jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Peng Ran | Yunzhi Wang | Kai Li | Shiman He | Subei Tan | Jiacheng Lv | Jiajun Zhu | Shaoshuai Tang | Jinwen Feng | Zhaoyu Qin | Yan Li | Lin Huang | Yanan Yin | Lingli Zhu | Wenjun Yang | Chen Ding
Source: Briefings in Bioinformatics (article, 2024-09-23)
Field: Chemistry — Spectroscopy

STAVER: a standardized benchmark dataset-based algorithm for effective variation reduction in large-scale DIA-MS data Mass spectrometry (MS)-based proteomics has become instrumental in comprehensively investigating complex biological systems. Data-independent acquisition (DIA)-MS, utilizing hybrid spectral library search strategies, allows for the simultaneous quantification of thousands of proteins, showing promise in enhancing protein identification and quantification precision. However, low-quality profiles can considerably undermine quantitative precision, resulting in inaccurate protein quantification. To tackle this challenge, we introduced STAVER, a novel algorithm that leverages standardized benchmark datasets to reduce non-biological variation in large-scale DIA-MS analyses. By eliminating unwanted noise in MS signals, STAVER significantly improved protein quantification precision, especially in hybrid spectral library searches. Moreover, we validated STAVER's robustness and applicability across multiple large-scale DIA datasets, demonstrating significantly enhanced precision and reproducibility of protein quantification. STAVER offers an innovative and effective approach for enhancing the quality of large-scale DIA proteomic data, facilitating cross-platform and cross-laboratory comparative analyses. This advancement significantly enhances the consistency and reliability of findings in clinical research. The complete package is available at https://github.com/Ran485/STAVER. Computer science ; Robustness (evolution) ; Benchmark (surveying) ; Data mining ; Scale (ratio) ; Consistency (knowledge bases) ; Data quality ; Reliability (semiconductor) ; Quantitative proteomics ; Proteomics ; Artificial intelligence ; Chemistry ; Engineering Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Tongcan Cui | Zehua Ying | Jianyu Zhang | Shubo Guo | Wei Chen | Guifang Zhou | Wenlong Li
Source: Phytochemical Analysis (article, 2024-01-28)
Field: Chemistry — Analytical Chemistry

Strategies for the quality control of Chrysanthemi Flos: Rapid quantification and end‐to‐end fingerprint conversion based on FT‐NIR spectroscopy This comprehensive strategy provides a novel and dependable approach for the rapid quality evaluation of CF. Chemistry ; Flos ; Fingerprint (computing) ; End-to-end principle ; Chromatography ; Quality (philosophy) ; Near-infrared spectroscopy ; Artificial intelligence ; Organic chemistry ; Optics Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Sean P. Cooper | Nimal Naser | Tanusree Chatterjee | Yeonjoon Kim | Goutham Kukkadapu | Gina M. Fioroni | Seonah Kim | Olivier Mathieu | Eric L. Petersen | William J. Pitz | Robert L. McCormick
Source: Combustion and Flame (article, 2024-06-21)
Field: Chemistry — Organic Chemistry

An experimental and chemical kinetic modeling study of 4-butoxyheptane combustion Combustion ; Kinetic energy ; Thermodynamics ; Chemistry ; Materials science ; Physical chemistry ; Physics ; Classical mechanics Chemical Thermodynamics and Molecular Structure ; Phase Equilibria and Thermodynamics ; Advanced Combustion Engine Technologies
Authors: Amin Shafiee | Linhong Chen | Mahdi Nikdast | Jie Yao
Source: Nano Research (article, 2024-12-17)
Field: Materials Science — Materials Chemistry

Polarization-encodable photonic memory cells using next-generation 2D phase-change materials Integration of phase-change materials (PCMs) created a unique opportunity to implement reconfigurable photonics devices that their performance can be tuned depending on the target application. Conventional PCMs such as Ge-Sb-Te (GST) and Ge-Sb-Se-Te (GSST) rely on melt-quench and high temperature annealing processes to change the organization of the molecules in the materials’ crystal. Such a reorganization leads to different optical, electrical, and thermal properties which can be exploited to implement photonic memory cells that are able to store the data at different resistance or optical transmission levels. Despite the great promise of conventional PCMs for realizing reconfigurable photonic memories, their slow and extremely power-hungry thermal mechanisms make scaling the systems based on such devices challenging. In addition, such materials do not offer a stable multi-level response over a long period of time. To address these shortcomings, the research carried out in this study shows the proof of concept to implement next-generation photonic memory cells based on two-dimensional (2D) birefringence PCMs such as SnSe, which offer anisotropic optical properties that can be switched ferroelectrically. We demonstrate that by leveraging the ultrafast and low-power crystallographic direction change of the material, the optical polarization state of the input optical signal can be changed. This enables the implementation of next-generation high-speed polarization-encodable photonic memory cells for future photonic computing systems. Compared to the conventional PCMs, the proposed SnSe-based photonic memory cells offer an ultrafast switching and low-loss optical response relying on ferroelectric property of SnSe to encode the data on the polarization state of the input optical signal. Such a polarization encoding scheme also reduces memory read-out errors and alleviates the scalability limitations due to the optical insertion loss often seen in optical transmission encoding. Polarization (electrochemistry) ; Photonics ; Materials science ; Phase change ; Optoelectronics ; Nanotechnology ; Physics ; Engineering physics ; Chemistry Phase-change materials and chalcogenides ; Photonic and Optical Devices ; Advanced Optical Network Technologies
Authors: Atilay Ayasli | Petra Tóth | Tim Michaelsen | Thomas Gstir | Fábio Zappa | Dóra Papp | Gábor Czakó | Roland Wester
Source: The Journal of Physical Chemistry A (article, 2024-04-10)
Field: Chemistry — Spectroscopy

Imaging the Ion–Molecule Reaction Dynamics of O<sup>–</sup> + CD<sub>4</sub> While neutral reactions involved in methane oxidation have been intensively studied, much less information is known about the reaction dynamics of the oxygen radical anion with methane. Here, we study the scattering dynamics of this anion-molecule reaction using crossed-beam velocity map imaging with deuterated methane. Differential scattering cross sections for the deuterium abstraction channel have been determined at relative collision energies between 0.2 and 1.5 eV and ab initio calculations of the important stationary points along the reaction pathway have been performed. At lower collision energies, direct backscattering and indirect complex-mediated reaction dynamics are observed, whereas at higher energies, sideways deuterium stripping dominates the reaction. Above 0.7 eV collision energy, a suppressed cross section is observed at low product ion velocities, which is likely caused by the endoergic pathway of combined deuteron/deuterium transfer, forming heavy water. The measured product internal energy is attributed mainly to the low-lying deformation and out-of-plane bending vibrations of the methyl radical product. The results are compared with a previous crossed-beam result for the reaction of oxygen anions with nondeuterated ̧methane and with the related neutral-neutral reactions, showing similar dynamics and qualitative agreement. Deuterium ; Chemistry ; Crossed molecular beam ; Reaction dynamics ; Ion ; Atomic physics ; Methane ; Molecule ; Chemical reaction ; Scattering ; Physics Mass Spectrometry Techniques and Applications ; Spectroscopy and Laser Applications ; Advanced Chemical Physics Studies
Authors: Robert J. Meier | Paul R. Rablen
Source: Applied Sciences (article, 2024-02-27)
Field: Chemistry — Organic Chemistry

Group Contribution Revisited: The Enthalpy of Formation of Organic Compounds with “Chemical Accuracy” Part V Group Contribution (GC) methods to predict thermochemical properties are eminently important in chemical process design. Following our earlier work in which a Group Contribution (GC) model was presented to account for the gas-phase heat of formation of organic molecules which, for the first time, revealed chemical accuracy (1 kcal/mol or 4 kJ/mol), we here present Group Contribution parameters for a range of additional series of molecules allowing the application to a wider range of molecules whilst, mostly, retaining chemical accuracy. The new classes of molecules include amines, alkylesters, and various substituted benzenes, including t-butyl-benzenes, phenols, methoxybenzenes, anilines, benzaldehydes, and acetophenones, and finally furans and indoles/indolines. As in our previous works on this theme, again the critical selection of experimental data was crucial. Not meeting the criterion for chemical accuracy occurred when steric interactions such as nearest neighbour substituents on a benzene ring were present, something which does not fit with the characteristics of the Group Contribution method. We also report some cases for which the experimental value does not seem correct, but where both the G4 and GC model values agree well. In general, in line with accounts in the literature, the G4 method performs really well. Contrary to other related works, we have applied conformational averaging to obtain a slightly more realistic G4 result. Although the difference is generally only a few kJ/mol, this may still be relevant when attempting the development of a model with chemical accuracy, e.g., 4.2 kJ/mol. Standard enthalpy of formation ; Enthalpy ; Thermodynamics ; Chemistry ; Group (periodic table) ; Group contribution method ; Computational chemistry ; Organic chemistry ; Physics ; Phase equilibrium Chemical Thermodynamics and Molecular Structure ; Phase Equilibria and Thermodynamics ; Thermal and Kinetic Analysis
Authors: Mohammed H. Alqarni | Faiyaz Shakeel | Sultan Alshehri | Ahmed I. Foudah | Tariq M. Aljarba | Fatma Mohamed Abdel Bar | Prawez Alam
Source: Green Processing and Synthesis (article, 2024-01-01)
Field: Chemistry — Analytical Chemistry

Simultaneous estimation of ibuprofen, caffeine, and paracetamol in commercial products using a green reverse-phase HPTLC method Abstract A fast, sensitive, and green reverse-phase “high-performance thin-layer chromatography” approach for the simultaneous estimation of ibuprofen (IBF), caffeine (CAF), and paracetamol (PCM) in marketed formulations was established and verified in this study. The binary combination of acetone and water (80:20 v/v) was used as the green eluent system. The current method’s greenness was predicted using four different approaches, namely National Environmental Method Index, Analytical Eco-Score (89), ChlorTox (1.08 g), and the Analytical GREENness (83) approaches, which demonstrated an outstanding greener profile. The present approach was linear in the range of 25–800 ng·band −1 for the simultaneous estimation of IBF, CAF, and PCM. In addition, the current method was accurate (% recoveries = 100 ± 2), precise (%CV &lt; 2%), robust (%CV &lt; 2), sensitive (LOD = 1.13–2.71 ng·band −1 and LOQ = 3.39–8.10 ng·band −1 ), and green. The amount of IBF, CAF, and PCM in commercial tablets was determined to be 99.51%, 98.25%, and 100.64%, respectively. The present method for the simultaneous determination of IBF, CAF, and PCM in marketed tablets is supported by these data. The findings of this study suggested that the current approach may be consistently applied to analyze IBF, CAF, and PCM in marketed tablets. Chromatography ; Ibuprofen ; Acetone ; Caffeine ; Chemistry ; Stationary phase ; Medicine Analytical Methods in Pharmaceuticals ; Analytical Chemistry and Chromatography ; Antibiotics Pharmacokinetics and Efficacy
Authors: Jacob P. Gorton | Joel McDuffee | Patrick L. Snarr | Christian Petrie | Andrew Nelson
Source: Nuclear Engineering and Design (article, 2024-01-04)
Field: Materials Science — Materials Chemistry

Heat transfer optimization of uo2-mo fuel using genetic algorithms Materials science ; Heat transfer ; Thermal conductivity ; Pellet ; Thermal conduction ; Heat flux ; Thermal ; RADIUS ; Nuclear engineering ; Composite material ; Thermodynamics ; Engineering ; Computer science ; Physics Nuclear Materials and Properties ; Nuclear reactor physics and engineering ; Radioactive element chemistry and processing
Authors: Ting-feng Shi | Tingtiao Pan | Ping Lü
Source: Food Chemistry X (article, 2024-11-01)
Field: Chemistry — Analytical Chemistry

Rapid and simultaneous determination of mixed pesticide residues in apple using SERS coupled with multivariate analysis This study aims to apply multivariate analysis algorithms for modeling the same spectra, for simultaneous determination of pymetrozine and carbendazim residues in apple. To mitigate the impact of competitive adsorption, SERS spectra are obtained from mixed solutions of pymetrozine and carbendazim at varying concentration ratios, which are then utilized for modeling. Results suggest that the PLSR model based on full-band SNV processed spectra shows the best performance for predicting pymetrozine and carbendazim contents, with R2 <i>p</i> of 0.9751 and 0.9779, RMSEP of 0.0492 and 0.5531 mg/L, RPD of 6.4297 and 6.8246, respectively. This model yielded R2 <i>p</i> of 0.9644 and 0.9671, RMSEP of 0.0747 and 0.8247 mg/L, RPD of 5.3857 and 5.6066 for pymetrozine and carbendazim in apple, respectively. The findings suggest that the proposed approach is suitable for simultaneous detection of pymetrozine and carbendazim in apples, offering a novel avenue for monitoring food safety. Pesticide residue ; Multivariate statistics ; Multivariate analysis ; Pesticide ; Chromatography ; Chemistry ; Analytical Chemistry (journal) ; Environmental chemistry ; Mathematics ; Statistics ; Agronomy ; Biology Spectroscopy and Chemometric Analyses ; Pesticide Residue Analysis and Safety ; Identification and Quantification in Food
Authors: Saeedeh Mohammadi | Aoife Gowen | Colm P. O’Donnell
Source: Heliyon (article, 2024-08-01)
Field: Chemistry — Analytical Chemistry

Vibrational spectroscopy data fusion for enhanced classification of different milk types The aim of this study is to classify seven types of Irish milk (butter, fresh, heart active, lactose free, light, protein, and slimline), supplied by a specific company, using vibrational spectroscopy methods: Near infrared (NIR), mid infrared (MIR), and Raman spectroscopy. In this regard, chemometric methods were used, and the impact of spectral data fusion on prediction accuracy was evaluated. A total of 105 samples were tested, with 21 used in the test set. The study assessed principal component analysis (PCA), partial least square discriminant analysis (PLS-DA), and sequential and orthogonalized partial least squares linear discriminant analysis (SO-PLS-LDA) for classifying different milk types. The prediction accuracy, when applying PLS-DA on individual blocks of data and low-level fused data, did not exceed 85.71 %. However, implementing the SO-PLS-LDA strategy significantly improved the accuracy to 95 %, suggesting a promising method for the development of classification models for milk using data fusion strategies. Spectroscopy ; Fusion ; Sensor fusion ; Infrared spectroscopy ; Materials science ; Computer science ; Chemistry ; Artificial intelligence ; Physics ; Organic chemistry Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Mineral Processing and Grinding
Authors: P. Bertrand | Cédric Huchette | Thibaut Archer | Thomas Vandellos | François Hild
Source: Journal of the European Ceramic Society (article, 2024-09-04)
Field: Materials Science — Ceramics and Composites

Determination of interfacial fracture energy of an environmental barrier coating on ceramic matrix composite substrate The present work concerns the study of the interface fracture energy between a SiC/SiC Ceramic Matrix Composite and an environmental barrier coating. Four-point flexural tests with no precrack were conducted. These tests enable for the stable propagation of two interfacial cracks. They were carried out at room temperature and were instrumented with visible light cameras. This instrumentation allowed for the analysis of the tests thanks to digital image correlation as well as comparisons between experimental and numerical results to locate crack tips and to calculate the interface fracture energy using numerical methods based on linear elastic fracture mechanics. The limits of the method as well as the uncertainties associated with the crack length and the fracture energy were assessed. Materials science ; Composite number ; Composite material ; Ceramic ; Ceramic matrix composite ; Substrate (aquarium) ; Matrix (chemical analysis) ; Coating ; Fracture (geology) ; Surface energy Advanced ceramic materials synthesis ; Numerical methods in engineering ; High-Velocity Impact and Material Behavior
Authors: Luke R. Sadergaski | Jeffrey D. Einkauf | Lætitia H. Delmau | Jonathan D. Burns
Source: Frontiers in Nuclear Engineering (article, 2024-08-08)
Field: Chemistry — Analytical Chemistry

Leveraging design of experiments to build chemometric models for the quantification of uranium (VI) and HNO3 by Raman spectroscopy Partial least squares regression (PLSR) and support vector regression (SVR) models were optimized for the quantification of U(VI) (10–320 g L −1 ) and HNO 3 (0.6–6 M) by Raman spectroscopy with optimized calibration sets chosen by optimal design of experiments. The designed approach effectively minimized the number of samples in the calibration set for PLSR and SVR by selecting sample concentrations with a quadratic process model, despite complex confounding and covarying spectral features in the spectra. The top PLS2 model resulted in percent root mean square errors of prediction for U(VI), HNO 3 , and NO 3 − of 3.7%, 3.6%, and 2.9%, respectively. PLS1 models performed similarly despite modeling an analyte with a majority linear response (i.e., uranyl symmetric stretch) and another with more covarying vibrational modes (i.e., HNO 3 ). Partial least squares (PLS) model loadings and regression coefficients were evaluated to better understand the relationship between weaker Raman bands and covarying spectral features. Support vector machine models outperformed PLS1 models, resulting in percent root mean square error of prediction values for U(VI) and HNO 3 of 1.5% and 3.1%, respectively. The optimal nonlinear SVR model was trained using a similar number of samples (11) compared with the PLSR model, even though PLS is a linear modeling approach. The generic D-optimal design presented in this work provides a robust statistical framework for selecting training set samples in disparate two-factor systems. This approach reinforces Raman spectroscopy for the quantification of species relevant to the nuclear fuel cycle and provides a robust chemometric modeling approach to bolster online monitoring in challenging process environments. Partial least squares regression ; Calibration ; Raman spectroscopy ; Mean squared error ; Chemometrics ; Overfitting ; Mathematics ; Collinearity ; Analytical Chemistry (journal) ; Support vector machine ; Statistics ; Linear regression ; Least-squares function approximation ; Biological system ; Chemistry ; Artificial intelligence ; Computer science ; Chromatography Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Water Quality Monitoring and Analysis
Authors: Monica Tirapelle | Dian Ning Chia | Fanyi Duanmu | Maximilian O. Besenhard | Luca Mazzei | Eva Sørensen
Source: Journal of Chromatography A (article, 2024-03-21)
Field: Chemistry — Spectroscopy

In-silico method development and optimization of on-line comprehensive two-dimensional liquid chromatography via a shortcut model Comprehensive two-dimensional liquid chromatography (LCxLC) represents a valuable alternative to conventional single column, or one-dimensional, liquid chromatography (1D-LC) for resolving multiple components in a complex mixture in a short time. However, developing LCxLC methods with trial-and-error experiments is challenging and time-consuming, which is why the technique is not dominant despite its significant potential. This work presents a novel shortcut model to in-silico predicting retention time and peak width within an RPLCxRPLC separation system (i.e., LCxLC systems that use reversed-phase columns (RPLC) in both separation dimensions). Our computationally effective model uses the hydrophobic-subtraction model (HSM) to predict retention and considers limitations due to the sample volume, undersampling and the maximum pressure drop. The shortcut model is used in a two-step strategy for sample-dependent optimization of RPLCxRPLC separation systems. In the first step, the Kendall's correlation coefficient of all possible combinations of available columns is evaluated, and the best column pair is selected accordingly. In the second step, the optimal values of design variables, flow rate, pH and sample loop volume, are obtained via multi-objective stochastic optimization. The strategy is applied to method development for the separation of 8, 12 and 16 component mixtures. It is shown that the proposed strategy provides an easy way to accelerate method development for full-comprehensive 2D-LC systems as it does not require any experimental campaign and an entire optimization run can take less than two minutes. Undersampling ; Column (typography) ; Computer science ; Volume (thermodynamics) ; Sample (material) ; Separation (statistics) ; Chromatography ; Biological system ; Chemistry ; Artificial intelligence ; Machine learning Analytical Chemistry and Chromatography ; Microfluidic and Capillary Electrophoresis Applications ; Protein purification and stability