jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Muhammad Mohsin | Ali Mohsin | Waqas Qamar Zaman | Xiaojuan Zhu | Xihua Zhao | Zain Abbas | Muhammad Hammad Hussain | Ali Shan | Salim‐ur‐Rehman | Muhammad Asif Nawaz | Rabia Omer | Yingping Zhuang | Meijin Guo | Jiaofang Huang
Source: Green Synthesis and Catalysis (article, 2024-02-05)
Field: Materials Science — Materials Chemistry

Efficient toluene degradation using Bacillus subtilis biofilm-supported Mn–Ce/zeolite catalysts This study investigated a new approach for synthesizing Bacillus subtilis biofilm-supported Mn-Ce/Zeolite catalysts for the degradation of gaseous toluene. Four different metal oxide nano-catalysts (ZMn, ZMnCe-10%, ZMnCe-20%, and ZMnCe-30%) were synthesized with varying ratios of manganese (Mn) and cerium (Ce) on zeolite nanoparticles. TEM, SEM, XRD, BET, XPS, and EDX mapping were used to examine these four samples, as well as simple zeolite. Based on these analyses, the catalytic activity of the prepared samples ZMn, ZMnCe-10%, ZMnCe-20%, and ZMnCe-30% for the complete oxidation of toluene and toluene intermediate products were tested with Non-thermal plasma (NTP) technology in a dielectric barrier discharge (DBD) reactor. Among all, ZMnCe-20% showed the highest toluene degradation efficiency (89%) at low concentrations (200 ppm) and humidity (>50%). Later, highly efficient and hydrophobic nano-biocatalysts were prepared by combining B. subtilis biofilm wild-type (WT) and engineered B. subtilis biofilm EPS with ZMnCe-20% catalyst. EPS is the main component found in biofilm matrix and plays a key role in influencing properties such as biofilm stability, electron transfer, surface roughness and hydrophobicity. Compared to WT B. subtilis biofilm, EPS overexpressed B. subtilis biofilm showed stronger growth and development on ZMnCe-20% nanocatalyst. Moreover, the NTP system packed with ZMnCe-20%/biofilm (EPS+) nano-biocatalyst exhibited the highest toluene degradation activity (99%) with (83%) CO2 selectivity, (up to 50%) reduction in NOx concentration and complete ozone decomposition at (250 ppm) toluene concentrations and increased humidity (>90%). High-energy electrons generated in the NTP system break the C-H and C-C bond between the rings of the toluene molecule, forming several byproducts which are later reacted with active radical species such as O•, OH•, and O3 and further converted into final degradation products (CO2 and H2O). The results demonstrated successful biofilm development and growth on the ZMnCe-20% catalyst with advanced features such as superhydrophobicity, H2O resistance, improved surface roughness, and electron generation. In short, the study's approach combines bioengineering and material science to develop sustainable nano-biocatalysts for removing VOCs in industrial and environmental settings. Toluene ; Biofilm ; Catalysis ; Chemistry ; Bacillus subtilis ; Zeolite ; Chemical engineering ; Nuclear chemistry ; Degradation (telecommunications) ; Organic chemistry ; Bacteria Catalytic Processes in Materials Science ; Plasma Applications and Diagnostics ; Gas Sensing Nanomaterials and Sensors
Authors: Atif Mahmood | Ahmed Muneeb | Usman Saeed | Shahid Alam | Essam A. Al‐Ammar | Jee-Hyun Kang | Wail Al Zoubi | Dongwhi Choi
Source: Nanotechnology Reviews (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Aging assessment of silicone rubber materials under corona discharge accompanied by humidity and UV radiation Abstract High voltage (HV) outdoor insulators are subjected to both electrical and environmental stresses, which may lead to their failure. Among the causes, corona discharge, humidity and UV radiation are considered to be the most damaging factors. Efforts are therefore underway to investigate new materials for improving the performance of insulating systems. In this research work, silicone-based room temperature vulcanized samples filled with alumina trihydrate (ATH), silicon dioxide and magnesium hydroxide (MH) were prepared and exposed to AC corona discharge for a duration of 110 h. The electric discharge was also accompanied by UV radiation and two different humidity levels. Following aging of the test samples, diagnosis was conducted to assess their integrity. Measurements based on determining the static contact angle demonstrated the loss of hydrophobicity of all the materials, while hydrophobicity recovery phenomena revealed that ATH-doped materials demonstrated a comparatively higher increase in the contact angle than in samples filled with silicone dioxide (silica) and MH. Scanning electron microscopy analysis revealed deep cracks and block-like structures on their surfaces. Similarly, energy-dispersive X-ray analysis indicated the signs of surface oxidation of the aged samples. However, the data of elemental composition exhibited the loss of filler contents as well as that of carbon from the base matrix. The overall assessment showed that resistance to suppress aging is influenced by both the filler type and its concentration in the investigated composites. The ATH-filled composites exhibited outstanding performance when exposed to the rigors of corona discharge and other environmental stresses. This research contributes to materials science and HV engineering by addressing the development of composites for enhanced insulator performance, with future aspects lying in the utilization of nano-composites for advanced functionality and durability. Silicone rubber ; Materials science ; Corona discharge ; Composite material ; Radiation ; Humidity ; Ultraviolet radiation ; Corona (planetary geology) ; Silicone ; Chemistry ; Radiochemistry ; Optics ; Astrobiology ; Meteorology ; Electrical engineering ; Engineering ; Physics ; Voltage High voltage insulation and dielectric phenomena ; Advanced Sensor and Energy Harvesting Materials ; Dielectric materials and actuators
Authors: Mashhoor Kattali | Keerthana P. Mampett | Hamna Fathima Kodoor | Sreejesh Govindankutty Ponnenkunnathu | S. V. Soman | Debarshi Kar Mahapatra | Tomy Muringayil Joseph | Józef T. Haponiuk | Sabu Thomas
Source: Eng—Advances in Engineering (article, 2024-04-15)
Field: Materials Science — Materials Chemistry

Biofabrication of Silver Nanoparticles by Azadirachta indica Rhizosphere Bacteria with Enhanced Antibacterial Properties Microorganisms (MOs) are prominent in ecological functioning and balance. The rhizosphere is considered one of the most diverse ecosystems on Earth and serves as a breeding spot for many MOs. Rhizosphere microbial diversity changes according to plant species, genotype, and the nature of the soil. The current study reports the possible use of bacteria isolated from the rhizosphere of Azadirachta indica for synthesizing silver nanoparticles (AgNPs). The physicochemical characterization and antibacterial activity of these green synthesized AgNPs are also reported. The gene (16S rRNA) sequence of bacteria isolated from the rhizosphere showed a maximum similarity of 99.25% with Bacillus subtilis. After incubation, the colorless reaction mixture transformed to brown, which indicates the formation of AgNPs, and UV-vis spectral analysis also confirmed the biosynthesis of AgNPs. Compared to lower temperatures, the efficiency of AgNP synthesis was high at the higher temperature. The scanning electron microscope image demonstrated spherical-shaped AgNPs with sizes ranging from 18 to 21 nm. Energy-dispersive X-ray analysis established the elemental analysis of synthesized AgNPs. The synthesized AgNPs showed strong bactericidal properties against pathogenic bacteria Klebsiella pneumonia, Pseudomonas aeruginosa, Escherichia coli, and methicillin-resistant Staphylococcus aureus. Rhizosphere ; Silver nanoparticle ; Azadirachta ; Bacteria ; Bacillus subtilis ; Chemistry ; Antibacterial activity ; Microorganism ; Klebsiella pneumonia ; Nuclear chemistry ; Pathogenic bacteria ; Botany ; Biology ; Microbiology ; Staphylococcus aureus ; Nanoparticle ; Materials science ; Nanotechnology Nanoparticles: synthesis and applications ; Graphene and Nanomaterials Applications ; Medicinal Plants and Neuroprotection
Authors: Parambalath Narendran Ashitha | T. Bhavani Shanker | K. P. Meena | K. T. Varughese
Source: Journal of Applied Polymer Science (article, 2024-04-24)
Field: Materials Science — Materials Chemistry

Nano and micro co‐filled aluminum nitride/magnesium dihydrate—Silicone rubber composites for high voltage insulation Abstract Co‐filled composites of micro magnesium dihydrate (MDH) and hydrophobically treated nano aluminum nitride ( nH ‐AlN) fillers in high temperature vulcanizing silicone gum are studied for thermal and dielectric characteristics for use as an insulating material in high voltage applications. The study begins with description of the compounding protocol adopted, followed by spectroscopic and optical characterization. Fourier transform infrared spectroscopy (FTIR) and energy dispersive X‐ray spectroscopy (EDX) analysis are used to verify the presence of nH ‐AlN and MDH in the composites. Thermal analysis through thermogravimetric analysis revealed a stark enhancement in the onset of polymer degradation temperature, with an improvement of 208 °C achieved at 30 phr by weight loading level of MDH. Volume resistivity of 9.71 E14 Ω‐cm is obtained at 30 phr weight loading of MDH and 5 phr of nH ‐AlN. AC, +DC, and −DC dielectric strength improved by 1.09, 2.06, and 1.08 times for co‐filled composites at 30 phr MDH and 5 phr nH ‐AlN loading levels over unitary filled composite. Effect of dielectric polarization on dielectric constant and dissipation factor is studied at various frequencies. Limitations while conducting dry arc resistance test at standard voltage of 12.50 kV is discussed, with modification in dry arc resistance test voltage to 16.95 kV. The results are presented and discussed here. Materials science ; Composite material ; Silicone rubber ; Dielectric ; Vulcanization ; Thermogravimetric analysis ; Fourier transform infrared spectroscopy ; Dielectric loss ; Magnesium ; Dielectric spectroscopy ; Natural rubber ; Electrode ; Electrochemistry ; Metallurgy ; Chemistry ; Chemical engineering High voltage insulation and dielectric phenomena ; Dielectric materials and actuators ; Polymer Nanocomposite Synthesis and Irradiation
Authors: Yuwei Sun | Anqiang Lv | Zhiyuan Xie
Source: IET Science Measurement & Technology (article, 2024-01-30)
Field: Materials Science — Materials Chemistry

Analysis of electric field and partial discharge characteristics of cable joint stress cone dislocation defects Abstract The mechanisms governing electric field and partial discharge (PD) under stress cone dislocation remain unclear. This study employs the finite element method to explore the relationship between the electric field, externally applied voltage, and length of dislocation. Subsequently, the correlation between voltage and electric field is determined. The relationship between voltage and PD is established through PD tests conducted under stress cone dislocation conditions. By considering the influence of voltage, the association between the electric field and PD is established, revealing the mechanism of PD initiation by electric field distortion in joints. The findings suggest that the lifetime and reliability were increased by wrapping semi‐conductive self‐adhesive tape around the exposed cross‐linked polyethylene (XLPE) insulation layer. The electric field at a specific location is approximately proportional to the applied voltage. However, the slope is influenced by the composite insulation interface and the radial distance of the copper conductor. When crossing the interface between the XLPE insulation layer and the accessory insulation layer, the slope decreases significantly. The threshold for electric field PD is determined by examining the electric field distribution and the PD inception voltage during testing. When the voltage is sufficiently high, significant electric field distortions can occur at multiple points in the joint, potentially leading to concurrent PDs at several locations. Electric field ; Partial discharge ; Materials science ; Voltage ; Cross-linked polyethylene ; Composite material ; Stress (linguistics) ; High voltage ; Electrical engineering ; Dislocation ; Electric potential ; Polyethylene ; Physics ; Engineering High voltage insulation and dielectric phenomena ; Electrical Fault Detection and Protection ; Electrostatic Discharge in Electronics
Authors: Patrick L. Y. Tang | Alejandra Méndèz Romero | Remi A. Nout | Caroline van Rij | Cleo Slagter | Annemarie T. Swaak‐Kragten | Marion Smits | Esther A. H. Warnert
Source: European Radiology Experimental (article, 2024-10-30)
Field: Materials Science — Materials Chemistry

Amide proton transfer-weighted CEST MRI for radiotherapy target delineation of glioblastoma: a prospective pilot study Integration of APT-weighted imaging into target delineation for radiotherapy is feasible. The integration of APT-weighted imaging yields larger GTVs in glioblastoma. APT-weighted LD mapping may be more robust against motion artifacts than APT-weighted MTR<sub>asym</sub>. Nuclear medicine ; Radiation therapy ; Magnetic resonance imaging ; Medicine ; Neuroradiology ; Magnetization transfer ; Glioblastoma ; Radiology ; Neurology Lanthanide and Transition Metal Complexes ; Advanced MRI Techniques and Applications ; Electron Spin Resonance Studies
Authors: Li Wang | Qin Hu | Xing Xu | Lipeng Rao | Xingliang Jiang
Source: Polymers (article, 2024-10-26)
Field: Materials Science — Materials Chemistry

Temperature and Physicochemical Properties of Abnormal Heating Composite Insulators Abnormal heating will reduce the insulation performance of composite insulators or even cause insulator fracture, and the abnormal heating phenomenon is more serious under high-humidity conditions. Therefore, in this paper, the voltage withstand test of abnormal heating composite insulators caused by aging and damp sheath, decay-like core rod, and contamination were carried out under different ambient humidity. The heating and discharge of composite insulators were observed by infrared thermal imager and ultraviolet imager, and its temperature characteristics were analyzed from the aspects of heating range, heating shape, and temperature difference. In addition, in order to study the abnormal heating mechanism of composite insulators, the micro-morphology, chemical groups and dielectric properties of the silicon rubber of composite insulators with aging and damp sheath and the decay-like core rod were also measured. It is found that the temperature characteristics of the three types of abnormal heating composite insulators are different, and the temperature difference increases with the increase of humidity. The deterioration of silicone rubber and core rod material is the internal reason for the abnormal heating of composite insulators, and high-humidity conditions will exacerbate the heating phenomenon. Materials science ; Composite material ; Composite number ; Humidity ; Silicone rubber ; Insulator (electricity) ; Dielectric ; Thermal insulation ; Electric heating ; Natural rubber ; Core (optical fiber) ; Optoelectronics High voltage insulation and dielectric phenomena ; Smart Materials for Construction ; Thermal Analysis in Power Transmission
Authors: Olga V. Doroshenko | Mikhail V. Golub | О. Yu. Kremneva | Pavel S. Shcherban’ | Andrey S. Peklich | Roman Danilov | Ksenia Gasiyan | A. V. Ponomarev | Ilya N. Lagutin | Ilya A. Moroz | Victor K. Postovoy
Source: Agronomy (article, 2024-08-28)
Field: Chemistry — Analytical Chemistry

Automated Assessment of Wheat Leaf Disease Spore Concentration Using a Smart Microscopy Scanning System An advanced approach to the automated assessment of a microscopic slide containing spores is presented. The objective is to develop an intelligent system for the rapid and precise estimation of phytopathogenic spore concentration on microscopic slides, thereby enabling automated processing. The smart microscopy scanning system comprises an electronic microscope, a coordinate table, and software for the control of the coordinate table and image processing. The developed smart microscopy scanning system processes the entire microscope slide with multiple exposed strips, which are automatically determined based on the novel two-stage algorithm. The analysis of trained convolutional neural networks employed for the detection of spore phytopathogens demonstrates high precision and recall metrics. The system is capable of identifying and counting the number of spores of phytopathogenic fungi species Blumeria graminis, Puccinia striiformis, and Pyrenophora tritici-repentis on each exposed strip. A methodology for estimating the spore distribution on a microscopic slide is proposed, which involves calculating the average spore concentration density. Spore ; Microscopy ; Microscope ; Blumeria graminis ; Computer science ; Biological system ; Artificial intelligence ; Materials science ; Biology ; Optics ; Botany ; Physics Spectroscopy and Chemometric Analyses ; Plant Pathogens and Fungal Diseases ; Smart Agriculture and AI
Authors: João Victor Jales de Melo | George R. S. Lira | Edson Guedes da Costa | Pablo Bezerra Vilar | F. L. M. Andrade | Ana Marotti | André Irani Costa | Antonio Francisco Leite Neto | Almir Carlos dos Santos Júnior
Source: Energies (article, 2024-08-02)
Field: Materials Science — Materials Chemistry

Separation and Classification of Partial Discharge Sources in Substations This work proposes a methodology for noise removal, separation, and classification of partial discharges in electrical system assets. Partial discharge analysis is an essential method for fault detection and evaluation of the operational conditions of high-voltage equipment. However, it faces several limitations in field measurements due to interference from radio signals, television transmissions, WiFi, corona signals, and multiple sources of partial discharges. To address these challenges, we propose the development of a clustering model to identify partial discharge sources and a classification model to identify the types of discharges. New features extracted from pulses are introduced to model the clustering and classification of discharge sources. The methodology is tested in the laboratory with controlled partial discharge sources, and field tests are conducted in substations to assess its practical applicability. The results of laboratory tests achieved an accuracy of 85% in classifying discharge sources. Field tests were performed in a substation of the Eletrobras group, allowing the identification of at least three potentially defective current transformers. Partial discharge ; Transformer ; Cluster analysis ; Identification (biology) ; Computer science ; Source separation ; Engineering ; Voltage ; Data mining ; Electronic engineering ; Reliability engineering ; Artificial intelligence ; Electrical engineering High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Lightning and Electromagnetic Phenomena
Authors: Takuya Taniguchi
Source: Faraday Discussions (article, 2024-07-18)
Field: Materials Science — Materials Chemistry

Knowledge distillation of neural network potential for molecular crystals Organic molecular crystals exhibit various functions due to their diverse molecular structures and arrangements. Computational approaches are necessary to explore novel molecular crystals from the material space, but quantum chemical calculations are costly and time-consuming. Neural network potentials (NNPs), trained on vast amounts of data, have recently gained attention for their ability to perform energy calculations with accuracy comparable to quantum chemical methods at high speed. However, NNPs trained on datasets primarily consisting of inorganic crystals, such as the Materials Project, may introduce bias when applied to organic molecular crystals. This study investigates the strategies to improve the accuracy of a pre-trained NNP for organic molecular crystals by distilling knowledge from a teacher model. The most effective knowledge transfer was achieved when fine-tuning using only soft targets, <i>i.e.</i>, the teacher model's inference values. As the ratio of hard target loss increased, the efficiency of knowledge transfer decreased, leading to overfitting. As a proof of concept, the NNP created through knowledge distillation was used to predict elastic properties, resulting in improved accuracy compared to the pre-trained model. Artificial neural network ; Vacuum distillation ; Distillation ; Computer science ; Process engineering ; Biochemical engineering ; Artificial intelligence ; Biological system ; Chemistry ; Materials science ; Organic chemistry ; Engineering ; Biology Machine Learning in Materials Science ; Crystallography and molecular interactions ; Computational Drug Discovery Methods
Authors: Habibollah Safari | Mona Bavarian
Source: Systems and Control Transactions (article, 2024-07-09)
Field: Materials Science — Materials Chemistry

Enhancing Polymer Reaction Engineering Through the Power of Machine Learning Copolymers are commonplace in various industries. Nevertheless, fine-tuning their properties bears significant cost and effort. Hence, an ability to predict polymer properties a priori can significantly reduce costs and shorten the need for extensive experimentation. Given that the physical and chemical characteristics of copolymers are correlated with molecular arrangement and chain topology, understanding the reactivity ratios of monomers�which determine the copolymer composition and sequence distribution of monomers in a chain�is important in accelerating research and cutting R&amp;D costs. In this study, the prediction accuracy of two Artificial Neural Network (ANN) approaches, namely, Multi-layer Perceptron (MLP) and Graph Attention Network (GAT), are compared. The results highlight the potency and accuracy of the intrinsically interpretable ML approaches in predicting the molecular structures of copolymers. Our data indicates that even a well-regularized MLP cannot predict the reactivity ratio of copolymers as accurately as GAT. This is attributed to the compatibility of GAT with the data structure of molecules, which are graph-representative. Computer science ; Polymer ; Power (physics) ; Materials science ; Composite material ; Physics Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Yifan Cao | Killian Sheriff | Rodrigo Freitas
Source: arXiv (Cornell University) (preprint, 2024-01-12)
Field: Materials Science — Materials Chemistry

Capturing short-range order in high-entropy alloys with machine learning potentials Chemical short-range order (SRO) affects the distribution of elements throughout the solid-solution phase of metallic alloys, thereby modifying the background against which microstructural evolution occurs. Investigating such chemistry-microstructure relationships requires atomistic models that act at the appropriate length scales while capturing the intricacies of chemical bonds leading to SRO. Here we consider various approaches for the construction of training data sets for machine learning potentials (MLPs) for CrCoNi and evaluate their performance in capturing SRO and its effects on materials quantities of relevance for mechanical properties, such as stacking-fault energy and phase stability. It is demonstrated that energy accuracy on test sets often does not correlate with accuracy in capturing material properties, which is fundamental in enabling large-scale atomistic simulations of metallic alloys with high physical fidelity. Based on this analysis we systematically derive design principles for the rational construction of MLPs that capture SRO in the crystal and liquid phases of alloys. Stacking-fault energy ; Materials science ; Stability (learning theory) ; Stacking ; Microstructure ; Fidelity ; Range (aeronautics) ; High entropy alloys ; Statistical physics ; Computer science ; Artificial intelligence ; Machine learning ; Physics ; Metallurgy Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Ion-surface interactions and analysis