jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Serdal Ünal | Mehmet Canbaz
Source: Challenge Journal of Structural Mechanics (article, 2024-12-10)
Field: Chemistry — Inorganic Chemistry

Mechanical properties of lightweight photocatalytic marbelite This study investigated the mechanical performance of lightweight photocatalytic marbelite (LPM). In the production of LPM, titanium dioxide (TiO2) and various fiber additives were used to impart self-cleaning properties to the LPM with photocatalytic effect. In the study, fibers were added to the LPM mix at different ratios (0%, 0.5%, 1%) and unit weight, ultrasound transmission rate, bending, splitting tensile and compressive strength tests were carried out on these specimens. The mixture was prepared with perlite and polyester resin and enriched with TiO2 and fiber additives. Perlite was used as an aggregate in the LPM and lightweighting properties were added to the specimens. The experimental results show that increasing the fiber content significantly improves the mechanical strength of the LPM. The improvement in bending strength reached 60%, while in compressive strength it reached 25% and in splitting tensile strength it reached 35%. With the addition of TiO2, the bending strength of LPM increased by 15%, while the compressive strength increased by 12% and the splitting tensile strength increased by 7%. These ratios were higher with increasing fiber content. These results suggest that LPM, which provide environmental benefits with their photocatalytic properties and improved mechanical performance, can be more effectively used in industrial applications. Ultimate tensile strength ; Materials science ; Perlite ; Compressive strength ; Composite material ; Photocatalysis ; Flexural strength ; Fiber ; Titanium dioxide ; Bending ; Catalysis Pigment Synthesis and Properties ; TiO2 Photocatalysis and Solar Cells
Authors: Juho Park | Ju Young Kim | Sunghyun Nam | Min Seok Jang
Source: arXiv (Cornell University) (preprint, 2024-10-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

High-directivity multi-level beam switching with single-gate tunable metasurfaces based on graphene The growing demand for ultra-fast telecommunications, autonomous driving, and futuristic technologies highlights the crucial role of active beam steering at the nanoscale. This is essential for applications like LiDAR, beam-forming, and holographic displays, especially as devices reduce in form-factor. Although device with active beam switching capability is a potential candidate for realizing those applications, there have been only a few works to realize beam switching in reconfigurable metasurfaces with active tuning materials. In this paper, we theoretically present a multi-level beam-switching dielectric metasurface with a graphene layer for active tuning, addressing challenges associated with achieving high directivity and diffraction efficiency, and doing so while using a single-gate setup. For two-level switching, the directivities reached above 95%, and the diffraction efficiencies were near 50% at the operation wavelength $λ_0$ = 8 $μ$m. Through quasi-normal mode expansion, we illustrate the physics of the beam switching metasurface inverse-designed by the adjoint method, highlighting the role of resonant modes and their response to charge carrier tuning. Under the same design scheme, we design and report characteristics of a three-level and four-level beam switching device, suggesting a possibility of generalizing to multi-level beam switching. Graphene ; Directivity ; Materials science ; Optoelectronics ; Beam (structure) ; Optics ; Physics ; Computer science ; Telecommunications ; Nanotechnology Metamaterials and Metasurfaces Applications ; Energy Harvesting in Wireless Networks ; Advanced Antenna and Metasurface Technologies
Authors: M. G. Lavrentev | M. V. Voronov | А. А. Иванов | В. П. Панченко | N. Yu. Tabachkova | M. K. Tapero | Ivan Yu. Yarkov
Source: Izvestiya Vysshikh Uchebnykh Zavedenii Materialy Elektronnoi Tekhniki = Materials of Electronics Engineering (article, 2024-03-05)
Field: Materials Science — Materials Chemistry

Mechanical properties of medium-temperature thermoelectric materials based on tin and lead tellurides The strength and thermoelectric properties of PbTe and Sn 0.9 Pb 0.1 Te medium-temperature polycrystalline specimens with p and n conductivity types, respectively, have been studied. The specimens have been produced using extrusion and spark plasma sintering. The strength parameters of the materials were studied using uniaxial compression at 20 to 500 °C. The structure of the materials was studied using X-ray diffraction and electron microscopy. The electrical conductivity and the Seebeck coefficient were measured simultaneously using the four-probe and differential methods. The temperature conductivity and the specific heat capacity were measured using the laser flash and differential scanning calorimetry methods. The PbTe and Sn 0.9 Pb 0.1 Te materials produced using extrusion and spark plasma sintering prove to be single-phase and have homogeneous compositions. For comparable synthesis methods, the dislocation density in the Sn 0.9 Pb 0.1 Te specimens is by an order of magnitude lower than in the PbTe ones. Study of the mechanical properties of n and p conductivity type specimens over a wide temperature range from 20 to 500 °C has shown that their deformation is plastic and has no traces of brittle fracture. For these plastic materials, the strength criterion has been accepted to be the arbitrary yield stress corresponding to the stress at a 0.2% deformation. The 20 °C yield stress of PbTe and Sn 0.9 Pb 0.1 Te is far higher for the specimens produced by extrusion. For all the test temperatures and synthesis methods the Sn 0.9 Pb 0.1 Te specimens have a higher strength than the PbTe ones. The PbTe and Sn 0.9 Pb 0.1 Te specimens produced by extrusion have better thermoelectric properties than the spark plasma sintered ones. The heat conductivity of the PbTe and Sn 0.9 Pb 0.1 Te specimens is almost the same regardless of compaction method. Tin ; Thermoelectric effect ; Materials science ; Lead (geology) ; Thermoelectric materials ; Lead telluride ; Metallurgy ; Composite material ; Engineering physics ; Geology ; Engineering ; Thermodynamics ; Thermal conductivity ; Physics Advanced Thermoelectric Materials and Devices ; Chalcogenide Semiconductor Thin Films ; Advanced Semiconductor Detectors and Materials
Authors: Lihuan Yu | Jiangdan Li | Jin‐Liang Wang | Jiajia Li | Jiao Li | Qiang Xi | Zhanwen Han
Source: arXiv (Cornell University) (preprint, 2024-04-05)
Field: Chemistry — Electrochemistry

Derivative Spectroscopy and its Application at Detecting the Weak Emission/Absorption Lines The development of spectroscopic survey telescopes like Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST), Apache Point Observatory Galactic Evolution Experiment and Sloan Digital Sky Survey has opened up unprecedented opportunities for stellar classification. Specific types of stars, such as early-type emission-line stars and those with stellar winds, can be distinguished by the profiles of their spectral lines. In this paper, we introduce a method based on derivative spectroscopy (DS) designed to detect signals within complex backgrounds and provide a preliminary estimation of curve profiles. This method exhibits a unique advantage in identifying weak signals and unusual spectral line profiles when compared to other popular line detection methods. We validated our approach using synthesis spectra, demonstrating that DS can detect emission signals three times fainter than Gaussian fitting. Furthermore, we applied our method to 579,680 co-added spectra from LAMOST Medium-Resolution Spectroscopic Survey, identifying 16,629 spectra with emission peaks around the Hα line from 10,963 stars. These spectra were classified into three distinct morphological groups, resulting in nine subclasses as follows. (1) Emission peak above the pseudo-continuum line (single peak, double peaks, emission peak situated within an absorption line, P Cygni profile, Inverse P Cygni profile); (2) Emission peak below the pseudo-continuum line (sharp emission peak, double absorption peaks, emission peak shifted to one side of the absorption line); (3) Emission peak between the pseudo-continuum line. Derivative (finance) ; Spectroscopy ; Absorption (acoustics) ; Absorption spectroscopy ; Second derivative ; Emission spectrum ; Materials science ; Analytical Chemistry (journal) ; Physics ; Chemistry ; Optics ; Spectral line ; Business ; Astronomy ; Mathematics ; Chromatography ; Mathematical analysis Electrochemical Analysis and Applications ; Advanced Chemical Sensor Technologies ; Analytical Chemistry and Sensors
Authors: Antoine Etourneau | Marta Oggioni | Sophia Belrhomari
Source: CHIMIA International Journal for Chemistry (article, 2024-08-21)
Field: Chemistry — Inorganic Chemistry

Second BeNeFri Student Spring Symposium 2024 – A Scientific Day in Neuchâtel Schlemper-Scheidt (UniNE) and Subhradip Kundu ( Spring (device) ; Physics Inorganic Chemistry and Materials
Authors: Mahsa Bank Tavakoli | Josh Morgan
Source: Microscopy and Microanalysis (article, 2024-05-03)
Field: Materials Science — Surfaces, Coatings and Films

Evaluating the Quality of Serial EM Sections with Deep Learning Automated image acquisition can significantly improve the throughput of serial section scanning electron microscopy (ssSEM). However, image quality can vary from image to image depending on autofocusing and beam stigmation. Automatically evaluating the quality of images is, therefore, important for efficiently generating high-quality serial section scanning electron microscopy (ssSEM) datasets. We tested several convolutional neural networks for their ability to reproduce user-generated evaluations of ssSEM image quality. We found that a modification of ResNet-50 that we term quality evaluation Network (QEN) reliably predicts user-generated quality scores. Running QEN in parallel to ssSEM image acquisition therefore allows users to quickly identify imaging problems and flag images for retaking. We have publicly shared the Python code for evaluating images with QEN, the code for training QEN, and the training dataset. Computer science ; Python (programming language) ; Image quality ; Artificial intelligence ; Convolutional neural network ; Computer vision ; Deep learning ; Pattern recognition (psychology) ; Image (mathematics) Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications ; Cell Image Analysis Techniques
Authors: Alexander Scheinker
Source: arXiv (Cornell University) (preprint, 2024-07-29)
Field: Materials Science — Materials Chemistry

cDVAE: Multimodal Generative Conditional Diffusion Guided by Variational Autoencoder Latent Embedding for Virtual 6D Phase Space Diagnostics Imaging the 6D phase space of a beam in a particle accelerator in a single shot is currently impossible. Single shot beam measurements only exist for certain 2D beam projections and these methods are destructive. A virtual diagnostic that can generate an accurate prediction of a beam's 6D phase space would be incredibly useful for precisely controlling the beam. In this work, a generative conditional diffusion-based approach to creating a virtual diagnostic of all 15 unique 2D projections of a beam's 6D phase space is developed. The diffusion process is guided by a combination of scalar parameters and images that are converted to low-dimensional latent vector representation by a variational autoencoder (VAE). We demonstrate that conditional diffusion guided by VAE (cDVAE) can accurately reconstruct all 15 of the unique 2D projections of a charge particle beam's 6 phase space for the HiRES compact accelerator. Autoencoder ; Embedding ; Generative grammar ; Space (punctuation) ; Phase (matter) ; Artificial intelligence ; Computer science ; Generative model ; Phase space ; Diffusion ; Pattern recognition (psychology) ; Statistical physics ; Mathematics ; Physics ; Deep learning ; Quantum mechanics Nuclear Materials and Properties
Authors: V Vani | Fathimathul Nafiya | A Harshad | Mahima Krishnan M M | N Naisam | S Saji
Source: Asian Journal of Pharmaceutical Research and Development (article, 2024-08-15)
Field: Chemistry — Organic Chemistry

In-Silico Design of Thiadiazole Derivatives as Anticonvulsant Agents This study focuses on the in-silico design and molecular docking of thiadiazole and its derivatives to evaluate their potential as anticonvulsant agents.Thiadiazoles are heterocyclic compounds known for their diverse biological activities, including anticonvulsant properties.The molecular structures were subjected to various in-silico screening methods to predict their drug-like properties and pharmacokinetic profiles. Molecular docking studies were performed to assess the binding affinity and interactions of the designed compounds with key targets associated with anticonvulsant activity, particularly the voltage-gated sodium channels and GABA receptors.The docking results revealed that several thiadiazole derivatives exhibited strong binding affinities towards the targeted proteins, with favourable binding energies and significant interactions at the active sites. In silico ; Anticonvulsant ; Pharmacology ; Chemistry ; Medicine ; Epilepsy ; Psychiatry ; Biochemistry Inorganic and Organometallic Chemistry
Authors: Lorena Mato | Arben Osmanaj | Ardiana Topi | Dritan Topi | Naser Bajraktari
Source: Research Square (preprint, 2024-10-25)
Field: Chemistry — Analytical Chemistry

NIR Spectroscopy Application for Quality Control of Wheat Grain Produced in Albania Quality (philosophy) ; Spectroscopy ; Control (management) ; Grain quality ; Wheat grain ; Agronomy ; Environmental science ; Agricultural engineering ; Materials science ; Business ; Computer science ; Engineering ; Physics ; Biology ; Artificial intelligence ; Astronomy Spectroscopy and Chemometric Analyses ; Agricultural Engineering and Mechanization ; Food composition and properties
Authors: Assil Halimi | Koroush Shirvan
Source:  (preprint, 2024-12-16)
Field: Materials Science — Materials Chemistry

Fuel Behavior Implications of Reactor Design Choices in Pressurized Water SMRs Small pressurized water reactors (PWRs) can feature boron free operation, natural circulation mode, reduced height assemblies and/or long refueling cycles. This paper attempts to explore core design optimization for each of these design evolutions. In consequence, five core design layouts are developed incorporating boron free operation with continuous control rods insertion, natural circulation with low burnup/low power density design, natural circulation with high burnup/low power density design, forced circulation with standard core power density design, and forced circulation with high power density design. These cores’ performance is compared to a standard 4-loop PWR. The design process aims to improve the fuel cycle cost under safety constraints through core design optimization using CASMO4E/SIMULATE3 reactor physics codes and FRAPCON4.1 fuel performance assessment tool. Core modeling assumes standard 17x17 PWR fuel assemblies loaded with low enriched uranium (LEU) up to 5wt% or LEU+ (i.e., below 10wt% enrichment) pellets with gadolinium oxide (Gd2O3) as the burnable poison. Satisfactory core and fuel performances are obtained for all the designed cores under steady state and considered overpower transients. For low power density operation, long cycle lengths are achieved reaching a 2.5- and a 5-year cycles and peak rod-average burnup is pushed to 83 MWd/kgU. Other cycle lengths are maintained at 18 months. Boron free operation exhibits the ability to achieve longer cycle lengths at the cost of higher peaking factors leading to high local power and fuel temperatures which prevents sizable power uprates and is deemed uneconomical. Fuel assembly height reduction allows coolant velocity retrofit which enables higher core power density without violating structural integrity of the fuel assembly. As a result, a core power density of 123 kW/l is reached where total cladding hoop strain becomes the limiting parameter. Pressurized water reactor ; Environmental science ; Nuclear engineering ; Waste management ; Engineering Nuclear Materials and Properties ; Nuclear reactor physics and engineering ; Nuclear and radioactivity studies
Authors: Yong Keun Koo | Yong Woon Jung | Dong Hwa Kim | Sang Won Kim | Eun Seol Kim | Byeong Jae Park | Seung Ju Lee | Seung Won Jung
Source: Food Engineering Progress (article, 2024-02-01)
Field: Chemistry — Analytical Chemistry

Development of a Secondary Model for the Growth of Salmonella enterica in Food by Applying Artificial Neural Networks and Databases (ComBase and FoodData Central) The secondary growth model for Salmonella was developed based on the artificial neural network (ANN) with data collected from ComBase and FoodData Central. In addition to the existing secondary model variables (temperature, pH, Na+, and water contents), more input variables (sugar, carbohydrate, lipid, and protein contents) were considered. The output variables were microbial growth parameters (lag phase duration [λ] and maximum growth rate [μmax]). A commercial ANN program (NeuralWorks Predict) was utilized with training at 80%, validation at 10%, and test data at 10%. ANN models were created using all data and cleansed data. Using the cleansed data, the training/testing root mean square error (RMSE) for μmax improved from 0.14/0.16 to 0.11/0.14, whereas the RMSE for λ was still not acceptable, from 11.94/33.03 to 7.09/4.18. The λ data were divided into two ranges with high and low goodness of fit, whereas the ANN model for each field was built, resulting in an optimally low RMSE. Salmonella enterica ; Salmonella ; Biology ; Food science ; Bacteria ; Genetics Spectroscopy and Chemometric Analyses ; Listeria monocytogenes in Food Safety ; Advanced Chemical Sensor Technologies
Authors: Harry Wilders | George S. Biggs | Sam M. Rowe | Emma E. Cawood | Alan R. Rendina | Emma K. Grant | Jonathan Pettinger | David J. Fallon | Mark Skehel | David House | Nicholas C. O. Tomkinson | Jacob T. Bush
Source: ChemRxiv (preprint, 2024-09-06)
Field: Chemistry — Organic Chemistry

Expedited SARS-CoV-2 main protease inhibitor discovery through modular ‘direct-to-biology’ screening Reactive fragment (RF) screening has emerged as an efficient method for ligand discovery across the proteome, irrespective of a target's perceived tractability. To date, however, the efficiency of subsequent optimisation campaigns has largely been low‐throughput, constrained by the need for synthesis and purification of target compounds. We report an efficient platform for ‘direct‐to‐biology’ (D2B) screening of cysteine‐targeting chloroacetamide RFs, wherein synthesis is performed in 384‐well plates allowing direct assessment in downstream biological assays without purification. Here, the developed platform was used to optimise inhibitors of SARS‐CoV‐2 main protease (MPro), an established drug target for the treatment of COVID‐19. An initial RF hit was developed into a series of potent inhibitors, and further exploration using D2B screening enabled a ‘switch’ to a reversible inhibitor series. This example of ligand discovery for MPro illustrates the acceleration that D2B chemistry can offer for optimising RFs towards covalent inhibitor candidates, as well as providing future impetus to explore the evolution of RFs into non‐covalent ligands. Drug discovery ; Computational biology ; High-throughput screening ; Protease inhibitor (pharmacology) ; Chemical biology ; Ligand efficiency ; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) ; Proteome ; Drug target ; Combinatorial chemistry ; Ligand (biochemistry) ; Chemistry ; Protease ; Cysteine ; Covalent bond ; Coronavirus disease 2019 (COVID-19) ; Biochemistry ; Biology ; Enzyme ; Virology ; Receptor ; Virus ; Medicine Click Chemistry and Applications ; Computational Drug Discovery Methods ; vaccines and immunoinformatics approaches
Authors: Kamil Khayrnasov
Source: International Journal of Engineering Trends and Technology (article, 2024-03-20)
Field: Materials Science — General Materials Science

Methods for Analyzing Robotic Systems Made of Composite Materials Methods for modeling, calculation, and analysis of robotic systems are given in the example of a dynamic of stand. A three-layer structure of a dynamic of the stand made of external load-bearing 8-layer composite materials and filler between the load-bearing layers is considered. Elements of movement of robotic systems such as bearings, gearboxes, and gear rims are modeled as a system of rod systems of identical rigidity. An algorithm and a program for calculating the stiffness of gearboxes, gear rims, bearing supports, and engines were developed. The influence of the location of the base layers of the composite material on the stress state of the stand under dynamic loading is considered. The problem is solved by the finite element method. The convergence of the calculation results was checked by increasing the number of finite elements and comparing the results obtained. To achieve maximum rigidity and strength of the stand, a method has been developed to arrange the base of the composite material layers along the lines of maximum stresses. As a result of the calculations, the stress-strain state of the stand materials was obtained. The criteria for the destruction of layered materials were used to assess the bearing capacity of a stand made of composite material under dynamic loading. The conducted studies are applicable to a wide class of robotic structures made of composite material under dynamic impact. Composite number ; Computer science ; Materials science ; Algorithm Material Properties and Applications ; Additive Manufacturing and 3D Printing Technologies ; Manufacturing Process and Optimization
Authors: Sudeep Kumar Sahoo | M. Thierry Douillard | Bianca Frincu | Michel Nardin | H. Langlois
Source: BIO Web of Conferences (article, 2024-01-01)
Field: Materials Science — Surfaces, Coatings and Films

Coupling Clustering and Channeling Contrast in the Scanning Electron Microscope International audience Contrast (vision) ; Scanning electron microscope ; Cluster analysis ; Coupling (piping) ; Optics ; Materials science ; Electron microscope ; Electron ; Microscope ; Physics ; Computer science ; Artificial intelligence ; Nuclear physics Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications ; Industrial Vision Systems and Defect Detection
Authors: John Findley | Samir Budhathoki | Janice A. Steckel
Source:  (article, 2024-08-18)
Field: Chemistry — Inorganic Chemistry

Machine Learned Force Field Modeling of Metal Organic Frameworks for CO2 Direct Air Capture Metal organic frameworks (MOFs) are a large class of porous materials and have garnered significant interest due to their large surface areas and their tunable physical and chemical properties. Numerous prior studies have been performed to screen large databases of this material class for promising DAC sorbent materials. These studies have often relied on classical model potentials. While density functional theory (DFT) calculations have been shown to be very accurate for modeling the interaction of CO2 with MOFs, such calculations are too computationally demanding for statistically significant adsorption predictions. To overcome this barrier, we developed methods for training models to achieve DFT-level accuracy for the forces and energies associated with MOF flexibility and CO2 adsorption using machine learned force fields (MLFFs). These methods were parametrized based on DFT calculations of CO2 in a flexible MOF and used to predict MOF structural properties as well as CO2 adsorption in several MOFs. Field (mathematics) ; Metal-organic framework ; Computer science ; Force field (fiction) ; Environmental science ; Artificial intelligence ; Chemistry Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Advanced Control Systems Optimization