jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Changjiu He | Xiaoyu Wang | Mingming Lyu | Xinye Bian
Source: Journal of Computer and Communications (article, 2024-01-01)
Field: Chemistry — Spectroscopy

Research on the Upper Limit of Accuracy for Predicting Theoretical Tandem Mass Spectrometry In recent years, numerous theoretical tandem mass spectrometry prediction methods have been proposed, yet a systematic study and evaluation of their theoretical accuracy limits have not been conducted. If the accuracy of current methods approaches this limit, further exploration of new prediction techniques may become redundant. Conversely, a need for more precise prediction methods or models may be indicated. In this study, we have experimentally analyzed the limits of accuracy at different numbers of ions and parameters using repeated spectral pairs and integrating various similarity metrics. Results show significant achievements in accuracy for backbone ion methods with room for improvement. In contrast, full-spectrum prediction methods exhibit greater potential relative to the theoretical accuracy limit. Additionally, findings highlight the significant impact of normalized collision energy and instrument type on prediction accuracy, underscoring the importance of considering these factors in future theoretical tandem mass spectrometry predictions. Limit (mathematics) ; Mass spectrometry ; Computer science ; Chemistry ; Chromatography ; Mathematics ; Mathematical analysis Mass Spectrometry Techniques and Applications ; Analytical Chemistry and Chromatography ; Metabolomics and Mass Spectrometry Studies
Authors: Omar Alejandro Alvarez-Gonzaga | Juan I. Rodríguez | Rodriguez, Juan I.
Source: arXiv (Cornell University) (preprint, 2024-10-30)
Field: Materials Science — Materials Chemistry

Machine learning models with different cheminformatics data sets to forecast the power conversion efficiency of organic solar cells Random Forest (RF) and Gradient Boosting Regression Trees (GBRT) regression models along with three cheminformatics data sets (RDkit, Mordred, Morgan) have been used to predict the power conversion efficiency (PCE) of organic solar cells (OSCs). The data consists of cheinformatics descriptors of the electron donor used in 433 OSCs for which the experimental PCE (target variable) is reported in the literature. The donor is either a polymer or a small organic molecule, and the acceptor the fullerene derivatives PCBM or PC71BM. Unlike previous methods, our ML approach considers the type of donor and the acceptor by adding four extra donor's features using the one-hot encoder tool. It is demonstrated that this additional information improves the prediction performance up to 34%. We have also exploited this feature to theoretically forecast the PCE of new OSCs by evaluating the ML model for a different acceptor. It is predicted that more than 50% of the OCSs obtained by exchanging the acceptor would have higher experimental PCE. The prediction accuracy of a given ML approach is analyzed for different PCE intervals. RF using RDkit descriptors resulted in the best ML approach with a Pearson's correlation coefficient for the training and testing sets equal to 0.96 and 0.62, respectively. Cheminformatics ; Artificial intelligence ; Computer science ; Machine learning ; Organic solar cell ; Power (physics) ; Energy conversion efficiency ; Engineering ; Photovoltaic system ; Bioinformatics ; Biology ; Physics ; Electrical engineering Machine Learning in Materials Science
Authors: Alexander Sychev | Eugene B. Postnikov
Source: Izvestiya VUZ Applied Nonlinear Dynamics (article, 2024-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

On the relationship between the observed dynamics of a colorimetric indicator and the nonlinear dynamics of the population growth under study in the case of microbial cultures The resazurin test is one of the most widespread approaches for studying the growth and metabolic activity of microorganisms. It is based on the colour change of the blue indicator, resazurin, to its pink reduced form, resorufin due to the reduction process catalyzed by the metabolic activity. At the same time, the quantitative characterization of the process needs to take into account the fact that one registers the results of the chemical transformation, which can differ from the underlying kinetics of the population growth. Purpose. The principal goal of this work is a sequential modelling of both coupled nonlinear growth processes aimed at obtaining the closed-form solution depending on the specificity and parameters of biological and chemical counterparts and its comparison with the curves obtained experimentally. Methods. The indicator concentration change is derived under the assumption of the logistic bacterial growth catalyzing the unidirectional chemical reaction considered and compared with the photometrically registered growth curve for a population of lactobacteria. Results. It is revealed that the biochemical growth curve will be logistic too only in the case of specially coordinated kinetic parameters and the systems’ carrying capacity. Otherwise, another functional form should be used to approximate the observable dynamics. Conclusion. Thus, the main conclusion consists of drawing attention to the importance of distinguishing between the underlying microbial and observable chemical growth curves. Their difference affects the value of the population growth rate, which is the target of such tests, and, therefore, the proper functional form should be used for the experimental data regression. Dynamics (music) ; Nonlinear system ; Econometrics ; Population ; Mathematics ; Biological system ; Biology ; Psychology ; Demography ; Sociology ; Physics thermodynamics and calorimetric analyses
Source: Chemistry International (article, 2024-10-01)
Field: Chemistry — Physical and Theoretical Chemistry

Winners of the 2024 IUPAC-Solvay International Award for Young Chemists Abstract The International Union of Pure and Applied Chemistry and Solvay announce the winners of the 2024 IUPAC-Solvay International Award for Young Chemists, presented for the best Ph.D. theses in the chemical sciences, as described in 1000-word essays. Chemical nomenclature ; Polymer science ; Engineering ; Chemistry ; Organic chemistry History and advancements in chemistry ; Radioactive element chemistry and processing ; Various Chemistry Research Topics
Authors: Deepak Kumar Awasthi
Source: African Journal of Biomedical Research (article, 2024-12-12)
Field: Chemistry — Analytical Chemistry

Multi-Level Classifier for Medicinal Plant Type and Health Prediction Plants fulfill the food, shelter, fuel and other life related requirement from ancient time. Human is continuously performing a lot of efforts to overcome the disease related to plant decays. To cure any disease it is necessary to identify whether a leaf is healthy or diseased. This work helps to identify two important attributes from an plant image i.e. plant leaf type and to predict leaf is either healthy or unhealthy. This combined phenomena is achieved&nbsp; by multilevel classification. Well accepted publically available SMVU Katra dataset is used. Multi-class classification paradigm is used for classification on both levels i.e. plant leaf type and plant leaf health. A brief discussion with six machine learning classifiers are presented. These six classifiers are Support Vector Machine(SVM), Native Bayes(NB), Logistic Regression(LR), Extreme Gradient Boosting (XGBoost), k-Nearest Neighbours (KNN) and Random Forest(RF).Random Forest classifier reports the best result with accuracy 86.32% and F1-Score 86.22 %. This work is presents a state of art framework for leaf type and leaf health detection. &nbsp; Classifier (UML) ; Artificial intelligence ; Machine learning ; Pattern recognition (psychology) ; Computer science ; Traditional medicine ; Medicine Spectroscopy and Chemometric Analyses ; Food Quality and Safety Studies ; Smart Agriculture and AI
Authors: Jasim Haider Hadi
Source: Algerian Journal of Research and Technology (article, 2024-12-20)
Field: Materials Science — Materials Chemistry

Evaluation of the Effects of Dissolved CO₂ Gas on the Corrosion Rate of API 5L X60 Steel Crude Oil Pipelines, Considering the Effect of Vibration Vibrations in crude oil pipelines can occur due to the flow of crude oil, pumps, wind, earthquakes, and human activities. When vibrations are combined with corrosive environments and dissolved gases, especially CO₂, they can accelerate corrosion and ultimately lead to failure. In this study, a novel vibration immersion test was conducted in the laboratory under various temperatures and low CO₂ dissolved gas pressures to investigate the corrosion rate in API 5L X60 steel pipelines. The experimental 1991 De Waard-Lotz-Milliams correlation and a neural network method were used to evaluate the effects of CO₂ gas levels on overall corrosion rates. The results indicate that temperature, CO₂ partial pressure, and vibration directly influence corrosion rates. The corrosion rate increases uniformly at constant vibration and CO₂ partial pressure as temperature rises, exceeding 1%. At constant vibration and temperature, corrosion rates increase uniformly by less than 1% with increasing CO₂ partial pressure. Corrosion ; Pipeline transport ; Metallurgy ; Crude oil ; Vibration ; Materials science ; Petroleum engineering ; Environmental science ; Engineering ; Physics ; Environmental engineering ; Acoustics Material Properties and Failure Mechanisms ; Engineering Diagnostics and Reliability ; Corrosion Behavior and Inhibition
Authors: Angelika Plota-Pietrzak | Leszek Czechowski | Sebastian Miszczak | Anna Masek
Source: Materials (article, 2024-04-16)
Field: Materials Science — Polymers and Plastics

Innovative Materials Based on Epoxy Resin for Use as Seat Elements in Bulk Transport The subject of this research is the development of epoxy composites with a defined service life for the purpose of seat elements in rail vehicles, which will be more environmentally friendly. The produced materials based on epoxy resin filled with PLA or PLA and quercetin were subjected to solar aging tests for 800 h to investigate the impact of the additives used on the aging behavior of the epoxy matrix. Firstly, the TGA analysis showed that the use of the proposed additives allowed for the maintenance of the thermal stability of the epoxy resin. Moreover, based on an optical microscopy test, it was noticed that the introduction of PLA and PLA with quercetin did not contribute to an increase in matrix defects. The one-directional tensile tests carried out before and after solar aging showed that the presence of polylactide in epoxy composites causes a slight growth of the stiffness and strength. Based on contact angle and color change measurements, it was found that quercetin was oxidized, thus ensuring protection for the epoxy matrix. This phenomenon was confirmed by FTIR study, where the carbonyl index (CI) value for the R-PLA-Q composite was lower than for the reference sample. The obtained composite structures may be a good alternative to traditionally used systems as seat elements in rail vehicles, which are not only characterized by high aging resistance but are also more eco-friendly. Epoxy ; Materials science ; Composite material ; Ultimate tensile strength ; Composite number ; Thermal stability ; Fourier transform infrared spectroscopy ; Environmentally friendly ; Tensile testing ; Chemical engineering ; Chemistry ; Organic chemistry Polymer composites and self-healing ; biodegradable polymer synthesis and properties ; Polymer Nanocomposites and Properties
Authors: Felipe Wesley de Vasconcelos Paulino | Murilo Sérgio da Silva Julião | Aldeneide Soares de Paiva | Leilane Gomes Rodrigues | Fabriciany Lourenço Moreira | Alexandre Magno Rodrigues Teixeira | Hélcio Silva dos Santos
Source: Concilium (article, 2024-03-21)
Field: Chemistry — Analytical Chemistry

Qualitative identification of synthesized isoniazid derivatives by atemporal technique: UV-visible spectrophotometry Ultramodern analytical techniques must also be hyphenated with cheaper and less complex techniques such as UV-visible spectrophotometry to confirm the formation of organic synthesis products. Here, the synthesis of 4 compounds (HC4ATIOF, HFEC, HCPA4NO2, and HICNM) from a clinically approved antitubercular drug, the Isoniazid, was preliminarily verified by UV-Vis spectrophotometry. Spectra recorded in the UV-Vis region for the four possible isoniazid derivatives allowed us to confirm the synthesis of just one acylhydrazone from isoniazid, the HICNM, since the absorption bands observed in the UV-Vis spectra of the reactants and the product obtained are completely different, indicating the formation of a new substance. Spectrophotometry ; Isoniazid ; Identification (biology) ; Qualitative analysis ; Chemistry ; Photochemistry ; Nuclear chemistry ; Chromatography ; Medicine ; Biology ; Qualitative research ; Tuberculosis ; Sociology Analytical Methods in Pharmaceuticals ; Synthesis and biological activity ; Synthesis and Characterization of Heterocyclic Compounds
Authors: Shunya Yamada | Kansei Kanayama | Kazuaki Toyoura
Source: arXiv (Cornell University) (preprint, 2024-11-19)
Field: Materials Science — Materials Chemistry

Nuclear Quantum Effects on Proton Diffusivity in Perovskite Oxides In the present study, the nuclear quantum effects (NQEs) on proton diffusivity in oxides were evaluated by molecular dynamics (MD) simulations with the quantum thermal bath (QTB) based on the Langevin dynamics. We employed the proton diffusion in barium zirconate (BaZrO3) with the cubic perovskite structure as the model system, in which protons migrate by rotation around single oxide ions and hopping between adjacent oxide ions. MD simulations with the standard classical thermal bath (CTB) and phonon calculations were also conducted to verify the conventionally used classical harmonic transition state theory (classical h-TST), in which the transition state theory (TST), the harmonic approximation, and the classical approximation are assumed. As a result, the h-TST are reasonable for the proton rotation, while significantly overestimate the activation energy and the pre-exponential factor of the jump frequency for the proton hopping. Furthermore, the classical approximation makes the proton jump frequencies close to linear in the Arrhenius plots, which should actually be nonlinear by the NQEs in the temperature range of 500-2000 K. This suggests the necessity of the treatment beyond the classical h-TST for accurate evaluation of the proton diffusivity in oxides even in the intermediate temperature range (573-873 K). Proton ; Thermal diffusivity ; Perovskite (structure) ; Quantum ; Materials science ; Nuclear engineering ; Chemistry ; Physics ; Nuclear physics ; Crystallography ; Thermodynamics ; Engineering ; Quantum mechanics Nuclear materials and radiation effects ; Nuclear Materials and Properties ; Catalysis and Oxidation Reactions
Authors: Thomas A. Shoff | Brielle Van Orman | Vivian C. Onwudiwe | Joseph C. Genereux | Ryan R. Julian
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2024-12-05)
Field: Chemistry — Spectroscopy

Unusually Rapid Isomerization of Aspartic Acid in Tau Spontaneous chemical modifications in long-lived proteins can potentially change protein structure in ways that impact proteostasis and cellular health. For example, isomerization of aspartic acid interferes with protein turnover and is anticorrelated with cognitive acuity in Alzheimer's disease. However, few isomerization rates have been determined for Asp residues in intact proteins. To remedy this deficiency, we used protein extracts from SH-SY5Y neuroblastoma cells as a source of a complex, brain-relevant proteome with no baseline isomerization. Cell lysates were aged <i>in vitro</i> to generate isomers, and extracted proteins were analyzed by data-independent acquisition (DIA) liquid chromatography-mass spectrometry (LC-MS). Although no Asp isomers were detected at Day 0, isomerization increased across time and was quantifiable for 105 proteins by Day 50. Data analysis revealed that isomerization rate is influenced by both primary sequence and secondary structure, suggesting that steric hindrance and backbone rigidity modulate isomerization. Additionally, we examined lysates extracted under gentle conditions to preserve protein complexes and found that protein-protein interactions often slow isomerization. Base catalysis was explored as a means to accelerate Asp isomerization due to findings of accelerated asparagine deamidation. However, no substantial rate enhancement was found for isomerization, suggesting fundamental differences in acid-base chemistry. With an enhanced understanding of Asp isomerization in proteins in general, we next sought to better understand Asp isomerization in tau. <i>In vitro</i> aging of monomeric and aggregated recombinant tau revealed that tau isomerizes significantly faster than any similar protein within our dataset, which is likely related to its correlation with cognition in Alzheimer's disease. Isomerization ; Aspartic acid ; Chemistry ; Biochemistry ; Amino acid ; Catalysis Mass Spectrometry Techniques and Applications ; Enzyme Structure and Function ; Protein Structure and Dynamics
Authors: Shihao Zhang | Shihao Zhu | Fanshun Meng | Shigenobu Ogata
Source: Institutional Repositories DataBase (IRDB) (article, 2024-11-11)
Field: Materials Science — Metals and Alloys

Hydrogen and vacancy concentrations in α-iron under high hydrogen gas pressure and external stress: A first-principles neural network simulation study Hydrogen ; Artificial neural network ; Vacancy defect ; Energy (signal processing) ; Gas pressure ; Hydrogen fuel Hydrogen embrittlement and corrosion behaviors in metals ; Hydrogen Storage and Materials ; Nuclear Materials and Properties
Authors: Zeheng Wang | Timothy van der Laan | Muhammad Usman
Source: arXiv (Cornell University) (preprint, 2024-08-28)
Field: Materials Science — Materials Chemistry

Self-Adaptive Quantum Kernel Principal Components Analysis for Compact Readout of Chemiresistive Sensor Arrays The rapid growth of Internet of Things (IoT) devices necessitates efficient data compression techniques to handle the vast amounts of data generated by these devices. Chemiresistive sensor arrays (CSAs), a simple-to-fabricate but crucial component in IoT systems, generate large volumes of data due to their simultaneous multi-sensor operations. Classical principal component analysis (cPCA) methods, a common solution to the data compression challenge, face limitations in preserving critical information during dimensionality reduction. In this study, we present self-adaptive quantum kernel (SAQK) PCA as a superior alternative to enhance information retention. Our findings demonstrate that SAQK PCA outperforms cPCA in various back-end machine-learning tasks, especially in low-dimensional scenarios where access to quantum bits is limited. These results highlight the potential of noisy intermediate-scale quantum (NISQ) computers to revolutionize data processing in real-world IoT applications by improving the efficiency and reliability of CSA data compression and readout, despite the current constraints on qubit availability. Kernel principal component analysis ; Principal component analysis ; Quantum ; Kernel (algebra) ; Principal (computer security) ; Physics ; Computer science ; Mathematics ; Kernel method ; Artificial intelligence ; Pure mathematics ; Quantum mechanics ; Support vector machine ; Computer security Machine Learning in Materials Science ; Electrochemical Analysis and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Mahmoud M. Elkhoudary | Aya A. Marie | Sherin F. Hammad | Mohamed M. Salim | Amira H. Kamal
Source: BMC Chemistry (article, 2024-11-29)
Field: Chemistry — Analytical Chemistry

Decoding the synergy: unveiling gradient boosting regression model for multivariate quantitation of pioglitazone, alogliptin and glimepiride in pure and tablet dosage forms This study represents a comparison among the performances of four multivariate procedures: partial least square (PLS) and artificial neural networks (ANN) in addition to support vector regression (SVR) and extreme gradient boosting (XG Boost) algorithm for the determination of the anti-diabetic mixture of pioglitazone (PIO), alogliptin (ALG) and glimepiride (GLM) in pharmaceutical formulations with aid of UV spectrometry. Key wavelengths were selected using knowledge-based variable selection and various preprocessing methods (e.g., mean centering, orthogonal scatter correction, and principal component analysis) to minimize noise and improve model precision. XG Boost effectively enhanced computing speed and accuracy by focusing on specific spectral features rather than the entire spectrum, demonstrating its advantages in resolving complex, overlapping spectral data. The independent test results of different models demonstrated that XG Boost outperformed other methods. XG Boost achieved the lowest root mean squared error of prediction (RMSEP) and standard deviation (SD) values across all compounds, indicating minimal prediction error and variability. For PIO, XG Boost recorded an RMSEP of 0.100 and SD of 0.369, significantly better than PLS and ANN. For ALG, XG Boost showed near-perfect performance with an RMSEP of 0.001 and SD of 0.005, outperforming SVR and PLS, which had higher error rates. In the case of GLM, XG Boost also excelled with an RMSEP of 0.001 and SD of 0.018, demonstrating superior precision compared to the much higher errors seen in PLS and ANN. These results highlight XG Boost's exceptional ability to handle complex, overlapping spectral data, making it the most reliable and accurate model in this study. Mean squared error ; Alogliptin ; Pioglitazone ; Glimepiride ; Mathematics ; Partial least squares regression ; Statistics ; Multivariate statistics ; Support vector machine ; Regression ; Computer science ; Pattern recognition (psychology) ; Artificial intelligence ; Medicine ; Type 2 diabetes Spectroscopy and Chemometric Analyses ; Analytical Methods in Pharmaceuticals ; Pesticide Residue Analysis and Safety
Authors: G B Sathishkumar | B. Asaithambi | V. Srinivasan | T. Balamurugan | S. Sundaraselvan | A. Joseph Arockiam | Padmanabhan RG
Source: Research Square (preprint, 2024-07-18)
Field: Materials Science — Biomaterials

Parameter Optimization of Laser Cladding for Stelcar Powder Coatings on AZ61 Magnesium Alloy Magnesium alloy ; Materials science ; Magnesium ; Alloy ; Metallurgy ; Laser ; Cladding (metalworking) ; Optics ; Physics Magnesium Alloys: Properties and Applications ; Aluminum Alloys Composites Properties ; Aluminum Alloy Microstructure Properties