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

Viruses are a group of widespread organisms that are often responsible for very dangerous diseases, as most of them follow a mechanism to multiply and infect their hosts as quickly as possible. Pathogen viruses also mutate regularly, with the result that measures to prevent virus transmission and recover from the disease caused are often limited. The development of new substances is very time-consuming and highly budgeted and requires the sacrifice of many living organisms. Computational chemistry methods allow faster analysis at a much lower cost and, most importantly, reduce the number of living organisms sacrificed experimentally to a minimum. Ionic liquids (ILs) are a group of chemical compounds that could potentially find a wide range of applications due to their potential virucidal activity. In our study, we conducted a complex computational analysis to predict the antiviral activity of ionic liquids against three surrogate viruses: two nonenveloped viruses, Listeria monocytogenes phage P100 and Escherichia coli phage MS2, and one enveloped virus, Pseudomonas syringae phage Phi6. Based on experimental data of toxic activity (logEC90), we assigned activity classes to 154 ILs. Prediction models were created and validated according to the Organization for Economic Co-operation and Development (OECD) recommendations using the Classification Tree method. Further, we performed an external validation of our models through virtual screening on a set of 1277 theoretically generated ionic liquids and then selected 10 active ionic liquids, which were synthesized to verify their activity against the analyzed viruses. Our study proved the effectiveness and efficiency of computational methods to predict the antiviral activity of ionic liquids. Thus, computational models are a cost-effective alternative approach compared with time-consuming experimental studies where live animals are involved.

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.

In this study, we investigated PFAS (per- and polyfluoroalkyl substances) binding potencies to nuclear hormone receptors (NHRs): peroxisome proliferator-activated receptors (PPARs) α, ß, and γ and thyroid hormone receptors (TRs) α and ß. We have simulated the docking scores of 43 perfluoroalkyl compounds and based on these data developed QSAR (Quantitative Structure-Activity Relationship) models for predicting the binding probability to five receptors. In the next step, we implemented the developed QSAR models for the screening approach of a large group of compounds (4464) from the NORMAN Database. The in silico analyses indicated that the probability of PFAS binding to the receptors depends on the chain length, the number of fluorine atoms, and the number of branches in the molecule. According to the findings, the considered PFAS group bind to the PPARα, ß, and γ only with low or moderate probability, while in the case of TR α and ß it is similar except that those chemicals with longer chains show a moderately high probability of binding.

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

Quantitative toxicity-toxicity relationship (QTTR) models have a great potential for improving the meaning of toxicological tests conducted on simple organisms. These models allow predicting the toxicological effect of a chemical based on its known toxicological effect in different toxicity tests, even against a different organism. This fact poses a great potential for predicting the toxicity of chemicals against higher organisms based on the results against lower ones. However, the possibility of developing such models is often restricted due to the low availability of data. We present a case study of developing the QTTR model for ionic liquids in different toxicological tests against the same species, in the face of insufficient experimental data (an additional confirmation for a different species is provided in the Supporting Information). In the presented case, we use a series of quantitative structure-activity relationship (QSAR) models developed to deliver the data concerning the toxicity of ionic liquids against human HeLa and MCF-7 cancer cell lines. We use these data to develop a QTTR model with an R2 as high as 0.8. The benefit of applying the multi-objective genetic algorithm (MOGA-a genetic algorithm allowing for selection of the best set of explanatory features for several different dependent variables at the same time) as a QSAR model feature selecting strategy is presented and discussed.

Towards the Application of Structure-Property Relationship Modeling in Materials Science: Predicting the Seebeck Coefficient for Ionic Liquid/Redox Couple Systems.

This work focuses on determining the influence of both ionic-liquid (IL) type and redox couple concentration on Seebeck coefficient values of such a system. The quantitative structure-property relationship (QSPR) and read-across techniques are proposed as methods to identify structural features of ILs (mixed with LiI/I2 redox couple), which have the most influence on the Seebeck coefficient (Se ) values of the system. ILs consisting of small, symmetric cations and anions with high values of vertical electron binding energy are recognized as those with the highest values of Se . In addition, the QSPR model enables the values of Se to be predicted for each IL that belongs to the applicability domain of the model. The influence of the redox-couple concentration on values of Se is also quantitatively described. Thus, it is possible to calculate how the value of Se will change with changing redox-couple concentration. The presence of the LiI/I2 redox couple in lower concentrations increases the values of Se , as expected.

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

Computational techniques, such as Quantitative Structure-Property Relationship (QSPR) modeling, are very useful in predicting physicochemical properties of various chemicals. Building QSPR models requires calculating molecular descriptors and the proper choice of the geometry optimization method, which will be dedicated to specific structure of tested compounds. Herein, we examine the influence of the ionic liquids' (ILs) geometry optimization methods on the predictive ability of QSPR models by comparing three models. The models were developed based on the same experimental data on density collected for 66 ionic liquids, but with employing molecular descriptors calculated from molecular geometries optimized at three different levels of the theory, namely: (1) semi-empirical (PM7), (2) ab initio (HF/6-311+G*) and (3) density functional theory (B3LYP/6-311+G*). The model in which the descriptors were calculated by using ab initio HF/6-311+G* method indicated the best predictivity capabilities ([Formula: see text] = 0.87). However, PM7-based model has comparable values of quality parameters ([Formula: see text] = 0.84). Obtained results indicate that semi-empirical methods (faster and less expensive regarding CPU time) can be successfully employed to geometry optimization in QSPR studies for ionic liquids.

A new metric for long-range transport potential of chemicals.

We propose a new metric for long-range transport potential (LRTP), GIF, based on source-receptor analyses and evaluate the LRTP and persistence of a wide variety of chlorinated and brominated organic compounds using GIF and overall persistence (POV), respectively. We calculated GIF and POV using our global 3D dynamic multimedia model (FATE). Physicochemical properties were obtained from quantitative structure-property relationship (QSPR) models. The FATE-QSPR combined model enabled us to systematically investigate the LRTP and persistence of a wide variety of chemical substances. On average, the estimated GIF and POV for chlorinated compounds were larger than those for their brominated counterparts, with the largest and smallest values found for polychlorinated biphenyls and polybrominated dibenzodioxins, respectively. We also compared GIF with four differently defined LRTP metrics and two LRTP metrics obtained from a simple model. The results of our analyses indicate that the LRTP ranks can differ considerably among LRTP metrics, the differences being dependent on the governing environmental processes, relevant physicochemical properties, and multimedia model.

Application of two-way hierarchical cluster analysis for the identification of similarities between the individual lipid fractions of Lucilia sericata.

The composition of the cuticular and internal lipids of larvae and pupae of Lucilia sericata was studied using chromatographic techniques. The lipids from both stages of L. sericata had similar free fatty acid (FFA) profiles and also contained alcohols and cholesterol. The range of the number of C-atoms detected for these classes of compounds was to some extent similar in larvae and pupae, but the relative amounts of each class differed between stages. Saturated as well as unsaturated FFAs with even and odd numbered C-atom chains were present in both cuticular and internal lipids. The alcohol fractions of L. sericata were represented by free, straight-chain primary alcohols containing an even number of C-atoms. The lipid composition of male and female L. sericata adults and the hydrocarbon composition of all stages of L. sericata had previously been analyzed. To have a full overview of the lipid composition and to identify similarities or dissimilarities between the individual lipid fractions in this insect species, two-way hierarchical cluster analysis (HCA) was performed using also the data from these previous publications. The content of FFA 18 : 1 (n-9) was noticed to be very high in the cuticular fractions of larvae and pupae as well as in all internal fractions (male, female, larvae, and pupae) and low in the cuticular fractions of male and female imago. The contents of FFAs 16 : 0 and 16 : 1 (n-9), cholesterol, and the n-alkanes n-C31 , n-C29 , n-C27 , n-C25 , and n-C23 varied between particular fractions, whereas the amounts of other compounds were similar in all fractions.

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

Per- and polyfluorinated alkyl substances (PFAS), known for their widespread environmental presence and slow degradation, pose significant concerns. Of the approximately 10,000 known PFAS, only a few have undergone comprehensive testing, resulting in limited experimental data. In this study, we employed a combination of physics-based methods and data-driven models to address gaps in PFAS bioaccumulation potential. Using the COnductor-like Screening MOdel for Realistic Solvents (COSMO-RS) method, we predicted n-octanol/water partition coefficients (logKOW), crucial for PFAS bioaccumulation. Our developed Quantitative Structure-Property Relationship (QSPR) model exhibited high accuracy (R2 = 0.95, RMSEC = 0.75) and strong predictive ability (Q2LOO = 0.93, RMSECV = 0.83). Leveraging the extensive NORMAN, we predicted logKOW for over 4,000 compounds, identifying 244 outliers out of 4519. Further categorizing the database into eight Organisation for Economic Co-operation and Development (OECD) categories, we confirmed fluorine atoms role in enhanced bioaccumulation. Utilizing predicted logKOW, water solubility logSW, and vapor pressure logVP values, we calculated additional physicochemical properties that are responsible for the transport and dispersion of PFAS in the environment. Parameters such as Henry's Law (kH), air-water partition coefficient (KAW), octanol-air coefficient (KOA), and soil adsorption coefficient (KOC) exhibited favorable correlations with literature data (R2 > 0.66). Our study successfully filled data gaps, contributing to the understanding of ubiquitous PFAS in the environment and estimating missing physicochemical data for these compounds.

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.

Water pollution has become a critical global concern requiring effective monitoring techniques and robust protection strategies. Contaminants of emerging concern (CECs) are increasingly detected in various water sources, with their harmful effects on humans and ecosystems continually evolving. Based on literature reports highlighting the promising sorption properties of metal-organic frameworks (MOFs), the aim of this study was to evaluate the suitability of NH2-MIL-125 (Ti) and UiO-66 (Ce) as sorbents in passive sampling devices (MOFs-PSDs) for the collection and extraction of a wide group of CECs. Solvothermal methods were used to synthesize MOFs, and the characterization of the obtained materials was performed using field-emission scanning electron microscopy (FE-SEM), powder X-ray diffractometry (pXRD) and Fourier-transform infrared (FTIR) spectroscopy. The research demonstrated the sorption capabilities of the tested MOFs, the ease and rapidity of their chemical regeneration and the possibility of reuse as sorbents. Using chemometric analysis, the structural properties of CECs determining the sorption efficiency on the surface of NH2-MIL-125 (Ti) were identified. The MOFs-PSDs were lab-calibrated to examine the kinetics of analytes sorption and determine the sampling rates (Rs). MOFs-PSDs and CNTs-PSDs (PSDs containing carbon nanotubes as a sorbent) were then placed in the Elblag River and the Vistula Lagoon to sampling and extraction of the target compounds from the water. CNTs-PSDs were selected, based on our previous research, for the comparison of the effectiveness of the MOFs-PSDs in environmental monitoring. MOFs-PSDs were successfully used in monitoring of CECs in water. The time-weighted average concentrations (CTWA) of 2-hydroxycarbamazepine, carbamazepine-10,11-epoxide, p-nitrophenol, 3,5-dichlorophenol and caffeine were determined in the Elblag River and CTWA of metoprolol, diclofenac, 2-hydroxycarbamazepine, carbamazepine-10,11-epoxide, p-nitrophenol, 3,5-dichlorophenol and caffeine were determine in the Vistula Lagoon using MOFs-PSDs and a high-performance liquid chromatography coupled with triple quadrupole mass spectrometer.

Linking nanomaterial-induced mitochondrial dysfunction to existing adverse outcome pathways for chemicals.

The adverse outcome pathway (AOP) framework plays a crucial role in the paradigm shift of tox­icity testing towards the development and use of new approach methodologies. AOPs developed for chemicals are in theory applicable to nanomaterials (NMs). However, only initial efforts have been made to integrate information on NM-induced toxicity into existing AOPs. In a previous study, we identified AOPs in the AOP-Wiki associated with the molecular initiating events (MIEs) and key events (KEs) reported for NMs in scientific literature. In a next step, we analyzed these AOPs and found that mitochondrial toxicity plays a significant role in several of them at the molecular and cellular levels. In this study, we aimed to generate hypothesis-based AOPs related to NM-induced mitochondrial toxicity. This was achieved by integrating knowledge on NM-induced mitochondrial toxicity into all existing AOPs in the AOP-Wiki, which already includes mitochondrial toxicity as a MIE/KE. Several AOPs in the AOP-Wiki related to the lung, liver, cardiovascular and nervous system, with extensively defined KEs and key event relationships (KERs), could be utilized to develop AOPs that are relevant for NMs. However, the majority of the studies included in our literature review were of poor quality, particularly in reporting NM physicochemical characteristics, and NM-relevant mitochondrial MIEs were rarely reported. This study highlights the potential role of NM-induced mitochondrial toxicity in human-relevant adverse outcomes and identifies useful AOPs in the AOP-Wiki for the development of AOPs for NMs.

This article investigates commonalities in the toxicity pathways of chemicals and nanomaterials. Nanomaterials have been found to affect the function of mitochondria, the powerhouses within every human cell. Mitochondrial dysfunction may cause harmful effects such as cellular damage and inflammation. By linking these findings to existing adverse outcome pathways for chemicals, the research provides valuable insights for assessing the risks associated with nanomaterial exposure. This work is crucial for understanding the potential health implications of nanomaterials and can contribute to informed decision-making in regulatory and risk assessment processes without the use of animals.

Exploring BPA alternatives - Environmental levels and toxicity review.

Bisphenol A alternatives are manufactured as potentially less harmful substitutes of bisphenol A (BPA) that offer similar functionality. These alternatives are already in the market, entering the environment and thus raising ecological concerns. However, it can be expected that levels of BPA alternatives will dominate in the future, they are limited information on their environmental safety. The EU PARC project highlights BPA alternatives as priority chemicals and consolidates information on BPA alternatives, with a focus on environmental relevance and on the identification of the research gaps. The review highlighted aspects and future perspectives. In brief, an extension of environmental monitoring is crucial, extending it to cover BPA alternatives to track their levels and facilitate the timely implementation of mitigation measures. The biological activity has been studied for BPA alternatives, but in a non-systematic way and prioritized a limited number of chemicals. For several BPA alternatives, the data has already provided substantial evidence regarding their potential harm to the environment. We stress the importance of conducting more comprehensive assessments that go beyond the traditional reproductive studies and focus on overlooked relevant endpoints. Future research should also consider mixture effects, realistic environmental concentrations, and the long-term consequences on biota and ecosystems.

A template wizard for the cocreation of machine-readable data-reporting to harmonize the evaluation of (nano)materials.

Making research data findable, accessible, interoperable and reusable (FAIR) is typically hampered by a lack of skills in technical aspects of data management by data generators and a lack of resources. We developed a Template Wizard for researchers to easily create templates suitable for consistently capturing data and metadata from their experiments. The templates are easy to use and enable the compilation of machine-readable metadata to accompany data generation and align them to existing community standards and databases, such as eNanoMapper, streamlining the adoption of the FAIR principles. These templates are citable objects and are available as online tools. The Template Wizard is designed to be user friendly and facilitates using and reusing existing templates for new projects or project extensions. The wizard is accompanied by an online template validator, which allows self-evaluation of the template (to ensure mapping to the data schema and machine readability of the captured data) and transformation by an open-source parser into machine-readable formats, compliant with the FAIR principles. The templates are based on extensive collective experience in nanosafety data collection and include over 60 harmonized data entry templates for physicochemical characterization and hazard assessment (cell viability, genotoxicity, environmental organism dose-response tests, omics), as well as exposure and release studies. The templates are generalizable across fields and have already been extended and adapted for microplastics and advanced materials research. The harmonized templates improve the reliability of interlaboratory comparisons, data reuse and meta-analyses and can facilitate the safety evaluation and regulation process for (nano) materials.

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

This work aimed to verify whether it is possible to extend the applicability domain (AD) of existing QSPR (Quantitative Structure-Property Relationship) models by employing a strategy involving additional quantum-chemical calculations. We selected two published QSPR models: for water solubility, logSW, and vapor pressure, logVP of PFAS as case studies. We aimed to enlarge set of compounds used to build the model by applying factorial planning to plan the augmentation of the set of these compounds based on their structural features (descriptors). Next, we used the COSMO-RS model to calculate the logSW and logVP for selected chemicals. This allowed filling gaps in the experimental data for further training QSPR models. We improved the published models by significantly extending number of compounds for which theoretical predictions are reliable (i.e., extending the AD). Additionally, we performed external validation that had not been carried out in original models. To test effectiveness of the AD extension, we screened 4519 PFAS from NORMAN Database. The number of compounds outside the domain was reduced comparing the original model for both properties. Our work shows that combining physics-based methods with data-driven models can significantly improve the performance of predictions of phys-chem properties relevant for the chemical risk assessment.

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

The goal of this study was to evaluate the effects of two kinds of 24-week dietary interventions in haemodialysis patients, a traditional nutritional intervention without a meal before dialysis (HG1) and implementation of a nutritional intervention with a meal served just before dialysis (HG2), in terms of analysing the differences in the serum metabolic profiles and finding biomarkers of dietary efficacy. These studies were performed in two homogenous groups of patients (n = 35 in both groups). Among the metabolites with the highest statistical significance between HG1 and HG2 after the end of the study, 21 substances were putatively annotated, which had potential significance in both of the most relevant metabolic pathways and those related to diet. After the 24 weeks of the dietary intervention, the main differences between the metabolomic profiles in the HG2 vs. HG1 groups were related to the higher signal intensities from amino acid metabolites: indole-3-carboxaldehyde, 5-(hydroxymethyl-2-furoyl)glycine, homocitrulline, 4-(glutamylamino)butanoate, tryptophol, gamma-glutamylthreonine, and isovalerylglycine. These metabolites are intermediates in the metabolic pathways of the necessary amino acids (Trp, Tyr, Phe, Leu, Ile, Val, Liz, and amino acids of the urea cycle) and are also diet-related intermediates (4-guanidinobutanoic acid, indole-3-carboxyaldehyde, homocitrulline, and isovalerylglycine).

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

Exposure to hexavalent chromium Cr(VI) may occur in several occupational activities, placing workers in many industries at risk for potential related health outcomes. Untargeted metabolomics was applied to investigate changes in metabolic pathways in response to Cr(VI) exposure. We obtained our data from a study population of 220 male workers with exposure to Cr(VI) and 102 male controls from Belgium, Finland, Poland, Portugal and the Netherlands within the HBM4EU Chromates Study. Urinary metabolite profiles were determined using liquid chromatography mass spectrometry, and differences between post-shift exposed workers and controls were analyzed using principal component analysis. Based on the first two principal components, we observed clustering by industrial chromate application, such as welding, chrome plating, and surface treatment, distinct from controls and not explained by smoking status or alcohol use. The changes in the abundancy of excreted metabolites observed in workers reflect fatty acid and monoamine neurotransmitter metabolism, oxidative modifications of amino acid residues, the excessive formation of abnormal amino acid metabolites and changes in steroid and thyrotropin-releasing hormones. The observed responses could also have resulted from work-related factors other than Cr(VI). Further targeted metabolomics studies are needed to better understand the observed modifications and further explore the suitability of urinary metabolites as early indicators of adverse effects associated with exposure to Cr(VI).

A strategy towards the generation of testable adverse outcome pathways for nanomaterials.

Manufactured nanomaterials (NMs) are increasingly used in a wide range of industrial applications leading to a constant increase in the market size of nano-enabled products. The increased production and use of NMs are raising concerns among different stakeholder groups with regard to their effects on human and environmental health. Currently, nanosafety hazard assessment is still widely performed using in vivo (animal) models, however the development of robust and reg­ulatory relevant strategies is required to prioritize and/or reduce animal testing. An adverse outcome pathway (AOP) is a structured representation of biological events that start from a molecular initiating event (MIE) leading to an adverse outcome (AO) through a series of key events (KEs). The AOP framework offers great advancement to risk assessment and regulatory safety assessments. While AOPs for chemicals have been more frequently reported, the AOP collection for NMs is limited. By using existing AOPs, we aimed to generate simple and testable strategies to predict if a given NM has the potential to induce a MIE leading to an AO through a series of KEs. Firstly, we identified potential MIEs or initial KEs reported for NMs in the literature. Then, we searched the identified MIE or initial KEs as keywords in the AOP-Wiki to find associated AOPs. Finally, using two case studies, we demonstrate how in vitro strategies can be used to test the identified MIE/KEs.

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

The process of encoding the structure of chemicals by molecular descriptors is a crucial step in quantitative structure-activity/property relationships (QSAR/QSPR) modeling. Since ionic liquids (ILs) are disconnected structures, various ways of representing their structure are used in the QSAR studies: the models can be based on descriptors either derived for particular ions or for the whole ionic pair. We have examined the influence of the type of IL representation (separate ions vs. ionic pairs) on the model's quality, the process of the automated descriptors selection and reliability of the applicability domain (AD) assessment. The result of the benchmark study showed that a less precise description of ionic liquid, based on the 2D descriptors calculated for ionic pairs, is sufficient to develop a reliable QSAR/QSPR model with the highest accuracy in terms of calibration as well as validation. Moreover, the process of a descriptors' selection is more effective when the possible number of variables can be decreased at the beginning of model development. Additionally, 2D descriptors usually demand less effort in mechanistic interpretation and are more convenient for virtual screening studies.

The Acid Strength of the Lewis-Brønsted Superacids - A QSPR Study.

The acidity of Lewis-Brønsted superacids can be derived from the theoretical calculations as the Gibbs free energy of the deprotonation reaction (ΔGacid ), which describes the tendency of a studied compound to donate a proton. This paper presents the first Quantitative Structure - Property Relationship (QSPR) model that correlates the ΔGacid of superacid (HF/MeX3 formula (X=F, Cl, Br)) with their structure. Developed model is well fitted, roubustness, has good predictive abilities, fulfills all OECD recommendation for good model. Obtained results provide the insight into the relation of structural features of superacids, which are responsible for their acid strength - the structures characterized by strong F-Me dative bond (with relatively large vibrational frequency), small positive partial atomic charge on Me central atom, possibly large polarity exhibit large acid strength. Such assumption can be used in the future as valuable information in the process of the designing new, stronger, more effective superacids.

Chemoinformatic Approach to Assess Toxicity of Ionic Liquids.

Chemoinformatic methods, such as multivariable explorative techniques and quantitative structure-activity relationship (QSAR) modeling, allow for discovering relationships between the activity and the structure of chemical compounds. These techniques can be applied, as preliminary screening methods for designing and/or selecting new compounds with defined activity.Here we describe step by step how to preliminarily screen ionic liquids (a set of 13 ILs) and assess their cytotoxic activity against leukemia cell line IPC-81 as well as ILs' potential to inhibit acetylcholinesterase enzyme using the TRIC method (toxicity ranking index of cations) combined with the QSAR approach.

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

HYPOTHESIS: Different ions constituting ionic liquids (ILs) change their properties, including the Critical Micelization Concentration (CMC). It is possible to identify and quantitatively describe specific structural ions' features having influence on the micelization of ILs. Moreover, it should be possible to verify, whether the phenomenon of micelization is governed by the influence of the single ion only, rather than being a sum of both ions' mutual influence. EXPERIMENTAL: The qualitative and quantitative description of the structural properties responsible for micelles formation was performed with the use of the Quantitative Structure-Property Relationship (QSPR) approach. Structural features were expressed with help of the molecular GEometry, Topology, and Atom-Weights AssemblY (GETAWAY) descriptors system. The QSPR model was properly validated and its quality and usability was additionally proven by applying it to predict the CMC for 15,000 computationally designed ILs. It was the first model to the CMC assessment for ILs. FINDINGS: The analysis showed that longer (containing big hydrophobic domain), less spherical and not "folded" cations as well as bigger anions are the main factors causing the decrease of CMC. According to the presented model, the influence of cations and anions is independent.