Scientific Reports最新文献

Aromatic compounds constitute the primary chemical components responsible for the distinctive flavor characteristics of cigars and play a pivotal role in cigar quality assessment. This study employed solid-phase microextraction/gas chromatography-mass spectrometry (SPME/GC-MS) and liquid-liquid extraction/gas chromatography-mass spectrometry (LLE/GC-MS) to analyze the aroma profiles of Chinese and international cigars under smoking and simulated oral environment conditions. Based on multidimensional statistical and model evaluations-including One-way analysis of variance (P ≤ 0.05), odor activity threshold (OAV ≥ 1), variable importance in projection (VIP ≥ 2), and fold change (≥ 1.5)-six key aromatic compounds were identified as significant contributors to the characteristic flavor of Chinese cigars: methylpyrazine, tabanone, 3-methylbutyric acid, limonene, 2,6-dimethylphenol, and malic acid. To elucidate the sensory perception mechanisms of these compounds, molecular dynamics simulations were conducted to investigate interactions between the human oral cavity receptor T1R1 and the identified aromatic compounds. The results revealed that 2,6-dimethylphenol, 3-methylbutyric acid, malic acid, and methylpyrazine exhibited specific binding interactions with the T1R1 receptor. These molecular interactions are hypothesized to facilitate receptor-mediated perception of the compounds in the oral environment, thereby contributing to the overall sensory experience of Chinese cigar.

The production of high-value-added products from biomass is a key strategy for advancing carbon neutrality. This approach not only reduces dependence on fossil resources but also enhances the economic value and utilization efficiency of renewable materials. 5-hydroxymethylfurfural (HMF) and 2,5-furan dicarboxylic acid (FDCA) are potential candidates for producing high-value-added chemicals via carbon-neutral pathways. This study conducts life cycle assessment (LCA) in accordance with ISO 14,040/44 standards, defining the functional unit as 1 ton of straw (for HMF production) and 284.34 kg of HMF (for FDCA production). Results show HMF_straw outperforms HMF_fructose in all categories, reducing 87.73 kg CO₂ eq and 7.87 kg 1,4-DB eq per unit product. HMF_fructose has 23.46%-27.83% higher aquatic/sediment ecotoxicity. Sensitivity analysis indicates that replacing the existing power structure (60% coal-fired power + 40% renewable energy) with 100% renewable energy can reduce global warming potential GWP by 74.56%. Replacing dichloromethane (DCM) with γ-valerolactone (GVL) reduces HT by 63.36%. Crystallization for FDCA is more sustainable than distillation, reducing abiotic depletion, acidification, human toxicity and photochemical oxidation by 50.22%-59.02%, and ~ 20% in fossil energy and global warming potential. The results confirm that straw is an environmentally viable feedstock for HMF production, crystallization represents more sustainable pathway for FDCA synthesis, and optimizing the power structure alongside solvent substitution can significantly reduce environmental impacts. This provides quantifiable reference criteria for green optimization in biomass chemical processes.

This research aimed to explore a novel and straightforward precipitation-based synthesis method for fabricating a NiO/ SrFe₁₂O₁₉ p-n heterojunction nanocomposite photocatalyst. Several characterization techniques were employed for evaluating the materials' properties: FTIR for functional groups, SEM for morphology, EDX for elemental composition, UV-DRS for bandgap energy, and EIS for charge transfer resistance, PL for recombination studies, XPS for surface chemical states, and XRD for crystalline phases. This work investigated the photocatalytic activity and reusability of the nanocomposite for the removal of rhodamine B (RhB) (cationic) dye and ibuprofen drug under solar light irradiation. To determine the ideal photodegradation conditions, intrinsic reaction parameters such as catalyst loading, solution pH, and beginning pollutant concentration and agitation of time were investigated. A binary NiO/SrFe₁₂O₁₉ catalyst with a 0.60 mg loading at pH 3.5 destroyed 93% of 60ppm RhB dye after 100 min of solar light irradiation. Furthermore, for ibuprofen degradation, at pH 7, catalyst dose 0.75 mg, pollutant concentration 10 ppm, and irradiation time 120 min, the composite showed 75% removal efficiency under solar light. Moreover, this composite catalyst demonstrated exceptional chemical stability and reusability, removing 60% of the RhB dye and 41% of ibuprofen after four recycling runs.Scavenger tests demonstrated that photogenerated holes (h⁺) and superoxide free radicals (^·O₂) are the primary reactive species causing degradation. This study gives information on the creation of highly effective nanomaterials for removing dyes from wastewater, as well as a possible reaction mechanism.

As virtual economies in the metaverse continue to grow, the need for real-time risk assessment in financial transactions has become critical. Traditional fraud detection systems often face challenges in keeping pace with the complexity and speed of metaverse data. To address this, we introduce a real-time anomaly detection and risk classification model designed specifically for metaverse transactions. The model is based on a one-dimensional convolutional neural network (1D-CNN) enhanced with residual connections and a self-attention mechanism, allowing it to focus on the most relevant features of each transaction for improved risk classification. We trained the model on benchmark metaverse financial datasets from Kaggle, achieving excellent results in accuracy, sensitivity, and specificity when classifying transactions into three risk levels-low, moderate, and high. To validate its robustness, we also tested it on the widely used Credit Card Fraud Detection dataset, where it maintained strong performance. However, we acknowledge that perfect scores can sometimes indicate overly clean or predictable data. To address this, we conducted an ablation study by introducing controlled noise into the dataset, evaluating the model's ability to handle uncertainty and imperfections in real-world scenarios. To enhance interpretability, we analyzed feature importance across several CNN-based variations and assessed performance using confusion matrices, ROC curves, and t-SNE visualizations, which confirmed clear separation of risk levels in high-dimensional space. Further comparisons with other machine learning and deep learning models demonstrate the confidence and effectiveness of the proposed 1D-CNN architecture for financial fraud detection in the metaverse.

Bovine coronavirus (BCoV) and SARS-CoV-2, both belonging to the Betacoronavirus genus, are major pathogens affecting cattle and humans, respectively. BCoV causes respiratory and enteric diseases in cattle, leading to significant economic losses, while the detection of SARS-CoV-2 in various animal species raises concerns about interspecies transmission. This study assessed the seroprevalence of both viruses in cattle and buffaloes from southern Italy, considering species and regional origin as potential risk factors. Among 945 animals analyzed (491 cattle, 454 buffaloes), 435 (46%) tested positive for BCoV and 27 (2.8%) for SARS-CoV-2. Significant differences were observed between species and regions (BCoV: p < 0.0001; SARS-CoV-2: p = 0.0029). Among BCoV-positive samples, 67.1% of cattle but no buffaloes showed neutralizing antibodies (p = 0.0006). Twelve SARS-CoV-2-positive cattle were also BCoV-positive. However, the observed SARS-CoV-2 ELISA reactivity cannot be conclusively interpreted as confirmed SARS-CoV-2 exposure and does not allow discrimination between true exposure and non-specific serological reactivity. Longitudinal and molecular studies are needed to further clarify infection dynamics and possible antibody cross-reactivity among Betacoronaviruses.