Sweet taste perception is primarily mediated by the T1R2/T1R3 receptor complex and plays a key role in dietary preference and human health. There is a growing significance in studying the relationship between sweet taste perception and metabolic responses. This study aimed to investigate metabolites changes associated with transient stimulation by different sweeteners (sucrose, neotame and sucralose) by a non-targeted metabolomic approach. The differential metabolite analysis and pathway enrichment revealed that different sweeteners activated distinct metabolic pathways and further impacted on cellular activity. As a nutritive sweetener, the transient stimulation by sucrose led to changes in numerous key intermediates in the tricarboxylic acid cycle within cells, suggesting the role of nutritive sweeteners in regulation of cellular energy metabolism. In contrast, many lipids were affected by neotame and sucralose transient stimulation, indicating that these non-nutritive sweeteners could impact cellular signaling and energy homeostasis. Additionally, we can effectively differentiate the metabolic profiles of these three sweeteners within 95% of the confidence interval through partial least squares-discriminant analysis. These findings offer insights into the diverse cellular metabolic responses induced by various sweeteners and provide a deeper understanding of receptor-mediated intracellular signaling.

Thermal treatment is an effective approach for sorbent regeneration, but its application is often limited by the decomposition of surface-active sites at elevated temperatures. In our previous work, sulfur-modified wastederived sorbents (SWSs) were synthesized through one-step co-pyrolysis of waste tires and biomass under SO2 atmosphere. These sorbents exhibited exceptional thermal stability in their sulfur-containing active sites, suggesting a promising strategy for efficient thermal regeneration. This study systematically investigated the physicochemical properties, regeneration performance, and underlying mechanisms of SWSs regenerated under N2 and SO2 atmospheres. After regeneration under N2 atmosphere, the pore structure exhibited slight development whereas the contents of elemental sulfur, aliphatic sulfur and sulfoxide underwent mild decomposition. The SWSs regenerated at 600 degrees C for 10 min under N2 atmosphere showed optimal regeneration performance, close to that of the fresh sorbent. Mechanistic analysis indicated that regeneration under N2 atmosphere primarily proceeded via thermal desorption of mercury species, accompanied by limited pore activation. In contrast, regeneration under SO2 atmosphere resulted in significant pore structure activation and a notable increase in the contents of elemental sulfur, aliphatic sulfur, and sulfoxide. The SWSs regenerated under SO2 atmosphere at 600 degrees C for 10 min achieved a regeneration efficiency exceeding that of fresh sorbent and maintained excellent stability over multiple regeneration cycles. Beyond thermal desorption of mercury species, the regeneration mechanism under SO2 atmosphere involved a more complex synergistic process, including sulfur replenishment, pore activation, and reformation of active sites. This process was driven primarily by the SO2-participating carbothermal reduction reaction.

This study proposes a novel approach utilizing Invertible Neural Networks (INNs) to address the complexity of predicting tobacco production parameters from specified tar content in a multimodal mapping task. The INN model takes advantage of bidirectional training and latent variables to accurately capture nonlinear relationships between inputs (Cigarette Paper Air Permeability (CPAP), Tipping Paper Air Permeability (TPAP), and Filter Rod Pressure Drop (FRPD)) and tar content. Experimental results show that the INN model achieves a Mean Normalized Percentage Error (MNPE) of 1.46%, outperforming traditional models like decision trees and linear regression in terms of prediction accuracy. These findings highlight the INN model’s potential for precise tar control in tobacco production.

Porous carbon-based QuEChERS purification material (CZIFA) was synthesized through a straightforward calcination and acid treatment process using Zeolitic Imidazolate Framework-67 (ZIF-67) as the precursor. The abundant adsorption sites and excellent clean-up capability of CZIFA resulting from acid treatment were confirmed through a series of characterization and performance evaluations. Combined with LC-MS/MS, a modified QuEChERS method using CZIFA as the adsorbent was developed for the simultaneous detection of 48 pesticide residues in tobacco. Under optimized conditions, the established method achieved low limits of detection (0.02-9.92 μg/kg), wide linear ranges (2-200 μg/L), and satisfactory precision. The average recoveries of the method were in the range of 80.1-119.7% with the relative standard deviations (RSDs) of <9.5%. This study provides a novel and efficient functionalized material for analyzing pesticide residues in complex matrices. Especially, it expands the application of ZIF-derived carbon materials in food safety detection.

The molecular weight (MW) of dextran involved in glycation is a key factor influencing the structural and functional properties of protein-dextran conjugates. In this study, soy protein isolate (SPI) was glycated with dextran of varying MWs via the Maillard reaction, and the resulting conjugates were evaluated for structural characteristics and flavor-binding capacity. Glycation significantly inhibited SPI thermal aggregation by decreasing surface hydrophobicity (H0) from 296 to 145-217 and free thiol group (R-SH) content from 10.8 to 4.7-7.9 mu mol/g, with both indicators negatively correlated with dextran MW distribution and grafting degree (DG). Multispectral analyses revealed that glycation promoted protein unfolding, induced secondary structure transitions from alpha-helix to beta-sheet, and enhanced overall molecular flexibility. Glycation predominantly occurred at the epsilon-amino groups of lysine and the imidazole groups of arginine, with LYS-(C6H10O5)2 identified as the predominant glycation adduct. Furthermore, glycation with medium- and high-MW dextran reduced SPI binding to off-flavor compounds, with the greatest reduction observed for nonanal (24.7 %), and the extent of reduction was positively correlated with DG. These findings elucidate the MW-dependent effects of glycation and offer mechanistic insights for the targeted design of protein modifications to enhance the flavor quality of plant-based foods.

Tobacco is a globally cultivated crop featuring distinct quality variations among leaves from different geographical origins. To develop a rapid, robust, and accurate method for multi-origin traceability, this study employed near-infrared spectroscopy combined with rapid chemical composition analysis to obtain 70 chemical components in samples from nine major tobacco-producing regions in China and four other countries (the United States, Brazil, Zimbabwe, and Zambia). One-way analysis of variance (ANOVA) and hierarchical cluster analysis (HCA) were used to investigate regional chemical differences. Discrimination models were built using a support vector machine (SVM), a backpropagation neural network, and a random forest. The best model was interpreted using permutation feature importance (PFI) to identify key markers for origin discrimination. One-way ANOVA revealed significant differences (p ≤ 0.001), and HCA demonstrated clear regional patterns. The SVM-hybrid kernel achieved the best performance with 97.96% test accuracy and macro-average recall, precision, and F1 scores of 0.9836, 0.9806, and 0.9821, respectively. The PFI algorithm was employed to identify and rank the top 20 key chemical components influencing the geographical origin discrimination. The top ten key components were Fru-Asn, succinic acid, rutin, Fru-Val, sulfate, serine, phosphate, starch, potassium, and Fru-Gly. This study integrated chemometrics, near-infrared, rapid chemical analysis, and interpretable machine learning to accurately distinguish tobacco origins, reveal regional traits, and offer insights into geographical traceability and chemical profiling.

The dynamic formation mechanism of biomass combustion aerosols is central to advancing fundamental multiphase combustion theory and developing targeted pollutant control strategies. However, these mechanisms remain elusive because of the absence of techniques for spatially and temporally resolved measurements at the microscale within a combustion environment. In this study, we used a cigarette-burning cone as a model system and introduced an innovative, synchronized measurement platform. By integrating microprobe sampling with ultrafine thermocouple arrays, this system enabled, for the first time, temperature-activated and simultaneous mapping of the dynamic temperature field and key aerosol properties, such as particle number concentration (PNC) and count median diameter (CMD) across the longitudinal section of the burning cone. Our results delineated the puff-induced dynamic evolution: the high-temperature zone (>700 degrees C) expanded continuously, with isotherms shifting markedly rearward axially. Aerosol distribution was highly heterogeneous: PNC and CMD peaked near the combustion line at an axial position of 25 mm (3.63 & times; 10(10) cm(-3) and 150 nm, respectively). The peak formation rates were located further downstream at an axial distance of approximately 22 mm, systematically lagging behind the absolute temperature peak, demonstrating spatial decoupling between precursor generation and aerosol formation. Therefore, we established a "Material Basis-Process Driver" duality framework, in which the absolute temperature provides the material foundation and the axial negative temperature gradient (partial derivative T/partial derivative x < 0) is the core driver initiating explosive homogeneous nucleation and regulating growth. This study provides multiscale experimental evidence and a theoretical framework for elucidating aerosol formation dynamics in combustion systems.

Differences between near-infrared (NIR) spectroscopy instruments make it difficult to apply calibration models universally across multiple instruments; hence, calibration transfer (CT) is crucial. To ensure that a model developed on one instrument is also applicable to a new instrument, this study establishes a CT method based on nonparametric techniques, referred to as nonparametric varying-coefficient regression calibration transfer (NVT). This method uses a varying-coefficient model (VCM) to build a functional relationship between the master and slave spectra using a set of standard sample spectra and employs B-splines as basis functions for function fitting. This functional relationship helps transfer the slave spectra of other samples into the master spectra, reducing the spectral differences caused by instrument variations. The performance of NVT was tested on the determination of moisture, oil, protein, and starch in corn, and the content of total plant alkaloids, reducing sugars, total sugars, and total nitrogen in tobacco using NIR spectroscopy. NVT was compared with two common CT methods: spectral space transformation (SST) and piecewise direct standardization (PDS). The results show that NVT can effectively eliminate some spectral differences caused by different instruments and significantly improve analytical accuracy. Compared with that of PDS, the CT effect of NVT is significantly improved, whereas compared with that of SST, it is slightly improved. This method is insensitive to parameters, making it easy to select parameters and providing a new idea for CT method design.

Benzene, as a ubiquitous and carcinogenic volatile organic compound (VOC), presents substantial health risks through inhalation exposure from ambient indoor environments and cigarette mainstream smoke, necessitating the development of advanced adsorbent materials. Herein, zeolitic imidazolate framework-8/cellulose acetate (ZIF-8/CA) composite porous carbon materials were synthesized via a dual emulsion-solvent evaporation method coupled with subsequent high-temperature carbonization. Systematic studies were conducted to evaluate the effects of ZIF-8/CA mass ratios on the microstructure, pore architecture, and the benzene adsorption property. The results of the relevant characterizations revealed that the composite prepared at a 4/6 mass ratio exhibited an excellent BET specific surface area with the highest value of 1380 m2/g and mesopore volume of 0.77 cm3/g, alongside a well-defined hierarchical pore network spanning microporous, mesoporous, and macroporous regimes. Dynamic benzene vapor adsorption tests demonstrated that this optimized formulation achieved a dynamic saturation adsorption capacity of 235.0 mg/g. Kinetic analysis employing the Apiratikul-Chu and Adams-Bohart models yielded high correlation coefficients, indicating that benzene adsorption was governed by surface adsorption and mass‑transfer mechanisms. When applied in cigarette mainstream smoke purification, the ZIF-8/CA composite exhibited a benzene removal efficiency of 49.0

BackgroundIdiopathic pulmonary fibrosis (IPF) is a progressive and irreversible interstitial lung disease with limited therapeutic options. Existing hiPSC-derived lung organoid models are largely restricted to single epithelial lineages and cannot endogenously integrate multiple pulmonary cell types, limiting mechanistic studies and drug screening.MethodsWe established a staged directed differentiation protocol to sequentially differentiate hiPSCs into definitive endoderm, anterior foregut endoderm, and lung progenitor cells, ultimately generating a multi-lineage lung model on Transwell inserts. The model was characterized by bright-field microscopy, hematoxylin-eosin (H&E) staining, scanning and transmission electron microscopy, immunofluorescence, quantitative real-time PCR (qRT-PCR), enzyme-linked immunosorbent assay (ELISA), and single-cell RNA sequencing. Fibrosis-like phenotypes were induced by transforming growth factor-beta 1 (TGF-β1) stimulation. Transcriptomic similarity to human IPF was assessed by RNA sequencing (RNA-seq) combined with bidirectional gene set enrichment analysis (GSEA), and drug responsiveness was validated with pirfenidone.ResultsWithout genetic editing or exogenous cell supplementation, the model endogenously generated proximal duct-like epithelium, distal alveolar epithelium, fibroblasts, endothelial cells, and CD68+ macrophage-like cells confirmed by immunofluorescence. Single-cell sequencing identified 13 transcriptional subclusters and revealed epithelial-mesenchymal co-development. TGF-β1 stimulation induced epithelial barrier disruption, ferroptosis-like mitochondrial ultrastructural alterations, extracellular matrix (ECM) remodeling, and aberrant secretion of multiple IPF-associated biomarkers. Transcriptomic analysis showed that upregulated genes in the fibrosis model group were significantly enriched in ECM organization, cell adhesion, and the PI3K-Akt pathway. Protein-protein interaction (PPI) network analysis identified COL1A1, FN1, and integrin family members as central hubs. Bidirectional GSEA validation confirmed that the transcriptomic signature of the model was highly similar to human IPF, with differentially expressed genes significantly enriched in three independent IPF cohorts. Pirfenidone reversed TGF-β1-induced ECM deposition and myofibroblast activation, and partially restored type II alveolar epithelial (AT2) cell marker expression.ConclusionWe successfully established a humanized multi-lineage lung model that captures epithelial-mesenchymal co-development in vitro. Its responsiveness to TGF-β1 stimulation and sensitivity to pirfenidone highlight its potential for antifibrotic drug screening. This platform offers a humanized tool with the potential to be standardized for investigating cell fate determination during early lung development and the pathogenesis of IPF.

To address complex chemical relationships and geographical sample imbalance in tobacco origin classification, a method combining chemical composition imaging with a two-dimensional convolutional neural network (2D-CNN) and the synthetic minority over-sampling technique (SMOTE) with threshold moving is proposed. In this approach, multidimensional chemical components are transformed into structured 2D images. Then, a 2D-CNN deep learning model is constructed to capture intricate correlations among chemical indicators through two-dimensional convolutions. Classifier bias arising from class imbalance is mitigated by combining SMOTE with threshold-moving techniques. The results show that the 2D-CNN classification model achieved an overall accuracy of 0.9764 on the test set, with an average precision of 0.9477, a recall of 0.9511, and an F1-score of 0.9492 across eight ecological areas, indicating high model performance. Under the same imbalance handling, the 2D-CNN outperformed a one-dimensional CNN (1D-CNN) by 2.51% in average F1-score, confirming that chemical composition imaging effectively extracts complex inter-indicator relationships. The integration of SMOTE and threshold moving effectively alleviates the impact of class imbalance, significantly enhancing the recognition rates for minority areas. Furthermore, to independently validate the effectiveness of the imbalance handling strategy, it was applied to a separate public image dataset (the Niphad Grape Leaf Disease Dataset). Compared to the baseline model without any imbalance mitigation, the absolute recall of the minority class increased by 40 percentage points.

A deep eutectic solvents-based ferrofluids (DES-FFs) material was prepared by combining small-sized Fe3O4 magnetic cores with a ternary choline chloride/sesamol/coumarin (1:3:1) DESs. The material was used for single-step magnetic-assisted liquid-liquid microextraction (MALLME) and determination of 15 pyrethroids (PYs) in vegetable oils by GC-MS/MS. The small-sized Fe3O4 material improved colloidal stability of DES-FFs and enabled rapid phase separation (<10 s). The DES-FFs might leverage enhanced π-π stacking interactions between sesamol/coumarin and PYs, and optimal hydrophilicity-polarity matching, which collectively contributed to the high extraction efficiency for PYs. Comprehensive characterization (HRTEM, XRD, FTIR, TGA, etc.) validated the material's structural integrity, thermal stability and magnetic property. The developed method demonstrated high sensitivity (LODs: 0.91-3.03 ng/mL; LOQs: 3.02-10.09 ng/mL), significantly reduced matrix effect, and good precision (RSD ≤6.92%) for 15 PYs in vegetable oils, with recoveries ranging from 79.88% to 107.90%. Determination of 15 PYs was successfully achieved in both refined and crude vegetable oils.

A series of amino acid-based ionic liquids (AAILs) have been synthesized by acid-base neutralization reaction of tetraalkylammonium hydroxides and amino acids. The pendent chain length and groups of AAILs were well adjusted by using glycine (Gly), L-alanine (L-Ala), L-valine (L-Val), L-leucine (L-Leu), L-Phenylalanine (L-Phe), Lglutamic acid (L-Glu) and L-lysine (L-Lys) as amino acid precursors. These AAILs was used for the effective removal of aldehydes in cigarette smoke, 41-66% of crotonaldehyde and 12-39% of acetaldehyde could be removed from the cigarette smoke with these AAILs as adsorbents. With the increasing of the pendent chain length, the remove efficiency of acetaldehyde and crotonaldehyde decreased from 32%, 66% to 16%, 41%, respectively due to the increase of steric hindrance. [N3333][Gly], the AAILs with the smallest chain length, has a highest removal efficiency of 66% for crotonaldehyde and 32% for acetaldehyde. The AAILs with pendent benzene groups, [N3333][Phe], has high crotonaldehyde removal efficiency of crotonaldehyde due to the strong it-it interaction, which could overcome the higher steric resistance of pendent chain. AAILs with pendent amino groups, [N3333][Lys], could reduce 60% of crotonaldehyde and 39% of acetaldehyde due to the enhanced chemadsorption of the aldehydes by the pendent amino groups. Its excellent performance stems from the synergy between amino chemical adsorption and polar side chain solubility. The high aldehydes removal efficiency of these AAILs, such as [N3333][Gly], [N3333][Phe], [N3333][Lys], demonstrates the great potential of these AAILs as adsorbents for the removal of aldehydes in cigarette smoke.

IntroductionTo identify the key chemical components affecting the sensory irritation of tobacco leaves, a binary classification model for high and low irritation was constructed based on 78 chemical components and sensory evaluation scores of 353 tobacco leaf samples.MethodsFirst, the median absolute deviation (MAD) method was applied to remove outliers from the high- and low-irritation samples. Then, the ReliefF algorithm was applied for dimensionality reduction, selecting 33 core features to eliminate data redundancy. Using the selected features, a random forest (RF) algorithm was employed to build the classification model, and the optimal number of decision trees was determined to be 60.ResultsThe ReliefF-RF model achieved an accuracy of 84.38% on an independent test set, with precision, recall, and F1-score all at 86.49%, outperforming the original RF model as well as other machine learning models such as support vector machine (SVM) and k-nearest neighbors (KNN). Through feature importance evaluation, eight key chemical indicators were identified: total nitrogen, total alkaloids, cryptochlorogenic acid, oleic acid + linolenic acid, reducing sugar, sugar-nitrogen ratio, Fru-Asp, and neochlorogenic acid.DiscussionSHapley Additive exPlanations (SHAP) analysis revealed that higher levels of nitrogenous compounds were strongly associated with increased irritation, whereas elevated levels of sugar components, specific organic acids, and amino acid derivatives were associated with reduced irritation. Notably, Fru-Asp exhibited a complex non-linear response, where both extremely high and low levels contributed to higher irritation. This study provides a useful reference and data support for the targeted regulation of cigarette irritation.

Molecular imprinting still faces the big challenge of template recovery, and the development of efficient template recovery methods is of great significance for the green synthesis of MIP. Herein, the performance of liquid-phase microextraction (LPME) and electromembrane extraction (EME) for template recovery was investigated for the first time using sulfamethazine as the model template. Under the optimal extraction conditions for low-concentration sulfamethazine using small-volume devices, the recovery of high-concentration sulfamethazine by large-volume LPME could still reach 89.9%, while the recovery by large-volume EME decreased sharply from 94.0% to 32.1%, which could be improved to 74.7% by increasing the extraction voltage. High recoveries of 88.1% and 72.6% were achieved for the actual MIP eluent using large-volume LPME and EME, respectively. This comparison of LPME and EME under different application scenarios will provide guidance for the rational selection of membrane-based microextraction techniques for the efficient recovery of templates in molecular imprinting.

Nicotine serves as the core active ingredient in electronic cigarette liquids (e-liquids) and a critical quality control indicator for e-cigarette products. Given the variable quality of commercially available e-cigarettes, the development of rapid and accurate methods for quantifying nicotine within e-cigarette cartridges is essential for effective quality control and assessing nicotine intake risks. However, existing methods suffer from complex sample preparation, high analytical costs, and an inability of fast inspection. This study establishes a rapid detection method for nicotine leveraging the inherent advantages of Raman spectroscopy, namely its capacity for rapid quantitative analysis without sample pretreatment. Three distinct types of simulated e-liquid matrices were formulated to systematically evaluate the impact of common organic solvents, additives, and authentic tobacco extracts on the quantitative analysis of nicotine. Our findings demonstrate that even in the presence of these complex matrices, nicotine quantification can be reliably achieved using the ratio of the Raman peak intensity of nicotine (1053 cm(-1)) to that of solvent (840 cm(-1)). The developed method yielded linear range of 0-30 mg g(-1), which meets the requirements for practical nicotine detection in e-liquids. The method was further validated with 28 commercial e-liquid products, showing good agreement with results obtained via gas chromatography. This Raman-based approach enables quantitative nicotine detection without diluting or pre-treatment. Its combination of speed, sensitivity, and easy way of performing positions it as a highly promising tool for widespread application in e-cigarette quality control and the rapid screening for non-compliant nicotine levels.

This dataset comprises 347 complete spectra of tobacco leaves, acquired using an Antaris II Fourier Transform Near-Infrared spectrometer equipped with an integrating sphere diffuse reflectance sampling system. The samples were collected from six countries: Argentina, Brazil, Zimbabwe, the United States, Tanzania, and Zambia. All samples were dried using an FD240 oven (Binder GmbH, Germany), ground using a ZM200 grinder (Retsch GmbH, Germany), and sieved through a 0.250 mm mesh screen. NIR spectra were acquired with the following parameters: a spectral range of 4000–10000 cm−1, a resolution of 8 cm−1, and 64 scans. The dataset was partitioned into a training set (70%) and a validation set (30%) using stratified sampling. Six preprocessing techniques were applied: Savitzky-Golay (SG) smoothing, Multiplicative Scatter Correction (MSC), Standard Normal Variate (SNV), First Derivative (1D), Second Derivative (2D), and Mean Centering. Partial Least Squares (PLS) regression was utilized to establish predictive models for 13 chemical indicators: total alkaloids, reducing sugars, total sugars, total nitrogen, K, Cl, pH, starch, neochlorogenic acid, chlorogenic acid, cryptochlorogenic acid, scopoletin, and rutin. The Random Forest (RF) algorithm was employed to create the origin classification model. Ultimately, through the selection of appropriate spectral preprocessing methods, the quantitative prediction models established using this dataset all achieved coefficients of determination (R2) exceeding 0.83, demonstrating robust predictive performance. Furthermore, the geographical origin classification models yielded an overall validation accuracy greater than 0.8, indicating strong classification performance. Consequently, this dataset is confirmed to be accurate and reliable, capable of providing foundational data for other researchers to construct near-infrared (NIR) spectral databases and develop NIR prediction models. The developed models exhibit excellent predictive capabilities and can be utilized in practical applications as an alternative to classical chemical analysis methods for these chemical indicators.

Tobacco leaf position is closely associated with its quality whose material basis is the chemical components of tobacco leaf. In recent years, near-infrared (NIR) spectroscopy combined with algorithmic models has emerged as a popular method for identifying the tobacco leaf position. However, when applied to leaf position discrimination, these models often rely on principal components derived from dimensionality-reduced spectral signals, resulting in limited interpretability and difficulty in identifying key chemical components. Chemical composition data combined with algorithmic models can also be used to discriminate tobacco leaf positions. However, the acquisition of chemical components relies on traditional instrumental analytical methods. As a result, the acquisition of chemical composition data is time-consuming and labor-intensive, involving only a limited number of compounds. The study proposes a novel approach that integrates machine learning with advanced interpretability techniques for both tobacco leaf position discrimination and analysis. Based on the 70 tobacco leaf chemical components obtained using near-infrared rapid analysis technology, tobacco leaf position discrimination models were built using Support Vector Machine (SVM), Back Propagation Neural Network (BPNN), and Random Forest (RF). Particle swarm optimization (PSO) was used to optimize parameters of each model. Chemical components were analyzed for statistical significance across leaf positions, and their influence on model predictions was interpreted using SHapley Additive exPlanations (SHAP). The experimental results showed that among all models, the SVM- hybrid kernel demonstrated the most robust and accurate performance, achieving discrimination accuracies of 98.17% and 96.33% on the training and test sets, respectively. SHAP analysis provided a clear ranking of feature importance and revealed the positive and negative contributions of individual chemical components. The proposed method can be useful for position traceability and chemical feature analysis of various crops.

The trace detection of both aqueous and gaseous nicotine is significant for health monitoring and environmental analysis. In this study, a novel covalent organic framework (COF)-based composite was designed to enrich and detect nicotine using surface-enhanced Raman scattering. The negatively charged TpPa-SO3H exhibited exceptional enrichment capabilities for nicotine, demonstrating an adsorption capacity of 148.0 mg center dot g-1. Chemical interactions, including it-it stacking and acid-base interactions, were observed between the COFs and nicotine molecules. Furthermore, chemical enhancement induced by electron transfer between TpPa-SO3H and nicotine was corroborated through energy level analysis. By leveraging the bimetallic synergism between core-shell Au and Ag to achieve electromagnetic enhancement, the TpPa-SO3H-Au@Ag composite achieved a detection limit of 6.0 x 10-10 mol center dot L-1 for aqueous nicotine. The detection of nicotine in e-cigarette oil and Cambridge filter demonstrates its application capability for different kinds of real samples. By the crosslinking with chitosan, the synthesized aerogel was successfully applied for the on-site gaseous nicotine detection in cigarette smoke. Additionally, the plasmonic aerogel enabled the identification of different kinds of cigarettes using artificial intelligence-assisted gaseous fingerprint spectra recognition and classification.