Retraction: Novel hybrid QSPR-GPR approach for modeling of carbon dioxide capture using deep eutectic solvents.

[This retracts the article DOI: 10.1039/D3RA05360A.].

Machine learning-powered estimation of malachite green photocatalytic degradation with NML-BiFeO3 composites.

This study explores the potential of photocatalytic degradation using novel NML-BiFeO3 (noble metal-incorporated bismuth ferrite) compounds for eliminating malachite green (MG) dye from wastewater. The effectiveness of various Gaussian process regression (GPR) models in predicting MG degradation is investigated. Four GPR models (Matern, Exponential, Squared Exponential, and Rational Quadratic) were employed to analyze a dataset of 1200 observations encompassing various experimental conditions. The models have considered ten input variables, including catalyst properties, solution characteristics, and operational parameters. The Exponential kernel-based GPR model achieved the best performance, with a near-perfect R2 value of 1.0, indicating exceptional accuracy in predicting MG degradation. Sensitivity analysis revealed process time as the most critical factor influencing MG degradation, followed by pore volume, catalyst loading, light intensity, catalyst type, pH, anion type, surface area, and humic acid concentration. This highlights the complex interplay between these factors in the degradation process. The reliability of the models was confirmed by outlier detection using William's plot, demonstrating a minimal number of outliers (66-71 data points depending on the model). This indicates the robustness of the data utilized for model development. This study suggests that NML-BiFeO3 composites hold promise for wastewater treatment and that GPR models, particularly Matern-GPR, offer a powerful tool for predicting MG degradation. Identifying fundamental catalyst properties can expedite the application of NML-BiFeO3, leading to optimized wastewater treatment processes. Overall, this study provides valuable insights into using NML-BiFeO3 compounds and machine learning for efficient MG removal from wastewater.

Mechanisms and factors affecting the removal of minocycline from aqueous solutions using graphene-modified resorcinol formaldehyde aerogels.

In recent years, concerns about the presence of pharmaceutical compounds in wastewater have increased. Various types of residues of tetracycline family antibiotic compounds, which are widely used, are found in environmental waters in relatively low and persistent concentrations, adversely affecting human health and the environment. In this study, a resorcinol formaldehyde (RF) aerogel was prepared using the sol-gel method at resorcinol/catalyst ratio of 400 and resorcinol/water ratio of 2 and drying at ambient pressure for removing antibiotics like minocycline. Next, RF aerogel was modified with graphene and to increase the specific surface area and porosity of the modified sample and to form the graphene plates without compromising the interconnected porous three-dimensional structure of the aerogel. Also, the pores were designed according to the size of the minocycline particles on the meso- and macro-scale, which bestowed the modified sample the ability to remove a significant amount of the minocycline antibiotic from the aqueous solution. The removal percentage of the antibiotic obtained by UV-vis spectroscopy. Ultimately, the performance of prepared aerogels was investigated under various conditions, including adsorbent doses (4-10 mg), solution pHs (2-12), contact times of the adsorbent with the adsorbate (3-24 h), and initial concentration of antibiotic (40-100 mg/l). The results from the BET test demonstrated that the surface area of the resorcinol formaldehyde aerogel sample, which included 1 wt% graphene (RF-G1), exhibited an augmentation in comparison to the surface area of the pure aerogel. Additionally, it was noted that the removal percentage of minocycline antibiotic for both the unmodified and altered samples was 71.6% and 92.1% at the optimal pH values of 4 and 6, respectively. The adsorption capacity of pure and modified aerogel for the minocycline antibiotic was 358 and 460.5 mg/g, respectively. The adsorption data for the modified aerogel was studied by the pseudo-second-order model and the results obtained from the samples for antibiotic adsorption with this model revealed a favorable fit, which indicated that the chemical adsorption in the rapid adsorption of the antibiotic by the modified aerogel had occurred.

Novel hybrid QSPR-GPR approach for modeling of carbon dioxide capture using deep eutectic solvents.

In recent years, deep eutectic solvents (DESs) have garnered considerable attention for their potential in carbon capture and utilization processes. Predicting the carbon dioxide (CO2) solubility in DES is crucial for optimizing these solvent systems and advancing their application in sustainable technologies. In this study, we presented an evolving hybrid Quantitative Structure-Property Relationship and Gaussian Process Regression (QSPR-GPR) model that enables accurate predictions of CO2 solubility in various DESs. The QSPR-GPR model combined the strengths of both approaches, leveraging molecular descriptors and structural features of DES components to establish a robust and adaptable predictive framework. Through a systematic evolution process, we iteratively refined the model, enhancing its performance and generalization capacity. By incorporating experimental CO2 solubility data in varied DES compositions and temperatures, we trained the model to capture the intricate solubility behaviour precisely. The analytical capability of the evolving hybrid model was validated against an extensive dataset of experimental CO2 solubility values, demonstrating its superiority over individual QSPR and GPR models. The model achieves high accuracy, capturing the complex interactions between CO2 and DES components under varying thermodynamic conditions. The versatility of the evolving hybrid model was highlighted by its ability to accommodate new experimental data and adapt to different DES compositions and temperatures. The proposed QSPR-GPR model presented a powerful tool for predicting CO2 solubility in DES, providing valuable insights for designing and optimizing solvent systems in carbon capture technologies. The model's remarkable performance enhances our understanding of CO2 solubility mechanisms and contributes to sustainable solutions for mitigating greenhouse gas emissions. As research in DESs progresses, the evolving hybrid QSPR-GPR model offers a versatile and accurate means for predicting CO2 solubility, supporting advancements in carbon capture and utilization processes towards a greener and more sustainable future.

Insights into the Adsorption Properties of Mixed Matrix Membranes (Pebax 1657-g-Chitosan-PVDF-Bovine Serum Albumin@ZIF-CO3-1) for the Antiviral COVID-19 Treatment Drugs Remdesivir and Nirmatrelvir: An In Silico Study.

The effect of the COVID-19 pandemic on the accumulation of environmental pollutants has been significant. In that way, waste management systems have faced problems, and the amount of hazardous and medical wastes has increased. As pharmaceuticals associated with the treatment of COVID-19 enter the environment, aquatic and terrestrial ecosystems have been negatively impacted, potentially disrupting natural processes and harming aquatic life. This analysis seeks to appraise the potential of mixed matrix membranes (MMMs) composed of Pebax 1657-g-chitosan-polyvinylidene fluoride (PEX-g-CHS-PVDF)-bovine serum albumin (BSA)@ZIF-CO3-1 as adsorbents for removing remdesivir (REMD) and nirmatrelvir (NIRM) from aqueous environments. An in silico study was conducted to explore the adsorption characteristics, physicochemical properties, and structural features of these MMMs, employing quantum mechanical (QM) calculations, molecular dynamics (MD) simulations, and Monte Carlo (MC) simulations as research methodologies. Incorporating BSA@ZIF-CO3-1 into the PEX-g-CHS-PVDF polymer matrix improved the physicochemical properties of MMMs by promoting the compatibility and interfacial adhesion between the two materials, facilitated by electrostatic interactions, van der Waals forces, and hydrogen bonding. Investigation of the interaction mechanism between the title pharmaceutical pollutants and the surfaces of MMMs, along with the description of their adsorption behavior, was also conducted by applying MD and MC approaches. Our observations indicate that the adsorption behavior of REMD and NIRM is influenced by molecular size, shape, and the presence of functional groups. Molecular simulation analysis demonstrated that the MMM membrane is a highly suitable adsorbent for the adsorption of REMD and NIRM drugs, with a higher affinity toward REMD adsorption. Our study emphasizes the significance of computational modeling in developing practical strategies for eliminating COVID-19 drug contaminants from wastewater. The knowledge obtained through our molecular simulations and QM calculations can assist in creating more efficient adsorption materials, resulting in a cleaner and healthier environment.