jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Shengyu Yang | Jijun Xue | Bo Zhang | Peng Wu | Yiwen Dong | Zhiwei Li | Fashen Li | Liang Qiao
Source: AIP Advances (article, 2024-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Exploring phase formation and magnetic transitions in Sm(Fe1−xMnx)14B by Mössbauer spectroscopy The Sm2(Fe1−xMnx)14B (0 ≤ x ≤ 0.3) rare earth alloy was prepared by a reduction-diffusion method, and a corresponding reduction-diffusion model was established. The crystal structure, microscopic morphology, and magnetic properties were systematically characterized to investigate the effect of Mn doping. The results show that the diffusion of Sm into FeB proceeded with the formation of the Sm2Fe14B phase from the very beginning of the reaction. Sm2(Fe1−xMnx)14B alloy undergoes a transition from the ferromagnetic phase (x = 0) to the paramagnetic phase (x = 0.2), and the average magnetic moment of Fe decreases from 2.066 to 0.719 µB as observed by 57Fe Mössbauer spectroscopy. In addition, the Curie temperature decreases rapidly from 620 to 230 K. In other words, a wide temperature-regulation of TC can be achieved by adjusting the Mn content, which is attributed to the fact that Mn doping greatly weakens the atomic exchange interactions. These results provide value for potential applications of Re–Fe-based alloys in room temperature magnetic refrigeration. Mössbauer spectroscopy ; Curie temperature ; Ferromagnetism ; Paramagnetism ; Alloy ; Magnetic moment ; Phase (matter) ; Doping ; Analytical Chemistry (journal) ; Diffusion ; Materials science ; Spectroscopy ; Condensed matter physics ; Chemistry ; Nuclear magnetic resonance ; Crystallography ; Metallurgy ; Thermodynamics ; Optoelectronics Magnetic Properties of Alloys ; Magnetic and transport properties of perovskites and related materials ; Rare-earth and actinide compounds
Authors: Ecem Özen Öner | Mediha Kök
Source: Turkish Journal of Science and Technology (article, 2024-01-13)
Field: Materials Science — Materials Chemistry

Gd Effect on Micro-Crystal Structure and Thermomagnetic Behavior of NiMnSn Magnetic Shape Memory Alloy In this study, the rare earth Gadolinium (Gd) element was added to the NiMnSn alloy, which is an alternative to the NiMnGa alloy group, with the increasing popularity of magnetic shape memory alloys. Since rare earth elements have strategic importance for our country in recent years, Gd element has been preferred in this study. X-rays and SEM-EDX analysis were performed to determine the morphological properties of the crystal structure and microstructure of the alloys. Magnetic measurements of the alloys were made with the physical property measuring device and it was determined that the magnetization values decreased with the addition of Gd. Thermomagnetic convection ; Alloy ; Materials science ; Microstructure ; Gadolinium ; Rare earth ; Rare-earth element ; Magnetization ; Crystal structure ; Metallurgy ; Crystallography ; Magnetic field ; Chemistry ; Physics Shape Memory Alloy Transformations ; Magnetic and transport properties of perovskites and related materials
Authors: Qiuyu Wang | Guang Yang | Jiaxing Zhu | Tongzhang Zheng | Bingbing Su | Bo Zhang
Source: Asia-Pacific Journal of Chemical Engineering (article, 2024-12-13)
Field: Materials Science — Materials Chemistry

A Mutual Information Based Multifeature Pattern Recognition Criterion for Identification and Optimization of Thermally Safe Operating Conditions for Single Diffusion‐Controlled Reactions Occurring in Isoperibolic Liquid–Liquid Semibatch Reactors ABSTRACT Prevention of thermal runaway and establishment of safe‐economic operating conditions are crucial for the practical operation of single diffusion‐controlled liquid–liquid reactions occurring in isoperibolic semibatch reactors. Hence, in this study, a mutual information‐based multiple feature recognition criterion (MI‐MFR) was innovatively proposed, for which the maximum relevance minimum redundancy approach was introduced in feature screening; in combination with the support vector machine algorithm, various thermal behaviors can be effectively recognized. On this basis, a series of boundary diagrams were established accordingly, the impact of the variation in model parameters on which was systematically explored. Subsequently, an improved generalized thermal safety region was developed by introducing the upper thresholds in normal and cooling failure scenarios. Meanwhile, the robustness and progressiveness of the established MI‐MFR criterion were confirmed by comparison with other conventional criteria. Finally, MI‐MFR‐based procedures were constructed to identify thermal behaviors and optimize safe‐economic operating conditions in practices, based on which the optimum operating conditions of toluene mononitration were obtained. Redundancy (engineering) ; Thermal diffusivity ; Computer science ; Thermal ; Robustness (evolution) ; Feature (linguistics) ; Diffusion ; Mutual information ; Control theory (sociology) ; Mathematics ; Thermodynamics ; Artificial intelligence ; Chemistry ; Control (management) ; Physics Thermal and Kinetic Analysis ; Catalysis and Hydrodesulfurization Studies ; Process Optimization and Integration
Authors: Kevin M. Lefrancois‐Gagnon | Robert C. Mawhinney
Source:  (preprint, 2024-12-04)
Field: Chemistry — Physical and Theoretical Chemistry

Development and Validation of Atomic Group Descriptors for Substituent Effects Substituent constants are often described with experimental proxies based on a simplified substituent effect model. While good insights have been derived from such proxies, the true properties of the substituent, through which a more thorough understa Substituent ; Group (periodic table) ; Computer science ; Chemistry ; Psychology ; Stereochemistry ; Organic chemistry History and advancements in chemistry ; Chemistry and Chemical Engineering
Authors: Maryam Nurhuda | Ken‐ichi Otake | Susumu Kitagawa | Daniel M. Packwood
Source: arXiv (Cornell University) (preprint, 2024-06-18)
Field: Materials Science — Materials Chemistry

Can it be detected? A computational protocol for evaluating MOF-metal oxide chemiresistive sensors for early disease detection Human breath contains over 3000 volatile organic compounds, abnormal concentrations of which can indicate the presence of certain diseases. Recently, metal-organic framework (MOF)-metal oxide composite materials have been explored for chemiresistive sensor applications, however their ability to detect breath compounds associated with specific diseases remains unknown. In this work, we present a new high-throughput computational protocol for evaluating the sensing ability of MOF-metal oxide towards small organic compounds. This protocol uses a cluster-based method for accelerated structure relaxation, and a combination of binding energies and density-of-states analysis to evaluate sensing ability, the latter measured using Wasserstein distances. We apply this protocol to the case of the MOF-metal oxide composite material NM125-TiO2 and show that it is consistent with previously reported experimental results for this system. We examine the sensing ability of NM125-TiO2 for over 100 human-breath compounds spanning 13 different diseases. Statistical inference then allows us to identifies ones which subsequent experimental efforts should focus on. Overall, this work provides new tools for computational sensor research, while also illustrating how computational materials science can be integrated into the field of preventative medicine. Protocol (science) ; Oxide ; Computer science ; Nanotechnology ; Chemistry ; Materials science ; Medicine ; Pathology ; Organic chemistry Advanced Nanomaterials in Catalysis ; Gas Sensing Nanomaterials and Sensors ; Electrochemical sensors and biosensors
Authors: Yannik Appiarius | Sandra M guez-Lago | Pim Puylaert | Noah Wolf | Sourabh Kumar | Martin Molkenthin | Delia Miguel | Tim Neudecker | Michal Jur | Araceli Ek | Anne Campa a | Staubitz | Stefan Herv-Hansen | Daoyang Lin | Kento Kasahara | Nobuyuki Matubayasi | Zuzana Osifov | Tade Kalvoda | Jakub Galgonek | Martin Culka | Ji Vondr  Ek | Petr Bou
Source: Chemical Science (article, 2024-01-01)
Field: Chemistry — Organic Chemistry

Contents list This article is Open Access All publication charges for this article have been paid for by the Royal Society of Chemistry Library science ; Chemistry ; World Wide Web ; Political science ; Computer science Synthesis and Properties of Aromatic Compounds ; Inorganic and Organometallic Chemistry
Authors: Miao Liu | Sheng Meng
Source: Materials Futures (preprint, 2024-02-28)
Field: Materials Science — Materials Chemistry

Recent breakthrough in AI-driven materials science: tech giants introduce groundbreaking models Abstract A close look at Google’s GNoME inorganic materials dataset (Merchant et al 2023 Nature 624 80–85), and 11 things you would like to know. Data science ; Computer science ; Nanotechnology ; Materials science Machine Learning in Materials Science
Authors: Cristian Cruz-Carrasco | Josefa Díaz‐Álvarez | Francisco Chávez | Abel Sánchez-Venegas | Juan Villegas-Cortéz
Source: Preprints.org (preprint, 2024-09-19)
Field: Chemistry — Analytical Chemistry

Detection of Aspergillus Flavus in Figs by Means of Hyperspectral Images and Deep Learning Algorithms Plant diseases not only cause economic losses, but they also pose a health risk. In this regard, aflatoxins are linked to the development of some type of liver cancer, especially the hepatocellular carcinoma. Aflatoxins are usually produced by the fungi Aspergillus flavus and Parsiticus in figs, and their identification is usually carried out using invasive and complex methods or by visual inspection at an advanced stage. Given that Spain is one of the main producers of figs and Extremadura is the leading region in the country, it is necessary to explore alternative ways of detecting the presence of aflatoxins in figs and preventing them from entering the food chain. The aim of this research is to address the early detection of Aspergillus flavus fungus using non-invasive techniques with the aid of hyperspectral imaging and the application of artificial intelligence techniques, in particular deep learning. The images were taken after inoculation of the microtoxin using 3 different concentrations, related to three different classes and healthy figs (healthy controls). The analysis of the hyperspectral images was performed at the pixel level. Firstly, a full connected neural network was used to analyse the spectral signature associated with each pixel; second, the wavelet transform was applied to each spectral signature. The resulting images are fed to a convolutional neural network. The hyperparameters of the proposed models were adjusted based on the parameter tuning process also performed. The results are promising, with 83% accuracy, 82.75% recall and 83.25% F1-measure for the single neural network, and 77.25% accuracy, recall and F1 for the convolution neural network. Hyperspectral imaging ; Aspergillus flavus ; Artificial intelligence ; Deep learning ; Computer science ; Algorithm ; Pattern recognition (psychology) ; Biology ; Microbiology Spectroscopy and Chemometric Analyses
Authors: Yuzuka Onaka | Renki Sakai | T. Fukunaga | Koki Ikemoto | Hiroyuki Isobe
Source: Angewandte Chemie (article, 2024-04-06)
Field: Chemistry — Organic Chemistry

Bayesian Inference for Model Analyses of Supramolecular Complexes: A Case Study with Nanocarbon Hosts Abstract A 126 π‐electron nanobowl molecule, phenine tridehydrosumanene, was synthesized in 12 steps through the development of a polygon cyclization strategy that assembled the polygonal precursors by Ni‐mediated macrocyclization. The bowl‐shaped structure accommodated C 70 as a guest at the concave site, and the ball‐in‐bowl structure was determined by X‐ray crystallography. The host–guest equilibrium in solution was studied with titration experiments using isothermal calorimetry, which provided an interesting test case for studying the host–guest stoichiometry. Bayesian inference was introduced for stoichiometric analyses of the equilibrium, and a procedure to estimate the volume of prior probability in the parameter space was developed. The Bayesian procedure functioned as Occam's razor and provided quantitative support for a specific stoichiometry. The method was examined with five host–guest examples comprising nanocarbon hosts, which suggested the versatility of Bayesian inference for studies of supramolecular complexes. Supramolecular chemistry ; Stoichiometry ; Bayesian inference ; Bayesian probability ; Isothermal titration calorimetry ; Titration ; Molecule ; Chemistry ; Computer science ; Artificial intelligence ; Physical chemistry ; Organic chemistry Synthesis and Properties of Aromatic Compounds ; Supramolecular Self-Assembly in Materials ; Supramolecular Chemistry and Complexes
Authors: Narjes Maleki | Davoud Koolivand
Source: Genetic Engineering and Biosafety Journal (article, 2024-05-01)
Field: Chemistry — Analytical Chemistry

The Application of Hyperspectral Imaging in Plant Pathology Annually, a significant portion of agricultural products is lost due to biotic and abiotic stresses, making the identification of plant diseases crucial for crop protection.Laboratory diagnostic methods are time-consuming and not ideal for large-scale applications.Modern agricultural approaches with innovative perspectives have the ability to detect diseases at an early stage before visible symptoms appear, providing an opportunity for intervention to control or prevent the spread of contamination prior to the infection or damage of the entire crop.Hyperspectral imaging technology is one of these approaches that has shown remarkable results in the early identification of plant diseases and stresses as a rapid and non-destructive measurement technology.This technique utilizes various sensors and platforms, combining spectral analysis and image analysis methods to simultaneously evaluate physiological and morphological components.This article focuses on the fundamentals of hyperspectral imaging technology, image acquisition, pre-processing, modeling, and analysis of hyperspectral data, as well as its application in plant pathology. Hyperspectral imaging ; Medical imaging ; Image processing ; Plant disease Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Optical Imaging and Spectroscopy Techniques
Authors: Pengjie Zhang | Bin Du | Jiwei Xu | Jiang Wang | Zhiwei Liu | Bing Liu | Fanhua Meng | Zhaoyang Tong
Source: Molecules (article, 2024-07-01)
Field: Chemistry — Analytical Chemistry

Identification and Removal of Pollen Spectral Interference in the Classification of Hazardous Substances Based on Excitation Emission Matrix Fluorescence Spectroscopy Sensitively detecting hazardous and suspected bioaerosols is crucial for safeguarding public health. The potential impact of pollen on identifying bacterial species through fluorescence spectra should not be overlooked. Before the analysis, the spectrum underwent preprocessing steps, including normalization, multivariate scattering correction, and Savitzky-Golay smoothing. Additionally, the spectrum was transformed using difference, standard normal variable, and fast Fourier transform techniques. A random forest algorithm was employed for the classification and identification of 31 different types of samples. The fast Fourier transform improved the classification accuracy of the sample excitation-emission matrix fluorescence spectrum data by 9.2%, resulting in an accuracy of 89.24%. The harmful substances, including <i>Staphylococcus aureus</i>, ricin, beta-bungarotoxin, and Staphylococcal enterotoxin B, were clearly distinguished. The spectral data transformation and classification algorithm effectively eliminated the interference of pollen on other components. Furthermore, a classification and recognition model based on spectral feature transformation was established, demonstrating excellent application potential in detecting hazardous substances and protecting public health. This study provided a solid foundation for the application of rapid detection methods for harmful bioaerosols. Normalization (sociology) ; Smoothing ; Principal component analysis ; Fourier transform ; Analytical Chemistry (journal) ; Chemistry ; Biological system ; Computer science ; Mathematics ; Artificial intelligence ; Statistics ; Environmental chemistry Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Indoor Air Quality and Microbial Exposure
Authors: Saeideh Hematian | Kiana Peyvandi | Mehdi Dadashpour
Source: Journal of Microbiota (article, 2024-11-26)
Field: Materials Science — Materials Chemistry

Evaluating the Antibacterial Properties of a Zeolite-Chitosan Biocomposite for Medical Applications : Antibacterial substances have garnered significant interest in recent years due to their applications in medical equipment, prostheses, and drug delivery systems. Consequently, it is essential to identify and develop biomaterials with antibacterial properties tailored to specific target sites. This study utilizes two natural biomaterials: Chitosan and zeolite. Zeolite, a bioceramic with an aluminosilicate structure, exhibits high mechanical resistance, making it ideal for use in areas of the body that endure significant pressure, such as bones and teeth. Chitosan, on the other hand, possesses excellent properties for application in various bodily tissues. However, natural polymers like chitosan are often combined with other materials in composites to enhance their mechanical resistance. This research investigates the antibacterial properties of a biocomposite made from zeolite and chitosan against two types of bacteria: Staphylococcus aureus (gram-positive) and Escherichia coli (gram-negative). The results revealed that the composite exhibited strong resistance against E. coli but comparatively lower resistance to S. aureus. Furthermore, the findings suggest that chitosan plays a more significant role in conferring these antibacterial properties. Biocomposite ; Chitosan ; Zeolite ; Materials science ; Antibacterial activity ; Chemical engineering ; Polymer science ; Chemistry ; Composite material ; Engineering ; Organic chemistry ; Biology ; Bacteria ; Composite number ; Catalysis Nanoparticles: synthesis and applications ; Antimicrobial agents and applications ; Essential Oils and Antimicrobial Activity
Authors: <p>Junxi Li</p>
Source: Academic Journal of Computing & Information Science (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Machine learning-based PM concentration prediction and model interpretability analysis Accurate PM<sub>2.5</sub> concentration forecasting is pivotal for environmental health and sustainable development. This study introduces a machine learning model leveraging the SHAP framework for enhanced interpretability and prediction accuracy. Utilizing 2023 meteorological data from Beijing's Wanshou Xigong meteorological station, we initially explored all characteristics and selected three algorithms, RF, SVR, and LightGBM, to construct machine learning models for PM<sub>2.5</sub> concentration prediction. The R<sup>2</sup> of each model on the validation set reached 0.9334, 0.9185, and 0.9472. Ultimately, we conducted SHAP framework interpretability analysis on the LightGBM model, removing features with minimal predictive impact. The R<sup>2</sup> of the final prediction model reaches 0.9501. This advancement significantly aids in precisely predicting PM<sub>2.</sub><sub>5</sub> concentration, supporting proactive environmental and health policies. Interpretability ; Machine learning ; Artificial intelligence ; Computer science Catalytic Processes in Materials Science ; Vehicle emissions and performance ; Surface Treatment and Coatings
Authors: Abiola Idowu | Benjamin Omotayo Adewuyi | D.O. Folorunso | Akeem Damilola Akinwekomi | Bayode Benson Adeyanju
Source: Engineering Science & Technology Journal (review, 2024-12-27)
Field: Materials Science — Materials Chemistry

Influence of thermal treatment on the wear resistance of autempered ductile iron – a review The wear resistance of ductile iron can be significantly influenced by thermal treatment methods, which alter its microstructure and mechanical properties. This paper review investigates the effects of three distinct thermal treatments—Austenitizing, Austempering, and Cryogenic treatment—on the wear resistance of ductile iron. Austenitizing involves heating the material to 900°C in a salt bath, facilitating the formation of austenite, which provides a uniform matrix for subsequent treatments. Austempering, achieved through rapid isothermal quenching in a salt mixture of potassium nitrate (KNO?) and sodium nitrate (NaNO?), promotes the development of bainitic structures known for their enhanced toughness and wear performance. Cryogenic treatment, conducted at sub-ambient temperatures (-170°C), refines the microstructure by reducing retained austenite and enhancing martensitic transformation, resulting in increased hardness. Microstructural analysis revealed significant transformations in graphite morphology and matrix phases after thermal treatments. Austenitizing prepared the microstructure for uniform transformation during austempering and cryogenic processing. Austempered samples exhibited superior wear resistance due to the presence of fine bainite and retained austenite, which provided an optimal balance between hardness and toughness. Cryogenic treatment further improves wear performance by enhancing hardness and dimensional stability, although excessive brittleness is a trade-off. Wear tests under abrasive and sliding conditions showes that austempering results in the best wear resistance, followed by cryogenic treatment, with austenitizing providing moderate improvement over untreated ductile iron. The findings highlight the importance of selecting the appropriate thermal treatment based on the desired combination of wear resistance, toughness, and application-specific requirements. This study underscores the potential of advanced thermal treatments to enhance the performance of ductile iron in demanding applications such as automotive, mining, and industrial machinery. Future research should explore the synergistic effects of alloying and thermal treatments to further optimize the material's wear resistance. Keywords: Ductile Iron, Thermal Treatment, Austenitizing, Austempering, Cryogenic Treatment, Bainite, Retained Austenite, Hardness, Wear Resistance, Microstructure. Materials science ; Wear resistance ; Metallurgy ; Resistance (ecology) ; Ductile iron ; Thermal ; Composite material ; Cast iron ; Physics ; Biology Metal Alloys Wear and Properties ; Metallurgy and Material Forming ; Microstructure and Mechanical Properties of Steels