Chemical Papers最新文献

This study integrates computational chemistry and machine learning to explore the relationship between topological indices and physicochemical properties of compounds. Artificial neural networks (ANNs) and random forest (RF) models were developed, with ANN consistently achieving superior predictive performance. ANN attained significantly lower errors than RF for enthalpy of vaporization, with similar trends across properties such as density, boiling point, and surface tension. The analysis revealed that topological indices played a key role in predicting physicochemical properties such as molar refractivity and polar surface area. These findings underscore the robustness of ANN in capturing complex nonlinear structural property relationships and highlight its potential as a scalable computational framework for molecular design and drug discovery.

An increasing number of contaminants, including micro-pollutants like pharmaceuticals, are showing up in water sources and might affect aquatic life. We need a reliable way to eliminate micro-pollutants so that people may live safely, as wastewater management is inadequate. Walnut shells manufactured using nanoparticles of zinc oxide (WaSh@ZnO) were used in the research into the adsorption and elimination of ciprofloxacin (CIP) from water. The WaSh@ZnO was created and defined utilizing energy-dispersive X-ray analysis, scanning electron microscopy, Fourier transform infrared spectroscopy, Brunauer–Emmett–Teller, and energy-dispersive X-ray (EDX). The produced WaSh@ZnO exhibited mesoporous properties, including a surface area of 29.24 m²/g, a pore volume of 0.0821 cm³/g, and a pore width of 11.243 nm. The Box–Behnken statistical design was used to study the batch adsorption parameters, which may be changed, including pH (4–10), temperature (25–35 °C), duration (10–110 min), adsorbent dosage (0.1 mg/L), and CIP concentration (10–100 mg/L). The evolutionary algorithm was used to assess the maximum removal at 52.2 mg/L, 7.4 pH, 95 min, and 26.3 °C. Multiple isotherms and kinetic models were used to analyze the adsorption data, and the sum of normalized errors methodology was used to estimate the best values. Researchers determined that 319,703 mg/g was the highest adsorption capacity for CIP. According to the kinetics, the data were well-fit by the pseudo-second-order model. The CIP adsorption was shown to be exothermic (ΔH°) and spontaneous (ΔG°), according to the thermodynamic research.

In the field of computer-aided synthesis planning, retrosynthetic pathway planning stands as a core issue. It aims to leverage data-driven strategies to explore synthetic routes that are both efficient and feasible for the target molecule. Current retrosynthesis methods rely on two core components: (a) an effective search methods, such as Monte Carol tree search, heuristic A* search, depth-first search, or breadth-first search; and (b) a useful evaluation methods. However, the existing evaluation methods cannot accurately depict the synthetic cost of molecules. In our study, we introduce a novel strategy for the evaluation of intermediate molecules in multi-step synthesis, namely similarity estimate. This strategy gauges the synthesis complexity of molecules by estimating their prevalence. Experimental results on classical datasets substantiate that our approach can effectively reduce the complexity of path search and augment the interpretability of multi-step retrosynthesis algorithms.

Graphical abstract

This study investigates the corrosion inhibition performance of thalidomide on mild steel in 1.0 M HCl solution using gravimetric, electrochemical (PDP and EIS), surface characterization SEM–EDX, AFM, XPS, and quantum chemical (DFT) methods. Thalidomide exhibited maximum inhibition efficiency of 98.71 ± 0.02% at 1600 ppm, with potentiodynamic polarization indicating mixed-type inhibition dominated by anodic behavior. EIS analysis revealed a significant increase in charge transfer resistance, suggesting the formation of a stable protective layer. Thermodynamic studies confirmed spontaneous adsorption following the Langmuir isotherm, whereas surface morphology using XPS, AFM, and SEM–EDX verified the development of a corrosion-inhibiting layer. Density functional theory (DFT) calculations further validated the experimental observations, indicating a strong interaction between thalidomide molecules and the steel surface. These findings establish thalidomide as a highly effective corrosion inhibitor for mild steel in acidic environments.

Graphical abstract

Water contamination remains a significant global concern, exacerbated by industrialization, agriculture, and population growth. Among various treatment strategies, adsorption has emerged as the most practical and sustainable method due to its simplicity, scalability, and versatility. Recent advances include nanostructured adsorbents, hybrid composites, and bio-derived materials, achieving 80–99% removal of dyes, heavy metals, and pharmaceuticals. Significant regional progress has also been reported in Africa, particularly Nigeria, where low-cost precursors such as coconut shells, rice husks, and cassava peels demonstrated removal efficiencies of 78–98% for Pb²⁺, Cd²⁺, and Cr⁶⁺. Mechanistic insights into physisorption and chemisorption, supported by kinetic and isotherm models, have further improved adsorption process optimization. Despite these achievements, major challenges remain, including limited regenerability, reduced selectivity in multi-contaminant systems, fouling and leaching risks, variability of bio-derived feedstocks, and energy or cost constraints during large-scale application. The review concludes by recommending intensified research into multifunctional, regenerable, and environmentally benign adsorbents, supported by mechanistic modelling and real-world validation to enable sustainable water treatment solutions globally.

Groundwater fluoride contamination is a serious global issue that impacts many areas, particularly in developing countries. While excessive quantities of fluoride in drinking water can have major negative health effects, lower dosages are beneficial for teeth. This study examines the adsorption of fluoride ions from water by tamarind seeds derived from household trash that have been doped with magnesium metal in the form of TAM SE-Mg NPs.The findings demonstrated that we investigated several elements that influenced the manner in which adsorption took place when employing tamarind seeds to extract fluoride from water. The defluoridation capability increases with temperature. Since the kinetic data consistently fit the Freundlich adsorption isotherm and pseudo second order kinetics, these models can be trusted to predict the behavior of the reaction and adsorption processes. The Tamarind-Mg extract was thoroughly characterized using SEM-EDX, TGA, DTA, and FTIR analysis, ensuring an acceptable identification and investigation of its characteristics. A well-diffusion method was used to test the antibacterial effectiveness of TAM SE-Mg NPs extract.The extract was evaluated against Bacillus megaterium and Streptococcus. At a dose of 5g/mL, TAM SE-Mg NPs extract showed zones of inhibition (ZOIs) of 1.05 ± 0.064 cm against B. megaterium and 1.19 ± 0.027 cm against Streptococcus. Ampicillin was used as a positive control in our experiment, yielding a ZOI of 2.01 ± 0.038 cm. The results of the variance analysis showed that the Response Surface Methodology (RSM) was successful in eliminating sodium fluoride and that the model suited the data well. Studies show that tamarind seed extract (TAM SE-Mg NPs) is more successful in stopping bacterial development. Overall, Tamarindus indica Seed’s extract with Mg doped nanoparticles successfully applied for fluoride detoxification and hindered microbial activities

Topological indices, particularly the Sombor index, have emerged as powerful tools for quantifying molecular structure–property relationships. This study investigates the predictive capacity of Sombor indices across two distinct systems: a tetrahedral diamond lattice (TDL) and twenty-eight polychlorobiphenyl (PCB) compounds. For the diamond lattice, we derived exact closed-form polynomial expressions for the Sombor index (SO) and its variants ((SO_3)–(SO_6)) as functions of the lattice dimension r, revealing their sensitivity to geometric regularity. For PCBs, Sombor indices were calculated and correlated with key physicochemical properties (melting point, relative retention time, log P, heat of formation, Henry’s constant). Remarkably strong linear relationships were observed, with correlation coefficients ((R^2)) exceeding 0.95 for relative retention time (RTT). Remarkably strong linear relationships were observed for relative retention time (RTT), with (SO, SO_3), and (SO_4) achieving (R^2>0.93) ((p<0.001)) exceeding 93% variance explanation while (SO_6) yielded (R^2=0.873). In contrast, correlations with melting point, log P, and Henry’s constant were significant for (SO, SO_3), (SO_4), and (SO_6) ((R^2=0.875-1.000)) but weaker for (SO_5) ((R^2 =0.319)). These results underscore the versatility of Sombor-type descriptors in bridging molecular topology with experimental behavior, offering a computationally efficient strategy for property prediction in materials design and environmental risk assessment.

The mitigation effects of Mangifera indica, Psidium guajava, and Carica papaya on mild steel were studied in water-in-diesel emulsion (WiDE) with different concentrations (0.1–0.5 g/L) of the plant extracts for 1344 h. The stability tests indicated that WiDE with 10% water content and 5% surfactant dosage exhibited the highest stability, showing minimal phase separation after 33 h. The corrosion inhibition results demonstrated that Mangifera indica leaf extract exhibited the highest inhibition efficiency (IE) (91.28% at 0.5 g/L), followed by Psidium guajava (87.78%) and Carica papaya (85.71%). Additionally, the extracts reduced the average surface roughness by 72.48%, 49.61%, and 48.17%, respectively. FT-IR confirmed the interaction of the extract’s bioactive components with the mild steel surface, demonstrating the creation of a protective barrier. These findings highlight the potential of eco-friendly, plant-based inhibitors to mitigate corrosion in WiDE systems, enhancing the durability of engine components and supporting the broader adoption of WiDE as a low-emission fuel technology.

This study presents a computational framework based on graph-theoretical descriptors and Multi-Criteria Decision-Making (MCDM) models to evaluate potential drug candidates for the treatment of ear disease. Molecular descriptors, particularly Banhatti indices, were computed to capture essential structural features of drug molecules. Statistical analysis using correlation and (R^{2}) values established strong links between these descriptors and physicochemical properties. To prioritize drugs, three MCDM methods, namely Entropy, Simple Additive Weighting (SAW), and Weighted Aggregated Sum Product Assessment (WASPAS), were applied. The integrated approach provides a reliable and comprehensive assessment, enabling more informed selection of effective treatments for ear disease.