Vibrational Spectroscopy最新文献

{"title":"NIR-based efficient and non-destructive moisture monitoring in leather manufacturing","authors":"Lixia Liu,&nbsp;Wenhao Chen,&nbsp;Xia Li,&nbsp;Xiu He,&nbsp;Ya-nan Wang,&nbsp;Yue Yu","doi":"10.1016/j.vibspec.2026.103967","DOIUrl":"10.1016/j.vibspec.2026.103967","url":null,"abstract":"<div><div>Moisture content is a key determinant of processing efficiency and product quality during leather manufacturing. However, traditional gravimetric methods are inherently destructive, labor-intensive, and incompatible with real-time process monitoring requirements. To overcome these limitations, a robust analytical framework for rapid moisture quantification using near-infrared (NIR) spectroscopy combined with chemometric modeling was developed. By optimizing spectral preprocessing through multiplicative scatter correction and first-derivative transformation, a robust partial least squares (PLS) model was established, yielding superior predictive performance across a broad range of moisture levels (5.45%–79.40%; coefficient of determination [R²_p] = 0.9988 and root mean square error of prediction [RMSEP] = 0.57%). The framework demonstrated exceptional universal robustness, maintaining high predictive accuracy throughout the tannery workflow, including the beamhouse, tanning, post-tanning, and finishing stages, despite substantial matrix heterogeneity and confounding spectral interferences arising from various industrial tanning and retanning agents. When applied to monitor dynamic moisture evolution during industrial rewetting and drying, and evaluated across a diverse array of raw hides (bull, cowhide, buffalo, and sheepskin) and finished goods (shoe uppers, bags, upholstery, and garments), this proposed method limited predictive deviations within an analytically acceptable margin of ±1.00% while reducing the analysis time from hours to less than 1 min per sample. In conclusion, this non-destructive and scalable solution effectively leverages advanced analytical chemistry to meet the demands of intelligent, automated leather manufacturing.</div></div>","PeriodicalId":23656,"journal":{"name":"Vibrational Spectroscopy","volume":"147 ","pages":"Article 103967"},"PeriodicalIF":3.1,"publicationDate":"2026-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149347647","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multimodal fusion of near-infrared spectroscopy and chemical semantics for small-sample pharmaceutical classification","authors":"Yitong Chen,&nbsp;Adriano Tavares,&nbsp;Carlos Lima,&nbsp;Xiaofei Li,&nbsp;Yanchun Liang","doi":"10.1016/j.vibspec.2026.103968","DOIUrl":"10.1016/j.vibspec.2026.103968","url":null,"abstract":"<div><div>Near-Infrared Spectroscopy (NIRS) enables non-destructive measurements supporting assistive pharmaceutical classification. However, under small-sample constraints and user-induced operational noise, traditional data-driven models may suffer performance degradation. We propose a multimodal framework combining NIR spectra with chemical priors represented by candidate drug SMILES (Simplified Molecular Input Line Entry System): a Transformer encodes spectra, ChemBERTa extracts molecular representations, and adaptive gated fusion integrates both. Five-fold out-of-fold evaluation on 413 acquisitions across 17 classes achieved 99.76% accuracy, exceeding the strongest conventional baseline (SVM with SNV preprocessing, 93.45%), with 100% accuracy on the transparent-softgel subset and 8.43 ms average inference latency on a standard CPU. In a 413-pair random mismatch test, the standalone classifier accepted incorrect candidates in 93.70% of trials, revealing a limitation of the classification objective. Under the same perturbation, an inference-level SVM + SNV spectral-guard cascade reduced mismatch false acceptance to 0.24% with 93.22% matched acceptance. Additionally, an auxiliary binary verifier across all 7021 spectrum–candidate pairs achieved 0.9829 pair AUROC and 0.08%–0.62% pair-level false acceptance (1.21%–9.20% per-spectrum any-wrong-candidate rate), with 47.70%–58.60% true-candidate coverage. These results support multimodal fusion for closed-set classification while quantifying verification coverage–risk trade-offs.</div></div>","PeriodicalId":23656,"journal":{"name":"Vibrational Spectroscopy","volume":"147 ","pages":"Article 103968"},"PeriodicalIF":3.1,"publicationDate":"2026-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149347795","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Polarized low‑frequency raman and two‑dimensional correlation spectroscopy for discriminating enantiomeric and racemic crystals of phenylalanine","authors":"Romiald Yéchi YAVO,&nbsp;Yasmine HAMMI,&nbsp;Grégoire HERZOG,&nbsp;Manuel DOSSOT","doi":"10.1016/j.vibspec.2026.103966","DOIUrl":"10.1016/j.vibspec.2026.103966","url":null,"abstract":"<div><div>This work demonstrates that polarized low‑frequency Raman (LFR) spectroscopy, combined with two‑dimensional correlation spectroscopy (2D‑COS), provides a powerful approach for distinguishing enantiopure and racemic crystals of phenylalanine (Phe). LFR modes below 200 cm⁻¹ , which probe collective intermolecular vibrations, are highly sensitive to crystal packing and chirality. While D‑ and L‑phenylalanine powders display identical spectra, the racemic DL form exhibits distinct features, particularly a mode near 57 cm⁻¹ . Polarization‑resolved measurements on oriented single crystals reveal strong modulation of this band with rotation in DL‑phenylalanine, contrasting with the enantiopure D crystal, which instead shows a polarization‑sensitive mode near 94 cm⁻¹ . 2D‑COS analysis enhances spectral contrast and identifies co‑varying vibrational contributions, isolating these two bands as the most reliable markers for discriminating the crystalline forms. Mixtures of D and DL phenylalanine further demonstrate that the intensity ratio I₉₄/I₅₇ varies linearly with composition, enabling semi-quantitative evaluation of enantiomeric content. Complementary density functional theory (DFT) calculations on simplified gas‑phase dimers reproduce the essential characteristics of the modes observed experimentally: a low‑frequency librational–torsional motion for the racemic form and a higher‑frequency hydrogen‑bond‑mediated stretching motion for the enantiopure structure. These insights confirm that the discriminating Raman markers arise from fundamentally different intermolecular dynamics governed by the underlying chiral or racemic packing. Overall, the study establishes polarized LFR spectroscopy combined with 2D‑COS as a sensitive and semi-quantitative tool for probing chirality dependent lattice dynamics in molecular crystals.</div></div>","PeriodicalId":23656,"journal":{"name":"Vibrational Spectroscopy","volume":"147 ","pages":"Article 103966"},"PeriodicalIF":3.1,"publicationDate":"2026-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149347646","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Use of NIR spectroscopy as an analytical tool for identifying and assessing wood quality: A review","authors":"Dayane Targino de Medeiros,&nbsp;Felipe Gomes Batista,&nbsp;Jhennyfer Nayara Nogueira Gomes Duarte,&nbsp;Thalles Loiola Dias,&nbsp;Adriano Reis Prazeres Mascarenhas,&nbsp;Paulo Ricardo Gherardi Hein","doi":"10.1016/j.vibspec.2026.103951","DOIUrl":"10.1016/j.vibspec.2026.103951","url":null,"abstract":"<div><div>This review provides an overview of the application of near-infrared (NIR) spectroscopy in the timber industry, highlighting its potential as a fast, accurate, and non-destructive tool for assessing wood quality. The technique allows the prediction of relevant physical and chemical properties, such as density, moisture, lignin, extractives, and fiber characteristics, with significant advances resulting from the development of spectrometers and more robust multivariate statistical models. Despite promising results obtained mainly in laboratory conditions with benchtop spectrometers, the adoption of portable NIR devices in industrial and field environments is still limited. Factors such as moisture variability, environmental conditions, and differences between instruments hinder their practical application. Although calibration transfer methods have the potential to expand the use of the technique, their application in the forestry context is still in its infancy. Overcoming these gaps is essential to enable real-time, on-site monitoring of wood quality and identification throughout harvesting, processing, and transportation, contributing to greater operational efficiency, traceability, and better product performance, as well as reinforcing the role of NIR spectroscopy in sustainable forest management.</div></div>","PeriodicalId":23656,"journal":{"name":"Vibrational Spectroscopy","volume":"146 ","pages":"Article 103951"},"PeriodicalIF":3.1,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148935305","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Early detection and firmness prediction of apple fruit infested by Bactrocera dorsalis using hyperspectral imaging combined with 1D convolutional neural network","authors":"Yuanyuan Shao,&nbsp;Miaomiao Jiang,&nbsp;Yongxian Wang,&nbsp;Guantao Xuan,&nbsp;Xiaomin Xue","doi":"10.1016/j.vibspec.2026.103935","DOIUrl":"10.1016/j.vibspec.2026.103935","url":null,"abstract":"<div><div>To achieve accurate quality assessment of <em>B. dorsalis</em>-infested apples, this study proposes an early detection and firmness prediction method based on hyperspectral imaging (HSI) integrated with a one-dimensional convolutional neural network (1D‑CNN). First, a hyperspectral imaging system (400–1000 nm) was used to acquire images over two consecutive years for three categories of apple samples: healthy, <em>B. dorsalis</em>-infested, and superficially similar scarred apples, while the firmness of each fruit was measured simultaneously. Subsequently, the successive projections algorithm (SPA) and competitive adaptive reweighted sampling (CARS) were applied to select characteristic bands related to fruit categories and firmness, respectively. Finally, early detection and firmness prediction models were developed by integrating partial least squares-discriminant analysis (PLS-DA), back-propagation neural network (BP), 1D-CNN, random forest (RF), support vector machine (SVM), and 1D-CNN. The results showed that the SPA–1D-CNN detection model with 10 selected characteristic bands achieved satisfactory performance, with an overall classification accuracy of 94%, precision of 0.94, recall of 0.94, and an F1-score of 0.94. For firmness prediction, the SPA–1D-CNN model also performed well, For the calibration set, <span><math><msubsup><mrow><mi>R</mi></mrow><mrow><mi>c</mi></mrow><mrow><mn>2</mn></mrow></msubsup></math></span> was 0.87, RMSEC was 0.76. For the prediction set, <span><math><msubsup><mrow><mi>R</mi></mrow><mrow><mi>p</mi></mrow><mrow><mn>2</mn></mrow></msubsup></math></span> was 0.86, RMSEP was 0.79. Moreover, pixel-wise firmness prediction was implemented on the spectral images, enabling the visualization of apple firmness distribution. This study demonstrates that HSI combined with 1D-CNN provides an effective approach for early quality assessment of <em>B. dorsalis</em>-infested apples, providing theoretical support for the development of relevant detection devices.</div></div>","PeriodicalId":23656,"journal":{"name":"Vibrational Spectroscopy","volume":"145 ","pages":"Article 103935"},"PeriodicalIF":3.1,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148452571","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A quartz glass fiber sheet substrate for enhancing Raman signal","authors":"Hiroaki Ito,&nbsp;Naoyuki Uragami,&nbsp;Tomokazu Miyazaki,&nbsp;William Yang,&nbsp;Kenji Issha,&nbsp;Toshimitsu Yamaoka,&nbsp;Yuri Ito,&nbsp;Satoshi Kimura,&nbsp;Machiko Kawamura,&nbsp;Tetsuo Noguchi,&nbsp;Takashi Fukagai,&nbsp;Junji Tsurutani","doi":"10.1016/j.vibspec.2026.103933","DOIUrl":"10.1016/j.vibspec.2026.103933","url":null,"abstract":"<div><div>Cancer diagnostic methods based on Raman spectroscopy are being actively investigated, and there is a strong need for simple approaches to amplify the intensity of Raman scattered light from biological fluids such as serum and urine, which contain only trace amounts of nucleic acids, proteins, amino acids, and other analytes together with highly autofluorescent background components. We evaluated two measurement methods. One was the needle method (NM), in which a laser irradiates a droplet of liquid sample held at the tip of a fine‑diameter stainless‑steel needle. The other was the quartz glass fiber sheet method (QSM), in which a quartz glass fiber sheet is imbued with a liquid sample, allowed to dry, and then irradiated at the sheet surface. Raman spectra of sodium benzoate, sodium sulfate, human serum, and human urine were recorded. For the model compounds, spectra obtained by QSM reproduced the Raman shifts of the solid state, whereas spectra of aqueous solutions measured by NM showed clear peak shifts, and the scattered‑light intensity increased monotonically with the number of drops on the sheet. Based on these findings, we infer that the samples crystallize and become concentrated within the quartz glass fiber sheet, enabling acquisition of spectra with high scattered‑light intensity even from low‑concentration solutions. For human serum and urine, QSM increased the intensity of characteristic bands by up to about seven‑fold compared with NM while preserving the spectral fingerprints. Our results indicate that a quartz glass fiber sheet is a practical low‑background substrate for obtaining FT‑Raman spectra of liquid biological samples whose components are present at low concentrations.</div></div>","PeriodicalId":23656,"journal":{"name":"Vibrational Spectroscopy","volume":"145 ","pages":"Article 103933"},"PeriodicalIF":3.1,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148452509","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fermi resonance revealed by infrared spectroscopy in thin aqueous layers contacting with germanium surface","authors":"Alexander V. Udal'tsov","doi":"10.1016/j.vibspec.2026.103936","DOIUrl":"10.1016/j.vibspec.2026.103936","url":null,"abstract":"<div><div>Aqueous thin layers on germanium surface have been investigated by infrared spectroscopy. Two doublets of water librations and one of atmospheric CO₂ bound in the thin layer imply the aqueous thin layer structuring, namely formation of quasi-crystal structure. The layer structuring and theoretical estimations based on polaronic exciton concept allow to assign a broad band at 1124 cm^–1 to longitudinal optical phonons observed in the IR spectrum. The building up of the broad band is interpreted in terms of Fermi resonance that involves two frequencies at 1556 and 1664 cm^–1. Fermi coupling coefficient with a slight (36 cm^–1) or strong perturbation of 220 cm^–1 partakes in the mixing with the energy levels. The mixing results in unusual features of the IR spectra of the thin aqueous layers observed on germanium plates. It seems that the appearance of Fermi resonance in the structure of aqueous layer arises because of a brake of spin-orbit coupling in polaronic exciton. Namely, it arises because the repulsion between the spins of the opposite charges.</div></div>","PeriodicalId":23656,"journal":{"name":"Vibrational Spectroscopy","volume":"145 ","pages":"Article 103936"},"PeriodicalIF":3.1,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148452569","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Influence of path length on the accuracy of infrared (IR) spectroscopy for bulk property measurements","authors":"Haruna Okada,&nbsp;Scott T. Sanders","doi":"10.1016/j.vibspec.2026.103932","DOIUrl":"10.1016/j.vibspec.2026.103932","url":null,"abstract":"<div><div>The optical path length of a transmission flow cell is a critical parameter that influences the quantitative accuracy of infrared (IR) spectroscopy. This study investigates how path length affects the accuracy of IR spectroscopy for measuring bulk properties using a generalizable machine learning (ML)-based framework. The approach was demonstrated using an onboard fuel sensing application aimed at predicting the derived cetane number (DCN) of jet fuels and fuel blends from mid-IR spectra in the wavelength range of 5.8 – 7.2 µm. A range of path lengths, from 1 × 10⁻⁵ to 500 µm, was examined by simulating spectra through scaling of experimentally measured data. The results showed that extremely short path lengths led to poor prediction accuracy due to insufficient absorption and low signal-to-noise ratio (SNR), while excessively long path lengths caused strong absorption saturation and amplified spectral artifacts, degrading model performance. A broad near-optimal range between 10 and 58.7 µm was identified within the simulation framework, where spectra exhibited adequate absorption, moderate saturation, and minimal distortion. These findings demonstrate that allowing moderate absorption saturation can enhance predictive accuracy by improving SNR at wavelengths most correlated with the target property and emphasize that optimal measurement conditions should be determined based on predictive performance rather than solely maximizing SNR while maintaining absorbance within traditional limits.</div></div>","PeriodicalId":23656,"journal":{"name":"Vibrational Spectroscopy","volume":"145 ","pages":"Article 103932"},"PeriodicalIF":3.1,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148452570","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantitative analysis of e-liquid solvent using ATR-FTIR and GC-FID: Method validation and regression modeling","authors":"Carlos Leonny Raimundo Fragoso,&nbsp;Leonardo Guimarães,&nbsp;Vinnicius F. Brant,&nbsp;Anna De Falco,&nbsp;Adriana Gioda","doi":"10.1016/j.vibspec.2026.103934","DOIUrl":"10.1016/j.vibspec.2026.103934","url":null,"abstract":"<div><div>The growing electronic cigarette market faces significant quality control issues regarding e-liquid compositions. Fast analytical methods are required to verify the proportions of the primary solvents, vegetable glycerin and propylene glycol. This study used Attenuated Total Reflectance Fourier Transform Infrared spectroscopy with partial least squares regression to quantify glycerin and propylene glycol in commercial e-liquids. Gas Chromatography with Flame Ionization Detection served as the reference method. A matrix-matched calibration model, built with real commercial samples, was developed to effectively account for spectral interferences. The optimized PLS model achieved high predictive accuracy (R² = 0.9995), strongly correlating with the chromatography results. Crucially, both techniques exposed severe discrepancies between the measured glycerin/propylene glycol ratios and the manufacturers declared labels. These findings validate this spectroscopic approach as a robust, non-destructive, and low-cost tool for the routine quality control and rapid screening of mislabeled formulations. Future work should expand the calibration dataset with a larger set of commercial samples to further enhance the model's universal applicability.</div></div>","PeriodicalId":23656,"journal":{"name":"Vibrational Spectroscopy","volume":"145 ","pages":"Article 103934"},"PeriodicalIF":3.1,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148452510","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Discriminating injury and estimating post-injury intervals in moderate TBI by ATR-FTIR combined with chemometrics and machine learning","authors":"Jiantao Li ,&nbsp;Shuo Wu ,&nbsp;Jianliang Luo ,&nbsp;Yudong Qin ,&nbsp;Gengwang Hu ,&nbsp;Haobin La ,&nbsp;Haoxin Wang ,&nbsp;Zhen Wei ,&nbsp;Lulu Sha ,&nbsp;Taiming Fan ,&nbsp;Jiayu Xie ,&nbsp;Jian Yang ,&nbsp;Qinru Sun","doi":"10.1016/j.vibspec.2026.103921","DOIUrl":"10.1016/j.vibspec.2026.103921","url":null,"abstract":"<div><div>Traumatic brain injury (TBI) is a common case type in forensic medicine. Accurately distinguishing injured from non-injured tissues and estimating the time elapsed since injury are crucial aspects of forensic practice. This study aimed to investigate the feasibility of using Attenuated Total Reflection Fourier Transform Infrared (ATR-FTIR) spectroscopy to discriminate between injured and non-injured tissues, as well as to differentiate various post-injury intervals following moderate TBI. A mouse model of moderate TBI was established using the Feeney weight-drop method, and individuals with moderate injury were strictly selected based on the modified Neurological Severity Score (mNSS). Firstly, Principal Component Analysis (PCA) was employed for dimensionality reduction and visualization of the spectral data. Secondly, classification models including Partial Least Squares Discriminant Analysis (PLS-DA) and Support Vector Machine (SVM) were constructed to distinguish injured from non-injured tissues across different time points, both of which demonstrated excellent classification performance. Subsequently, regression models, including Partial Least Squares Regression (PLS-R), Constrained Linear Regression (CLR), Principal Component Regression (PCR), Multiple Linear Regression (MLR), Support Vector Regression (SVR), and Artificial Neural Network (ANN), were developed based on the spectral data to predict the survival time post-TBI. Among these, the SVR model delivered the best prediction performance with R² of 0.936 and RMSE of 1.485 days. This preliminary study demonstrates that FTIR spectroscopy combined with chemometrics enables rapid and accurate identification of trauma occurrence and estimation of the post-injury interval, thereby providing a scientific basis for case investigation and judicial proceedings.</div></div>","PeriodicalId":23656,"journal":{"name":"Vibrational Spectroscopy","volume":"144 ","pages":"Article 103921"},"PeriodicalIF":3.1,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147951584","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}