jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Tulsi Ojha | Susmita Limbu | Prakash M. Shrestha | Suresh Prasad Gupta | Krishna Bahadur
Source: Himalayan Journal of Science and Technology (article, 2023-12-31)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Comparative Computational Study on Molecular Structure, Electronic and Vibrational Analysis of Vinyl Bromide based on HF and DFT Approach In this study, we have used the Hartree-Fock and Density Functional Theory method of calculation and compared the equilibrium configuration, electronic and vibrational mode of Vinyl Bromide molecule. The molecule is geometrically optimized initially by using 6-31G basis set with B3LYP functional and then bond angles, bond lengths, dihedral angles and IR spectra are compared respectively. Various groups of atoms in Vinyl Bromide molecule by DFT has more accurate bond length, bond angle values rather than by HF computation when comparing with the experimental values. The ground state energies are found at angle 10° or 180° or 360° using HF and DFT method of calculation for the H4-C1-C2-Br6 position. Values of the carbon-hydrogen, carbon-carbon and carbon-bromine bond lengths and bond angles for optimization state of C2H3Br molecule using Hartree-Fock and Density Functional Theory methods with respect to the basis set 6-31G have been analyzed. The C−H in-plane bending vibration and C−H out-of-plane bending vibrations occur in the region 1400–1050 cm-1 and 1000–675 cm-1 respectively. The electronic properties, such as Highest Occupied Molecular Orbital and Lowest Unoccupied Molecular Orbital energies are performed by HF and DFT approach and the difference in Highest Occupied Molecular Orbital and Lowest Unoccupied Molecular Orbital energy gap for HF and DFT method are 14.0847 eV and 6.8994 eV respectively Bond length ; Density functional theory ; Basis set ; Molecular geometry ; Vinyl bromide ; Chemistry ; Dihedral angle ; Molecular orbital ; Molecular vibration ; Molecule ; Atomic physics ; Computational chemistry ; Molecular physics ; Hydrogen bond ; Physics Nonlinear Optical Materials Research ; Inorganic and Organometallic Chemistry ; Free Radicals and Antioxidants
Authors: Björn-Ivo Bachmann | Martin Müller | Dominik Britz | Thorsten Staudt | Frank Mücklich
Source: Preprints.org (preprint, 2023-07-10)
Field: Materials Science — Materials Chemistry

Reproducible Quantification of the Microstructure of Complex Quenched and Quenched and Tempered Steels using Modern Methods of Machine Learning Current conventional methods of evaluating microstructures are characterized by a high degree of subjectivity and a lack of reproducibility. Modern machine learning (ML) approaches have already shown great potential in overcoming these challenges. Once trained with representative data in combination with an objective ground truth, the ML model is able to perform a task properly in a reproducible and automated manner. However, in highly complex use cases, it is often not possible to create a definite ground truth. This study addresses this problem using the underlying showcase of microstructures of highly complex quenched and quenched and tempered (Q/QT) steels. A patch-wise classification approach combined with a sliding window technique provides a solution for segmenting entire microphotographs where pixel-wise segmentation is not applicable due to the problem of reproducibly creating unambiguous training masks. Using correlative microscopy, consisting of light optical microscope (LOM) and scanning electron microscope (SEM) micrographs as well as corresponding data from electron backscatter diffraction (EBSD), a training dataset of reference states that covers a wide range of microstructures was acquired in order to train accurate and robust ML models. Despite the enormous complexity associated with the steels treated here, classification accuracies of 88.8% in the case of LOM images and 93.7% for high-resolution SEM images were achieved. These high accuracies are close to super-human performance, especially in consideration of the reproducibility of the automated ML approaches compared to conventional methods based on subjective evaluations through experts. Microstructure ; Ground truth ; Segmentation ; Reproducibility ; Artificial intelligence ; Computer science ; Pixel ; Electron backscatter diffraction ; Materials science ; Pattern recognition (psychology) ; Microscope ; Optics ; Physics ; Mathematics ; Statistics ; Composite material Machine Learning in Materials Science ; Non-Destructive Testing Techniques ; Electron and X-Ray Spectroscopy Techniques
Authors: Yanming Zuo | Yibo Ying | Zhiyang Huang | Jiamen Shen | Xiaokun Li | Zhouguang Wang
Source: Brain‐X (article, 2023-09-01)
Field: Materials Science — Materials Chemistry

Reactive oxygen species targeted biomaterials for spinal cord injury therapy Abstract Spinal cord injuries (SCIs) often cause individuals to suffer from painful illnesses and debilitating disabilities. Excessive reactive oxygen species (ROS) generation in injured tissues hampers treatment effectiveness. Unfortunately, there is presently no established clinical remedy for addressing SCI, particularly the injuries related to ROS. However, the materials science and technology field has made remarkable progress, resulting in the development of a wide range of biomaterials with unique properties for regulating ROS. This review aims to summarize the latest advancements in ROS‐targeted biomaterials designed specifically for the treatment of SCIs. Key scientific challenges in the evolution of ROS‐targeted neuroprotection strategies are also discussed. We anticipate that this comprehensive summary will be valuable to new researchers and highlight specific future avenues of research, contributing to the further advancement of ROS‐targeted biomaterials for SCI treatment. Spinal cord injury ; Medicine ; Reactive oxygen species ; Neuroprotection ; Intensive care medicine ; Bioinformatics ; Neuroscience ; Nanotechnology ; Spinal cord ; Biology ; Pharmacology ; Materials science Advanced Nanomaterials in Catalysis ; Nanoplatforms for cancer theranostics ; Spinal Cord Injury Research
Authors: Umber Cheema
Source: Journal of Tissue Engineering (review, 2023-01-01)
Field: Materials Science — Biomaterials

Position Paper Progress in the development of biomimetic engineered human tissues Tissue engineering (TE) is the multi-disciplinary approach to building 3D human tissue equivalents in the laboratory. The advancement of medical sciences and allied scientific disciplines have aspired to engineer human tissues for three decades. To date there is limited use of TE tissues/organs as replacement body parts in humans. This position paper outlines advances in engineering of specific tissues and organs with tissue-specific challenges. This paper outlines the technologies most successful for engineering tissues and key areas of advancement. Tissue engineering ; Position (finance) ; Biomedical technology ; Engineering ethics ; Engineering ; Biomedical engineering ; Mechanical engineering ; Business Electrospun Nanofibers in Biomedical Applications ; Tissue Engineering and Regenerative Medicine ; 3D Printing in Biomedical Research
Authors: Omneya Attallah
Source: ISPRS annals of the photogrammetry, remote sensing and spatial information sciences (article, 2023-12-05)
Field: Chemistry — Analytical Chemistry

DEEP LEARNING-BASED MODEL FOR PADDY DISEASES CLASSIFICATION BY THERMAL INFRARED SENSOR: AN APPLICATION FOR PRECISION AGRICULTURE Abstract. The rice plant is an extremely valuable food crop worldwide. Paddy diseases not only reduce the cultivation of rice but most significantly, they contribute to environmental damage. The identification of paddy diseases before the onset of any visible signs has gained consideration with the development of deep learning (DL) and thermal infrared sensors. According to previous investigations, certain internal alterations in the paddy occur before signs of the infection become apparent. Such modifications couldn't be seen by exterior visible light sensors. On the other hand, thermal infrared sensors may be able to detect these variations, which will aid in predicting illness at earlier phases. However, there are few research articles regarding this topic. This study suggests a DL-based model for identifying paddy diseases from thermal images. The proposed DL-based model, in contrast to earlier approaches for classifying plant diseases, uses three convolutional neural networks (CNNs) with distinctive configurations. In addition, it makes use of discrete wavelet transform (DWT) to give a time-frequency illustration of the spatial deep features gathered via the three CNNs to develop the classification models instead of relying on solely spatial data like current models. Furthermore, it merges the spatial-time-frequency features of the three CNNs and uses a feature selection method based on Relief-F to choose the most beneficial attributes and reduce the dimensionality of the feature space. The outcomes of the proposed DL-based model show that spatial-time-frequency demonstrations are preferable to spatial data. The results additionally demonstrate that integrating high-level features from various CNNs can improve classification performance reaching an accuracy of 96.5% using a cubic support vector machine (SVM) classifier. Furthermore, the findings attained in this study outperform current DL-based models for plant disease classification. Artificial intelligence ; Convolutional neural network ; Computer science ; Pattern recognition (psychology) ; Feature (linguistics) ; Deep learning ; Identification (biology) ; Spatial analysis ; Curse of dimensionality ; Dimensionality reduction ; Machine learning ; Remote sensing ; Geography Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Advanced Chemical Sensor Technologies
Authors: Ali Talib Shomran | Batool Mardan Faisal | Emad Kamil Hussein | Thiago F. Santos | Fátima Kies
Source: Eastern-European Journal of Enterprise Technologies (article, 2023-02-28)
Field: Materials Science — Polymers and Plastics

Identifying some regularities of the fatigue behavior of the reinforced carbon-fiber with Al2O3 nanoparticles composite structure of the prosthesis foot In this research Carbon-Fiber with AL2O3 Nanoparticles Composite Structure of the Prosthesis foot has been examined and analysed numerically explain the fatigue behaviour of the prosthesis. Nanoparticles made of AL2O3 were incorporated into the production process of the composite structure of the prosthesis foot in the appropriate manner. The life forecast, the damage indicator, and the Biaxiliray indexation were the three primary considerations that went into the process of studying the composite construction of the prosthesis foot. The life prediction was the most important factor. Experiments on the phenomena of fatigue have been carried out with the stress being entirely reversed as the variable in order to ensure that the findings are in keeping with the theory that was proposed by GoodMan. In order to develop an estimate for these characteristics, the dynamic load that was applied, which was 1000 N, was utilised. It used the dynamic load that was applied in order to produce an estimate for these characteristics so that we could better understand them. The results of the computational research showed that the life prediction could be increased to 106 cycles by applying a primary force of 1000 N. This was shown by the findings of the studyThis was demonstrated by the findings. While the same load application was being carried out, the Biaxiliray indexation attained a value of 0.99. In addition to the research that was done on the damage indicator, the numerical findings demonstrated that the damage can be seen after the initial 1000 cycles of stress have been applied. This was demonstrated both by the research that was done on the damage indicator as well as by the numerical findings Prosthesis ; Composite number ; Materials science ; Structural engineering ; Stress (linguistics) ; Composite material ; Fiber ; Computer science ; Engineering ; Artificial intelligence Conducting polymers and applications ; Muscle activation and electromyography studies ; Tribology and Wear Analysis
Authors: Ridwan Nahar | Ka Ming Law | Thomas Roden | Michael Zengel | Justin Lewis | Sujan Budhathoki | Riley Nold | Harshil Avlani | Babajide Akintunde | Naomi Derksen | Adam J. Hauser
Source: Data in Brief (article, 2023-12-19)
Field: Materials Science — Materials Chemistry

Dataset on density functional theory investigation of ternary Heusler alloys This paper contains data and results from Density Functional Theory (DFT) investigation of 423 distinct <i>X</i><sub>2</sub><i>YZ</i> ternary full Heusler alloys, where <i>X</i> and <i>Y</i> represent elements from the D-block of the periodic table and <i>Z</i> signifies element from main group. The study encompasses both "regular" and "inverse" Heusler phases of these alloys for a total of 846 potential materials. For each specific alloy and each phase, a range of information is provided including total energy, formation energy, lattice constant, total and site-specific magnetic moments, spin polarization as well as total and projected density of electronic states. The aim of creating this dataset is to provide fundamental theoretical insights into ternary <i>X</i><sub>2</sub><i>YZ</i> Heusler alloys for further theoretical and experimental analysis. Ternary operation ; Density functional theory ; Materials science ; Research article ; Computer science ; Chemistry ; Computational chemistry ; Library science Machine Learning in Materials Science ; Heusler alloys: electronic and magnetic properties ; Boron and Carbon Nanomaterials Research
Authors: Dandan Feng | Xiaojing Wang | Shike Zhao | Bai‐Peng Song | Guanjun Zhang
Source: Materials (article, 2023-07-17)
Field: Materials Science — Materials Chemistry

Study of TiO2 on the Voltage Holdoff Capacity of Cr/Mn-Doped Al2O3 Ceramic in Vacuum With the development of vacuum electronic devices toward high power, high frequency, and miniaturization, the voltage holdoff capacity of the insulation materials in devices has also been raised to a higher demand. Cr/Mn/Ti-doped Al<sub>2</sub>O<sub>3</sub> ceramics were prepared, and the bulk density, micromorphology, phase composition, resistivity, secondary electron emission coefficient, and surface flashover threshold in the vacuum of the Al<sub>2</sub>O<sub>3</sub> were characterized. The results show that the addition of TiO<sub>2</sub> to the Al<sub>2</sub>O<sub>3</sub> ceramic can promote the sintering of the ceramic. The Cr/Mn/Ti-doped Al<sub>2</sub>O<sub>3</sub> ceramic with a homogeneous microstructure can be obtained by an appropriate amount of TiO<sub>2</sub> addition. In the process of the heat treatment, the TiO<sub>2</sub> in the ceramics was reduced to a certain degree, which had an impact on the microstructure of the Al<sub>2</sub>O<sub>3</sub> ceramic. Adding a small amount of TiO<sub>2</sub> can improve the voltage holdoff performance in the vacuum. The value of the surface flashover threshold in the vacuum of the Cr/Mn/Ti-doped Al<sub>2</sub>O<sub>3</sub> ceramic containing 1 wt.% TiO<sub>2</sub> reached a value of 33 kV, which is 32% higher than that of the basic Al<sub>2</sub>O<sub>3</sub> ceramic. The preparation of Al<sub>2</sub>O<sub>3</sub> ceramics with a high voltage holdoff capacity in a vacuum provides fundamental technical support for the development of vacuum electronic devices. Materials science ; Ceramic ; Microstructure ; Sintering ; Doping ; Composite material ; Miniaturization ; Metallurgy ; Optoelectronics ; Nanotechnology High voltage insulation and dielectric phenomena ; Vacuum and Plasma Arcs ; Lightning and Electromagnetic Phenomena
Authors: Zihan Li | Qingchuan Zhang | Wei Dong | Yingjie Liu | Siwei Wei | Min Zuo
Source: Foods (article, 2023-04-18)
Field: Chemistry — Analytical Chemistry

FEDformer-Based Paddy Quality Assessment Model Affected by Toxin Change in Different Storage Environments The storage environment can significantly impact paddy quality, which is vital to human health. Changes in storage can cause growth of fungi that affects grain quality. This study analyzed grain storage monitoring data from over 20 regions and found that five factors are essential in predicting quality changes during storage. The study combined these factors with the FEDformer (Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting) model and k-medoids algorithm to construct a paddy quality change prediction model and a grading evaluation model, which showed the highest accuracy and lowest error in predicting quality changes during paddy storage. The results emphasize the need for monitoring and controlling the storage environment to preserve grain quality and ensure food safety. Reliability engineering ; Environmental science ; Computer science ; Engineering Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Hydrological Forecasting Using AI
Authors: Nadia Jasim Ghdeeb | Aseel Mustafa Abdul-Majeed | Asma Hadi Mohammed
Source: Iraqi Journal of Science (article, 2023-04-30)
Field: Materials Science — Materials Chemistry

Role of Extracted Nano-metal Oxides From Factory Wastes In Medical Applications Nanometal oxides were extracted from cement factories wastes and were employed to proliferate skin cancer cells using MTT (Methyl Thiazolyl Tetraolium) assay. MTT assay results for skin cancer cell line A375,for CaO NPs the best concentration was 200 μg/mL,The viability was reduced to 57.28% while the IC50 was (69.66) g/mL for A375 and normal cell WRL68 was significantly higher (231.2 g/mL). The cytotoxicity results of CaO: MgO: Fe2O3 NPs, at higher concentrations (200 and 400) μg/mL showed a significant difference. The IC50 was (106.4) μg/mL for A375and normal cell WRL68 was significantly higher (173.3) μg/ml CaO:MgO:Fe2O3 NPs. The nanometal oxides, calcium oxide (CaO) and the mixture (CaO:MgO: Fe2O3) were extracted from wastes of cement factory, and then they were oxidized at a temperature of 400C° for (5) hours. The structural and surface properties of the prepared oxides were characterized by X-ray diffraction patterns,FE-SEM and FTIR. The X-ray data reflected that all the oxides have a polycrystalline cubic structure, with a preferred orientation along (111) for calcium oxide, with a preferred orientation along (002) for calcium oxide: magnesium oxide: iron oxide. Calcium oxide ; Oxide ; Crystallite ; Nuclear chemistry ; MTT assay ; Metal ; Materials science ; Magnesium ; Calcium ; Chemistry ; Chemical engineering ; Metallurgy ; Cell ; Biochemistry Nanoparticles: synthesis and applications ; Graphene and Nanomaterials Applications ; Healthcare and Environmental Waste Management
Authors: Thomas G. Mayerhöfer | Isao Noda | Susanne Pahlow | Rainer Heintzmann | Jürgen Popp
Source: PLoS ONE (article, 2023-04-20)
Field: Chemistry — Analytical Chemistry

Correcting systematic errors by hybrid 2D correlation loss functions in nonlinear inverse modelling Recently a new family of loss functions called smart error sums has been suggested. These loss functions account for correlations within experimental data and force modeled data to obey these correlations. As a result, multiplicative systematic errors of experimental data can be revealed and corrected. The smart error sums are based on 2D correlation analysis which is a comparably recent methodology for analyzing spectroscopic data that has found broad application. In this contribution we mathematically generalize and break down this methodology and the smart error sums to uncover the mathematic roots and simplify it to craft a general tool beyond spectroscopic modelling. This reduction also allows a simplified discussion about limits and prospects of this new method including one of its potential future uses as a sophisticated loss function in deep learning. To support its deployment, the work includes computer code to allow reproduction of the basic results. Computer science ; Multiplicative function ; Algorithm ; Nonlinear system ; Inverse ; Function (biology) ; Code (set theory) ; Multiplicative inverse ; Experimental data ; Theoretical computer science ; Applied mathematics ; Mathematics ; Statistics ; Physics ; Biology ; Mathematical analysis Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Spectroscopy and Quantum Chemical Studies
Authors: Claudio Zeni | Andrea Anelli | Aldo Glielmo | Stefano de Gironcoli | Kevin Rossi
Source: Digital Discovery (article, 2023-11-15)
Field: Materials Science — Materials Chemistry

Divide-and-conquer potentials enable scalable and accurate predictions of forces and energies in atomistic systems A divide-and-conquer strategy - where small datasets are extracted from a larger one and utilised to train multiple models, which are then carefully combined for prediction - provides an avenue for accurate machine learning potentials. Divide and conquer algorithms ; Scalability ; Computer science ; Statistical physics ; Machine learning ; Artificial intelligence ; Algorithm ; Physics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Xiaoyang Zhu | Ke Zhu | Pingzeng Liu | Yan Zhang | Honghua Jiang
Source: Applied Sciences (article, 2023-09-07)
Field: Chemistry — Analytical Chemistry

A Special Robot for Precise Grading and Metering of Mushrooms Based on Yolov5 In order to accomplish accurate mushroom classification and measurement, it is necessary to optimize the existing classification algorithm and measurement devices, as well as to design a specific robot to improve classification accuracy and measurement efficiency. In order to achieve the above objectives, a research-level verification of mushroom grading using Yolov5 + OpenCV and a mushroom measuring system using a resistance strain gauge sensor was carried out. In the aspect of mushroom grading, a method based on the OpenCV visual library was used to identify the minimum quadrilateral outside the mushroom contour, allowing the size of the mushroom to be measured. The experiment’s results show that the method can assess target objects that are occluded with each other under different illumination conditions with 96% accuracy. In the measurement of mushrooms, the strain of the resistance is converted into an analog signal, and the weight of different grades of mushrooms is converted according to the linear relationship after processing by the detection circuit module. Through this method, the error range is successfully controlled within ±0.02 kg, which meets the requirements of accurate measurement of mushrooms. The results of field experiments show that the proposed accurate grading and measurement method of Lentinula edodes is effective and feasible, and provides technical support for the intelligent grading and measurement of Lentinula edodes in production units. Lentinula ; Artificial intelligence ; Mushroom ; Grading (engineering) ; Computer science ; Computer vision ; Strain gauge ; Pattern recognition (psychology) ; Engineering ; Electrical engineering Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Advanced Measurement and Detection Methods
Authors: Vancho Kocevski | M. Cooper | David A. Andersson
Source: Journal of Nuclear Materials (article, 2023-06-15)
Field: Materials Science — Materials Chemistry

Modeling of fission gas diffusion and release for doped Uranium dioxide ; Fission ; Diffusion ; Thermal diffusivity ; Xenon ; Fission products ; Irradiation ; Chemistry ; Cluster (spacecraft) ; Gaseous diffusion ; Materials science ; Neutron ; Radiochemistry ; Atomic physics ; Thermodynamics ; Nuclear physics ; Uranium ; Physical chemistry ; Physics Nuclear Materials and Properties ; Nuclear reactor physics and engineering ; Radioactive element chemistry and processing