Journal of Chemometrics最新文献

{"title":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"40 5","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148069023","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"40 5","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148068374","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Deep Learning Approach for OER Electrocatalyst Kinetics Prediction","authors":"Umme Javaria, Manzar Sohail, Tahir Mehmood, Ayesha Nazuk, Aqsa Shabbir","doi":"10.1002/cem.70129","DOIUrl":"https://doi.org/10.1002/cem.70129","url":null,"abstract":"<div>\u0000 \u0000 <p>Over the past decades, machine learning has been increasingly applied in electrochemistry, but challenges such as feature extraction and data scarcity persist. Here, we introduce a deep learning workflow that predicts Tafel slopes for the oxygen evolution reaction (OER) directly from electrochemical responses, overcoming data scarcity and enabling rapid evaluation of electrocatalyst performance. By capturing complex activity–structure relationships, this approach bypasses conventional parameter engineering, transfer learning, and DFT calculations, providing efficient data-driven insights for sustainable water-splitting electrocatalysts. This study proposes a novel application-driven workflow that integrates conditional variational autoencoder (CVAE)-based synthetic data generation with a one-dimensional convolutional neural network (1D-CNN) to predict Tafel slopes from OER electrocatalyst electrochemical measurements. CVAEs and CNNs are integrated deep learning architectures whose combined use augments sparse electrochemical datasets, validates synthetic data consistency, and enables accurate Tafel slope prediction in the context of OER kinetics for sustainable hydrogen production. The CVAE demonstrated capability in generating realistic synthetic data from only six real samples, with transfer tests confirming consistency between synthetic and real domains (TSTR: <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <msup>\u0000 <mrow>\u0000 <mi>R</mi>\u0000 </mrow>\u0000 <mrow>\u0000 <mn>2</mn>\u0000 </mrow>\u0000 </msup>\u0000 <mo>=</mo>\u0000 <mn>0</mn>\u0000 <mo>.</mo>\u0000 <mn>996</mn>\u0000 </mrow>\u0000 <annotation>$$ {R}^2=0.996 $$</annotation>\u0000 </semantics></math>, RMSE = 2.736; TRTS: <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <msup>\u0000 <mrow>\u0000 <mi>R</mi>\u0000 </mrow>\u0000 <mrow>\u0000 <mn>2</mn>\u0000 </mrow>\u0000 </msup>\u0000 <mo>=</mo>\u0000 <mn>0</mn>\u0000 <mo>.</mo>\u0000 <mn>986</mn>\u0000 </mrow>\u0000 <annotation>$$ {R}^2=0.986 $$</annotation>\u0000 </semantics></math>, RMSE = 5.474). Building on this, the 1D-CNN provides predictions that align with the dataset, yielding an <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <msup>\u0000 <mrow>\u0000 <mi>R</mi>\u0000 </mrow>\u0000 <mrow>\u0000 <mn>2</mn>\u0000 </mrow>\u0000 </msup>\u0000 </mrow>\u0000 <annotation>$$ {R}^2 $$</annotation>\u0000 </sema","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"40 5","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148133185","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Augmented Reality as a Tool to Immerse Students in Chemistry Education","authors":"Jonathan F. Prather,&nbsp;Grant R. Bowman,&nbsp;Alan Buss,&nbsp;Kyle Summerfield,&nbsp;Samantha C. Patterson","doi":"10.1002/cem.70128","DOIUrl":"https://doi.org/10.1002/cem.70128","url":null,"abstract":"<div>\u0000 \u0000 <p>Understanding molecular-scale processes such as diffusion and Brownian motion requires students to grasp processes that occur on spatial and temporal scales far removed from everyday human experience. Instructional approaches often rely on static representations that obscure the stochastic and quantitative nature of these processes. Here, we investigated whether an immersive augmented reality (AR) environment could improve students' conceptual understanding and use of scientific terminology related to molecular motion. The lesson was developed as a socially mediated augmented reality (SoAR) experience in which teams of four participants collaboratively explored a shared simulation of molecules diffusing in solution. Participants manipulated system parameters such as temperature using gesture-based controls while observing real-time graphical representations of molecular motion and statistical relationships between temperature and molecular movement. Learning outcomes were assessed through written responses to prelesson and postlesson questions, which were categorized and scored to measure a participant's information content and use of expert language in their answers. Results revealed significant increases in both information context and use of expert language after engagement with the SoAR lesson. These gains were independent of participants' prior educational experience and were not influenced by social familiarity within working groups. These findings demonstrate the potential of immersive SoAR environments to support collaborative learning of complex molecular phenomena.</p>\u0000 </div>","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"40 5","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148133184","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Partial Least Squares Estimates and Regression Coefficients","authors":"Richard G. Brereton","doi":"10.1002/cem.70071","DOIUrl":"https://doi.org/10.1002/cem.70071","url":null,"abstract":"<p>The original and still main motivation of PLS is estimation by multivariate regression [<span>1</span>]. Measurements such as spectra at several wavelengths or chromatograms at several elution times can be used to predict a quantity such as the concentration of a metabolite or the activity of a pharmaceutical extract.</p><p>In practical terms, there is not a great deal of difference in performance between PCR and PLS; however, PLS is widespread in most chemometrics software and is often used as a default for most multivariate calibration or regression estimates, so we will focus on this technique.</p><p>Both the Wold (NIPALS) and Martens algorithms provide identical estimates of c, and so we will restrict this article to the Wold algorithm for brevity.</p><p>Hence, PLS can be used for estimation (or prediction) of future unknowns, and this is one of the commonest uses of multivariate calibration models. We will discuss training and test sets and predictions in future articles in more detail.</p><p>The author declares no conflicts of interest.</p><p>The author has nothing to report.</p>","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"40 5","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cem.70071","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148131610","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"40 5","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148067626","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A New Double-Algorithmic Architecture for Wavelet-Domain Multiway Separation of Overlapping UV Spectra and Co-Quantification of Analytes","authors":"Erdal Dinç","doi":"10.1002/cem.70130","DOIUrl":"10.1002/cem.70130","url":null,"abstract":"<p>In this study, a new double-algorithmic chemometric architecture employing continuous wavelet transform (CWT) and parallel factor analysis (PARAFAC) was developed for wavelet-domain separation of overlapping UV spectra and co-quantification of analytes in laboratory-prepared binary mixtures and a commercial tablet formulation. Within this framework, one-dimensional UV absorbance spectra were transformed into the wavelet domain across multiple scale factors to generate structured pseudo-three-way data arrays. These multiway arrays were subsequently decomposed by PARAFAC, enabling the simultaneous extraction of pure spectral profiles, concentration profiles, and scale-dependent features for each component. Unlike prediction-based calibration approaches such as PLS and PCR, the proposed CWT-PARAFAC architecture enables true analytical separation of overlapping spectral components. The wavelet-domain representation was optimized by evaluating different wavelet families over multiple scale ranges, and those providing the best spectral consistency and recovery performance were selected for subsequent multiway analysis. The applicability of the proposed architecture was demonstrated using a representative pharmaceutical binary mixture containing moexipril hydrochloride (MOEX) and hydrochlorothiazide (HCTZ), whose UV absorption spectra strongly overlap. The CWT-PARAFAC framework provided effective analytical separation of overlapping spectral signals and enabled accurate and robust quantification of both analytes. Model performance was confirmed through validation studies, including linearity, precision, and recovery, and the method was successfully applied to the analysis of a commercial tablet formulation. Overall, the results demonstrate that the proposed CWT-PARAFAC double-algorithmic architecture enables effective analysis of complex multicomponent mixture systems exhibiting signal overlap and provides a practical and cost-effective alternative analytical approach to conventional high-cost analytical techniques.</p>","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"40 5","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cem.70130","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148039948","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"40 5","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148096175","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"40 5","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148086154","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"40 5","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148086153","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}