Exploring QSPR modeling for adsorption of hazardous synthetic organic chemicals (SOCs) by SWCNTs

Towards safer pesticide management: A quantitative structure-activity relationship based hazard prediction model

Pesticides are recognized as common environmental contaminants. The potential pesticide hazard to non-target organisms, including various mammal species, is a global concern. The global problem requires a comprehensive risk assessment. To assess the toxic effects of pesticides at the early stage, a toxicological risk analysis is conducted to determine pesticide hazard levels. World Health Organization (WHO) has established five pesticide hazard classes based on lethal dose (LD50) values to perform these assessments. In this paper, we have developed one-vs-all quantitative structure-activity relationship (OvA-QSAR) models using five machine-learning techniques with the selected optimum molecular descriptors. Descriptor selection was conducted based on correlation to evaluate the relevance and significance of individual features in our dataset. Our OvA-QSAR model was built using a dataset obtained from the WHO, covering a wide range of chemical pesticides. These models can predict the hazard category for a pesticide within the five available categories. Notably, our experiments demonstrate the outstanding performance and robustness of the Random Forest (RF) model in addressing the challenge of multi-class classification with the selected descriptors.

Effect of Aging on Physicochemical Properties and Size Distribution of PET Microplastic: Influence on Adsorption of Diclofenac and Toxicity Assessment

Microplastics (MPs) are detected in the water, sediments, as well as biota, mainly as a consequence of the degradation of plastic products/waste under environmental conditions. Due to their potentially harmful effects on ecosystems and organisms, MPs are regarded as emerging pollutants. The highly problematic aspect of MPs is their interaction with organic and inorganic pollutants; MPs can act as vectors for their further transport in the environment. The objective of this study was to investigate the effects of ageing on the changes in physicochemical properties and size distribution of polyethylene terephthalate (PET), as well as to investigate the adsorption capacity of pristine and aged PET MPs, using pharmaceutical diclofenac (DCF) as a model organic pollutant. An ecotoxicity assessment of such samples was performed. Characterization of the PET samples (bottles and films) was carried out to detect the thermooxidative aging effects. The influence of the temperature and MP dosage on the extent of adsorption of DCF was elucidated by employing an empirical modeling approach using the response surface methodology (RSM). Aquatic toxicity was investigated by examining the green microalgae Pseudokirchneriella subcapitata. It was found that the thermooxidative ageing process resulted in mild surface changes in PET MPs, which were reflected in changes in hydrophobicity, the amount of amorphous phase, and the particle size distribution. The fractions of the particle size distribution in the range 100-500 μm for aged PET are higher due to the increase in amorphous phase. The proposed mechanisms of interactions between DCF and PET MPs are hydrophobic and π-π interactions as well as hydrogen bonding. RSM revealed that the adsorption favors low temperatures and low dosages of MP. The combination of MPs and DCF exhibited higher toxicity than the individual components.

Next-generation graphene oxide additives composite membranes for emerging organic micropollutants removal: Separation, adsorption and degradation

In the past two decades, membrane technology has attracted considerable interest as a viable and promising method for water purification. Emerging organic micropollutants (EOMPs) in wastewater have trace, persistent, highly variable quantities and types, develop hazardous intermediates and are diffusible. These primary issues affect EOMPs polluted wastewater on an industrial scale differently than in a lab, challenging membranes-based EOMP removal. Graphene oxide (GO) promises state-of-the-art membrane synthesis technologies and use in EOMPs removal systems due to its superior physicochemical, mechanical, and electrical qualities and high oxygen content. This critical review highlights the recent advancements in the synthesis of next-generation GO membranes with diverse membrane substrates such as ceramic, polyethersulfone (PES), and polyvinylidene fluoride (PVDF). The EOMPs removal efficiencies of GO membranes in filtration, adsorption (incorporated with metal, nanomaterial in biodegradable polymer and biomimetic membranes), and degradation (in catalytic, photo-Fenton, photocatalytic and electrocatalytic membranes) and corresponding removal mechanisms of different EOMPs are also depicted. GO-assisted water treatment strategies were further assessed by various influencing factors, including applied water flow mode and membrane properties (e.g., permeability, hydrophily, mechanical stability, and fouling). GO additive membranes showed better permeability, hydrophilicity, high water flux, and fouling resistance than pristine membranes. Likewise, degradation combined with filtration is two times more effective than alone, while crossflow mode improves the photocatalytic degradation performance of the system. GO integration in polymer membranes enhances their stability, facilitates photocatalytic processes, and gravity-driven GO membranes enable filtration of pollutants at low pressure, making membrane filtration more inexpensive. However, simultaneous removal of multiple contaminants with contrasting characteristics and variable efficiencies in different systems demands further optimization in GO-mediated membranes. This review concludes with identifying future critical research directions to promote research for determining the GO-assisted OMPs removal membrane technology nexus and maximizing this technique for industrial application.

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

The study of the structural features affecting the adsorption of organics, especially contaminants of emerging concern (CECs), onto TiO₂ P25 in aqueous medium has far-reaching implications for the understanding and modification of TiO₂ P25 in the roles such as an adsorbent and photocatalyst. The effect of pH and γ(TiO₂ P25) as variables on the extent of removal of organics by adsorption on TiO₂ P25 was investigated by response surface methodology (RSM) and quantitative structure-property relationship (QSPR) modeling. Experimentally determined coefficients of adsorption were used as responses in RSM, yielding a quadratic polynomial equation (QPE) for each of the studied organics. Furthermore, coefficients (A, B, C, D, E, and F) obtained from QPEs were used as responses in QSPR modeling to establish their dependence on the structural features of the studied organics. The functional stability and predictive power of the resulting QSPR models were confirmed with internal and external cross validation. The influence of structural features of organics on the adsorption process is explained by molecular descriptors included in the derived QSPR models. The most influential descriptors on the adsorption of organics on TiO₂ P25 are found to be those correlated with ionization potential, molecular mass, and volume, then molecular fragments (e.g., -CH =) and particular topological features such as C and N atoms, or two heteroatoms (e.g., N and N or O and Cl) at certain distance. Derived QSPR models can be considered as robust predictive tools for evaluating efficiency of adsorption processes onto TiO₂ P25, providing insights into influential structural features facilitating adsorption process.

Prediction reliability of QSAR models: an overview of various validation tools

The reliability of any quantitative structure-activity relationship (QSAR) model depends on multiple aspects such as the accuracy of the input dataset, selection of significant descriptors, the appropriate splitting process of the dataset, statistical tools used, and most notably on the measures of validation. Validation, the most crucial step in QSAR model development, confirms the reliability of the developed QSAR models and the acceptability of each step in the model development. The present review deals with various validation tools that involve multiple techniques that improve the model quality and robustness. The double cross-validation tool helps in building improved quality models using different combinations of the same training set in an inner cross-validation loop. This exhaustive method is also integrated for small datasets (< 40 compounds) in another tool, namely the small dataset modeler tool. The main aim of QSAR researchers is to improve prediction quality by lowering the prediction errors for the query compounds. 'Intelligent' selection of multiple models and consensus predictions integrated in the intelligent consensus predictor tool were found to be more externally predictive than individual models. Furthermore, another tool called Prediction Reliability Indicator was explained to understand the quality of predictions for a true external set. This tool uses a composite scoring technique to identify query compounds as 'good' or 'moderate' or 'bad' predictions. We have also discussed a quantitative read-across tool which predicts a chemical response based on the similarity with structural analogues. The discussed tools are freely available from https://dtclab.webs.com/software-tools or http://teqip.jdvu.ac.in/QSAR_Tools/DTCLab/ and https://sites.google.com/jadavpuruniversity.in/dtc-lab-software/home (for read-across).

Interactions between carbon-based nanoparticles and steroid hormone micropollutants in water

The occurrence of micropollutants (MPs) including steroid hormones is a global environmental and health challenge. Carbon-based nanoparticles can be incorporated with water treatment processes to allow MP removal by adsorption. The aim was to compare the suitability of such nanoparticles (graphene, graphene oxide, carbon nanotubes and C₆₀) to adsorb steroid hormones for later incorporation in membrane composites. All nanoparticles displayed fast kinetics; carbon nanotubes and graphene showed high adsorption capacities for hormones undeterminable in isotherm studies (over 10 mg/g). External surface adsorption appears to be the most prominent factor impacting adsorption performance. Structure, conformation, geometry and surface charge of nanoparticles can influence the accessibility of surface area through colloidal instability in aqueous solution. Mechanism inspection shows that adsorption initiates at long ranges (up to 10 nm) through hydrophobic and electrostatic interactions. At relatively short ranges (0.2-0.5 nm), adsorption is enhanced by π/π stacking, XH / π (X = C, O) interactions, van der Waals forces and hydrogen bonding. Both long- and short-range forces transporting hormones from the liquid bulk into the adsorbed phase could control the rate. With relatively short residence time required and high adsorption capacity, carbon nanotubes and graphene are promising for incorporation in a membrane composite.

Synthetic Organic Compounds From Paper Industry Wastes: Integrated Biotechnological Interventions

Synthetic organic compounds (SOCs) are reported as xenobiotics compounds contaminating the environment from various sources including waste from the pulp and paper industries: Since the demand and production of paper is growing increasingly, the release of paper and pulp industrial waste consisting of SOCs is also increasing the SOCs' pollution in natural reservoirs to create environmental pollution. In pulp and paper industries, the SOCs viz. phenol compounds, furans, dioxins, benzene compounds etc. are produced during bleaching phase of pulp treatment and they are principal components of industrial discharge. This review gives an overview of various biotechnological interventions for paper mill waste effluent management and elimination strategies. Further, the review also gives the insight overview of various ways to restrict SOCs release in natural reservoirs, its limitations and integrated approaches for SOCs bioremediation using engineered microbial approaches. Furthermore, it gives a brief overview of the sustainable remediation of SOCs via genetically modified biological agents, including bioengineering system innovation at industry level before waste discharge.

Computational Modeling of Environmental Co-exposure on Oil-Derived Hydrocarbon Overload by Using Substrate-Specific Transport Protein (TodX) with Graphene Nanostructures

BACKGROUND

Bioremediation is a biotechnology field that uses living organisms to remove contaminants from soil and water; therefore, they could be used to treat oil spills from the environment.

METHODS

Herein, we present a new mechanistic approach combining Molecular Docking Simulation and Density Functional Theory to modeling the bioremediation-based nanointeractions of a heterogeneous mixture of oil-derived hydrocarbons by using pristine and oxidized graphene nanostructures and the substrate-specific transport protein (TodX) from Pseudomonas putida.

RESULTS

The theoretical evidences pointing that the binding interactions are mainly based on noncovalent bonds characteristic of physical adsorption mechanism mimicking the "Trojan-horse effect".

CONCLUSION

These results open new horizons to improve bioremediation strategies in over-saturation conditions against oil-spills and expanding the use of nanotechnologies in the context of environmental modeling health and safety.

Predicting Aqueous Adsorption of Organic Compounds onto Biochars, Carbon Nanotubes, Granular Activated Carbons, and Resins with Machine Learning

Predictive models are useful tools for aqueous adsorption research; existing models such as multilinear regression (MLR), however, can only predict adsorption under specific equilibrium concentrations or for certain adsorption isotherm models. Also, few studies have discussed data processing beyond applying different modeling algorithms to improve the prediction accuracy. In this research, we employed a cosine similarity approach that focused on mining the available data before developing models; this approach can mine the most relevant data concerning the prediction target to build models and was found to considerably improve the prediction accuracy. We then built a machine-learning modeling process based on neural networks (NN), a group-selection data-splitting strategy for grouped adsorption data for adsorbent-adsorbate pairs under different equilibrium concentrations, and polyparameter linear free energy relationships (pp-LFERs) for aqueous adsorption of 165 organic compounds onto 50 biochars, 34 carbon nanotubes, 35 GACs, and 30 polymeric resins. The final NN-LFER models were successfully applied to various equilibrium concentrations regardless of the adsorption isotherm models and showed less prediction deviations than the published models with the root-mean-square errors 0.23-0.31 versus 0.23-0.97 log unit, and the predictions were improved by adding two key descriptors (BET surface area and pore volume) for the adsorbents. Finally, interpreting the NN-LFER models based on the Shapley values suggested that not considering equilibrium concentration and properties of the adsorbents in the existing MLR models is a possible reason for their higher prediction deviations.

A robust model for estimating thermal conductivity of liquid alkyl halides

Thermal conductivity is an essential thermodynamic property in chemical engineering application. As a result, estimating the thermal conductivity of organic compounds is of significance in industry production. Alkyl halides are important organic intermediates and raw materials, but little investigations have been performed to estimate their thermal conductivity. In this study, the structures of compounds were optimized in Gaussian 09W and molecular descriptors were extracted by Dragon software. Finally, we developed a 6-descriptor linear quantitative structure-property relationship (QSPR) model to estimate the thermal conductivity of alkyl halides using the genetic function approximation (GFA) method. Validation proved that the developed model had goodness-of-fit, robustness and predictive ability. The r²_pred and root-mean-square error (RMSE_P) of prediction set for the model were equal to 0.9745 and 0.0035, respectively. Meanwhile, the applicability domain was visualized by means of the Williams plot. This study provides a new model for estimating the thermal conductivity of this important class of chemicals.