jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Osama E. Gouda | Mohamed M. F. Darwish | Karar Mahmoud | Matti Lehtonen | Tamer M. Elkhodragy
Source: IEEE Access (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Pollution Severity Monitoring of High Voltage Transmission Line Insulators Using Wireless Device Based on Leakage Current Bursts The present article describes a smart wireless online device for the severity monitoring of the contaminated insulators of high voltage transmission networks. Accordingly, the wireless developed monitor works by continuously sensing the magnitudes of the leakage current bursts and calculating its average root-mean-square (RMS) value at every second or minute as the monitor software is calibrated. Regarding, if the average of the leakage current RMS value is adjudicated by the monitor as corresponding to a probably significant scale of a power outage, the monitor transfers an alarm signal and sends a warning message to the maintenance crew members to take the action to wash the high voltage line insulators on time before the unexpected outage of the high voltage network has been occurring. The developed monitor contains the following main units: current transformer with burden resistor, Node MCU (ESP8266), solar power bank, cloud-based data storage, and smart device (Mobile or tablet). These units can be assembled to work without the need for a power source. The proposed monitor has the following merits if it is compared with the other monitoring devices: it enjoys little cost, easy of handling and calibration. It has a high degree of safety, it is an online system, and its design is simple. The developed monitor is tested in the Laboratory using insulators with different pollution layers’ conductivity and the findings show that the accuracy of the proposed monitor reached 91.66 % after carrying out 50 tests. Electrical engineering ; Wireless ; Computer science ; Voltage ; Resistor ; Transformer ; Microcontroller ; Real-time computing ; Engineering ; Telecommunications High voltage insulation and dielectric phenomena ; Electrical Fault Detection and Protection ; Power Line Inspection Robots
Authors: Sammar Fathy Elhabal | Hanan M. Elwy | Soha Osama Hassanin | Ahmed A. El‐Rashedy | Alaaeldin Ahmed Hamza | Mohammad Ahmad Khasawneh
Source: International Journal of Nanomedicine (article, 2022-12-01)
Field: Materials Science — Materials Chemistry

Biosynthesis and Characterization of Gold and Copper Nanoparticles from Salvadora persica Fruit Extracts and Their Biological Properties These AuNPs and CuNPs are particularly appealing in a variety of applications in the pharmaceutical and medicinal industries due to their economical and environmentally friendly production. Colloidal gold ; Zeta potential ; Nanoparticle ; Fourier transform infrared spectroscopy ; Biomolecule ; Antimicrobial ; Reducing agent ; Nuclear chemistry ; Materials science ; Antibacterial activity ; Copper ; Chemistry ; Nanotechnology ; Bacteria ; Chemical engineering ; Organic chemistry ; Biology Nanoparticles: synthesis and applications ; Medicinal Plants and Neuroprotection ; Phytochemicals and Medicinal Plants
Authors: Hui Hu | Bin Qi | Yufan Zhao | Xiaoju Zhang | Yue Wang | Xinning Huang
Source: Frontiers in Physics (article, 2022-09-14)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A graphene-based THz metasurface sensor with air-spaced structure Owing to the unique electromagnetic response ability, electromagnetic metasurface have potential applications in medical, imaging, sensing, and other fields. In this paper, a graphene-based THz metasurface sensor with an air spacer layer is designed by combining the advantages of the air spacer structure and the dynamic tunability of graphene materials. The proposed metasurface sensor consists of six layers, which are silicon dioxide (SiO 2 ) substrate, metal reflector, air layer, graphene metasurface etched with microstructure, ion-gel layer and silicon dioxide dielectric layer from bottom to top. A comprehensive study of the absorption properties and sensing performance are carried out which is simulated and analyzed with the help of CST Microwave Studio software. The calculation results show that the two obvious resonance absorption peaks located at 0.95 THz and 1.53 THz with absorption of 94.9% and 79%, respectively, and there is a good linear relationship between the absorption peak and dielectric parameters of analyte. The frequency shift sensitivity of the two resonance peaks M1 and M2 can reach 450 GHz/RIU and 717 GHz/RIU, respectively. By changing the thickness of the air layer, when the two resonance peaks M1 and M2 reach the maximum absorption at <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m1"><mml:mrow><mml:msub><mml:mi>h</mml:mi><mml:mn mathvariant="italic">2</mml:mn></mml:msub><mml:mo>=</mml:mo><mml:mn mathvariant="italic">140</mml:mn><mml:mtext> </mml:mtext><mml:mi>μ</mml:mi><mml:mi mathvariant="normal">m</mml:mi></mml:mrow></mml:math> and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m2"><mml:mrow><mml:msub><mml:mi>h</mml:mi><mml:mn mathvariant="italic">2</mml:mn></mml:msub><mml:mo>=</mml:mo><mml:mn mathvariant="italic">280</mml:mn><mml:mtext> </mml:mtext><mml:mi>μ</mml:mi><mml:mi mathvariant="normal">m</mml:mi></mml:mrow></mml:math> , respectively, the frequency shift sensitivity is still as high as close to 450 GHz/RIU. The influence of structure parameters and incident angle on the absorption spectrum shows that the proposed structure has good stability and reliability. The proposed graphene-based absorption sensor has good biocompatibility, broadening the application range of terahertz functional devices. Graphene ; Materials science ; Terahertz radiation ; Absorption (acoustics) ; Dielectric ; Permittivity ; Silicon ; Silicon dioxide ; Resonance (particle physics) ; Analytical Chemistry (journal) ; Optoelectronics ; Optics ; Nanotechnology ; Chemistry ; Physics ; Composite material ; Atomic physics Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Advanced Antenna and Metasurface Technologies
Authors: Yuchen Liu | Henry Wu | Tam Mayeshiba | Benjamin Afflerbach | Ryan Jacobs | Josh Perry | Jerit George | Josh Cordell | Jinyu Xia | Hao Yuan | Aren Lorenson | Haotian Wu | Matthew Parker | Fenil Doshi | Alexander Politowicz | Linda Xiao | Dane Morgan | Peter Wells | Nathan Almirall | Takuya Yamamoto | G.R. Odette
Source: npj Computational Materials (article, 2022-04-27)
Field: Materials Science — Materials Chemistry

Machine learning predictions of irradiation embrittlement in reactor pressure vessel steels Abstract Irradiation increases the yield stress and embrittles light water reactor (LWR) pressure vessel steels. In this study, we demonstrate some of the potential benefits and risks of using machine learning models to predict irradiation hardening extrapolated to low flux, high fluence, extended life conditions. The machine learning training data included the Irradiation Variable for lower flux irradiations up to an intermediate fluence, plus the Belgian Reactor 2 and Advanced Test Reactor 1 for very high flux irradiations, up to very high fluence. Notably, the machine learning model predictions for the high fluence, intermediate flux Advanced Test Reactor 2 irradiations are superior to extrapolations of existing hardening models. The successful extrapolations showed that machine learning models are capable of capturing key intermediate flux effects at high fluence. Similar approaches, applied to expanded databases, could be used to predict hardening in LWRs under life-extension conditions. Fluence ; Embrittlement ; Irradiation ; Materials science ; Reactor pressure vessel ; Hardening (computing) ; Nuclear engineering ; Light-water reactor ; Neutron flux ; Pressure vessel ; Flux (metallurgy) ; Metallurgy ; Composite material ; Nuclear physics ; Engineering ; Physics ; Neutron Nuclear Materials and Properties ; Fusion materials and technologies ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Alexander Howarth | Jonathan M. Goodman
Source: Chemical Science (article, 2022-01-01)
Field: Chemistry — Spectroscopy

The DP5 probability, quantification and visualisation of structural uncertainty in single molecules Whenever a new molecule is made, a chemist will justify the proposed structure by analysing the NMR spectra. The widely-used DP4 algorithm will choose the best match from a series of possibilities, but draws no conclusions from a single candidate structure. Here we present the DP5 probability, a step-change in the quantification of molecular uncertainty: given one structure and one <sup>13</sup>C NMR spectra, DP5 gives the probability of the structure being correct. We show the DP5 probability can rapidly differentiate between structure proposals indistinguishable by NMR to an expert chemist. We also show in a number of challenging examples the DP5 probability may prevent incorrect structures being published and later reassigned. DP5 will prove extremely valuable in fields such as discovery-driven automated chemical synthesis and drug development. Alongside the DP4-AI package, DP5 can help guide synthetic chemists when resolving the most subtle structural uncertainty. The DP5 system is available at https://github.com/Goodman-lab/DP5. Molecule ; Visualization ; Chemistry ; Computer science ; Data mining ; Organic chemistry Molecular spectroscopy and chirality ; Computational Drug Discovery Methods ; Analytical Chemistry and Chromatography
Authors: Yifei Cao | Peisen Yuan | Huanliang Xu | José Fernán Martínez-Ortega | Jiarui Feng | Zhaoyu Zhai
Source: Frontiers in Plant Science (article, 2022-07-13)
Field: Chemistry — Analytical Chemistry

Detecting Asymptomatic Infections of Rice Bacterial Leaf Blight Using Hyperspectral Imaging and 3-Dimensional Convolutional Neural Network With Spectral Dilated Convolution Rice is one of the most important food crops for human beings. Its total production ranks third in the grain crop output. Bacterial Leaf Blight (BLB), as one of the three major diseases of rice, occurs every year, posing a huge threat to rice production and safety. There is an asymptomatic period between the infection and the onset periods, and BLB will spread rapidly and widely under suitable conditions. Therefore, accurate detection of early asymptomatic BLB is very necessary. The purpose of this study was to test the feasibility of detecting early asymptomatic infection of the rice BLB disease based on hyperspectral imaging and Spectral Dilated Convolution 3-Dimensional Convolutional Neural Network (SDC-3DCNN). First, hyperspectral images were obtained from rice leaves infected with the BLB disease at the tillering stage. The spectrum was smoothed by the Savitzky-Golay (SG) method, and the wavelength between 450 and 950 nm was intercepted for analysis. Then Principal Component Analysis (PCA) and Random Forest (RF) were used to extract the feature information from the original spectra as inputs. The overall performance of the SDC-3DCNN model with different numbers of input features and different spectral dilated ratios was evaluated. Lastly, the saliency map visualization was used to explain the sensitivity of individual wavelengths. The results showed that the performance of the SDC-3DCNN model reached an accuracy of 95.4427% when the number of inputs is 50 characteristic wavelengths (extracted by RF) and the dilated ratio is set at 5. The saliency-sensitive wavelengths were identified in the range from 530 to 570 nm, which overlaps with the important wavelengths extracted by RF. According to our findings, combining hyperspectral imaging and deep learning can be a reliable approach for identifying early asymptomatic infection of the rice BLB disease, providing sufficient support for early warning and rice disease prevention. Hyperspectral imaging ; Multispectral image ; Asymptomatic ; Convolutional neural network ; Convolution (computer science) ; Decorrelation ; Pattern recognition (psychology) ; Crop ; Principal component analysis ; Mathematics ; Medicine ; Artificial intelligence ; Computer science ; Remote sensing ; Agronomy ; Biology ; Artificial neural network ; Statistics ; Pathology ; Geography Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Remote Sensing in Agriculture
Authors: Priyanka Reddy | Kathryn M. Guthridge | Joe Panozzo | Emma J. Ludlow | Germán Spangenberg | Simone Rochfort
Source: Sensors (review, 2022-03-03)
Field: Chemistry — Analytical Chemistry

Near-Infrared Hyperspectral Imaging Pipelines for Pasture Seed Quality Evaluation: An Overview Near-infrared (800-2500 nm; NIR) spectroscopy coupled to hyperspectral imaging (NIR-HSI) has greatly enhanced its capability and thus widened its application and use across various industries. This non-destructive technique that is sensitive to both physical and chemical attributes of virtually any material can be used for both qualitative and quantitative analyses. This review describes the advancement of NIR to NIR-HSI in agricultural applications with a focus on seed quality features for agronomically important seeds. NIR-HSI seed phenotyping, describing sample sizes used for building high-accuracy calibration and prediction models for full or selected wavelengths of the NIR region, is explored. The molecular interpretation of absorbance bands in the NIR region is difficult; hence, this review offers important NIR absorbance band assignments that have been reported in literature. Opportunities for NIR-HSI seed phenotyping in forage grass seed are described and a step-by-step data-acquisition and analysis pipeline for the determination of seed quality in perennial ryegrass seeds is also presented. Hyperspectral imaging ; Near-infrared spectroscopy ; Absorbance ; Remote sensing ; Environmental science ; Calibration ; Computer science ; Materials science ; Mathematics ; Chemistry ; Optics ; Geography ; Chromatography Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Leaf Properties and Growth Measurement
Authors: Ke-Jun Fan | Wen‐Hao Su
Source: Biosensors (review, 2022-01-28)
Field: Chemistry — Analytical Chemistry

Applications of Fluorescence Spectroscopy, RGB- and MultiSpectral Imaging for Quality Determinations of White Meat: A Review Fluorescence spectroscopy, color imaging and multispectral imaging (MSI) have emerged as effective analytical methods for the non-destructive detection of quality attributes of various white meat products such as fish, shrimp, chicken, duck and goose. Based on machine learning and convolutional neural network, these techniques can not only be used to determine the freshness and category of white meat through imaging and analysis, but can also be used to detect various harmful substances in meat products to prevent stale and spoiled meat from entering the market and causing harm to consumer health and even the ecosystem. The development of quality inspection systems based on such techniques to measure and classify white meat quality parameters will help improve the productivity and economic efficiency of the meat industry, as well as the health of consumers. Herein, a comprehensive review and discussion of the literature on fluorescence spectroscopy, color imaging and MSI is presented. The principles of these three techniques, the quality analysis models selected and the research results of non-destructive determinations of white meat quality over the last decade or so are analyzed and summarized. The review is conducted in this highly practical research field in order to provide information for future research directions. The conclusions detail how these efficient and convenient imaging and analytical techniques can be used for non-destructive quality evaluation of white meat in the laboratory and in industry. Multispectral image ; Quality (philosophy) ; RGB color model ; Computer science ; Meat packing industry ; Digital imaging ; Imaging spectroscopy ; Artificial intelligence ; Environmental science ; Image processing ; Digital image ; Food science ; Hyperspectral imaging ; Biology Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Meat and Animal Product Quality
Authors: Elizabeth Bello | Yutao Chen | Marianne Alleyne
Source: Insects (review, 2022-12-31)
Field: Materials Science — Surfaces, Coatings and Films

Staying Dry and Clean: An Insect’s Guide to Hydrophobicity Insects demonstrate a wide diversity of microscopic cuticular and extra-cuticular features. These features often produce multifunctional surfaces which are greatly desired in engineering and material science fields. Among these functionalities, hydrophobicity is of particular interest and has gained recent attention as it often results in other properties such as self-cleaning, anti-biofouling, and anti-corrosion. We reviewed the historical and contemporary scientific literature to create an extensive review of known hydrophobic and superhydrophobic structures in insects. We found that numerous insects across at least fourteen taxonomic orders possess a wide variety of cuticular surface chemicals and physical structures that promote hydrophobicity. We discuss a few bioinspired design examples of how insects have already inspired new technologies. Moving forward, the use of a bioinspiration framework will help us gain insight into how and why these systems work in nature. Undoubtedly, our fundamental understanding of the physical and chemical principles that result in functional insect surfaces will continue to facilitate the design and production of novel materials. Biology ; Biofouling ; Insect ; Nanotechnology ; Variety (cybernetics) ; Diversity (politics) ; Biochemical engineering ; Ecology ; Computer science ; Artificial intelligence ; Engineering ; Materials science Surface Modification and Superhydrophobicity ; Adhesion, Friction, and Surface Interactions ; Insect and Arachnid Ecology and Behavior
Authors: Stephan Freitag | Michael Sulyok | Natasha Logan | Christopher T. Elliott | Rudolf Krska
Source: Comprehensive Reviews in Food Science and Food Safety (review, 2022-10-10)
Field: Chemistry — Analytical Chemistry

The potential and applicability of infrared spectroscopic methods for the rapid screening and routine analysis of mycotoxins in food crops Infrared (IR) spectroscopy is increasingly being used to analyze food crops for quality and safety purposes in a rapid, nondestructive, and eco-friendly manner. The lack of sensitivity and the overlapping absorption characteristics of major sample matrix components, however, often prevent the direct determination of food contaminants at trace levels. By measuring fungal-induced matrix changes with near IR and mid IR spectroscopy as well as hyperspectral imaging, the indirect determination of mycotoxins in food crops has been realized. Recent studies underline that such IR spectroscopic platforms have great potential for the rapid analysis of mycotoxins along the food and feed supply chain. However, there are no published reports on the validation of IR methods according to official regulations, and those publications that demonstrate their applicability in a routine analytical set-up are scarce. Therefore, the purpose of this review is to discuss the current state-of-the-art and the potential of IR spectroscopic methods for the rapid determination of mycotoxins in food crops. The study critically reflects on the applicability and limitations of IR spectroscopy in routine analysis and provides guidance to non-spectroscopists from the food and feed sector considering implementation of IR spectroscopy for rapid mycotoxin screening. Finally, an outlook on trends, possible fields of applications, and different ways of implementation in the food and feed safety area are discussed. Mycotoxin ; Environmental science ; Biochemical engineering ; Food supply ; Food quality ; Food safety ; Computer science ; Biotechnology ; Chemistry ; Food science ; Biology ; Engineering ; Agricultural science Spectroscopy and Chemometric Analyses ; Mycotoxins in Agriculture and Food ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Zi‐Lan Deng | Zhiqiang Wang | Fengjun Li | Meng-Xia Hu | Xiangping Li
Source: Nanophotonics (review, 2022-01-25)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Multi‐freedom metasurface empowered vectorial holography Optical holography capable of the complete recording and reconstruction of light's wavefront, plays significant roles on interferometry, microscopy, imaging, data storage, and three-dimensional displaying. Conventional holography treats light as scalar field with only phase and intensity dimensions, leaving the polarization information entirely neglected. Benefiting from the multiple degrees of freedom (DOFs) for optical field manipulation provided by the metasurface, vectorial holography with further versatile control in both polarization states and spatial distributions, greatly extended the scope of holography. As full vectorial nature of light field has been considered, the information carried out by light has dramatically increased, promising for novel photonic applications with high performance and multifarious functionalities. This review will focus on recent advances on vectorial holography empowered by multiple DOFs metasurfaces. Interleaved multi-atom approach is first introduced to construct vectorial holograms with spatially discrete polarization distributions, followed by the versatile vectorial holograms with continuous polarizations that are designed usually by modified iterative algorithms. We next discuss advances with further spectral response, leading to vivid full-color vectorial holography; and the combination between the far-field vectorial wavefront shaping enabled by vectorial holography and the near-field nano-printing functionalities by further exploiting local polarization and structure color responses of the meta-atom. The development of vectorial holography provides new avenues for compact multi-functional photonic devices, potentially useful in optical encryption, anticounterfeiting, and data storage applications. Holography ; Wavefront ; Optics ; Polarization (electrochemistry) ; Spatial light modulator ; Photonics ; Computer science ; Physics ; Light field Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Orbital Angular Momentum in Optics
Authors: A. Shcherbakov | Peyton J. Spreacker | Aurelio J. Dregni | Katherine A. Henzler‐Wildman | Mei Hong
Source: Nature Communications (article, 2022-02-18)
Field: Chemistry — Spectroscopy

High-pH structure of EmrE reveals the mechanism of proton-coupled substrate transport The homo-dimeric bacterial membrane protein EmrE effluxes polyaromatic cationic substrates in a proton-coupled manner to cause multidrug resistance. We recently determined the structure of substrate-bound EmrE in phospholipid bilayers by measuring hundreds of protein-ligand H<sup>N</sup>-F distances for a fluorinated substrate, 4-fluoro-tetraphenylphosphonium (F<sub>4</sub>-TPP<sup>+</sup>), using solid-state NMR. This structure was solved at low pH where one of the two proton-binding Glu14 residues is protonated. Here, to understand how substrate transport depends on pH, we determine the structure of the EmrE-TPP complex at high pH, where both Glu14 residues are deprotonated. The high-pH complex exhibits an elongated and hydrated binding pocket in which the substrate is similarly exposed to the two sides of the membrane. In contrast, the low-pH complex asymmetrically exposes the substrate to one side of the membrane. These pH-dependent EmrE conformations provide detailed insights into the alternating-access model, and suggest that the high-pH conformation may facilitate proton binding in the presence of the substrate, thus accelerating the conformational change of EmrE to export the substrate. Protonation ; Substrate (aquarium) ; Deprotonation ; Chemistry ; Crystallography ; Proton ; Membrane ; Conformational change ; Ligand (biochemistry) ; Protein structure ; Stereochemistry ; Biophysics ; Biochemistry ; Ion ; Biology ; Organic chemistry Advanced NMR Techniques and Applications ; Bacterial Genetics and Biotechnology ; DNA and Nucleic Acid Chemistry