jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Rachel C. Ollier | Thomas T. Nguyen | Hrithik Agarwal | Jeremy R. Phifer | Larissa Ferreira da Silva | Gabriel Gonçalves Nogueira | Ana Pereira Barbosa | Ryan T. Ley | Elizabeth J. O’Loughlin | Brett T. Rygelski | Spencer J. Sabatino | Andrew S. Paluch
Source: Processes (article, 2022-03-09)
Field: Chemistry — Spectroscopy

Predicting the Solubility of Nonelectrolyte Solids Using a Combination of Molecular Simulation with the Solubility Parameter Method MOSCED: Application to the Wastewater Contaminants Monuron, Diuron, Atrazine and Atenolol Methods to predict the equilibrium solubility of nonelectrolyte solids are indispensable for early-stage process development, design, and feasibility studies. Conventional analytic methods typically require reference data to regress parameters, which may not be available or limited for novel systems. Molecular simulation is a promising alternative, but is computationally intensive. Here, we demonstrate the ability to use a small number of molecular simulation free energy calculations to generate reference data to regress model parameters for the analytical MOSCED (modified separation of cohesive energy density) model. The result is an efficient analytical method to predict the equilibrium solubility of nonelectrolyte solids. The method is demonstrated for the wastewater contaminants monuron, diuron, atrazine and atenolol. Predictions for monuron, diuron and atrazine are in reasonable agreement with MOSCED parameters regressed using experimental solubility data. Predictions for atenolol are inferior, suggesting a potential limitation in the adopted molecular models, or the solvents selected to generate the necessary reference data. Atenolol ; Solubility ; Chemistry ; Hildebrand solubility parameter ; Thermodynamics ; Process engineering ; Physical chemistry ; Physics Analytical Chemistry and Chromatography ; Analytical chemistry methods development ; Pharmaceutical and Antibiotic Environmental Impacts
Authors: K.V.N. Rajesh | D. Lalitha Bhaskari
Source: International Journal of Computational Science and Engineering (article, 2022-01-01)
Field: Chemistry — Analytical Chemistry

Multi-class classification using convolution neural networks for plant leaf recognition of Ayurvedic plants Ayurveda is the traditional medicine system of India. The ingredients from which Ayurvedic medicines are made are mostly herbal and mineral in nature. Also, there are many herbal home remedies in India for general ailments. This knowledge has been passed down from generation to generation in large joint families. This knowledge is slowly fading away in the current generation of nuclear families. The current generation is unable to identify even locally available plants. The authors have come up with the idea of using convolution neural networks for solving this problem. In this solution, the images of leaves are used to identify the plant. This problem is a case of multi-class classification. A leaf image database is created and a neural network model is built using convolutional neural network (CNN). Keras deep learning framework with tensorflow as backend, is used for this purpose. The work presented in this paper is a part of larger research work in this area. This paper explains the developed CNN model and presents the results corresponding to six Ayurvedic leaves commonly available in and around the City of Visakhapatnam in the State of Andhra Pradesh. Class (philosophy) ; Convolution (computer science) ; Pattern recognition (psychology) ; Artificial intelligence ; Computer science ; Botany ; Traditional medicine ; Mathematics ; Biology ; Artificial neural network ; Medicine Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Leaf Properties and Growth Measurement
Authors: Prabhat Ranjan | Kumar Gaurav | Tanmoy Chakraborty
Source: Quantitative Biology (article, 2022-01-14)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Computational study of some potential inhibitors of COVID‐19: A DFT analysis Background There is an urgent demand of drug or therapy to control the COVID‐19. Until July 22, 2021 the worldwide total number of cases reported is more than 192 million and the total number of deaths reported is more than 4.12 million. Several countries have given emergency permission for use of repurposed drugs for the treatment of COVID‐19 patients. This report presents a computational analysis on repurposing drugs—tenofovir, bepotastine, epirubicin, epoprostenol, tirazavirin, aprepitant and valrubicin, which can be potential inhibitors of the COVID‐19. Method Density functional theory (DFT) technique is applied for computation of these repurposed drug. For geometry optimization, functional B3LYP/6‐311G (d, p) is selected within DFT framework. Results DFT based descriptors—highest occupied molecular orbital (HOMO)‐lowest unoccupied molecular orbital (LUMO) gap, molecular hardness, softness, electronegativity, electrophilicity index, nucleophilicity index and dipole moment of these species are computed. IR and Raman activities are also analysed and studied. The result shows that the HOMO‐LUMO gap of these species varies from 1.061 eV to 5.327 eV. Compound aprepitant with a HOMO‐LUMO gap of 1.419 eV shows the maximum intensity of IR (786.176 km mol ‒1 ) and Raman spectra (15036.702 a.u.). Conclusion Some potential inhibitors of COVID‐19 are studied by using DFT technique. This study shows that epirubicin is the most reactive compound whereas tenofovir is found to be the most stable. Further analysis and clinical trials of these compounds will provide more insight. HOMO/LUMO ; Density functional theory ; Electronegativity ; Computational chemistry ; Chemistry ; Molecule ; Organic chemistry Nonlinear Optical Materials Research ; Synthesis and biological activity ; Synthesis and Characterization of Heterocyclic Compounds
Authors: Manuel Sanguinetti | Lucianna Helene Santos | Juliette Dourron | Catalina Alamón | Juan Idiarte | Sotiris Amillis | Sergio Pantano | Ana Ramón
Source: International Journal of Molecular Sciences (article, 2022-12-16)
Field: Materials Science — Materials Chemistry

Substrate Recognition Properties from an Intermediate Structural State of the UreA Transporter Through a combination of comparative modeling, site-directed and classical random mutagenesis approaches, we previously identified critical residues for binding, recognition, and translocation of urea, and its inhibition by 2-thiourea and acetamide in the <i>Aspergillus nidulans</i> urea transporter, UreA. To deepen the structural characterization of UreA, we employed the artificial intelligence (AI) based AlphaFold2 (AF2) program. In this analysis, the resulting AF2 models lacked inward- and outward-facing cavities, suggesting a structural intermediate state of UreA. Moreover, the orientation of the W82, W84, N279, and T282 side chains showed a large variability, which in the case of W82 and W84, may operate as a gating mechanism in the ligand pathway. To test this hypothesis non-conservative and conservative substitutions of these amino acids were introduced, and binding and transport assessed for urea and its toxic analogue 2-thiourea, as well as binding of the structural analogue acetamide. As a result, residues W82, W84, N279, and T282 were implicated in substrate identification, selection, and translocation. Using molecular docking with Autodock Vina with flexible side chains, we corroborated the AF2 theoretical intermediate model, showing a remarkable correlation between docking scores and experimental affinities determined in wild-type and UreA mutants. The combination of AI-based modeling with classical docking, validated by comprehensive mutational analysis at the binding region, would suggest an unforeseen option to determine structural level details on a challenging family of proteins. Docking (animal) ; Acetamide ; AutoDock ; Chemistry ; Stereochemistry ; Binding site ; Urea ; Homology modeling ; Protein Data Bank (RCSB PDB) ; Mutagenesis ; Biochemistry ; Mutant ; In silico ; Enzyme ; Gene Enzyme Structure and Function ; Bacterial Genetics and Biotechnology ; Biochemical and Molecular Research
Authors: Chuanbao Liu | Changxin Wang | Junhong Chen | Yanjing Su | Lijie Qiao | Ji Zhou | Yang Bai
Source: Research (article, 2022-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Ultrasensitive Frequency Shifting of Dielectric Mie Resonance near Metallic Substrate Dielectric resonators on metallic surface can enhance far-field scattering and boost near-field response having promising applications in nonlinear optics and reflection-type devices. However, the dependence of gap size between dielectric resonator and metallic surface on Mie resonant frequency is complex and desires a comprehensive physical interpretation. Here, we systematically study the effect of metallic substrate on the magnetic dipole (MD) resonant frequency at X-band by placing a high permittivity CaTiO<sub>3</sub> ceramic block on metallic substrate and regulating their gap size. The simulated and experimental results show that there are two physical mechanisms to codetermine the metallic substrate-induced MD frequency. The greatly enhanced electric field pair in the gap and the coupling of MD resonance with its mirror image are decisive for small and large gaps, respectively, making the MD resonant frequency present an exponential blue shift first and then a slight red shift with increasing gap size. Further, we use the two mechanisms to explain different frequency shifting properties of ceramic sphere near metallic substrate. Finally, taking advantage of the sharp frequency shifting to small gaps, the ceramic block is demonstrated to accurately estimate the thickness or permittivity of thin film on metallic substrate through a governing equation derived from the method of symbolic regression. We believe that our study will help to understand the resonant frequency shifting for dielectric particle near metallic substrate and give some prototypes of ultrasensitive detectors. Materials science ; Permittivity ; Substrate (aquarium) ; Resonance (particle physics) ; Dielectric ; Resonator ; Optoelectronics ; Electric field ; Optics ; Dipole ; Condensed matter physics ; Physics ; Atomic physics Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Plasmonic and Surface Plasmon Research
Authors: Mutiullah Jamil | Hafeez ul Rehman | SaleemUllah | Imran Ashraf | Saqib Ubaid
Source: Computers, materials & continua/Computers, materials & continua (Print) (article, 2022-12-29)
Field: Chemistry — Analytical Chemistry

Smart Techniques for LULC Micro Class Classification Using Landsat8營magery Wheat species play important role in the price of products and wheat production estimation. There are several mathematical models used for the estimation of the wheat crop but these models are implemented without considering the wheat species which is an important independent variable. The task of wheat species identification is challenging both for human experts as well as for computer vision-based solutions. With the use of satellite remote sensing, it is possible to identify and monitor wheat species on a large scale at any stage of the crop life cycle. In this work, nine popular wheat species are identified by using Landsat8 operational land imager (OLI) and thermal infrared sensor (TIRS) data. Two thousand samples of each wheat crop species are acquired every fifteen days with a temporal resolution of ten multispectral bands (band two to band eleven). This study employs random forest (RF), artificial neural network, support vector machine, Naïve Bayes, and logistic regression for nine types of wheat classification. In addition, deep neural networks are also developed. Experimental results indicate that RF shows the best performance of 91% accuracy while DNN obtains a 90.2% accuracy. Results suggest that remotely sensed data can be used in wheat type estimation and to improve the performance of the mathematical models. Multispectral image ; Support vector machine ; Bayes' theorem ; Artificial neural network ; Random forest ; Machine learning ; Scale (ratio) ; Naive Bayes classifier ; Remote sensing ; Identification (biology) ; Artificial intelligence ; Computer science ; Estimation ; Geography ; Engineering ; Ecology ; Cartography ; Bayesian probability Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Remote Sensing in Agriculture
Authors: Siti Mariatul Hazwa Mohd Huzir | Noratikah Zawani Mahabob | Aqib Fawwaz Mohd Amidon | Nurlaila Ismail | Zakiah Mohd Yusoff | Mohd Nasir Taib
Source: Indonesian Journal of Electrical Engineering and Computer Science (article, 2022-08-10)
Field: Chemistry — Organic Chemistry

A Ppreliminary study on the intelligent model of k-nearest neighbor for agarwood oil quality grading Essential &lt;span&gt;oils extracted from trees has various usages like perfumes, incense, aromatherapy and traditional medicine which increase their popularity in global market. In Malaysia, the recognition system for identifying the essential oil quality still does not reach its standard since mostly graded by using human sensory evaluation. However, previous researchers discovered new modern techniques to present the quality of essential oils by analyse the chemical compounds. Agarwood essential oil had been chosen for the proposed integrated intelligent models with the implementation of k-nearest neighbor (k-NN) due to the high demand and an expensive natural raw world resource. k-NN with Euclidean distance metrics had better performance in terms of its confusion matrix, sensitivity, precision accuracy and specificity. This paper presents an overview of essential oils as well as their previous analysis technique. The review on k-NN is done to prove the technique is compatible for future research studies based on its performance.&lt;/span&gt; Agarwood ; Incense ; Euclidean distance ; Essential oil ; Confusion ; Computer science ; Confusion matrix ; Grading (engineering) ; Aromatherapy ; Popularity ; Artificial intelligence ; Machine learning ; Pattern recognition (psychology) ; Engineering ; Chemistry ; Chromatography Wood and Agarwood Research ; Spectroscopy and Chemometric Analyses ; Image Processing and 3D Reconstruction
Authors: Nupur Raghav | Shweta Sharma | Parul SIngh | Sanjay Yadav | Rajesh Nigam
Source: The Applied Biology & Chemistry Journal (article, 2022-06-30)
Field: Materials Science — Materials Chemistry

A comprehensive case review of microbial remediation of heavy metals and pesticides in Yamuna River Water resources (mainly surface and groundwater) support two-thirds of the global population but have been of great concern in recent years. Most civilizations all around the world evolved on the river's banks. River Yamuna is the major tributary to River Ganga (India’s largest river) and one of the major rivers in India. The extreme cause of pollution in rivers is the excessive discharge of domestic waste water from adjacent towns and residents, contributing to about two-thirds of pollution. Agricultural and industrial effluents cause the rest one-third. Organic pollutants can be treated or removed through appropriate sewage water treatment before final discharge into the river. The status of river water is advantageous because it determines the life cycle of animals, plants, and human beings. The direct use of river water for drinking causes severe hazards due to anthropogenic activities causing environmental pollution in rivers. The noxious discharge of toxic industrial waste consisting of heavy metals and pesticides into the water bodies, especially rivers, prevails in water bodies and accumulates through the food chain. Biomagnification of toxic heavy metals and pesticides through the food chain causes severe health hazards to humans and other living creatures. However, extensive farming depends on pesticides, accelerating water and land contamination. Long-duration contact with pesticides can harm living organisms and disrupt the function of different body organs. Various chemical and biological methods are available for reducing the water pollution level. Still, the emergence of an astonishing technology of multi-cultures of aerobic and anaerobic effective and beneficial microorganisms is gaining popularity because of its eco-friendly nature. Tributary ; Environmental science ; Pollution ; Food chain ; Surface water ; Pesticide ; Biomagnification ; Water pollution ; Sewage ; Pollutant ; Environmental protection ; Environmental engineering ; Environmental chemistry ; Ecology ; Geography ; Biology ; Chemistry Nanoparticles: synthesis and applications
Authors: Gaoxue Wang | Evgenya Simakov | Danny Pérez
Source: Applied Physics Letters (article, 2022-03-28)
Field: Materials Science — Materials Chemistry

<i>Ab initio</i> Cu alloy design for high-gradient accelerating structures Operation of normal conducting accelerator structures at high accelerating gradients is beneficial for many accelerator applications in basic science, industry, medicine, and National Security. RF breakdown is the major factor that limits the achievable accelerating gradients. Previous experiments on copper (Cu) have demonstrated that RF breakdown probability can be significantly decreased by hardening the material and alloying Cu with solutes such as silver (Ag). In this paper, we propose a figure-of-merit (FOM) that characterizes the ability of Cu alloys to withstand high-gradients. The FOM represents a trade-off between hardening through solid solution strengthening and the additional thermal stress induced by incremental RF pulse heating resulting from changes in electronic properties induced by alloying. We performed high-throughput ab initio calculations and computed the FOM for a large number of binary Cu alloys. Several promising candidate alloys for high-gradient accelerating structures were identified, such as CuAg, CuCd, CuHg, CuAu, CuIn, and CuMg. CuAg alloys have previously exhibited low RF breakdown rates in experiments. The results provide guidance for selecting alloys for the future high-gradient normal conducting accelerating structures operating at very high gradients. Alloy ; Materials science ; Figure of merit ; Hardening (computing) ; Ab initio ; Copper ; Ab initio quantum chemistry methods ; Temperature gradient ; Condensed matter physics ; Metallurgy ; Optoelectronics ; Composite material ; Chemistry ; Physics Fusion materials and technologies ; Boron and Carbon Nanomaterials Research ; Metal and Thin Film Mechanics
Authors: Costanzo Bellini | Vittorio Di Cocco | Francesco Iacoviello | Larisa Patricia Mocanu
Source: Metals (article, 2022-11-26)
Field: Materials Science — Materials Chemistry

Impact of Copper, Tin and Titanium Addition on Bending-Induced Damage of Intermetallic Phases in Hot Dip Galvanizing Hot dip galvanizing is among the cheapest methods for protecting ferrous alloys against corrosion. The success is due to both the low cost of the process and the high degree of protection in many corrosive environments, where the coatings serve as sacrificial protection. The purpose of this analysis is to study the mechanical characteristics of steel plates, that have been hot dip coated with five different zinc alloy molten bath for different time periods. The mechanical tests performed is a non-standardized four-point bending test considering three distinct bending angles. Results are examined in terms of both mechanical behaviour and coating phase damage. The development of intermetallic phases and their damage are both influenced by the chemical compositions of the zinc bath, demonstrating that fractures arise mostly at the substrate-coating interface. All the coatings showed the arising of micro-cracks except for the Aluminium, which demonstrated its ductility. In addition, Zn-Ti coatings showed the arising of a new compact phase rich in iron, characterized by a great hardness. More research is needed to explore the aluminium impact on the zinc bath, the lack of tiny fractures in the phase, and the lesser thickness compared to the other coatings tested. Galvanization ; Materials science ; Intermetallic ; Metallurgy ; Aluminium ; Corrosion ; Bending ; Coating ; Alloy ; Tin ; Ductility (Earth science) ; Copper ; Zinc ; Brass ; Titanium ; Aluminium alloy ; Composite material ; Creep ; Layer (electronics) Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals ; Mechanical Behavior of Composites
Authors: E. J. Moon | Anjali Goyal
Source: Scientific Reports (article, 2022-03-07)
Field: Materials Science — Materials Chemistry

Combinatorial synthesis of heteroepitaxial, multi-cation, thin-films via pulsed laser deposition coupled with in-situ, chemical and structural characterization Combinatorial synthesis via a continuous composition spread is an excellent route to develop thin-film libraries as it is both time- and cost-efficient. Creating libraries of functional, multicomponent, complex oxide films requires excellent control over the synthesis parameters combined with high-throughput analytical feedback. A reliable, high-throughput, in-situ characterization analysis method is required to meet the crucial need to rapidly screen materials libraries. Here, we report on the combination of two in-situ techniques-(a) Reflection high-energy electron diffraction (RHEED) for heteroepitaxial characterization and a newly developed compositional analysis technique, low-angle x-ray spectroscopy (LAXS), to map the chemical composition profile of combinatorial heteroepitaxial complex oxide films deposited using a continuous composition spread method via pulsed laser deposition. This is accomplished using a unique state-of-the-art combinatorial growth system with a fully synchronized four-axis mechanical substrate stage without shadow masks, alternating acquisition of chemical compositional data using LAXS at various different positions on the [Formula: see text] 41 mm [Formula: see text] 41 mm range and sequential deposition of multilayers of SrTiO[Formula: see text] and [Formula: see text] on a 2-inch (50.8 mm) [Formula: see text] wafer in a single growth run. Rutherford backscattering spectrometry (RBS) is used to calibrate and validate the compositions determined by LAXS. This study shows the feasibility of combinatorial synthesis of heteroepitaxial, functional complex oxide films at wafer-scale via two essential in-situ characterization tools-RHEED for structural analysis or heteroepitaxy and LAXS for compositional characterization. This is a powerful technique for development of new films with optimized heteroepitaxy and composition. In situ ; Characterization (materials science) ; Pulsed laser deposition ; Materials science ; Thin film ; Deposition (geology) ; Laser ; Optoelectronics ; Chemical engineering ; Nanotechnology ; Chemistry ; Optics ; Biology ; Physics ; Organic chemistry Electronic and Structural Properties of Oxides ; Semiconductor materials and devices ; Ferroelectric and Negative Capacitance Devices