jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: V A Kukartsev | Alina Igorevna Trunova | В В Кукарцев | В С Тынченко | Sergei Kurashkin | K A Bashmur | Yadviga Tynchenko | Ruslan Sergienko | Sergei Vasilievich Tynchenko
Source: Metals (article, 2023-07-05)
Field: Materials Science — General Materials Science

Influence of Temperature Regimes of Synthetic Iron Smelting on Casting Production Efficiency The purpose of the foundry is to provide the consumer with blanks for general machine-building (special) purposes which are as close as possible to the size of the future part in full compliance with the requirements. The competitiveness of these products is primarily dependent on the use of efficient and reliable smelting equipment which meets the necessary cost. The replacement of high-value ironworks and ironworks iron with steel scrap using induction melting furnaces (ICFs) reduces the cost of producing synthetic cast iron. However, this results in temperatures greater than 1500 °C, reduced lining stability and increased downtime of the smelter. As a result of the research carried out, a technology for the use of quartzite is proposed. Thereby, the purpose of this work is to establish temperature regimes for the smelting of synthetic pig iron, allowing the use in metal filling up to 70–90% of steel scrap; this leads to a reduction in the cost of purchasing bulk materials (depending on the brand of cast iron) up to 50% and, thus, increases the efficiency of synthetic cast iron smelting and castings production in general. After removal of the original moisture and the subsequent sintering of the manufactured lining, it provides the possibility of melting using the melting temperatures 1550–1600 °C. It increases the efficiency of the operation of the melting furnaces and eliminates the consumption of the ironworks and the melting of the cast iron in the blast furnace, as well as the cost of the lost alloy. As a result, metallurgical production will be able to reduce the volume of production and supply of cast iron for ironworks, which will improve their environmental situation during the production and processing of necessary raw materials. Smelting ; Scrap ; Cast iron ; Metallurgy ; Foundry ; Materials science ; Casting ; Blast furnace ; Production cost ; Pig iron ; Induction furnace ; Mold ; Environmental science ; Alloy ; Engineering ; Composite material ; Mechanical engineering Material Properties and Applications ; Materials Engineering and Processing ; Surface Treatment and Coatings
Authors: Kamel El Omari | Ramona Duman | Vitaliy Mykhaylyk | Christian M. Orr | Merlyn Latimer-Smith | Graeme Winter | Vinay Grama | Feng Qu | Kiran Bountra | Hok Sau Kwong | Maria Romanò | Rosana I. Reis | Lutz Vogeley | Luca Vecchia | David Owen | Sina Wittmann | Max Renner | Miki Senda | Naohiro Matsugaki | Yoshiaki Kawano | Thomas A. Bowden | Isabel Moraes | Jonathan M. Grimes | Erika J. Mancini | Martin Walsh | Cristiane Rodrigues Guzzo | Raymond J. Owens | E. Yvonne Jones | D. Brown | David I. Stuart | Konstantinos Beis | Armin Wagner
Source: Communications Chemistry (article, 2023-10-12)
Field: Materials Science — Materials Chemistry

Experimental phasing opportunities for macromolecular crystallography at very long wavelengths Despite recent advances in cryo-electron microscopy and artificial intelligence-based model predictions, a significant fraction of structure determinations by macromolecular crystallography still requires experimental phasing, usually by means of single-wavelength anomalous diffraction (SAD) techniques. Most synchrotron beamlines provide highly brilliant beams of X-rays of between 0.7 and 2 Å wavelength. Use of longer wavelengths to access the absorption edges of biologically important lighter atoms such as calcium, potassium, chlorine, sulfur and phosphorus for native-SAD phasing is attractive but technically highly challenging. The long-wavelength beamline I23 at Diamond Light Source overcomes these limitations and extends the accessible wavelength range to λ = 5.9 Å. Here we report 22 macromolecular structures solved in this extended wavelength range, using anomalous scattering from a range of elements which demonstrate the routine feasibility of lighter atom phasing. We suggest that, in light of its advantages, long-wavelength crystallography is a compelling option for experimental phasing. Phaser ; Wavelength ; Diffraction ; Anomalous scattering ; Synchrotron ; Optics ; Materials science ; Diamond ; Crystallography ; Optoelectronics ; Chemistry ; Physics Enzyme Structure and Function ; Advanced Electron Microscopy Techniques and Applications ; Porphyrin Metabolism and Disorders
Authors: Seyed Mojtaba Alirahmi | Truls Gundersen | Haoshui Yu
Source: International Journal of Hydrogen Energy (article, 2023-02-08)
Field: Materials Science — Materials Chemistry

A comprehensive study and tri-objective optimization for an efficient waste heat recovery from solid oxide fuel cell Since the solid oxide fuel cell (SOFC) has fuel flexibility, high electrical efficiency, and environmental benefits, it is considered a promising electrochemical energy-conversion device. However, it is important to include a waste heat recovery (WHR) unit in SOFC due the high operating temperature. In the present work, the integration of SOFC and WHR is developed. Accordingly, a parametric evaluation of the critical variables from energy, exergy, exergoeconomic, and environmental (4E) viewpoints is carried out. To determine the optimal decision variables, a tri-objective optimization is performed on the novel integrated SOFC-WHR system; the Pareto frontier solution is also provided. On the Pareto curve, the ultimate solution is selected employing the TOPSIS method. The objective functions are cost rate, output power, and CO2 emission, and the corresponding optimal values are identified as 28.5 $/GJ, 1368 kW, and 0.205 kg/kWh, respectively. Moreover, the scattered distribution of nine design variables is explored, and the behavior of decision variables is obtained. Solid oxide fuel cell ; Multi-objective optimization ; Exergy ; Process engineering ; Pareto principle ; Exergy efficiency ; Waste heat recovery unit ; Flexibility (engineering) ; TOPSIS ; Environmental science ; Computer science ; Mathematical optimization ; Mechanical engineering ; Engineering ; Heat exchanger ; Mathematics ; Chemistry ; Operations research Advancements in Solid Oxide Fuel Cells ; Fuel Cells and Related Materials ; Thermodynamic and Exergetic Analyses of Power and Cooling Systems
Authors: Zhiwei Jiang | Aimin Lv | Lingjiao Zhong | Jingjing Yang | Xiaowei Xu | Yuchan Li | Yuchen Liu | Qiuju Fan | Qingsong Shao | Ailian Zhang
Source: Foods (article, 2023-07-30)
Field: Chemistry — Analytical Chemistry

Rapid Prediction of Adulteration Content in Atractylodis rhizoma Based on Data and Image Features Fusions from Near-Infrared Spectroscopy and Hyperspectral Imaging Techniques <i>Atractylodis rhizoma</i> (AR) is an herb and food source with great economic, medicinal, and ecological value. <i>Atractylodes chinensis</i> (DC.) Koidz. (AC) and <i>Atractylodes lancea</i> (Thunb.) DC. (AL) are its two botanical sources. The commercial fraud of AR adulterated with <i>Atractylodes japonica</i> Koidz. ex Kitam (AJ) frequently occurs in pursuit of higher profit. To quickly determine the content of adulteration in AC and AL powder, two spectroscopic techniques, near-infrared spectroscopy (NIRS) and hyperspectral imaging (HSI), were introduced. The partial least squares regression (PLSR) algorithm was selected for predictive modeling of AR adulteration levels. Preprocessing and feature variable extraction were used to optimize the prediction model. Then data and image feature fusions were developed to obtain the best predictive model. The results showed that if only single-spectral techniques were considered, NIRS was more suitable for both tasks than HSI techniques. In addition, by comparing the models built after the data fusion of NIRS and HSI with those built by the single spectrum, we found that the mid-level fusion strategy obtained the best models in both tasks. On this basis, combined with the color-texture features, the prediction ability of the model was further optimized. Among them, for the adulteration level prediction task of AC, the best strategy was combining MLF data (at CARS level) and color-texture features (C-TF), at which time the R<sup>2</sup><sub>T</sub>, RMSET, R<sup>2</sup><sub>P</sub>, and RMSEP were 99.85%, 1.25%, 98.61%, and 5.06%, respectively. For AL, the best approach was combining MLF data (at SPA level) and C-TF, with the highest R<sup>2</sup><sub>T</sub> (99.92%) and R<sup>2</sup><sub>P</sub> (99.00%), as well as the lowest RMSET (1.16%) and RMSEP (2.16%). Therefore, combining data and image features from NIRS and HSI is a potential strategy to predict the adulteration content quickly, non-destructively, and accurately. Partial least squares regression ; Hyperspectral imaging ; Pattern recognition (psychology) ; Artificial intelligence ; Mathematics ; Medicinal herbs ; Near-infrared spectroscopy ; Computer science ; Statistics ; Traditional medicine ; Physics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Miranda L. Lynch | M. Elizabeth Snell | Stephen A. Potter | Edward H. Snell | Sarah Bowman
Source: Acta Crystallographica Section D Structural Biology (article, 2023-02-21)
Field: Materials Science — Materials Chemistry

20 years of crystal hits: progress and promise in ultrahigh-throughput crystallization screening Diffraction-based structural methods contribute a large fraction of the biomolecular structural models available, providing a critical understanding of macromolecular architecture. These methods require crystallization of the target molecule, which remains a primary bottleneck in crystal-based structure determination. The National High-Throughput Crystallization Center at Hauptman-Woodward Medical Research Institute has focused on overcoming obstacles to crystallization through a combination of robotics-enabled high-throughput screening and advanced imaging to increase the success of finding crystallization conditions. This paper will describe the lessons learned from over 20 years of operation of our high-throughput crystallization services. The current experimental pipelines, instrumentation, imaging capabilities and software for image viewing and crystal scoring are detailed. New developments in the field and opportunities for further improvements in biomolecular crystallization are reflected on. Crystallization ; Bottleneck ; Throughput ; Computer science ; Pipeline (software) ; Software ; Nanotechnology ; Materials science ; Engineering ; Embedded system ; Chemical engineering Enzyme Structure and Function ; Protein Structure and Dynamics ; Metabolomics and Mass Spectrometry Studies
Authors: A. Jabłoński | Shigeo Tanuma | C. J. Powell
Source: Surface and Interface Analysis (article, 2023-05-04)
Field: Materials Science — Surfaces, Coatings and Films

Calculations of electron inelastic mean free paths (IMFPs). XIV. Calculated IMFPs for LiF and Si <sub>3</sub> N <sub>4</sub> and development of an improved predictive IMFP formula We report inelastic mean free paths (IMFPs) of Si 3 N 4 and LiF for electron energies from 50 eV to 200 keV that were calculated from their optical energy‐loss functions using the relativistic full Penn algorithm including the correction of the bandgap effect in insulators. Our calculated IMFPs, designated as optical IMFPs, could be fitted to a modified form of the relativistic Bethe equation for inelastic scattering of electrons in matter from 50 eV to 200 keV. The root mean square (RMS) deviations in these fits were less than 1% for Si 3 N 4 and LiF. The IMFPs were also compared with the relativistic version of our predictive Tanuma–Powell–Penn (TPP‐2M) equation. We found that IMFPs calculated from the TPP‐2M equation are systematically larger than the optical IMFPs for both LiF and Si 3 N 4 . The RMS differences between IMFPs from the TPP‐2M equation and the optical IMFPs were 49.3% for LiF and 17.3% for Si 3 N 4 for energies between 50 eV and 200 keV. These RMS differences are much larger than those for most of the inorganic compounds in our previous IMFP calculations where the average RMS difference was 10.7% for 42 inorganic compounds. We also report the development of an improved predictive IMFP formula, which we designate as the JTP equation. This formula is a refinement of the TPP‐2M equation and is based on the recent IMFP calculations for 100 materials including the present IMFPs for Si 3 N 4 and LiF (41 elemental solids, 45 inorganic compounds, and 14 organic compounds) for 83 electron energies between 50 eV and 200 keV. Our predictive JTP equation gave satisfactory results in comparisons of optical IMFPs and IMFPs calculated from the JTP equation. The RMS difference between the 8300 optical IMFPs used for optimization and the IMFPs calculated from the JTP equation was 10.2%. This value is appreciably less than the RMS difference of 16.0% found in a similar comparison of the optical IMFPs and IMFPs from the TPP‐2M equation. Furthermore, IMFPs from the JTP equation were compared with measured IMFPs for energies between 50 eV and 200 keV for 16 elemental solids and 37 inorganic compounds. We found that the JTP equation gave satisfactory results that were comparable with previous comparisons of the optical IMFPs and measured IMFPs. We believe that the JTP equation will be applicable to a wider range of materials than the TPP‐2M equation. Electron ; Inelastic mean free path ; Atomic physics ; Chemistry ; Physics ; Mean free path ; Nuclear physics Electron and X-Ray Spectroscopy Techniques ; Semiconductor materials and devices ; X-ray Spectroscopy and Fluorescence Analysis
Authors: Caio Bezerra Souto Maior | Lavìnia Maria Mendes Araújo | Isis Didier Lins | Márcio das Chagas Moura | Enrique López Droguett
Source: IEEE Access (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Prognostics and Health Management of Rotating Machinery via Quantum Machine Learning Prognostics and Health Management (PHM) concerns predicting machines’ behavior to support maintenance decisions through failure modes diagnosis and prognosis. Diagnosis is broadly applied in the context of rotating machines’ state classification using several traditional Machine Learning (ML) and Deep Learning (DL) methods. Recently, Quantum Computing (QC), a new and expanding research field, has contributed to different purposes and contexts, such as optimization, artificial intelligence, simulation, cybersecurity, pharmaceutics, and the energy sector. Despite the current limitations in terms of hardware, QC has been studied as an alternative for improving models’ speed and computational efficiency. Specifically, this paper proposes a Quantum Machine Learning (QML) approach to diagnose rolling bearings, which are essential components in rotating machinery, based on vibration signals. We apply hybrid models involving the encoding and construction of parameterized quantum circuits (PQC) connected to a classical neural network, the Multi-Layer Perceptron (MLP). We consider combinations of the Variational Quantum Eigensolver (VQE) framework with rotation gates and different entanglement (two-qubits) gates (CNOT, CZ and iSWAP). For each PQC configuration, we assess the impact of the number of layers (1, 5 and 10). We use two databases of different complexity levels not previously explored with QML, namely CWRU and JNU, with 10 and 12 failure modes, respectivel. For CWRU, all QML models presented higher accuracy than the classical MLP. For JNU, all QML models were superior to classical MLP as well. These results suggest that, despite the current limitations of quantum environments, QML models are promising tools to be further investigated in PHM. Prognostics ; Computer science ; Health management system ; Quantum ; Physics ; Medicine ; Data mining Machine Learning in Materials Science ; Fault Detection and Control Systems
Authors: Yinjiang Jia | Yaoyao Shi | Jiaqi Luo | Hongmin Sun
Source: Sensors (article, 2023-01-29)
Field: Chemistry — Analytical Chemistry

Y–Net: Identification of Typical Diseases of Corn Leaves Using a 3D–2D Hybrid CNN Model Combined with a Hyperspectral Image Band Selection Module Corn diseases are one of the significant constraints to high-quality corn production, and accurate identification of corn diseases is of great importance for precise disease control. Corn anthracnose and brown spot are typical diseases of corn, and the early symptoms of the two diseases are similar, which can be easily misidentified by the naked eye. In this paper, to address the above problems, a three-dimensional-two-dimensional (3D-2D) hybrid convolutional neural network (CNN) model combining a band selection module is proposed based on hyperspectral image data, which combines band selection, attention mechanism, spatial-spectral feature extraction, and classification into a unified optimization process. The model first inputs hyperspectral images to both the band selection module and the attention mechanism module and then sums the outputs of the two modules as inputs to a 3D-2D hybrid CNN, resulting in a Y-shaped architecture named Y-Net. The results show that the spectral bands selected by the band selection module of Y-Net achieve more reliable classification performance than traditional feature selection methods. Y-Net obtained the best classification accuracy compared to support vector machines, one-dimensional (1D) CNNs, and two-dimensional (2D) CNNs. After the network pruned the trained Y-Net, the model size was reduced to one-third of the original size, and the accuracy rate reached 98.34%. The study results can provide new ideas and references for disease identification of corn and other crops. Hyperspectral imaging ; Artificial intelligence ; Convolutional neural network ; Pattern recognition (psychology) ; Selection (genetic algorithm) ; Feature selection ; Identification (biology) ; Computer science ; Support vector machine ; Feature extraction ; Biology ; Botany Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Remote-Sensing Image Classification
Authors: Leena R. Pade | Kaitlyn E. Stepler | Erika P. Portero | Kellen DeLaney | Péter Nemes
Source: Mass Spectrometry Reviews (review, 2023-01-16)
Field: Chemistry — Spectroscopy

Biological mass spectrometry enables spatiotemporal ‘omics: From tissues to cells to organelles Abstract Biological processes unfold across broad spatial and temporal dimensions, and measurement of the underlying molecular world is essential to their understanding. Interdisciplinary efforts advanced mass spectrometry (MS) into a tour de force for assessing virtually all levels of the molecular architecture, some in exquisite detection sensitivity and scalability in space‐time. In this review, we offer vignettes of milestones in technology innovations that ushered sample collection and processing, chemical separation, ionization, and 'omics analyses to progressively finer resolutions in the realms of tissue biopsies and limited cell populations, single cells, and subcellular organelles. Also highlighted are methodologies that empowered the acquisition and analysis of multidimensional MS data sets to reveal proteomes, peptidomes, and metabolomes in ever‐deepening coverage in these limited and dynamic specimens. In pursuit of richer knowledge of biological processes, we discuss efforts pioneering the integration of orthogonal approaches from molecular and functional studies, both within and beyond MS. With established and emerging community‐wide efforts ensuring scientific rigor and reproducibility, spatiotemporal MS emerged as an exciting and powerful resource to study biological systems in space‐time. Data science ; Chemistry ; Scalability ; Computational biology ; Nanotechnology ; Proteome ; Metabolomics ; Computer science ; Biology Mass Spectrometry Techniques and Applications ; Advanced Proteomics Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Ting An | Zheli Wang | Guanglin Li | Shuxiang Fan | Wenqian Huang | Dandan Duan | Chunjiang Zhao | Xi Tian | Chunwang Dong
Source: Food Chemistry X (article, 2023-05-22)
Field: Chemistry — Analytical Chemistry

Monitoring the major taste components during black tea fermentation using multielement fusion information in decision level Hitherto, the intelligent detection of black tea fermentation quality is still a thought-provoking problem because of one-side sample information and poor model performance. This study proposed a novel method for the prediction of major chemical components including total catechins, soluble sugar and caffeine using hyperspectral imaging technology and electrical properties. The multielement fusion information were used to establish quantitative prediction models. The performance of model using multielement fusion information was better than that of model using single information. Subsequently, the stacking combination model using fusion data combined with feature selection algorithms for evaluating the fermentation quality of black tea. Our proposed strategy achieved better performance than classical linear and nonlinear algorithms, with the correlation coefficient of the prediction set (Rp) for total catechins, soluble sugar and caffeine being 0.9978, 0.9973 and 0.9560, respectively. The results demonstrated that our proposed strategy could effectively evaluate the fermentation quality of black tea. Black tea ; Fermentation ; Sugar ; Fusion ; Artificial intelligence ; Caffeine ; Mathematics ; Computer science ; Set (abstract data type) ; Food science ; Chemistry ; Machine learning ; Biology Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Tea Polyphenols and Effects
Authors: Rong Li | Zhu Xiao | Zhou Li | Xiangpeng Meng | Xu Wang
Source: Materials (article, 2023-01-02)
Field: Materials Science — Materials Chemistry

Work Hardening Behavior and Microstructure Evolution of a Cu-Ti-Cr-Mg Alloy during Room Temperature and Cryogenic Rolling A Cu-1.79Ti-0.39Cr-0.1Mg (wt.%) alloy was prepared by a vacuum induction melting furnace in a high-purity argon atmosphere. The effects of room temperature rolling and cryogenic rolling on the microstructure, textures, and mechanical properties of the alloy were investigated by means of electron backscatter diffraction, transmission electron microscopy, and X-ray diffraction. The results show that the hardness of the cryogenically rolled alloy is 18-30 HV higher than that of the room temperature rolled alloy at any tested rolling reduction. The yield strength and tensile strength of the alloy cryogenically rolled by 90% reduction are 723 MPa and 796 MPa, respectively. With the increase of rolling reduction, the orientation density of the Cube texture decreases, while the Brass texture increases. The Brass texture is preferred especially during the cryogenic rolling, suggesting that the cross-slip is inhibited at the cryogenic temperature. The dislocation densities of Cu-Ti-Cr-Mg alloy increase significantly during the deformation, finally reaching 23.03 × 10<sup>-14</sup> m<sup>-2</sup> and 29.98 × 10<sup>-14</sup> m<sup>-2</sup> after a 90% reduction for the room temperature rolled and cryogenically rolled alloys, respectively. This difference could be attributed to the impediment effect of cryogenic temperature on dynamic recovery and dynamic recrystallization. The cryogenic temperature promotes the formation of the dislocation and the nano-twins, leading to the improvement of the mechanical properties of the alloy. Microstructure ; Materials science ; Alloy ; Cryogenic temperature ; Metallurgy ; Hardening (computing) ; Work hardening ; Composite material Microstructure and mechanical properties ; Aluminum Alloy Microstructure Properties ; Aluminum Alloys Composites Properties