distributions (CTDs). The impact of group of ILs to individual aquatic species is assessed by species sensitivity distributions (SSDs). Acute toxicity data of imidazolium ILs with chloride (Cl^-), bromide (Br^-), tetrafluoroborate (BF4^-), and hexafluorophosphate (PF6^-) anions are used in CTD and SSD. Allowable concentrations for a group of Imidazolium ILs with the same mode of action (SMOA) to five aquatic species; Daphnia magna, Vibrio fischeri, Algae, Zebrafish, and Escherichia coli are estimated by CTDs. It has been concluded that 1-Butyl-3-methylimidazolium chloride (BMIMCl) possess the lowest risk at an acceptable risk value of 750 × 10^-5 mmol/L which is 12% less than that of OMIMCl. Furthermore, the sensitivities towards the aquatic species reveal that from the studied ILs, BMIMBF4 with an acceptable risk value of 3200 × 10^-5 mmol/L is the most suitable IL towards the selected aquatic species. Hence, current work provides cumulative allowable concentrations and acceptable risk values for ILs which release to aquatic compartment of ecosystem.

A review on machine learning algorithms for the ionic liquid chemical space

There are thousands of papers published every year investigating the properties and possible applications of ionic liquids. Industrial use of these exceptional fluids requires adequate understanding of their physical properties, in order to create the ionic liquid that will optimally suit the application. Computational property prediction arose from the urgent need to minimise the time and cost that would be required to experimentally test different combinations of ions. This review discusses the use of machine learning algorithms as property prediction tools for ionic liquids (either as standalone methods or in conjunction with molecular dynamics simulations), presents common problems of training datasets and proposes ways that could lead to more accurate and efficient models.

Reductive desulfurization of aromatic sulfides with nickel boride in deep eutectic solvents

Deep eutectic solvents were first used as the solvents in the reductive desulfurization process with nickel boride, and the desulfurization performance of nickel boride was greatly improved.

Discovering Low Toxicity Ionic Liquids for Saccharomyces cerevisiae by Using the Agar Well Diffusion Test

Ionic liquids (ILs) are new solvents widely used in many technologies due to their unique and advantageous physicochemical properties. In biotechnological applications, ILs can be used along with microorganisms such as Saccharomyces cerevisiae. Due to the enormous number of ILs that can be synthesized through the combination of different anions and cations, it is necessary to have an easy and quick tool for the preliminary screening of their biocompatibility for being used in biotechnological applications. In this work, the agar well diffusion test was successfully applied as a rapid method to identify toxic/nontoxic ILs toward S. cerevisiae. Sixty-three ILs containing a diverse set of cations and anions were used. Through this methodology, nine fully biocompatible ILs toward S. cerevisiae were identified, including: [Bmim+] [NO3−], [HOPmim+] [NO3−], [Bmim+] [NTf2−], [N8,8,8,1+] [NTf2−], [S2,2,2+] [NTf2−], [EMPyr+] [NTf2−], [BMPi+] [NTf2−], [Moxa+] [MeSO4−] and [Chol+] [H2PO4−]. The analysis of the results also provides preliminary rules to enable the design of biocompatible ILs with S. cerevisiae. In this context, the toxicity was mainly determined by the cation nature although some anions can also display a strong influence on the IL biocompatibility as the bistriflimide anion. Besides, it was observed that an increase in the alkyl chain length of cations, such as imidazolium or pyridinium, involves an increase in the IL toxicity.