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Sputter deposited silver film as an alternative tool for Raman signal enhancement in plasma-activated water 溅射沉积银膜作为等离子体活化水中拉曼信号增强的替代工具
IF 2.7 3区 化学 Q2 CHEMISTRY, ANALYTICAL Pub Date : 2025-01-01 DOI: 10.1016/j.vibspec.2024.103765
Nilton F. Azevedo Neto , Samuel A. Marques , Felipe S. Miranda , Pedro W.P. Moreira Junior , Andre L.J. Pereira , Carlos J.L. Constantino , José H. Dias da Silva , Rodrigo S. Pessoa
Plasma-activated water (PAW), generated by non-thermal plasma, has shown great potential in various applications, including bacterial inactivation, agriculture, and disinfection, primarily attributed to the presence of reactive oxygen and nitrogen species (RONS). Traditional characterization methods for RONS in PAW often encounter limitations in sensitivity and specificity, particularly at low concentrations. In this study, we investigated the application of surface-enhanced Raman spectroscopy (SERS) for the characterization of PAW. A SERS substrate was prepared by sputter-depositing a silver (Ag) film onto a cover glass. The structural, topographic, and optical properties of the film were characterized by X-ray diffraction (XRD), atomic force microscopy (AFM), and reflectance spectroscopy. Utilizing the Ag film substrates, we observed a substantial enhancement in the Raman signals of deionized water compared to measurements on glass substrates, achieving an analytical enhancement factor (AEF) of approximately 30 for the O–H stretching band. The characterization of PAW using the SERS substrate enabled the acquisition of well-defined Raman spectra and facilitated the detection of nitrate ions (NO₃⁻) in PAW generated by a dielectric barrier discharge reactor. The results obtained from the PAW Raman spectra were further supported by changes in physicochemical properties, such as decreased pH and increased conductivity, as well as UV-Vis spectroscopy results. These findings demonstrate that sputter-deposited Ag films can serve as a valuable methodological tool for the characterization of PAW using Raman spectroscopy.
等离子体活化水(PAW)由非热等离子体产生,在各种应用中显示出巨大的潜力,包括细菌灭活,农业和消毒,主要归因于活性氧和氮物种(RONS)的存在。传统的表征方法在敏感性和特异性上往往存在局限性,特别是在低浓度下。在这项研究中,我们研究了表面增强拉曼光谱(SERS)在PAW表征中的应用。通过溅射沉积银(Ag)膜到覆盖玻璃上制备了SERS衬底。利用x射线衍射(XRD)、原子力显微镜(AFM)和反射光谱对膜的结构、形貌和光学性能进行了表征。利用Ag薄膜衬底,我们观察到去离子水的拉曼信号与玻璃衬底的测量结果相比有显著增强,O-H拉伸带的分析增强因子(AEF)约为30。使用SERS衬底对PAW进行表征,可以获得定义良好的拉曼光谱,并有助于在介质阻挡放电反应器产生的PAW中检测硝酸盐离子(NO₃⁻)。从PAW拉曼光谱得到的结果进一步得到了物理化学性质变化的支持,如pH值降低和电导率增加,以及UV-Vis光谱结果。这些发现表明,溅射沉积Ag薄膜可以作为一种有价值的方法工具,用拉曼光谱来表征PAW。
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引用次数: 0
Influence of dielectric environments on Raman non-coincidence effects in the CO stretching and NH2 bending modes of formamide 介电环境对甲酰胺CO拉伸和NH2弯曲模式拉曼非符合效应的影响
IF 2.7 3区 化学 Q2 CHEMISTRY, ANALYTICAL Pub Date : 2025-01-01 DOI: 10.1016/j.vibspec.2025.103767
Abduvakhid Jumabaev , Utkirjon Holikulov , Shavkatjon Yormatov , Th. Gomti Devi
This paper investigates the vibrational properties and intermolecular interactions of formamide (FA) in polar solvents with varying dielectric constants and dipole moments, including dimethyl sulfoxide (DMSO), acetonitrile (AcN), and 1,4-dioxane (DiX). Raman non-coincidence effects (NCE) in the CO stretching and NH2 bending modes were observed to decrease monotonically in all solvents, indicating a systematic solute-solvent interaction trend. To explain such trends, experimental results were compared with the Onsager-Fröhlich dielectric continuum model, revealing strong agreement in solvents with high dielectric constants and dipole moments. Complementary DFT analyses of the Raman spectra for various FA self-associations identified a closer match with experimental results in odd-numbered molecular associations. The nature and strength of intermolecular forces in FA-solvent complexes were further investigated by topological methods (AIM, NCI, RDG), which confirmed findings from experiments. These findings advance our understanding of solute-solvent dynamics in polar environments and have broad implications for studies of intermolecular forces in chemical and biological systems.
本文研究了甲酰胺(FA)在不同介电常数和偶极矩极性溶剂(包括二甲亚砜(DMSO)、乙腈(AcN)和1,4-二恶烷(DiX))中的振动特性和分子间相互作用。在所有溶剂中,CO拉伸模式和NH2弯曲模式的拉曼非符合效应(NCE)单调降低,表明存在系统的溶质-溶剂相互作用趋势。为了解释这种趋势,将实验结果与Onsager-Fröhlich介电连续体模型进行了比较,结果表明,在具有高介电常数和偶极矩的溶剂中,实验结果具有很强的一致性。对各种FA自缔合的拉曼光谱进行互补DFT分析,发现其与奇数分子缔合的实验结果更接近。通过拓扑方法(AIM, NCI, RDG)进一步研究了fa -溶剂配合物分子间力的性质和强度,证实了实验结果。这些发现促进了我们对极性环境中溶质-溶剂动力学的理解,并对化学和生物系统中分子间作用力的研究具有广泛的意义。
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引用次数: 0
Application of ATR-FTIR and chemometrics for rapid lard adulteration assessment in confectionery ATR-FTIR和化学计量学在糖果中猪油掺假快速评定中的应用
IF 2.7 3区 化学 Q2 CHEMISTRY, ANALYTICAL Pub Date : 2025-01-01 DOI: 10.1016/j.vibspec.2024.103762
Sobia Kunbhar, Farah Naz Talpur, Sarfraz Ahmed Mahesar, Hassan Imran Afridi, Ghulam Fareed, Noshad Razzaque, Mehr-un Nisa
Consumption of confectionery products such as chocolates and biscuits are popular among all age groups in the population around the globe. Adulteration of confectionery fats with cheaper animal fats, such as lard, has become an issue in recent years. A simple and rapid analytical method of attenuated total reflectance in Fourier transform infrared spectroscopy (ATR-FTIR) was developed in order to determine the lard content in imported chocolates and biscuits. For quantitative measurement, the partial least square (PLS) model with multivariate calibration for adulterant prediction was developed. A lard oil calibration graph was drawn (2–35 %) with palm oil in the region of 3035–2984 cm−1, with the correlation coefficient (R2) = 0.9994 with minimum errors in root mean square error of calibration (RMSEC) and root mean square error of prediction (RMSEP) with the value of 0.320 and 0.315 respectively. The root mean square error of cross-validation (RMSECV) was used to find the calibration model accuracy which was found to be 1.17 with the best limit of detection (LOD) 0.10 % and limit of quantification (LOQ) 0.35 %. The developed ATR-FTIR method is robust accurate and precise in terms of lard detection in confectionery products (chocolate and biscuits).
巧克力和饼干等糖果产品的消费在全球各年龄段的人口中都很受欢迎。近年来,在糖果脂肪中掺入廉价动物脂肪(如猪油)已成为一个问题。建立了一种简单快速的傅立叶变换红外光谱(ATR-FTIR)衰减全反射法测定进口巧克力和饼干中猪油含量的方法。在定量测量方面,建立了多变量校正的偏最小二乘(PLS)模型用于掺假预测。在3035 ~ 2984 cm−1范围内绘制猪油校正图(2 ~ 35 %),相关系数(R2) = 0.9994,校正均方根误差(RMSEC)和预测均方根误差(RMSEP)的最小误差分别为0.320和0.315。采用交叉验证均方根误差(RMSECV)求出校准模型精度为1.17,最佳检出限(LOD) 0.10 %,定量限(LOQ) 0.35 %。所建立的ATR-FTIR方法对糖果产品(巧克力和饼干)中的猪油检测具有鲁棒性、准确性和精密度。
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引用次数: 0
Prediction of the probability of metastasis to the sentinel lymph node in breast cancer by ATR-FTIR spectroscopy and chemometrics 用ATR-FTIR光谱和化学计量学预测乳腺癌前哨淋巴结转移的可能性
IF 2.7 3区 化学 Q2 CHEMISTRY, ANALYTICAL Pub Date : 2025-01-01 DOI: 10.1016/j.vibspec.2025.103770
Nikolas Mateus Pereira de Souza , Dhuli Kimberli Abeg da Rosa , Caroline de Moraes , Licerio Vicente Padoin , Daniel Prá , André Poisl Fay , Valeriano Antonio Corbellini , Alexandre Rieger
The status of axillary lymph nodes is considered one of the most critical prognostic indicators for patients diagnosed with invasive breast cancer (BC). The Breast Service of Memorial Sloan Kettering Cancer Center (MSKCC) nomogram was developed to obtain the likelihood of metastasis to the sentinel lymph node (SLN). Blood plasma from 28 patients with BC stages I, II, and III were collected and analyzed by Attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR). Therefore, the aim of the study is to use ATR-FTIR spectroscopy and chemometrics in blood plasma to predict the probability of SLN metastases in breast cancer in ≥ 50 % (Model 1) or > 30 % (Model 2) determined by the MSKCC nomogram. The chemometric models were performed with orthogonal partial least squares discriminant analysis (OPLS-DA). Both Models 1 and 2 obtained sensitivity and specificity of 100 %. Model 1 was built with 7 latent variables (LV) having a cumulative variance of 92.56 % and root mean square error of cross-validation (RMSECV) of 0.0096. Model 2 for 6 LV had a cumulative variance of 94.17 % and RMSECV of 0.0031. External validation was only tested for Model 1, obtaining 100 % accuracy and RMSE of validation (RMSEV) of 0.274. Proteins and nucleic acids were the biomolecules with the highest contribution to this prediction. Therefore, ATR-FTIR spectroscopy generates valuable insights into the tumor aggressiveness and the probability of metastasis to the SLN.
腋窝淋巴结的状态被认为是浸润性乳腺癌(BC)患者最重要的预后指标之一。纪念斯隆-凯特琳癌症中心(MSKCC)乳腺服务部(Breast Service of Memorial Sloan Kettering Cancer Center,简称MSKCC)的nomogram放射图是为了获得转移到前哨淋巴结(sentinel lymph node,简称SLN)的可能性。收集28例BC I、II、III期患者血浆,采用衰减全反射-傅里叶变换红外光谱(ATR-FTIR)进行分析。因此,本研究的目的是利用血浆中的ATR-FTIR光谱和化学计量学来预测乳腺癌中SLN转移的概率≥ 50 %(模型1)或>; 30 %(模型2)由MSKCC nomogram确定。采用正交偏最小二乘判别分析(OPLS-DA)建立化学计量模型。模型1和模型2的敏感性和特异性均为100 %。模型1采用7个潜在变量(LV),累积方差为92.56 %,交叉验证均方根误差(RMSECV)为0.0096。模型2对6 LV的累积方差为94.17 %,RMSECV为0.0031。仅对模型1进行外部验证,获得100 %的准确率,验证的RMSE (RMSEV)为0.274。蛋白质和核酸是对这一预测贡献最大的生物分子。因此,ATR-FTIR光谱对肿瘤的侵袭性和转移到SLN的可能性产生了有价值的见解。
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引用次数: 0
IR spectra of the CH3 stretching region of methylamine monomer, dimer and trimer in helium nanodroplets 氦纳米液滴中甲胺单体、二聚体和三聚体CH3拉伸区的红外光谱
IF 2.7 3区 化学 Q2 CHEMISTRY, ANALYTICAL Pub Date : 2025-01-01 DOI: 10.1016/j.vibspec.2024.103761
Hamad Ashraf, Philipp Meyer, Stefan Henkel, Gerhard Schwaab, Martina Havenith
We report the infrared spectra of the CH3 stretching region in methylamine monomer and its clusters in superfluid helium nanodroplets. Using mass-selective IR spectroscopy in combination with band intensities as the function of methylamine pressure, we were able to assign the vibrations of the (methylamine)1–3 clusters. In addition to the fundamental vibrations, overtone and combination bands are observed. As an example, in the high-resolution spectrum of methylamine monomer, 15 spectral features were observed, compared to 9 features reported in the literature. All the bands could be assigned based on recent anharmonic calculations.
本文报道了甲胺单体及其超流氦纳米液滴中CH3拉伸区的红外光谱。利用质量选择红外光谱结合波段强度作为甲胺压力的函数,我们能够分配(甲胺)1-3簇的振动。除了基本振动外,还观察到泛音和组合带。例如,在甲胺单体的高分辨率光谱中,观察到15个光谱特征,而文献报道的光谱特征为9个。所有的频带都可以根据最近的非谐波计算来分配。
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引用次数: 0
Developing a fast Fourier transform infrared spectroscopy system for precise and reliable grade differentiation of Gastrodia elata 建立一种快速傅立叶变换红外光谱系统,用于天麻的精确、可靠的品级鉴别
IF 2.7 3区 化学 Q2 CHEMISTRY, ANALYTICAL Pub Date : 2025-01-01 DOI: 10.1016/j.vibspec.2025.103769
Shuting Lin , Zerong Liu , Hai Qin , Liqin Jiang , Di Zhao , Qinzhu Li , Quanhong Ou
Gastrodia elata belongs to the rare medicinal herbs, in China classified as a plant under state protection (Category II). This research utilizes Fourier Transform Infrared Spectroscopy (FTIR) combined with machine learning algorithms to develop an intelligent grading detection model for Gastrodia elata, with different grades of spring and winter Gastrodia elata harvested in Zhaotong, Yunnan as the main research objects. An initial exploration of both the original spectra (OS) and the preprocessed spectra (PS), which were processed through eleven distinct pretreatment methodologies, was conducted using Principal Component Analysis (PCA) within the spectral range spanning 4000 to 400 cm-¹. Remarkably, the FTIR of the diverse grades of Gastrodia elata exhibited a discernible clustering pattern, with the preprocessed spectral data exhibiting a superior clustering effect compared to the original spectral data. To ascertain the optimal detection model, we employed a diverse array of machine learning algorithms, including Naive Bayes (NB), Support Vector Machine (SVM), Tree (T), Logistic Regression (LR), and Multi-layer Perceptron (MLP), to establish a comprehensive grade detection model for Gastrodia elata, leveraging both the original spectral data and those subjected to effective preprocessing. Upon comparison, the MLP model emerged as the superior choice, demonstrating exceptional overall classification performance. Notably, the MLP model achieved 100 % accuracy on the test set, irrespective of the preprocessing method applied to the spectral data. Drawing upon these findings, the authors designed an intelligent detection system, namely "Intelligent Detection System for the Grade of Gastrodia elata Based on MLP Model and FTIR Technology". This research highlights the potential of machine learning classification models using FTIR technology to accurately detect and distinguish Gastrodia elata grades.
天麻属于珍稀药材,在中国属于国家二类保护植物。本研究采用傅里叶变换红外光谱(FTIR)技术结合机器学习算法,以云南昭通不同品级的春冬天麻为主要研究对象,建立了天麻智能分级检测模型。通过11种不同的预处理方法,对原始光谱(OS)和预处理光谱(PS)进行了初步探索,利用主成分分析(PCA)在4000 ~ 400 cm-¹的光谱范围内进行了分析。值得注意的是,天麻不同等级的FTIR表现出明显的聚类模式,预处理后的光谱数据比原始光谱数据具有更好的聚类效果。为了确定最佳检测模型,我们利用朴素贝叶斯(NB)、支持向量机(SVM)、树(T)、逻辑回归(LR)和多层感知器(MLP)等多种机器学习算法,利用原始光谱数据和经过有效预处理的数据,建立了天麻综合等级检测模型。经过比较,MLP模型显示出卓越的整体分类性能,成为更好的选择。值得注意的是,无论光谱数据采用何种预处理方法,MLP模型在测试集上的准确率都达到了100 %。在此基础上,笔者设计了一套智能检测系统,即“基于MLP模型和FTIR技术的天麻品位智能检测系统”。本研究强调了使用FTIR技术的机器学习分类模型在准确检测和区分天麻等级方面的潜力。
{"title":"Developing a fast Fourier transform infrared spectroscopy system for precise and reliable grade differentiation of Gastrodia elata","authors":"Shuting Lin ,&nbsp;Zerong Liu ,&nbsp;Hai Qin ,&nbsp;Liqin Jiang ,&nbsp;Di Zhao ,&nbsp;Qinzhu Li ,&nbsp;Quanhong Ou","doi":"10.1016/j.vibspec.2025.103769","DOIUrl":"10.1016/j.vibspec.2025.103769","url":null,"abstract":"<div><div><em>Gastrodia elata</em> belongs to the rare medicinal herbs, in China classified as a plant under state protection (Category II). This research utilizes Fourier Transform Infrared Spectroscopy (FTIR) combined with machine learning algorithms to develop an intelligent grading detection model for <em>Gastrodia elata</em>, with different grades of spring and winter <em>Gastrodia elata</em> harvested in Zhaotong, Yunnan as the main research objects. An initial exploration of both the original spectra (OS) and the preprocessed spectra (PS), which were processed through eleven distinct pretreatment methodologies, was conducted using Principal Component Analysis (PCA) within the spectral range spanning 4000 to 400 cm<sup>-</sup>¹. Remarkably, the FTIR of the diverse grades of <em>Gastrodia elata</em> exhibited a discernible clustering pattern, with the preprocessed spectral data exhibiting a superior clustering effect compared to the original spectral data. To ascertain the optimal detection model, we employed a diverse array of machine learning algorithms, including Naive Bayes (NB), Support Vector Machine (SVM), Tree (T), Logistic Regression (LR), and Multi-layer Perceptron (MLP), to establish a comprehensive grade detection model for <em>Gastrodia elata</em>, leveraging both the original spectral data and those subjected to effective preprocessing. Upon comparison, the MLP model emerged as the superior choice, demonstrating exceptional overall classification performance. Notably, the MLP model achieved 100 % accuracy on the test set, irrespective of the preprocessing method applied to the spectral data. Drawing upon these findings, the authors designed an intelligent detection system, namely \"Intelligent Detection System for the Grade of <em>Gastrodia elata</em> Based on MLP Model and FTIR Technology\". This research highlights the potential of machine learning classification models using FTIR technology to accurately detect and distinguish <em>Gastrodia elata</em> grades.</div></div>","PeriodicalId":23656,"journal":{"name":"Vibrational Spectroscopy","volume":"136 ","pages":"Article 103769"},"PeriodicalIF":2.7,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143161051","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}
引用次数: 0
ATR-FTIR as a green tool for rapid identity authentication of Gastrodia elata Blume under the influence of multi-biological variability ATR-FTIR作为天麻多生物变异影响下的绿色快速鉴定工具
IF 2.7 3区 化学 Q2 CHEMISTRY, ANALYTICAL Pub Date : 2025-01-01 DOI: 10.1016/j.vibspec.2024.103766
Zhiyi Ji , Honggao Liu , Jieqing Li , Yuanzhong Wang
Rapid authentication of labelling information for medicinal and edible plants interfered by multiple biological variability (species, origin, growth pattern, etc.) has always been an important challenge for market supervision and management as well as consumers purchase orientation. Gastrodia elata Blume (G.elata) is used both as a food and as an herbal medicine for the treatment of migraine, hyperglycaemia and epilepsy. The advantages of Attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy, which is a practical, fast and reliable spectroscopic technique used for solid samples, can be combined with chemometric models allows for fast confirmation of the desired label under the influence of different factors. A total of 344 G.elata samples from five geographic origins, three growth patterns and four species were discriminatively analyzed using ATR-FTIR spectra combined with the Orthogonal partial least squares discriminant analysis (OPLS-DA) model and Support Vector Machine (SVM) model. Spectra were preprocessed using S-G, FD, SD, MSC and SNV and their combinations to eliminate scattering and baseline drift, and the clustering results of PCA showed that S-G+SD preprocessing was the most effective. The models developed all had good accuracy with 97.09–100.00 % and Matthews correlation coefficient (Mcc) values of 0.80–1.00. The linear model PLSR and the nonlinear model SVR were used to predict the weight of dried individuals of G.elata for quality grade information, and the PLSR model with S-G+SD preprocessing could obtain a prediction accuracy of R2P = 0.94 and RPD= 4.19. This study can provide a green and feasible method for the authenticity identification of G.elata labels under the influence of multiple biological variability, which is of great significance for the quality control and rapid detection of medicinal and edible plants in the market.
受多种生物变异(物种、原产地、生长方式等)干扰的药用和食用植物标签信息的快速认证一直是市场监督管理和消费者购买导向的重要挑战。天麻既可作为食物,也可作为治疗偏头痛、高血糖和癫痫的草药。衰减全反射傅里叶变换红外光谱(ATR-FTIR)是一种实用、快速、可靠的固体样品光谱技术,它可以与化学计量模型相结合,在不同因素的影响下快速确定所需的标记。利用ATR-FTIR光谱结合正交偏最小二乘判别分析(OPLS-DA)模型和支持向量机(SVM)模型,对5个产地、3种生长模式和4个物种共344份天麻样本进行了判别分析。采用S-G、FD、SD、MSC和SNV及其组合对光谱进行预处理以消除散射和基线漂移,PCA聚类结果表明,S-G+SD预处理效果最好。所建立的模型准确率均达到97.09 ~ 100.00 %,Matthews相关系数(Mcc)值为0.80 ~ 1.00。利用线性模型PLSR和非线性模型SVR对干果单株的质量等级进行预测,经S-G+SD预处理的PLSR模型预测精度分别为R2P = 0.94和RPD= 4.19。本研究可为多重生物变异影响下的elata标签真伪鉴定提供一种绿色可行的方法,对市场上药用和食用植物的质量控制和快速检测具有重要意义。
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引用次数: 0
Real-time monitoring of multiparameter in 1,3-propanediol production process with near-infrared spectroscopy 1,3-丙二醇生产过程多参数的近红外光谱实时监测
IF 2.7 3区 化学 Q2 CHEMISTRY, ANALYTICAL Pub Date : 2025-01-01 DOI: 10.1016/j.vibspec.2024.103760
Zijian Liang, Chijian Zhang, Liyun Hu, Yunlong Cai, Maolang Deng
1,3-propanediol (1,3-PDO) is a significant product of fermentation, with glycerol serving as the primary substrate in most cases. Bioprocess control based on real-time information of feedstock and main products is crucial for reducing the cost of production. However, rapid quantification of 1,3-PDO and glycerol remains challenging due to their highly similar molecular structures. In this study, the feasibility of near-infrared (NIR) spectroscopy to monitor 1,3-PDO, glycerol, acetate, and butyrate concentrations in the fermentation process using strain Clostridium pasteurianum was evaluated. NIR spectra were acquired through at-line measurement involving sampling and ex-situ analysis or on-line measurement with a fiber optic probe immersed in fermentation broth, integrated with Partial Least Squares (PLS) regression to establish calibration models on a laboratory-scale and pilot-scale. The best PLS regressions of 1,3-PDO, glycerol, acetate, and butyrate with two measurement approaches provided excellent performance, with the root-mean-squared errors of prediction (RMSEP) of 1.656 g/L, 1.502 g/L, 0.746 g/L, and 0.557 g/L in at-line measurement and 1.113 g/L, 1.581 g/L, 0.415 g/L, and 0.526 g/L in on-line measurement. The cross-scale application performance of at-line measurement was evaluated by an external fermentation trial and an acceptable result was achieved. At-line measurement technique represents a superior choice for the optimization of fermentation process since the robustness across varying fermentation scales and its applicability in multiple bioreactors. Thus, a calibration model developed for one bioreactor is likely to be used in other bioreactors, which enables the reduction of modeling costs. On-line measurement technique, owing to its automated operation and frequent data acquisition, enables real-time monitoring and precise control of the fermentation process, thereby reducing cost and improving production efficiency.
1,3-丙二醇(1,3- pdo)是发酵的重要产物,在大多数情况下甘油作为主要底物。基于原料和主要产品实时信息的生物过程控制对于降低生产成本至关重要。然而,1,3- pdo和甘油的快速定量仍然具有挑战性,因为它们的分子结构非常相似。在本研究中,评估了近红外(NIR)光谱监测巴氏梭菌发酵过程中1,3- pdo、甘油、乙酸和丁酸浓度的可行性。近红外光谱通过采样和非原位分析的在线测量或浸泡在发酵液中的光纤探针在线测量获得,并结合偏最小二乘(PLS)回归建立实验室规模和中试规模的校准模型。两种测量方法对1,3- pdo、甘油、乙酸酯和丁酸酯的最佳PLS回归具有良好的性能,在线测量的均方根预测误差(RMSEP)分别为1.656 g/L、1.502 g/L、0.746 g/L和0.557 g/L,在线测量的RMSEP分别为1.113 g/L、1.581 g/L、0.415 g/L和0.526 g/L。通过外部发酵试验,对在线测量的跨尺度应用性能进行了评价,取得了可接受的结果。在线测量技术由于其在不同发酵规模上的稳健性和在多种生物反应器中的适用性,是优化发酵过程的一种优越选择。因此,为一个生物反应器开发的校准模型可能用于其他生物反应器,从而降低建模成本。在线测量技术由于其自动化操作和频繁的数据采集,可以实时监测和精确控制发酵过程,从而降低成本,提高生产效率。
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引用次数: 0
Research on vehicle-mounted measurement of NO2 based on cavity ring-down spectroscopy 基于腔衰荡光谱的车载二氧化氮测量研究
IF 2.7 3区 化学 Q2 CHEMISTRY, ANALYTICAL Pub Date : 2024-11-28 DOI: 10.1016/j.vibspec.2024.103752
Dehao Li , Dan Wang , Yunfei Gao , Jun Duan , Zhiyan Li , Hao Chen , Yuanyuan Qian
Nitrogen dioxide (NO2) is a significant atmospheric pollutant with notable impacts on the environment and human health. The measurement of NO2 concentration is crucial for gas phase analysis in atmospheric chemistry, assessment of regional pollution levels, and improvement of data management systems. In this paper, a highly sensitive cavity ring-down spectroscopy (CRDS) technique has been used to measure the concentration of NO2. The CRDS system utilizes a diode laser and high reflectivity mirrors (over 99.99 %) at 405 nm, achieving a minimum detection limit of 43 ppt (1σ, 40 s) and 92 ppt (1σ, 10 s) as determined by Allan variance. In order to improve the measurement accuracy and stability, the Kalman filtering has been used to process the background ring-down time and ring-down time, and the minimum detection limit can be increased by about 2.06 times to 45 ppt (1σ, 10 s), while preserving temporal response and reducing noise. Under the condition of atmospheric NO2 measurement, a comparative experiment was carried out in Hefei with the CEAS system. The linear fitting slope was 1.033±0.017, and the correlation coefficient reached about 92 %. The CRDS device was integrated for vehicle-mounted measurements of NO2 concentrations in suburban, urban, and industrial areas of Maanshan City. The experimental result has demonstrated that this system can achieve high-precision, high-sensitivity, and stable measurements of NO2.
二氧化氮(NO2)是一种重要的大气污染物,对环境和人类健康有显著影响。NO2浓度的测量对于大气化学中的气相分析、区域污染水平评估以及数据管理系统的改进具有重要意义。本文采用高灵敏度的腔衰荡光谱(CRDS)技术测量了NO2的浓度。CRDS系统利用二极管激光器和高反射率反射镜(超过99.99 %)在405 nm处,通过Allan方差确定的最小检测限为43 ppt (1σ, 40 s)和92 ppt (1σ, 10 s)。为了提高测量精度和稳定性,采用卡尔曼滤波对背景灭环时间和灭环时间进行处理,在保持时间响应和降低噪声的同时,将最小检测限提高了约2.06倍,达到45 ppt (1σ, 10 s)。在大气NO2测量条件下,与CEAS系统在合肥进行了对比实验。线性拟合斜率为1.033±0.017,相关系数约为92%。将CRDS装置集成到马鞍山市郊区、市区和工业区的车载NO2浓度测量中。实验结果表明,该系统能够实现高精度、高灵敏度、稳定的二氧化氮测量。
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引用次数: 0
Harnessing the past: Vibration analysis of organic additives in ancient plasters for sustainable building solutions 利用过去:古代石膏中有机添加剂的振动分析,用于可持续建筑解决方案
IF 2.7 3区 化学 Q2 CHEMISTRY, ANALYTICAL Pub Date : 2024-11-28 DOI: 10.1016/j.vibspec.2024.103751
Bhushan Dhige , Manager Rajdeo Singh
This study investigates plant-based organic additives and grains used in ancient earthen plasters from the caves of Karla, Bhaja, Pitalkhora, Ellora, and Aurangabad in Maharashtra, India, highlighting their relevance for sustainable modern construction. Stereo microscopy, FTIR spectrophotometry, and phytochemical analysis identified rice husk, millet, wheat, Chenopodium album, Paspalum, barnyard millet, foxtail millet, and Fumaria seeds as key organic components. Starch grain analysis confirmed the presence of wheat, millet, and mung bean starches, while polarized light microscopy revealed distinctive birefringence patterns, such as the Maltese cross. FTIR analysis of solvent extractions identified natural compounds, including starches, gums, proteins, oils, waxes, and tree resins. These organic additives, especially rice husk, prevalent in Karla, Bhaja, and Pitalkhora samples, and the balanced composition of hemp fibers, seeds, and gums in Ellora samples, contributed significantly to the strength and durability of the plasters. The use of beeswax and tree resins as natural binders further underscores the ingenuity of these ancient techniques. This research not only highlights the botanical diversity in ancient construction but also draws critical connections to modern sustainable practices. The carbon-negative potential of these materials, combined with their renewability and resilience, aligns closely with contemporary green building strategies, offering inspiration for eco-friendly construction rooted in traditional Indian architecture.
本研究调查了印度马哈拉施特拉邦Karla、Bhaja、Pitalkhora、Ellora和Aurangabad洞穴中使用的古代泥塑中植物性有机添加剂和谷物,强调了它们与可持续现代建筑的相关性。立体显微镜、FTIR分光光度法和植物化学分析鉴定出稻壳、小米、小麦、Chenopodium album、Paspalum、稗子、foxtail millet和Fumaria种子是主要的有机成分。淀粉粒分析证实了小麦、小米和绿豆淀粉的存在,而偏振光显微镜显示出独特的双折射模式,如马耳他十字。溶剂萃取物的FTIR分析鉴定出天然化合物,包括淀粉、树胶、蛋白质、油、蜡和树树脂。这些有机添加剂,特别是稻壳,普遍存在于Karla, Bhaja和Pitalkhora样品中,以及Ellora样品中大麻纤维,种子和树胶的平衡组成,对石膏的强度和耐久性有显著贡献。蜂蜡和树树脂作为天然粘合剂的使用进一步强调了这些古老技术的独创性。这项研究不仅突出了古代建筑中的植物多样性,而且还将其与现代可持续实践联系起来。这些材料的碳负潜力,加上它们的可再生和弹性,与当代绿色建筑策略紧密结合,为植根于传统印度建筑的生态友好型建筑提供了灵感。
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Vibrational Spectroscopy
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