jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ashraf El-Bardawil | Nehad A. Zidan | Noha H. El-Amary | W. Abbas | Mostafa Fedawy
Source: Journal of Advanced Research in Applied Sciences and Engineering Technology (article, 2024-07-02)
Field: Materials Science — Polymers and Plastics

Perovskite Silicon Solar Cell Emulation using Multi-Layer Perceptron Deep Neural Network Perovskite silicon solar cells have an unprecedentedly high-power conversion efficiency compared to other solar cell technologies. This manuscript aims to accomplish two specific goals. The first objective is to investigate the impact of perovskite layer thickness and doping concentration on the solar cell's power conversion efficiency. The second one is to conduct a comparative study to identify the best artificial intelligence technique for simulating the complex nonlinear behaviour of the variation of material parameters versus power conversion efficiency. A solar cell capacitance simulator is used to examine the photovoltaic properties of perovskite silicon solar cells. The simulation is conducted in three stages. Firstly, studying the silicon base structure efficiency to determine the absorber layer c-Si(p) thickness and doping, and the buffer layer c-Si (n) thickness and doping. The second stage is the single-junction of solar cell structure in which c-Si (p++) is used as back surface field. Finally, the perovskite silicon solar cells study the impact of perovskite layer thickness and carrier concentration on power conversion efficiency. The efficiency increases linearly from 26.5% to 28.5% with the perovskite layer thickness. Solar cell behaviour is simulated utilizing multi-layer perceptron. It represents satisfied results. Emulation ; Artificial neural network ; Silicon solar cell ; Solar cell ; Perceptron ; Layer (electronics) ; Silicon ; Materials science ; Computer science ; Perovskite (structure) ; Multilayer perceptron ; Artificial intelligence ; Optoelectronics ; Nanotechnology ; Chemical engineering ; Engineering ; Psychology Conducting polymers and applications ; Industrial Vision Systems and Defect Detection ; Machine Learning and ELM
Authors: Rafał Powalski | Bazyli Klockiewicz | Maciej Jaśkowski | Bartosz Topolski | Paweł Dąbrowski-Tumański | Maciej Wiśniewski | Łukasz Kuciński | Piotr Miłoś | Dariusz Plewczyński
Source: arXiv (Cornell University) (preprint, 2024-10-16)
Field: Chemistry — Organic Chemistry

RapidDock: Unlocking Proteome-scale Molecular Docking Accelerating molecular docking -- the process of predicting how molecules bind to protein targets -- could boost small-molecule drug discovery and revolutionize medicine. Unfortunately, current molecular docking tools are too slow to screen potential drugs against all relevant proteins, which often results in missed drug candidates or unexpected side effects occurring in clinical trials. To address this gap, we introduce RapidDock, an efficient transformer-based model for blind molecular docking. RapidDock achieves at least a $100 \times$ speed advantage over existing methods without compromising accuracy. On the Posebusters and DockGen benchmarks, our method achieves $52.1\%$ and $44.0\%$ success rates ($\text{RMSD}&lt;2$Å), respectively. The average inference time is $0.04$ seconds on a single GPU, highlighting RapidDock's potential for large-scale docking studies. We examine the key features of RapidDock that enable leveraging the transformer architecture for molecular docking, including the use of relative distance embeddings of $3$D structures in attention matrices, pre-training on protein folding, and a custom loss function invariant to molecular symmetries. Proteome ; Computational biology ; Docking (animal) ; Scale (ratio) ; Chemistry ; Biology ; Biochemistry ; Geography ; Cartography ; Medicine Click Chemistry and Applications ; Advanced Biosensing Techniques and Applications ; Biotin and Related Studies
Authors: Danial Md Nor | Nur Nisha Camelia Syukri | Mohd Helmy Abd Wahab | Fahmy Rinanda Saputri | Izanoordina Ahmad | Jean-Marc Ogier
Source: Journal of Advanced Research in Applied Sciences and Engineering Technology (article, 2024-12-17)
Field: Chemistry — Analytical Chemistry

Fresh Meat Classification Using Image Processing Technique Meat is considered one of the most nutritious animal food products as it contains carbohydrates, proteins, lipids, vitamins, and minerals. The level of freshness in meat plays a significant role in determining its quality for consumption. It is therefore crucial to maintain meat quality to ensure that consumers receive high-grade meat. Traditionally, meat quality assessment has been done visually by comparing actual meat with reference photographs of each meat class. However, this subjective and time-consuming process has its limitations. To overcome these limitations, an automated image processing-based system is required to detect and assess meat quality. In this project, a convolutional neural network (CNN) approach will be implemented to detect the freshness of meat. The meat samples used in the project will be categorized into three types: fresh, half-spoiled, and spoiled. The objective of this project is to develop an image-processing method that analyzes digital pictures to determine the quality of meat. The system's implementation aims to achieve consistent and objective measurements; as different human examiners may yield different results. This will be accomplished by developing an image classification system that utilizes the CNN algorithm along with image processing techniques. By utilizing the developed model, it can be concluded that the method is effectively applied in the classification of meat freshness with an accuracy rate of 80%. Artificial intelligence ; Image processing ; Pattern recognition (psychology) ; Computer science ; Computer vision ; Image (mathematics) Spectroscopy and Chemometric Analyses
Authors: Thomas Specht | Kerstin Münnemann | Hans Hasse | Fabian Jirasek
Source:  (preprint, 2024-01-03)
Field: Chemistry — Analytical Chemistry

Rational Method for Defining and Quantifying Pseudo-components Based on NMR Spectroscopy Poorly specified mixtures, whose composition is unknown, are ubiquitous in chemical and biochemical engineering.In the present work, we propose a rational method for defining and quantifying pseudo-components in such mixtures that is free of ad-hoc assumptions.The new method requires only standard nuclear magnetic resonance (NMR) experiments and can be fully automated.In the first step, the method analyzes the composition of the poorly specified mixture in terms of structural groups, which is much easier than obtaining the component speciation.The structural groups are then clustered into pseudo-components based on information on the self-diffusion coefficients measured by pulsed-field gradient (PFG) NMR spectroscopy.We demonstrate the performance of the new method on several aqueous mixtures.The method is broadly applicable and provides a sound basis for modeling and simulation of processes with poorly specified mixtures, without the need for tedious and expensive structure elucidation.It is also attractive for process monitoring. Nuclear magnetic resonance spectroscopy ; Spectroscopy ; Computer science ; Chemistry ; Materials science ; Physics ; Stereochemistry Spectroscopy and Chemometric Analyses ; Analytical Chemistry and Chromatography
Authors: Retno Damayanti
Source: International Journal of Agriculture and Biology (article, 2024-12-25)
Field: Chemistry — Analytical Chemistry

Reflectance versus Fluorescence Imaging: ANN-Based Approach for Predicting Phenol Content on Red Betel Leaves Identification systems using computer vision are important in image recognition technology and artificial intelligence in the agricultural sector. Identifying the phenol content in red betel leaves (Piper crocatum Ruiz &amp; Pav.) using computer vision made it possible to predict the phenol content quickly and efficiently, simplifying the process of classifying product quality. This research used computer vision methods combined with artificial neural networks (ANN) using reflectance and fluorescence images to obtain predictive modeling of phenol content in red betel leaves. The research was aimed to compare the ANN model to predict the phenol content in red betel leaves using reflectance and fluorescence images. The ANN model used 75 data from reflectance images, 75 data from fluorescence images, and 75 data from total phenol content. The color features of the image were used as input data and the total phenol content was used as output data. The dataset used was 75% as cumulative data and 25% as validation data. From the reflectance data, the best architecture chosen was 20-30-1 with a learning rate of 0.1, a momentum of 0.9, tansig as the activation function in the hidden layers, purelin as the activation function in the output layer, and trainlm as the learning function, which produced an MSE value of 0.0090, validation MSE of 0.0880, training R of 0.9777, and validation R 0.7944. Meanwhile, from the fluorescence data, the best architecture chosen was 20-10-1 with a learning rate of 0.1, momentum 0.9, tansig as activation function in the hidden layers, purelin as activation function in the output layer, and trainlm as a learning function, which produced an MSE value of 0.0099, validation MSE of 0.4368, training R of 0.9796, validation R of 0.4436. The reflectance imaging produced the best ANN modeling for predicting the phenol content of red betel leaves. Biology ; Reflectivity ; Fluorescence ; Botany ; Optics Spectroscopy and Chemometric Analyses
Authors: Wakutaka Nakano
Source: arXiv (Cornell University) (preprint, 2024-10-22)
Field: Materials Science — Materials Chemistry

Comparison of the Migdal transition probabilities in electron-atom inelastic cross sections The Migdal transition probabilities for dark matter scattering are compared to the total single-electron inelastic cross sections of electron-atom scattering for isolated Ar and Xe. The comparison is done by expressing the electron-atom scattering cross section by connecting the Migdal probability. The resultant differences are around $30 \ \%$ for Ar and $80 \ \%$ for Xe in $\sim$ 1 keV of incoming electron energy. The transition is dominated from the $3p$ shell electrons for Ar. For Xe, $5p$ states dominate the contribution, but, $4d$ states give $\sim 40\ \%$ contribution at 1 keV. Physics ; Atomic physics ; Atom (system on chip) ; Electron ; Nuclear physics ; Computer science ; Parallel computing Nuclear Materials and Properties ; Electron and X-Ray Spectroscopy Techniques ; Advanced Materials Characterization Techniques
Authors: Parna Nandi | Dipayan Das
Source: Polymer Composites (article, 2024-04-15)
Field: Materials Science — Polymers and Plastics

Static and dynamic mechanical characterization and thermal analysis for multi‐ply structure of woven nettle (<i>Girardinia diversifolia</i>) reinforced poly(lactic acid) biocomposites Abstract Poly(lactic acid) fiberwebs and multi‐ply assemblies of nettle woven reinforcement are used to develop a series of nettle/PLA biocomposites with different reinforcement orientations. The tensile strength, flexural strength, impact strength, and thermal properties of the biocomposites are found to improve by piling nettle fabrics and are also found to depend strongly on the piling architecture and the test direction. It is possible to obtain a biocomposite with higher degrees of isotropy and enhanced mechanical properties by using cross‐ply laminates. The three‐layered woven fabric reinforced PLA biocomposite with a reinforcement orientation 0°/45°/90° shows the best results among all the biocomposites developed in this study. The tensile strength, Young's (tensile) modulus, flexural strength, impact strength, storage modulus, and loss modulus of the biocomposite are found as 55.25 MPa, 6.30 GPa, 85.83 MPa, 63.74 J/m 2 , 10.08 GPa, and 0.75 GPa, respectively. In this biocomposite, a fair adhesion between matrix and reinforcement is noticed, and this is justified by retardation of crack propagation as well as by substantial energy dissipation. The thermal stability of the biocomposite does not get effected much, but the percent crystallinity increases by 25.84%. The degradation kinetics and the activation energy of the biocomposite are determined through a differential fitting of Arrhenius model. The soil burial test reveals 15.06% of weight loss and 50.56% strength loss for this biocomposite just after 20 days of burial under soil. Highlights Multi‐ply architecture of woven fabric reinforcement in nettle/PLA biocomposites. Nearly isotropic biocomposite through angle‐ply orientation of reinforcement. Biocomposite with remarkable static and dynamic mechanical properties. Augmented kinetic parameters through model fitting of Arrhenius equation. Biocomposite with excellent thermo‐recyclability and biodegradability. Biocomposite ; Materials science ; Composite material ; Flexural strength ; Ultimate tensile strength ; Dynamic mechanical analysis ; Flexural modulus ; Izod impact strength test ; Composite number ; Polymer Natural Fiber Reinforced Composites ; biodegradable polymer synthesis and properties ; Nanocomposite Films for Food Packaging
Authors: Fransiska Angelina Rezekinta | Anwar Kasim | Edi Syafri | Firman Ridwan | Irawati Chaniago | Hideaki Ichiura
Source: Journal of Applied Agricultural Science and Technology (article, 2024-11-24)
Field: Materials Science — Biomaterials

The Effect of Using Cellulose Nanofiber from Kapok (Ceiba pentandra, L) as Reinforcement on The Properties of Recycled Papers Recycling is important for achieving environmentally sustainable products and waste reduction. The trend has led to the addition of reinforcement to recycling process to ensure the effective usage of wasted papers. Therefore, this study was conducted to determine the effect of using cellulose nanofiber (CNF) synthesized from kapok fibers as reinforcement in recycled papers. CNF was applied at different concentrations of 2, 4, 6, 8, and 10% of recycled paper production and the analysis was conducted by testing tensile index, density, and brightness. Moreover, X-ray diffraction (XRD), Fourier Transform Infrared Spectroscopy (FTIR), and UV-Vis analysis were used to determine the characteristics of the paper. The results showed that CNF addition influenced tensile index, brightness, and density of recycled papers. This was observed from an increase in tensile strength by up to 76.32% and density at approximately 0.58 g/cm3 for 10% CNF addition. Meanwhile, the brightness level was reduced due to the compact nature of paper produced. The trend led to the conclusion that the addition of CNF could impact the characteristics of recycled papers. Ceiba ; Cellulose ; Nanofiber ; Materials science ; Reinforcement ; Composite material ; Polymer science ; Pulp and paper industry ; Chemistry ; Organic chemistry ; Engineering ; Geography ; Archaeology Advanced Cellulose Research Studies ; Coastal Management and Development ; Natural Fiber Reinforced Composites
Authors: Mohammad A. Al‐Zubi | Mahmood Ahmad | Shahriar Abdullah | Beenish Jehan Khan | Wajeeha Qamar | Gamil M. S. Abdullah | Roberto Alonso González‐Lezcano | S. Paul
Source: Research Square (preprint, 2024-09-16)
Field: Materials Science — Materials Chemistry

Long Short-Term Memory Networks for Predicting Resilient Modulus of Stabilized Base Material Subject to Wet-Dry Cycles Term (time) ; Subject (documents) ; Base (topology) ; Modulus ; Environmental science ; Mathematics ; Materials science ; Computer science ; Composite material ; Mathematical analysis ; Physics ; World Wide Web Material Properties and Failure Mechanisms ; Non-Destructive Testing Techniques ; Ultrasonics and Acoustic Wave Propagation
Authors: Sheng Hu | Wei Yu | Xiang Gong | Yuhong Liu
Source: Academic Journal of Science and Technology (article, 2024-12-26)
Field: Materials Science — Surfaces, Coatings and Films

A Method for Preparing Gallium-nickel Alloy Solid Targets by Alternating Current Plating In this paper, an alternating current electroplating platform is built. Gallium-nickel alloy plating is prepared on copper targets by using alternating current electroplating methods. The mixed solution of gallium and nickel metal ions is used as the electroplating solution. The differences of gallium-nickel alloy targets prepared by alternating current plating under different conditions of temperature, current density and pH of the plating solution were investigated, and a set of electroplating conditions for gallium-nickel alloy targets prepared by AC electroplating platform were summarized in this article. It is concluded that the gallium-nickel alloy coating with excellent surface quality can be obtained under the conditions of the temperature of the plating solution below 80℃, the current density of 25-50 mA/cm2 and the pH of the plating solution of 1.75-2.25 by the analysis of a series of experimental results. At the same time, it seems that the quality of gallium-nickel alloy coating obtained by AC plating is better than that of DC plating under the same conditions through the characterization results. Therefore, the scheme of using an AC electroplating platform to prepare gallium-nickel alloy targets is feasible. Nickel ; Metallurgy ; Gallium ; Materials science ; Plating (geology) ; Alloy ; Nickel alloy ; Current (fluid) ; Electrical engineering ; Engineering ; Physics Electron and X-Ray Spectroscopy Techniques ; Semiconductor materials and interfaces ; Integrated Circuits and Semiconductor Failure Analysis
Authors: Anastasiia Kucherenko | V. I. Dovhyi | Ľudmila Dulebová | M.Ya. Kuznetsova | Volodymyr Moravskyi
Source: Chemistry Technology and Application of Substances (article, 2024-06-01)
Field: Materials Science — General Materials Science

Рhysico-mechanical properties of epoxy composites filled with metallized polyamide granule The physical and mechanical properties of epoxy composites filled with copper-plated polyamide granules were investigated. Physico-mechanical properties were evaluated based on the results of tensile and impact toughness studies. It is shown that the obtained composites have high strength properties, which are preserved at the level of the unfilled matrix. It was established that the presence of polyamide granules of a copper shell on the surface has little effect on the change in the physical and mechanical properties of epoxy composites. An attempt was made to explain the obtained results using the values of the strength of the adhesive layer formed between the epoxy matrix and the surface of the filler, which is different in nature. Composite material ; Materials science ; Epoxy ; Polyamide ; Ultimate tensile strength ; Toughness ; Izod impact strength test ; Copper ; Granule (geology) ; Adhesive ; Layer (electronics) Material Properties and Applications ; Polymer Science and Applications ; Epoxy Resin Curing Processes
Authors: Damien Toquer | Lydéric Bocquet | Paul Robin
Source: arXiv (Cornell University) (preprint, 2024-10-04)
Field: Chemistry — Physical and Theoretical Chemistry

Ionic association and Wien effect in 2D confined electrolytes Recent experimental advances in nanofluidics have allowed to explore ion transport across molecular-scale pores, in particular for iontronic applications. Two dimensional nanochannels -- in which a single molecular layer of electrolyte is confined between solid walls -- constitute a unique platform to investigate fluid and ion transport in extreme confinement, highlighting unconventional transport properties. In this work, we study ionic association in 2D nanochannels, and its consequences on non-linear ionic transport, using both molecular dynamics simulations and analytical theory. We show that under sufficient confinement, ions assemble into pairs or larger clusters in a process analogous to a Kosterlitz-Thouless transition, here modified by the dielectric confinement. We further show that the breaking of pairs results in an electric-field dependent conduction, a mechanism usually known as the second Wien effect. However the 2D nature of the system results in non-universal, temperature-dependent, scaling of the conductivity with electric field, leading to ionic coulomb blockade in some regimes. A 2D generalization of the Onsager theory fully accounts for the non-linear transport. These results suggest ways to exploit electrostatic interactions between ions to build new nanofluidic devices. Association (psychology) ; Electrolyte ; Ionic bonding ; Chemistry ; Inorganic chemistry ; Ion ; Psychology ; Physical chemistry ; Organic chemistry ; Electrode ; Psychotherapist Electrostatics and Colloid Interactions ; Spectroscopy and Quantum Chemical Studies ; Ionic liquids properties and applications
Authors: Emilianto Sefri Bere | Binastya Anggara Sekti
Source: Journal of Systems Engineering and Information Technology (JOSEIT) (article, 2024-10-26)
Field: Chemistry — Analytical Chemistry

The Convolutional Neural Network as a Tool for Predicting Fruit Quality and Freshness Based on Images The lack of technology that can monitor changes in fruit quality in the supply chain can result in a large amount of fruit being wasted. Digital imagery can be used to reduce fruit wastage in monitoring and predicting fruit quality throughout its life. In post-harvest engineering, digital images can be used as virtual representations of real products. This research will present a new approach to monitor the quality changes of banana fruit with machine learning-based imagery, using a thermal camera to acquire data with its ability to detect surface area and physiological changes of banana fruit. In this research, model training has been performed using intelligent technology from SAP after the thermal data dataset has been built. By using thermal information to monitor the status of the fruit, this solution utilizes a deep artificial neural network. The training process has shown that it is more accurate. Therefore, the thermal imaging technique has been used as a data source to create a machine learning-based digital twin of the fruit that can reduce waste in the food supply chain. Thus, 99% prediction accuracy has been achieved. Convolutional neural network ; Computer science ; Quality (philosophy) ; Artificial intelligence ; Pattern recognition (psychology) Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI
Authors: Ryotaro Koshoji | Taisuke Ozaki
Source: arXiv (Cornell University) (preprint, 2024-04-22)
Field: Chemistry — Physical and Theoretical Chemistry

Mathematical Crystal Chemistry Efficient heuristics have predicted many functional materials such as high-temperature superconducting hydrides, while inorganic structural chemistry explains why and how the crystal structures are stabilized. Here we develop the paired mathematical programming formalism for searching and systematizing the structural prototypes of crystals. The first is the minimization of the volume of the unit cell under the constraints of only the minimum and maximum distances between pairs of atoms. We show the capabilities of linear relaxations of inequality constraints to optimize structures by the steepest-descent method, which is computationally very efficient. The second is the discrete optimization to assign five kinds of geometrical constraints including chemical bonds for pairs of atoms. Under the constraints, the two object functions, formulated as mathematical programming, are alternately optimized to realize the given coordination numbers of atoms. This approach successfully generates a wide variety of crystal structures of oxides such as spinel, pyrochlore-$α$, and $\mathrm{K}_2 \mathrm{NiF}_4$ structures. Crystal chemistry ; Chemistry ; Crystal structure ; Crystallography History and advancements in chemistry