Chemometrics and Intelligent Laboratory Systems最新文献

{"title":"Enhanced CO2 leak detection in soil: High-fidelity digital colorimetry with machine learning and ACES AP0","authors":"Chairul Ichsan ,&nbsp;Navinda Ramadhan ,&nbsp;Komang Gede Yudi Arsana ,&nbsp;M. Mahfudz Fauzi Syamsuri ,&nbsp;Rohmatullaili","doi":"10.1016/j.chemolab.2024.105268","DOIUrl":"10.1016/j.chemolab.2024.105268","url":null,"abstract":"<div><div>The importance of effective carbon capture and storage (CCS) in addressing climate change issues highlights the need for robust CO₂ leak monitoring systems. Limitations of conventional methods have prompted interest in alternative approaches, such as optical CO₂ sensors, which offer non-invasive and continuous monitoring. Here, we present a novel methodology for high-fidelity digital colorimetry to enhance CO₂ leak detection in soil, integrating machine learning algorithms with the ACES AP0 color space. Optical CO₂ sensors, utilizing a cresol red-based detection solution, were calibrated and validated in a controlled environment chamber designed to simulate CO₂ leakage. Digital images of the sensor's colorimetric response to varying CO₂ levels were analyzed in five color spaces. The ACES AP0 color space, renowned for its expansive color gamut and perceptual uniformity, exhibited optimal performance in discerning subtle color variations induced by changes in CO₂ concentration. Ten machine learning regression models were evaluated, and Multivariate Polynomial Regression (MPR) emerged as the most effective in converting ACES AP0 color data into precise CO₂ concentration estimates, achieving a Mean Absolute Percentage Error (MAPE) of 2.9 % and a Root Mean Square Error (RMSE) of 0.0731. Field validation at a carbon capture and storage (CCS) facility corroborated the robustness and accuracy of this method, showcasing its potential for real-world applications in CCS and environmental monitoring.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"255 ","pages":"Article 105268"},"PeriodicalIF":3.7,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142571294","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantitative structure properties relationship (QSPR) analysis for physicochemical properties of nonsteroidal anti-inflammatory drugs (NSAIDs) usingVe degree-based reducible topological indices","authors":"Rong Fan ,&nbsp;Abdul Rauf ,&nbsp;Manal Elzain Mohamed Abdalla ,&nbsp;Arif Nazir ,&nbsp;Muhammad Faisal ,&nbsp;Adnan Aslam","doi":"10.1016/j.chemolab.2024.105266","DOIUrl":"10.1016/j.chemolab.2024.105266","url":null,"abstract":"<div><div>Nonsteroidal Anti-Inflammatory Drugs (NSAIDs) are a class of medications that are used for different therapeutic uses. They effectively alleviate pain, reduce inflammation, and manage fever. These drugs are available in various forms. NSAIDs are prescribed by healthcare professionals to address a wide range of symptoms, from headaches and dental pain to conditions like arthritis and muscle stiffness. In this work, we use ve-degree-based reducible topological descriptors in quantitative structure-property relationship (QSPR) analysis to estimate the physicochemical properties of NSAIDs. In the first step, we have developed a MAPLE-based code to compute the reducible ve-degree-based topological descriptors of NSAIDs. Then, a linear regression model was used to estimate four physicochemical properties of seventy NSAIDs. It has been observed that two physicochemical properties, namely Molecular Weight and Complexity show a very strong correlation with the reducible ve-degree-based topological descriptors. For both cases, the value of correlation coefficient is greater than 0.9. Finally, quadratic and cubic regression models were constructed, and a comparative analysis with these models is presented. These results may help enhance the understanding of NSAIDs medication structures and aid in predicting their pharmacological activity.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"255 ","pages":"Article 105266"},"PeriodicalIF":3.7,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142578433","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"On-the-fly spectral unmixing based on Kalman filtering","authors":"Hugues Kouakou ,&nbsp;José Henrique de Morais Goulart ,&nbsp;Raffaele Vitale ,&nbsp;Thomas Oberlin ,&nbsp;David Rousseau ,&nbsp;Cyril Ruckebusch ,&nbsp;Nicolas Dobigeon","doi":"10.1016/j.chemolab.2024.105252","DOIUrl":"10.1016/j.chemolab.2024.105252","url":null,"abstract":"<div><div>This work introduces an on-the-fly (i.e., online) linear spectral unmixing method which is able to sequentially analyze spectral data acquired on a spectrum-by-spectrum basis. After deriving a sequential counterpart of the conventional linear mixing model, the proposed approach recasts the linear unmixing problem into a linear state-space estimation framework. Under Gaussian noise and state models, the estimation of the pure spectra can be efficiently conducted by resorting to Kalman filtering. Interestingly, it is shown that this Kalman filter can operate in a lower-dimensional subspace to lighten the computational burden of the overall unmixing procedure. Experimental results obtained on synthetic and real Raman data sets show that this Kalman filter-based method offers a convenient trade-off between unmixing accuracy and computational efficiency, which is crucial for operating in an on-the-fly setting. The proposed method constitutes a valuable building block for benefiting from acquisition and processing frameworks recently proposed in the microscopy literature, which are motivated by practical issues such as reducing acquisition time and avoiding potential damages being inflicted to photosensitive samples. The code associated with the numerical illustrations reported in this paper is freely available online at <span><span>https://github.com/HKouakou/KF-OSU</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"255 ","pages":"Article 105252"},"PeriodicalIF":3.7,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142652925","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Regression analysis with spatially-varying coefficients using generalized additive models (GAMs)","authors":"Francisco de Asis López ,&nbsp;Javier Roca-Pardiñas ,&nbsp;Celestino Ordóñez","doi":"10.1016/j.chemolab.2024.105254","DOIUrl":"10.1016/j.chemolab.2024.105254","url":null,"abstract":"<div><div>Regression models for spatial data have attracted the attention of researchers from different fields given their widespread application. In this work we analyze the utility of generalized additive models (GAMs) as regression methods with spatially-dependent coefficients. Particularly, three different aspects of the regression analysis were addressed: model definition and estimation, testing spatial heterogeneity, and variable selection. Spatial heterogeneity was addressed through bootstrapping, while and algorithm using the Bayesian Information Criterion (BIC) was implemented for variable selection to reduce computation time. In addition, this study makes a comparison of GAMs with two of the most common methods for regression with spatially-varying coefficients: Geographically Weighted Regression (GWR) and Multiscale Geographically Weighted Regression (MGWR), using both synthetic and real data.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"255 ","pages":"Article 105254"},"PeriodicalIF":3.7,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142571293","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of metrological correlation on the total combined risk in pharmaceutical equivalence evaluations","authors":"Maria Luiza de Godoy Bertanha,&nbsp;Felipe Rebello Lourenço","doi":"10.1016/j.chemolab.2024.105267","DOIUrl":"10.1016/j.chemolab.2024.105267","url":null,"abstract":"<div><div>Pharmaceutical equivalence evaluation requires a multiparametric conformity assessment for both generic and reference medicines. This paper investigates the impact of metrological correlations on the total combined risk in pharmaceutical equivalence evaluations. The study focused on the equivalence between ranitidine hydrochloride tablets, assessed by determining the average weight, the assay of the active pharmaceutical ingredient, and the uniformity of dosage units. The risks of false conformity decisions were evaluated using Monte Carlo method simulations across four scenarios, each reflecting different correlation conditions. The results of the study focus on evaluating pharmaceutical equivalence between ranitidine hydrochloride tablets from two manufacturers. The tablets were tested for three parameters: average weight, active pharmaceutical ingredient (API) assay, and uniformity of dosage units. The measured values were within the regulatory specifications for both medicines A and B. Four scenarios of metrological correlation were assessed: #1 – actual correlation from shared analytical steps, #2 – correlation between parameters within the same medicine, #3 – correlation between generic and reference medicines, and #4 – uncorrelated parameters. The study revealed that correlations significantly affect total and combined risk values. The correlations between different parameters of the same medicine affect the total risk values, while the correlations between generic and reference medicines for a given parameter influence the combined particular risk values. Correlations between parameters of the same medicine affect total risk values, while correlations between generic and reference medicines impact combined particular risk values. Both types of correlations significantly influence combined total risk values, making metrological correlations crucial in pharmaceutical equivalence evaluations. Proper consideration of these correlations ensures the quality, efficacy, and safety of generic and reference medicines.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"255 ","pages":"Article 105267"},"PeriodicalIF":3.7,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142571292","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adaptive soft-sensor update by Latest Sample Targeting Frustratingly Easy Domain Adaptation","authors":"Kaito Katayama ,&nbsp;Kazuki Yamamoto ,&nbsp;Koichi Fujiwara","doi":"10.1016/j.chemolab.2024.105246","DOIUrl":"10.1016/j.chemolab.2024.105246","url":null,"abstract":"<div><div>Soft-sensors are widely used in manufacturing processes to estimate key process variables; however, their performance may deteriorate when process characteristics change. Although Just-In-Time (JIT) modeling techniques have been proposed for adaptive soft-sensor design, they do not always adapt to abrupt changes. Transfer learning (TL) has been suggested as a means to address this issue, with Frustratingly Easy Domain Adaptation (FEDA) being used for soft-sensor design. This study proposes a new TL method called Latest Sample Targeting-FEDA (LST-FEDA) for JIT-based soft-sensor, which can handle both sudden and gradual changes in process characteristics. LST-FEDA updates soft-sensors using a fixed number of latest samples whenever a new sample is obtained. The effectiveness of the proposed method was demonstrated using simulation data from a vinyl acetate monomer (VAM) process and actual operation data from a fluorine-based monomer (FM) process. LST-FEDA accurately estimated objective variables during sudden malfunctions and scheduled maintenance, contributing to efficient and safe process operation.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"255 ","pages":"Article 105246"},"PeriodicalIF":3.7,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142539096","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Estimation of soil organic carbon content using visible and near-infrared spectroscopy in the Red River Delta, Vietnam","authors":"Nguyen-Xuan Hau ,&nbsp;Nguyen-Thanh Tuan ,&nbsp;Lai-Quang Trung,&nbsp;Tran-Thuy Chi","doi":"10.1016/j.chemolab.2024.105253","DOIUrl":"10.1016/j.chemolab.2024.105253","url":null,"abstract":"<div><div>Accurate estimation of Soil Organic Carbon (SOC) is vital for assessing soil fertility, health, and carbon sequestration. Visible and Near-Infrared (Vis-NIR) spectroscopy has gained popularity worldwide for SOC estimation due to its cost-effectiveness and environmental benefits. However, inconsistencies arise from varying preprocessing techniques and regression models applied across different datasets and regions. Few studies explore combinations of spectral preprocessing, modeling algorithms, and resampling techniques. This study presents the first SOC estimation using Vis-NIR spectroscopy in the Red River Delta, Vietnam. We assessed estimation performances incorporating fifteen preprocessing techniques, four regression models, and three resampling methods to identify the most effective strategies. Standard Normal Variate (SNV) emerged as the top preprocessing technique, while Partial Least Squares Regression (PLSR) demonstrated the highest accuracy with minimal discrepancies between calibration and validation. Regarding resampling methods, repeated cross-validation (repeatedcv) proved most robust, with simple cross-validation as an alternative. By utilizing SNV, PLSR, and repeatedcv, we achieved the first successful Vis-NIR spectroscopy-based SOC estimation in the Red River Delta and Vietnam. This approach satisfied stringent statistical criteria for predictive models, yielding validation performance metrics of R² = 0.740, RMSE = 0.166, RPD = 2.337, and RPIQ = 2.321. Our findings highlight the importance of optimizing preprocessing, regression, and resampling techniques for accurate Vis-NIR spectroscopy-based SOC prediction.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"255 ","pages":"Article 105253"},"PeriodicalIF":3.7,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142652926","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Chemometrics and electrochemistry joined hands to develop a novel and intelligent electronic device for simultaneous determination of malathion and diazinon in fruit juices: A progress in multidisciplinary studies","authors":"Leila Zare ,&nbsp;Ehsan Sadeghi ,&nbsp;Meghdad Pirsaheb ,&nbsp;Maziar Farshadnia ,&nbsp;Ali R. Jalalvand","doi":"10.1016/j.chemolab.2024.105249","DOIUrl":"10.1016/j.chemolab.2024.105249","url":null,"abstract":"<div><div>In this work, chemometrics and electrochemistry connected to each other to open a new way for assisting food industry specialists based on developing a novel electrochemical sensor for simultaneous determination of malathion (MT) and diazinon (DZ) in the presence of patulin (PT) and citrinin (CT) as uncalibrated interference in fruit juices. The sensor was fabricated based on modification of a glassy carbon electrode (GCE) by chitosan-ionic liquid (Ch-IL), electrodeposition of gold nanoparticles (Au NPs), drop-casting of multiwalled carbon nanotubes-IL (MWCNTs-IL), and electrochemical synthesis of dual templates molecularly imprinted polymers (DTMIPs) in which MT and DZ were used as templates. Effects of experimental variables on structure and response of the sensor were screened and optimized by Min Run screening and central composite design, respectively. After optimization, the third-order hydrodynamic differential pulse voltammetric (HDPV) data were generated based on changing modulation times and modulation amplitudes as instrumental parameters and modeled by N-PLS/RTL, U-PLS/RTL, U-PCA/RTL, APARAFAC, PARAFAC2 and MCR-ALS to select the best one to assist the sensor for ultra selective simultaneous determination of MT and DZ in the presence of PT and CT as uncalibrated interference in fruit samples. The results confirmed the MCR-ALS was the best assistance for DTMIPs/MWCNTs-IL/Au NPs/Ch-IL/GCE for simultaneous determination of MT and DZ in the presence of PT and CT as uncalibrated interference in both synthetic and real samples. Performance of the sensor assisted by MCR-ALS for ultra selective simultaneous determination of MT (0.1 pM–12.5 pM, LOD = 0.01 pM) and DZ (0.25 pM–8.5 pM, LOD = 0.15 pM) was really admirable which was comparable with HPLC with UV detection while it was faster, simpler and low-cost in comparison to HPLC-UV which motivated us to introduce it as a reliable method to assist food industry specialists for quality assurance purposes.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"255 ","pages":"Article 105249"},"PeriodicalIF":3.7,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142531123","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Temperature correction of near-infrared spectra of raw milk","authors":"Jose A. Diaz-Olivares ,&nbsp;Stef Grauwels ,&nbsp;Xinyue Fu ,&nbsp;Ines Adriaens ,&nbsp;Wouter Saeys ,&nbsp;Ryad Bendoula ,&nbsp;Jean-Michel Roger ,&nbsp;Ben Aernouts","doi":"10.1016/j.chemolab.2024.105251","DOIUrl":"10.1016/j.chemolab.2024.105251","url":null,"abstract":"<div><div>Accurate milk composition analysis is crucial for improving product quality, economic efficiency, and animal health in the dairy industry. Near-infrared (NIR) spectroscopy can quantify milk composition quickly and nondestructively. However, external factors, such as temperature fluctuations, can alter the molecular vibrations and hydrogen bonding in milk, altering the NIR spectra and leading to errors in predicting key constituents such as fat, protein, and lactose. This study compares the effectiveness of Piecewise Direct Standardization (PDS), Continuous PDS (CPDS), External Parameter Orthogonalization (EPO), and Dynamic Orthogonal Projection (DOP in correcting the impact of temperature-induced variations on predictions in milk long-wave NIR spectra (LW-NIR, 1000–1700 nm).</div><div>A total of 270 raw milk samples were analyzed, collecting both reflectance and transmittance spectra at five different temperatures (20 °C, 25 °C, 30 °C, 35 °C, and 40 °C). The experimental setup ensured precise temperature control and accurate spectral measurements. PLSR models were calibrated at 30 °C to predict milk fat, protein, and lactose content. The performance of these models was assessed before and after applying the temperature correction methods, with a primary focus on reflectance spectra.</div><div>Results indicate that EPO and DOP significantly enhance model robustness and prediction accuracy across all temperatures, outperforming PDS and CPDS, especially for lactose prediction. These orthogonalization methods were compared against PLSR models calibrated with spectra from all temperatures. EPO and DOP showed comparable or superior performance, highlighting their effectiveness without requiring extensive temperature-specific calibration data. These findings suggest that orthogonalization methods are particularly suitable for in-line milk quality measurements under farm conditions where temperature control is challenging. This study highlights the potential of advanced chemometric techniques to improve real-time, on-farm milk composition analysis, facilitating better farm management and enhanced dairy product quality.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"255 ","pages":"Article 105251"},"PeriodicalIF":3.7,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142531122","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mathematical modeling of ions adsorption from water/wastewater sources via porous materials: A machine learning-based approach","authors":"Guang Yang ,&nbsp;Nadhir N.A. Jafar ,&nbsp;Rafid Jihad Albadr ,&nbsp;Mariem Alwan ,&nbsp;Zainab Sadeq Yousif ,&nbsp;Suhair Mohammad Husein Kamona ,&nbsp;Safaa Mohammed Ibrahim ,&nbsp;Usama S. Altimari ,&nbsp;Ashwaq Talib Kareem ,&nbsp;Raghu Jettie ,&nbsp;Raaid Alubady ,&nbsp;Ahmed Alawadi","doi":"10.1016/j.chemolab.2024.105250","DOIUrl":"10.1016/j.chemolab.2024.105250","url":null,"abstract":"<div><div>This paper developed the predictive modeling of substance concentration (<em>C</em>) utilizing the input parameters <em>x</em> and <em>y</em>, for analysis of adsorption process. Employing three distinct machine learning models—Multilayer Perceptron (MLP), polynomial regression (PR), and Support Vector Machine (SVM)—the study investigates the efficacy of models in capturing the relationships between the inputs and output. The models are trained from data obtained from mass transfer calculations for removal of solute from solution via porous adsorbent. Furthermore, the hyper-parameters for each model are optimized through the utilization of the Political Optimizer (PO). The Multilayer Perceptron model emerges as a standout performer, showcasing an exceptional R-squared score of 0.9981, indicative of a robust fit to the data. Complemented by impressively low MAE and MSE values (7.94043E-01 and 2.0420E+00, respectively), the MLP model attests to its ability to provide accurate predictions and discern underlying patterns within the dataset. The polynomial regression model, while slightly trailing behind the MLP in terms of R-squared score (0.95929), revealed commendable predictive performance. Support Vector Machine also proves to be a formidable contender, boasting a robust R-squared score of 0.96055.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"255 ","pages":"Article 105250"},"PeriodicalIF":3.7,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142553145","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}