Frontiers in Chemistry最新文献

[This corrects the article DOI: 10.3389/fchem.2025.1708033.].

A cooling sensation is primarily elicited by cooling agents through activation of cold-sensitive receptors such as TRPM8 and TRPA1. Coolants are widely used as functional additives in various industries including food, personal care, pharmaceuticals, and tobacco. In this study, a gas chromatography-mass spectrometry (GC-MS) method was developed to quantify three representative cooling agents-menthol, WS-3(N-ethyl-2-(isopropyl)-5-methylcyclohexanecarboxamide), and WS-23 (2-Isopropyl-N,2,3-trimethylbutyramide)-in aerosol samples. The test aerosols were generated from laboratory-formulated e-liquids under optimized conditions. Aerosols were obtained from an electronic vaping device manufactured by RELX (China). The results demonstrated that: (1) the analytical method exhibited good linearity (R ² ≥ 0.9994), with limits of detection (LOD, from 0.137 ng/mL to 0.114 μg/mL), limits of quantification (LOQ, from 0.456 ng/mL to 0.380 μg/mL), relative standard deviations (RSDs, 1.40%-4.15%), and spiked recovery rates (from 91.32% to 113.25%) all meeting the requirements of analytical validation; (2) the cooling agents were detected in both gas and particle phases of the aerosol, with the concentrations in gas-phase being significantly lower than those in the particle phase due to aerosols condensation. Specifically, the gas-phase proportions of menthol, WS-23 and WS-3 ranged from 1.94% to 5.72%, 0.03%-0.08%, and 0.10%-0.18%, respectively. Therefore, the developed GC-MS method satisfies methodological validation criteria and is suitable for application to commercial aerosol samples. It provides a reliable analytical foundation for studying sensory perception of cooling agents under aerosol exposure and offers more precise guidance for their use.

[This retracts the article DOI: 10.3389/fchem.2023.1131935.].

The present work reports the catalytic function of the planar tetracoordinate carbon (ptC) molecule, CAl₃MgH₂¯, for the first time. The hydrogenation of alkyne and alkene using CAl₃MgH₂¯ as a catalyst has been computationally examined through density functional theory calculations. Various quantum chemical tools are employed to analyze the reaction pathways systematically. The study also highlights that the reaction is favourable in the gas phase as compared to the solvent phase, suggesting the practical feasibility of using the CAl₃MgH₂ ^- catalyst in the industry. Intrinsic reaction coordinate analysis confirms that the transition states are truly connected to the local minima. Furthermore, natural atomic charges and elongated bond lengths confirm the heterolytic cleavage of H₂. Non-covalent interaction analysis illustrates the significant role of van der Waals interactions in coordinating reactants and stabilizing products. This study highlights the potential of the ptC molecule CAl₃MgH₂ ^- as a catalyst for hydrogenation reactions, eventually opening up new avenues for planar hypercoordinate and main-group metal-based catalysts.

Introduction: Accurate monitoring of oxide compositions is critical for ensuring cement quality and performance in industrial production. Conventional analytical techniques for this purpose are often time-consuming, costly, and lack real-time capability. While Near-infrared (NIR) spectroscopy offers a rapid and non-destructive alternative, traditional chemometric models struggle to capture the highly nonlinear, high-dimensional spectral characteristics and exhibit limited interpretability.

Methods: To address these challenges, this paper proposes an interpretable TabNet-based multi-output regression method for predicting multiple oxide concentrations from NIR spectra. The proposed method integrates sparse feature selection with adaptive information aggregation, enabling it to dynamically prioritize the most informative spectral regions during processing. This architecture facilitates both automatic wavelength selection and accurate oxide content prediction.

Results: Extensive experiments on two cement datasets demonstrate that the proposed TabNet model consistently outperformed established baseline models in predictive accuracy. A key advantage of the TabNet framework is its enhanced interpretability, achieved by generating sequential attention masks that highlight chemically meaningful wavebands associated with each oxide component.

Discussion: This framework provides a scalable and insightful solution for spectral-based analysis, not only for cement quality monitoring but also for other materials science applications. The code is available at https:// github.com/Andrew-Leopard/CementOxidePredictor.

[This retracts the article DOI: 10.3389/fchem.2018.00250.].

Introduction: Breast cancer, one of the most prevalent malignancies in women begins in the milk ducts or lobules and is divided into invasive and non-invasive variants. The kind stage and molecular features of the cancer determine the treatment strategy which may include surgery, chemotherapy, and targeted drugs. Early identification through screening is critical to increasing patient survival rates.

Methods: In this study, we look at the efficacy of numerous breast cancer drugs, including Toremifene, Tucatinib, Ribociclib, Olaparib, Abemaciclib, Anastrozole, Letrozole, Thiotepa, Tamoxifen, and Megestrol Acetate. We investigate their chemical and physical properties, including molar volume (MV), polarizability (P), molar refractivity (MR), polar surface area (PSA), and surface tension (ST). We employ Quantitative Structure Property Relationship (QSPR) analytical approaches, including curvilinear regression and multiple linear regression (MLR), to model and predict the physicochemical properties of these medications by analyzing the impact of molecular descriptors on these properties.

Results: A comparison of the two regression techniques is done to see how accurate their predictions are and to find the best way to model the data. Furthermore, resolving topological indices examines the relationship between molecular structure and therapeutic effectiveness.

Discussion: The outcomes of these studies help to further our understanding of breast cancer treatments and the development of more focused and customized therapeutics.

Introduction: The increasing incidence and high mortality rate of necrotizing fasciitis (NF), a rapidly progressing infection of the fascia and subcutaneous tissue, highlights the urgent need for effective drug evaluation strategies. Traditional clinical trials for NF antibiotics are costly and time-consuming, necessitating the development of computational approaches that can reliably capture drug behavior.

Methods: The study employs degree-based topological indices to represent molecular structures of NF antibiotics and develops QSPR models to predict their physicochemical properties. Calculating topological indices, performing regression analyses to identify significant indices, and using these indices in multi-criteria decision-making techniques to rank the antibiotics.

Results: This study demonstrates the potential of degree-based TIs combined with regression and multi-criteria decision-making techniques to predict and rank the physicochemical properties of antibiotics used to treat necrotizing fasciitis (NF).

Discussion: This integrated approach demonstrates the utility of topological indices in predicting drug properties, prioritizing candidates, and supporting the rational design and repurposing of NF therapeutics.

The issue of adulteration and misclassification of Ganoderma species is addressed in this research. In the study, we present a novel and comprehensive framework for Ganoderma authentication by analyzing attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectra using a combined approach of a chemometric analysis and deep learning (DL) with a convolutional neural network (CNN). The three Ganoderma species involved in this study were as follows: Ganoderma lucidum, Ganoderma sinense, and Ganoderma tsugae. Among chemometric models, orthogonal partial least squares discriminant analysis (OPLS-DA) yielded a high accuracy of 98.61%, a sensitivity of 97.92%, and a specificity of 98.96%. Additionally, the root-mean-squared error of estimation (RMSEE), root-mean-squared error of prediction (RMSEP), and root-mean-squared error of cross-validation (RMSECV) values for the OPLS-DA model were <0.3, confirming its reliability. The CNN model also performed well, achieving 89.84% accuracy, 84.75% sensitivity, and 92.38% specificity, with minimal variation during random segregation testing. Additionally, the model exhibited a precision of 0.87 ± 0.02, a recall of 0.85 ± 0.03, and an F1 score of 0.86 ± 0.03 for 10 random segregation tests. As a conclusion, both chemometric and CNN models developed in this study are efficient and robust for classifying Ganoderma species. To further validate this combined approach, we aim to implement chemometric and CNN models in other medicinal herb authentication in the future.