Vibrational Spectroscopy最新文献

{"title":"Evaluation of pesticide acetamiprid on Candida parapsilosis biofilm using Raman spectroscopy","authors":"Kursad Osman Ay ,&nbsp;Bükay Yenice Gürsu ,&nbsp;Betül Yılmaz Öztürk ,&nbsp;İlknur Dağ","doi":"10.1016/j.vibspec.2026.103893","DOIUrl":"10.1016/j.vibspec.2026.103893","url":null,"abstract":"<div><div>Acetamiprid is a widely used pesticide recommended for its relatively low toxicity; however, prolonged application may cause extensive environmental problems. Reports have highlighted its adverse effects on non-target organisms and its potential to alter soil microbial activity due to substantial environmental exposure. Moreover, pesticide residues may enter the human body through various routes, influencing the microbiota. <em>Candida</em> species, normally commensal on mucosal surfaces, can cause opportunistic infections under immunocompromised conditions. Among them, <em>C. parapsilosis</em> has emerged as one of the most frequently isolated yeast species in recent fungal infections, primarily due to its biofilm-forming capacity. In this study, the impact of acetamiprid on <em>C. parapsilosis</em> biofilm was assessed using Raman spectroscopy, a label-free technique with advantages such as high sensitivity, specificity, and the ability to quantitatively reveal biochemical changes in biofilm structures. Biofilms were developed on glass slides and subsequently treated with acetamiprid, while untreated biofilms served as controls. Raman spectral analysis demonstrated significant reductions in band intensities related to proteins, lipids, polysaccharides, and DNA in acetamiprid-treated samples compared to controls. These findings indicate that Raman spectroscopy can serve as a powerful approach to rapidly and effectively examine pesticide effects on biofilms and provide insights into potential antibiofilm strategies. Furthermore, the data raise the possibility of considering acetamiprid for novel antibacterial applications, combination therapies, and implications in food and agricultural safety. Nevertheless, <em>in vitro</em> findings should be supported with comprehensive toxicological studies to validate the broader impact of such applications.</div></div>","PeriodicalId":23656,"journal":{"name":"Vibrational Spectroscopy","volume":"143 ","pages":"Article 103893"},"PeriodicalIF":3.1,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146080491","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":"Infrared spectroscopy of binary nucleobase mixtures: Vibrational fingerprints of intermolecular interactions","authors":"Ana L.F. de Barros ,&nbsp;Enio Frota da Silveira","doi":"10.1016/j.vibspec.2026.103885","DOIUrl":"10.1016/j.vibspec.2026.103885","url":null,"abstract":"<div><div>Nitrogenous bases such as adenine, guanine, cytosine, and thymine are central to molecular biology, yet their intermolecular interactions in the solid state remain incompletely characterized. In this work, we present a systematic FTIR study of all six binary mixtures of canonical nucleobases under ambient solid-state conditions, comparing the experimental spectra of the mixtures with the arithmetic mean of their pure components. Significant non-linear spectral deviations are observed, including the appearance of new bands, shifts in peak position, changes in intensity, and modifications of band shape; pronounced effects are particularly noted in both the 1700–1500 cm⁻¹ and fingerprint (∼1500–600 cm⁻¹) regions. Representative interaction-induced wavenumber shifts on the order of 5–20 cm⁻¹ are observed for key vibrational modes associated with C<img>O, C<img>N, and ring deformation bands, depending on the specific nucleobase pairing<em>.</em> They provide direct spectroscopic evidence that intermolecular interactions, rather than simple additive behavior, govern the vibrational response of binary nucleobase assemblies. These effects indicate that hydrogen bonding, π–π stacking, and local environment perturbations strongly influence vibrational behavior when different base molecules coexist. Our analysis builds upon established solid-state spectroscopic assignments of individual nucleobases and previous investigations of non-irradiated binary systems, extending them through a systematic, comparative experimental approach. By mapping and cross-comparing all six binary combinations, this study demonstrates that each pairing generates a distinct non-linear vibrational fingerprint, reflecting base-specific intermolecular organization and supramolecular packing. Such results serve as a chemical/ biochemical baseline for understanding intermolecular interactions in nucleobase mixtures. Importantly, this baseline provides a robust reference for disentangling purely interaction-driven vibrational effects from radiation-induced chemical modifications in future studies. In this context, the present data set establishes a reference framework for future comparisons with irradiated systems, supporting investigations relevant to supramolecular chemistry, molecular recognition, and prebiotic chemistry. Moreover, the identification of interaction-sensitive infrared markers contributes to the interpretation of spectroscopic observations of complex organic matter in astrochemical and cosmic environments.</div></div>","PeriodicalId":23656,"journal":{"name":"Vibrational Spectroscopy","volume":"143 ","pages":"Article 103885"},"PeriodicalIF":3.1,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145996498","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":"ATR FTIR combined with PCA as a tool to determine metabolic changes in the vetch root nodules after treatment with a Nod-factor-based biofertilizer","authors":"Mikolaj Krysa ,&nbsp;Dominika Kidaj ,&nbsp;Iwona Komaniecka ,&nbsp;Joanna Depciuch ,&nbsp;Anna Sroka-Bartnicka","doi":"10.1016/j.vibspec.2025.103884","DOIUrl":"10.1016/j.vibspec.2025.103884","url":null,"abstract":"<div><div>The common vetch (<em>Vicia sativa</em>) is a plant used as an animal forage and aftercrop. To address evolving agricultural demands, optimize nutrient use efficiency, and mitigate environmental impacts caused by conventional fertilizer use, there is a need for the development of new fertilizers for the common vetch. A promising candidates for such are specific lipochitooligosaccharides produced by rhizobia – which are called Nod factors. These signaling molecules trigger symbiotic processes and are considered to accelerate the growth of the vetch. However, the specific biochemical alterations or the developmental stage at which Nod-factor-based biofertilizers exerted their maximum activity within the root nodules remained unknown. This study therefore aimed to answer those questions. For this purpose, vetch was cultivated for 21 and 42 days with Nod-factor-based biofertilizer. The impact of the biofertilizer on the metabolites was afterward assessed using Attenuated Total Reflection Fourier Transform Infrared (ATR FTIR) spectroscopy combined with Principal Component Analysis (PCA). The findings revealed that 21-day biofertilizer-treated nodules exhibited increased protein and ester levels but decreased concentrations of carbohydrates and nucleic acids compared to control nodules. The 42-day nodules treated with biofertilizer displayed altered carbohydrate composition, higher lipid levels, and reduced concentrations of proteins and polyphenols. The impact of biofertilizer was however much higher in the 21-day root nodules than in the 42-day root nodules suggesting a higher impact of the biofertilizer on the earlier stages of development.</div></div>","PeriodicalId":23656,"journal":{"name":"Vibrational Spectroscopy","volume":"142 ","pages":"Article 103884"},"PeriodicalIF":3.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925791","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":"Atomic force infrared microscopy studies of SO2 induced atmospheric corrosion of ZnAlMg- coated steel in humid air","authors":"D. Persson ,&nbsp;A. Wärnheim ,&nbsp;S. Sainis ,&nbsp;N. Lebozec ,&nbsp;D. Thierry","doi":"10.1016/j.vibspec.2025.103883","DOIUrl":"10.1016/j.vibspec.2025.103883","url":null,"abstract":"<div><div>Atomic Force Infrared Microscopy (AFM-IR), also known as Photothermal Induced Resonance (PTIR), is a scanning probe spectroscopic technique that enables chemical characterization with submicron spatial resolution. This capability is especially valuable in corrosion science, where the chemical composition and phase distribution of metal alloy microstructures play a critical role in corrosion initiation, progression, and the formation of reaction products—key factors that determine long-term material performance. In this study, PTIR and infrared microspectroscopy were employed to investigate the influence of microstructure on corrosion product formation on ZnAlMg-coated steel exposed to SO₂-containing humid air. The analysis revealed that MgSO₄·7H₂O and MgSO₃·nH₂O predominantly form in eutectic regions. High-resolution PTIR measurements further showed preferential corrosion of microscopic MgZn₂ phases within the lamellar binary eutectic. This localized corrosion is attributed to micro-galvanic coupling, where MgZn₂ acts as the anodic site and Zn-rich phases serve as cathodic regions. These findings underscore the significant role of microstructural phase distribution in corrosion behavior and demonstrate the utility of PTIR for spatially resolved chemical analysis for complex metallic alloys.</div></div>","PeriodicalId":23656,"journal":{"name":"Vibrational Spectroscopy","volume":"142 ","pages":"Article 103883"},"PeriodicalIF":3.1,"publicationDate":"2025-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145839909","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":"Artificial neural networks combined with quotients to preprocess Raman Spectra from different setups","authors":"Joel Wahl ,&nbsp;Dirce Pineda Vazquez ,&nbsp;Elisabeth Klint ,&nbsp;Jan Hillman ,&nbsp;Johan Richter ,&nbsp;Peter Milos ,&nbsp;Karin Wårdell ,&nbsp;Kerstin Ramser","doi":"10.1016/j.vibspec.2025.103882","DOIUrl":"10.1016/j.vibspec.2025.103882","url":null,"abstract":"<div><div>Raman spectroscopy is widely used in chemistry, material science and in biomedical applications such as cancer detection. A Raman spectrum of tissue shows DNA, amino acids, lipids, and proteins simultaneously, which makes the evaluation of the spectral content both complete but also challenging. The impact of the technique can increase substantially by access to big databases, but variations in the setup, e.g. quantum efficiency of Raman detectors, transmission profiles and disturbances of optical filters and components, may hinder direct data comparison. We here introduce a step prior to preprocessing that calculates spectral quotients to address system-dependent multiplicative differences and system-inherent background noise, thereby enabling analysis of Raman spectral data from different setups. Pre-processing was performed using an artificial neural network (ANN) trained on synthetic data to deal with fluorescent background and noise. Validation by multivariate analysis of the spectral quotients combined with ANN was performed on randomized synthetic data and, as a proof of principle, on experimental data from brain tumor biopsies. The results demonstrated clustering and feature extraction that was not possible without the introduction of the quotients. Data exploration revealed that the method enabled spectral feature identification even for weak Raman signals that are not in resonance with the excitation wavelength. Note, some distortions persist due to data dependency and additive errors but a system independent clustering was achieved. ANN-based preprocessing combined with spectral quotients for combined evaluation of Raman spectroscopic data from multiple setups opens the possibility for more robust multivariate studies in biomedical and other applications.</div></div>","PeriodicalId":23656,"journal":{"name":"Vibrational Spectroscopy","volume":"142 ","pages":"Article 103882"},"PeriodicalIF":3.1,"publicationDate":"2025-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145839908","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":"Model upgrading method based on domain-adversarial neural networks (DANN) for near-infrared spectroscopy analysis","authors":"Yong Hao ,&nbsp;Jingjing Peng ,&nbsp;Xinyu Chen ,&nbsp;Chuangfeng Huai","doi":"10.1016/j.vibspec.2025.103881","DOIUrl":"10.1016/j.vibspec.2025.103881","url":null,"abstract":"<div><div>In near-infrared spectroscopy (NIRS) analysis, variations in component contents across different batches of samples cause changes in spectral characteristics, limiting the effectiveness of NIRS-based analysis models. Model Upgrade (MU) can address this by sharing the original model's knowledge with new batches, thereby reducing the time and material costs associated with remodelling. This study developed quantitative analysis models for mango dry matter content (DMC) and peach soluble solids content (SSC) using near-infrared (NIR) spectra collected at different seasons or maturation stages. A domain-adversarial training of neural networks (DANN) method was proposed to upgrade NIRS models for these fruits across various periods, overcoming limitations of traditional methods that require extensive calibration samples or linear corrections. Spectral preprocessing methods and the number of upgrade samples were optimized, and two recognized model upgrade methods including piecewise direct standardization (PDS) and slope/bias correction (SBC) were used for comparison with DANN. Results showed that preprocessing improved the NIR spectral quality for both fruits; DANN, using only 256 mango and 96 peach upgrade samples, simultaneously raised R_p to 0.983 (mango) and 0.916 (peach) while cutting RMSEP by 36.5 % (0.936–0.594) and 40.7 % (1.121–0.665), respectively, achieving the best cross-batch accuracy with &lt; 50 % of the reference samples previously required. The proposed DANN method aligns feature distributions between domains through adversarial training, demonstrates high accuracy with fewer upgrade samples, and can further simplify the model construction issues related to numerous batches, large quantities, and long-time spans.</div></div>","PeriodicalId":23656,"journal":{"name":"Vibrational Spectroscopy","volume":"142 ","pages":"Article 103881"},"PeriodicalIF":3.1,"publicationDate":"2025-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145839910","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":"Rapid diagnosis of alveolar echinococcosis: Serum shifted excitation Raman difference spectroscopy combined with machine learning algorithms","authors":"Xiangxiang Zheng ,&nbsp;Haoran Chen ,&nbsp;Liang Xu ,&nbsp;Guohua Wu ,&nbsp;Hui Zhao ,&nbsp;Renyong Lin ,&nbsp;Guodong Lü","doi":"10.1016/j.vibspec.2025.103880","DOIUrl":"10.1016/j.vibspec.2025.103880","url":null,"abstract":"<div><div>Alveolar echinococcosis (AE) is a life-threatening parasitic disease for which early and accurate diagnosis is essential to improve clinical outcomes. Here, we present a rapid, non-invasive AE screening strategy that integrates shifted excitation Raman difference spectroscopy (SERDS) with machine learning. SERDS employs dual-wavelength excitation and differential signal prbormalocessing to effectively suppress fluorescence background, markedly enhancing the signal-to-noise ratio of serum Raman spectra. Serum samples from AE patients and normal controls were processed into three spectral datasets, and classification was performed using Principal Component Analysis–Linear Discriminant Analysis (PCA-LDA) and Support Vector Machine (SVM) algorithms. Both methods achieved high diagnostic performance on Difference and Reconstructed Spectra, with SVM reaching Area Under the Receiver Operating Characteristic Curve (AUC) of 0.978 and 0.972, respectively. Notably, SVM maintained robust accuracy (AUC = 0.967) on Baseline-corrected Spectra, outperforming PCA-LDA (AUC = 0.855, p &lt; 0.05). Compared with conventional Raman spectroscopy (RS) and surface-enhanced Raman spectroscopy (SERS), SERDS achieved comparable accuracy and showed reduced fluorescence interference and operational simplicity under our acquisition conditions. These findings demonstrate that SERDS-based machine learning provides a robust and practical platform for rapid AE diagnosis, with strong potential for deployment in resource-limited, high-prevalence settings.</div></div>","PeriodicalId":23656,"journal":{"name":"Vibrational Spectroscopy","volume":"142 ","pages":"Article 103880"},"PeriodicalIF":3.1,"publicationDate":"2025-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145840818","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":"Study on liquid core optical fiber enhanced Raman scattering and micro detecting liquid-phase joint synovial fluid","authors":"Jinjin Wu ,&nbsp;Jiachun Dong ,&nbsp;Shuang Xiong ,&nbsp;Linwei Shang ,&nbsp;Huijie Wang ,&nbsp;Jianhua Yin","doi":"10.1016/j.vibspec.2025.103879","DOIUrl":"10.1016/j.vibspec.2025.103879","url":null,"abstract":"<div><div>The low concentration and weak Raman activity of synovial fluid (SF) make it difficult to be detected in the liquid-phase by conventional Raman spectroscopy. Therefore, liquid-core optical fiber enhanced Raman scattering (LCOF-ERS, LERS) was introduced to detect liquid-phase SF since its obvious scattering enhancement. This study reveals that when the LCOF material (refraction index, RI) and length are determined to collect LERS spectra of ethanol solution, the LERS performance increases with decreasing both the objective lens magnification and the LCOF inner diameter (ID). It reaches optimum at 4 × objective and 200μm ID, which was set for LERS measurement of SF. It’s found that the SNR of the LERS (4 ×/200-ID) spectra of the liquid-phase SF is 15.3 times of the conventional Raman spectra, simultaneously avoiding the spectral distortion and molecular configuration change caused by the phase change of SF. Moreover, the enhancement factor of LERS increases from 6.5 to 25.6 by diluting 10 times with water, confirming that LERS is highly feasible and worth expecting for the practical detection of micro-SF with low-RI. The significant enhancements for LERS in intensity and Signal to Noise Ratio (SNR) are very helpful to facilitate the micro clinical detection, disease diagnosis and scientific research of SF and other bodily fluids in liquid.</div></div>","PeriodicalId":23656,"journal":{"name":"Vibrational Spectroscopy","volume":"142 ","pages":"Article 103879"},"PeriodicalIF":3.1,"publicationDate":"2025-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145737729","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":"Multivariable prediction of concentration and temperature pressure from absorption spectra using B-LSTM-transformer model","authors":"Hongda Chen","doi":"10.1016/j.vibspec.2025.103876","DOIUrl":"10.1016/j.vibspec.2025.103876","url":null,"abstract":"<div><div>Direct absorption spectroscopy is extensively utilized in combustion diagnostics, offering valuable assistance in industrial production and aerospace applications. However, current approaches for processing substantial volumes of overlapped spectra encounter challenges related to slow processing speeds and inadequate precision. This research introduces a time series prediction model that integrates a Transformer self-attention mechanism with Long Short-Term Memory networks for spectroscopy and thermodynamic diagnostics, as well as spectral prediction in absorption spectroscopy. The proposed model proficiently adeptly captures spectral characteristics, including concentration, temperature, and pressure, from intricate unknown spectra. The model was verified in TDLAS. The prediction standard deviation for simulated spectra is less than 0.1, while the relative error for actual spectra is less than 1 %. Future advancements may involve the integration of spectral denoising and analysis techniques, along with few-shot learning methods, validation and optimization of the use of broadband spectrometers, to further optimize combustion gas detection solutions in industrial and aerospace domains.</div></div>","PeriodicalId":23656,"journal":{"name":"Vibrational Spectroscopy","volume":"142 ","pages":"Article 103876"},"PeriodicalIF":3.1,"publicationDate":"2025-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145683319","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":"Can we use visible-near infrared and mid infrared spectroscopy as a tool for wetland soil identification?","authors":"Caleb R. Whatley ,&nbsp;Nuwan K. Wijewardane ,&nbsp;Chamika A. Silva ,&nbsp;Mary Love Tagert ,&nbsp;Raju Bheemanahalli ,&nbsp;Prem Parajuli","doi":"10.1016/j.vibspec.2025.103875","DOIUrl":"10.1016/j.vibspec.2025.103875","url":null,"abstract":"<div><div>The wetland delineation process is primarily based on the visual recognition of anaerobic soil indicators by trained individuals, and is a complex and subjective task that is prone to error. Therefore, an objective alternative is needed to identify wetland soil; however, no such method currently exists that is rapid and easy to deploy. Accordingly, the objective was to evaluate soil spectroscopic classification approach as a rapid, deployable alternative by testing its feasibility to differentiate wetland from non-wetland soils. This study used visible-near infrared and mid-infrared (MIR) ranges for this task. A total of 440 wetland and non-wetland soils were sampled across Mississippi followed by obtaining visible/near-infrared and MIR spectra under both fresh and dried conditions. Support Vector Classification (SVC) and Random Forest (RF) methods were then used to classify spectra based on wetland/non-wetland status with a 75 %/25 % calibration and validation split. This split was repeated for 50 iterations to obtain randomized calibration and validation sets for model calibration and achieve average model performance. The average classification accuracy across all models was ∼91 %, with the highest accuracy of 99.6 % achieved on MIR spectra. The accuracy, precision, and recall scores showed similar performances between SVC and RF ranging their values from ∼80 % - 100 %. This study showed the reliability and ease of wetland determinations using spectroscopy as an objective and rapid wetland recognition method, while reducing the need for an expert for determination.</div></div>","PeriodicalId":23656,"journal":{"name":"Vibrational Spectroscopy","volume":"142 ","pages":"Article 103875"},"PeriodicalIF":3.1,"publicationDate":"2025-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145625077","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}