jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: A. V. Malinin | В. Д. Ситдиков | V. E. Tkacheva | A.K. Makatrov | I. V. Valekzhanin | A. N. Markin
Source: Frontier materials & technologies (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Characteristic properties of the microstructure and microtexture of medium-carbon steel subjected to sulfide stress cracking Increasing the resistance of steel products to sulfide stress cracking (SSC) is one of the topical issues of the oil and gas industry. Among various factors determining the SSC resistance of a material is the structure-phase state of the material itself and the crystallographic texture associated with it. This paper analyzes these features using the scanning electron microscopy (SEM), transmission electron microscopy (TEM), and microroentgen electron backscattered diffraction (EBSD) techniques. As the research material, a production string (PS) coupling made of medium-carbon steel was selected, which collapsed by the mechanism of hydrogen embrittlement and subsequent SSC. For the first time, by the SEM method, using the location and mutual orientation of cementite (Fe3C) particles, at high magnifications, the authors demonstrated the possibilities of identifying the components of upper bainite, lower bainite, and tempered martensite in steels. The presence of the detected structural components of steel was confirmed by transmission electron microscopy (TEM). Using the EBSD method, the detailed studies of microtexture were conducted to identify the type and nature of the microcrack propagation. It is established that the processes of hydrogen embrittlement and subsequent SSC lead to the formation of {101} &lt;0¯10&gt;, {100} &lt;001&gt;, {122} &lt;2¯10&gt;, {013} &lt;211&gt;, {111} &lt;¯100&gt;, {133} &lt;1 ̅2 ̅1&gt;, {32 ̅6 ̅} &lt;201&gt; grain orientations. It is shown that the strengthening of orientations of {001} &lt;110&gt;, {100} &lt;001&gt;, {112} &lt;111&gt;, and {133} &lt;1 ̅2 ̅1&gt; types worsens the SSC resistance of the material. Using the EBSD analysis method, the influence of coincident site lattice (CSL) grain boundaries on the nature of microcrack propagation is estimated. It is found that the Σ 3 CSL grain boundaries between the {122} &lt;2¯10&gt; and {111} &lt;¯100&gt;, {012} &lt;1 ̅1 ̅0&gt;, {100} &lt;001&gt; plates of the upper bainite inhibit the microcrack development, and the Σ 13b, Σ 29a, and Σ 39a CSL grain boundaries, contribute to the accelerated propagation of microcracks. For comparative analysis, similar studies were carried out in an unbroken (original) coupling before operation. Electron backscatter diffraction ; Materials science ; Cementite ; Bainite ; Metallurgy ; Transmission electron microscopy ; Martensite ; Microstructure ; Scanning electron microscope ; Austenite ; Hydrogen embrittlement ; Composite material ; Corrosion ; Nanotechnology Material Properties and Failure Mechanisms ; Engineering Diagnostics and Reliability ; Engineering and Environmental Studies
Authors: Muh. Wahyu Syabani | Iswahyuni Iswahyuni | Warmiati Warmiati | Kutut Aji Prayitno | Henny Saraswati | Rahmandhika Firdauzha Hary Hernandha
Source: Indonesian Journal of Halal Research (article, 2023-08-31)
Field: Materials Science — Biomaterials

Unveiling the Signature of Halal Leather: A Comparative Study of Surface Morphology, Functional Groups and Thermal Characteristics The halal certification of products holds significant importance for Muslim consumers, necessitating the development of reliable techniques for identifying leather products made from raw materials. This study employed rapid and accurate analytical methods to distinguish between cowhide, pigskin, and artificial leather. A combination of scanning electron microscope (SEM), Fourier transform infrared spectroscopy (FTIR), and differential scanning calorimetry (DSC) was used to assess the variations in collagen fiber structures and thermal stability among the leather samples. The findings revealed that morphological surface analysis, including grain patterns and pores, facilitated swift differentiation between different leather types. Pigskins exhibit three-hole patterns on their morphological surface compared to cowhide, with random pores and tighter grain patterns, whereas artificial leather lacks natural grain patterns and pores altogether. While FTIR spectra exhibited similarities between cowhide and pigskin leathers, variations in vibration intensity enabled effective discrimination. Artificial leather, particularly PVC-based materials, displayed distinct spectra, allowing FTIR spectroscopy to effectively discern between halal and non-halal leather. Cowhide possesses strong and sharp vibration at wavenumber 1736, 1277, and 817 cm-1 compared to pigskin, which has stronger vibration at 1534 cm-1. Meanwhile, PVC-based artificial leather exhibited stretching at 1723 and 744 cm-1 wavenumbers. DSC analysis proved valuable in differentiating between genuine and artificial leather based on unique peaks and thermal behavior. These three techniques provide reliable means to determine the raw material origins of leather products. Fourier transform infrared spectroscopy ; Differential scanning calorimetry ; Materials science ; Thermal stability ; Scanning electron microscope ; Raw material ; Composite material ; Chemical engineering ; Chemistry ; Engineering Collagen: Extraction and Characterization ; Cultural Heritage Materials Analysis ; Identification and Quantification in Food
Authors: Giacomo Tenti | Kousuke Nakano | Andrea Tirelli | Sandro Sorella | Michele Casula
Source: arXiv (Cornell University) (preprint, 2023-01-09)
Field: Materials Science — Materials Chemistry

Principal deuterium Hugoniot via Quantum Monte Carlo and $Δ$-learning We present a study of the principal deuterium Hugoniot for pressures up to $150$ GPa, using Machine Learning potentials (MLPs) trained with Quantum Monte Carlo (QMC) energies, forces and pressures. In particular, we adopted a recently proposed workflow based on the combination of Gaussian kernel regression and $Δ$-learning. By fully taking advantage of this method, we explicitly considered finite-temperature electrons in the dynamics, whose effects are highly relevant for temperatures above $10$ kK. The Hugoniot curve obtained by our MLPs shows a good agreement with the most recent experiments, particularly in the region below 60 GPa. At larger pressures, our Hugoniot curve is slightly more compressible than the one yielded by experiments, whose uncertainties generally increase, however, with pressure. Our work demonstrates that QMC can be successfully combined with $Δ$-learning to deploy reliable MLPs for complex extended systems across different thermodynamic conditions, by keeping the QMC precision at the computational cost of a mean-field calculation. Quantum Monte Carlo ; Monte Carlo method ; Statistical physics ; Kernel (algebra) ; Gaussian ; Computer science ; Deuterium ; Work (physics) ; Quantum ; Physics ; Mathematics ; Quantum mechanics ; Statistics ; Discrete mathematics Machine Learning in Materials Science ; Quantum, superfluid, helium dynamics ; Phase Equilibria and Thermodynamics
Authors: Peter P. Toma | Md Ali Muntaha | Joel B. Harley | Michael Tonks
Source: arXiv (Cornell University) (preprint, 2023-10-12)
Field: Materials Science — Materials Chemistry

Modeling Fission Gas Release at the Mesoscale using Multiscale DenseNet Regression with Attention Mechanism and Inception Blocks Mesoscale simulations of fission gas release (FGR) in nuclear fuel provide a powerful tool for understanding how microstructure evolution impacts FGR, but they are computationally intensive. In this study, we present an alternate, data-driven approach, using deep learning to predict instantaneous FGR flux from 2D nuclear fuel microstructure images. Four convolutional neural network (CNN) architectures with multiscale regression are trained and evaluated on simulated FGR data generated using a hybrid phase field/cluster dynamics model. All four networks show high predictive power, with $R^{2}$ values above 98%. The best performing network combine a Convolutional Block Attention Module (CBAM) and InceptionNet mechanisms to provide superior accuracy (mean absolute percentage error of 4.4%), training stability, and robustness on very low instantaneous FGR flux values. Mesoscale meteorology ; Robustness (evolution) ; Convolutional neural network ; Computer science ; Regression ; Fission ; Artificial intelligence ; Pattern recognition (psychology) ; Statistics ; Physics ; Mathematics ; Chemistry ; Nuclear physics ; Meteorology Nuclear Materials and Properties ; Nuclear reactor physics and engineering ; Nuclear Physics and Applications
Authors: Sarah Kowsari Mogadam | Mahdi Salimi | Seyyed Mohammad Taghi Bathaee | Davar Mirabasi
Source: IET Power Electronics (article, 2023-12-09)
Field: Materials Science — Materials Chemistry

Experimental evaluation of the backstepping‐based input resistance controller in step‐up DC–DC converter for maximum power point tracking of the thermoelectric generators Abstract In this paper, a novel non‐linear model‐based approach is presented for maximum power point (MPP) tracking of thermoelectric generators (TEGs) using the backstepping controller. Considering the output voltage range of the thermoelectric devices, a step‐up DC–DC converter is employed as an interface between the load and input power source. According to the maximum power transfer theorem, if the equivalent input resistance of the converter ( R in ) is equal to the internal resistance of the input source ( R TEG ), the TEG operation at the MPP will be achieved. Hence, defining the R TEG as a reference value and R in as a feedback variable for a closed‐loop controller, the backstepping non‐linear controller is developed for input resistance control of the boost DC–DC converter. Owing to the non‐linear nature of the error variable in the input resistance control of the converters, conventional linear controllers cannot guarantee the system's closed‐loop stability within an extensive operational range. However, despite changes in generator's open‐circuit voltage ( V OC ) and R TEG , the designed closed‐loop controller can successfully stabilize the thermoelectric converter in different operational conditions. Considering the Lyapunov theorem and the Barbalat lemma, the asymptotic stability of the backstepping controller is proved. During the steady‐state operation, the actual values of the V OC and R TEG are updated periodically by the measurement of the converter input voltage/current values. To verify the functionality of the designed control method, PC‐based simulations are carried out in MATLAB/Simulink software. Moreover, by using TMS320F28335 digital signal processor from Texas Instruments and a simple thermoelectric simulator, the experimental response of the proposed controller is evaluated in dynamic and steady‐state conditions. The developed closed‐loop system can track the MPP of a TEG with zero steady‐state error, regardless of uncertain parameter variations. Control theory (sociology) ; Controller (irrigation) ; Backstepping ; Converters ; Boost converter ; Computer science ; Thermoelectric generator ; Operating point ; Power (physics) ; Voltage ; Engineering ; Adaptive control ; Electronic engineering ; Thermoelectric effect ; Electrical engineering ; Physics ; Control (management) Advanced Thermoelectric Materials and Devices ; Photovoltaic System Optimization Techniques ; Advanced Battery Technologies Research
Authors: Maimoonah Ahmed | Abdelkader Ouda | Mohamed Abusharkh | Sandeep Singh Kohli | Khushwant Rai
Source: Preprints.org (preprint, 2023-05-06)
Field: Materials Science — Materials Chemistry

An Optimized Approach to Translate Technical Patents from English to Japanese Using Machine Translation Models Over the years, machine learning has emerged as a tool for automated translation and has been studied relentlessly for decades. RBMT, SMT, and NMT models have been used to achieve machine translation and the results have drastically improved from when research in this field first began. Although a few general-purpose translators such as Google Translate or Microsoft Translator have accurate translations compared to that of a human translator, many pieces of text containing highly technical terms or homonyms are often mistranslated completely. When considering the necessity and importance of translating technical patents from different domains, accuracy in translation is not something that can be compromised. This motivates the need to improve the performance of machine translation further. The scope of this paper covers three open-source machine translation models for the purpose of patent documentation translation from English to Japanese, evaluates their performance on patent data, and proposes a methodology that enabled us to improve one of the model’s BLEU score by 41.22%, achieving a BLEU score of 46.18. Machine translation ; Computer science ; Artificial intelligence ; Documentation ; Evaluation of machine translation ; Natural language processing ; Scope (computer science) ; Translation (biology) ; Technical documentation ; Field (mathematics) ; Machine translation software usability ; Machine learning ; Example-based machine translation ; Programming language Machine Learning in Materials Science ; Big Data and Digital Economy ; Intellectual Property and Patents
Authors: Zhixuan Pan | Jia Liu | Jing Liu | Xiaoge Ning | Zheng Qin | Lulu He
Source: Applied Sciences (article, 2023-03-06)
Field: Materials Science — Materials Chemistry

Active Disturbance Rejection Optimization Control for SOFCs in Offshore Wind Power With the development of offshore wind power (OWP)-based hydrogen production technology, hydrogen fuel cells play a critical role in buffering the mismatch between energy supply and demand in OWP systems. Benefitting from high efficiency, cleanliness, and nontoxicity, solid oxide fuel cells (SOFCs) have been extensively investigated. However, OWP-based SOFC systems are characterized by strong nonlinearity and uncertainty and are vulnerable to disturbance, which leads to appreciable fluctuations and even instability to the system output voltage. Since conventional PID control schemes cannot achieve favorable performance, a more advanced control method is imperative. In response, this paper proposes a linear active disturbance rejection control (LADRC) method to reduce the influence of disturbance and ensure the stability of SOFC systems. In addition, an improved firefly algorithm (IFA) was adopted to optimize LADRC parameters. A step inertia weight was introduced, and a random generation mechanism was adopted to replace 30% of individuals with low luminous degrees. Using optimized LADRC parameters, a series of Monte Carlo experiments were carried out to verify the system’s robustness. The experimental results show that the overshoot of the LADRC method optimized by the IFA can be reduced by 5.7% compared with the traditional PID controller, i.e., the influence of the voltage disturbance can be well suppressed. Active disturbance rejection control ; PID controller ; Control theory (sociology) ; Electric power system ; Computer science ; Engineering ; Automotive engineering ; Power (physics) ; Control engineering ; Nonlinear system ; Control (management) ; Physics ; Temperature control Advancements in Solid Oxide Fuel Cells ; Fuel Cells and Related Materials ; Maritime Transport Emissions and Efficiency
Authors: Syed Ahsan Haider | Abhishek Raj
Source: Research Square (preprint, 2023-03-22)
Field: Materials Science — Surfaces, Coatings and Films

Liquid drops on compliant and non-compliant substrates: An ellipsoid-based fitting for approximating drop shape and volume Abstract We present a novel method of 3-dimensional surface fitting of a droplet using ellipsoids such that the droplet is a combination of segments of two to four distinct ellipsoids. Further, this fitting method has been used to develop an analytical model estimating the volume of a droplet resting over compliant as well as non-compliant substrate. Here, we have used Glass and Poly(methyl methacrylate) (PMMA) substrates as rigid, and, Polydimethylsiloxane (PDMS) free-hanging thin membranes (with thickness ranging from 20 − 40 𝜇𝑚) as compliant substrates. The analytical model considers the base length, width, height, and contact angles of the droplet captured from the experiment, and estimates the droplet volume. The proposed analytical model could predict the volume correctly for droplets resting over compliant as well as non-compliant substrates with a maximum deviation of 16.6 % for the volume range of 5 − 70 𝜇𝐿. Further, the predictions from the proposed analytical model are compared with the spherical cap-based model for droplets placed over compliant as well as non-compliant substrates. The spherical cap-based model failed to estimate the droplet volume over a compliant substrate with an error of &gt; 50 %. Whereas, the proposed ellipsoid-based model could predict the droplet volume over compliant substrate correctly with a maximum error of 16.6 %. Also, the proposed analytical model predicts the volume of droplets even at high contact angle hysteresis (&gt; 50 𝑜 ) where the droplet has high radial asymmetry. Further, the study also illustrates how Artificial Neural Networks (ANNs) can be used to forecast droplet width and contact angle hysteresis (CAH). The droplet width predicted from ANN could be used to eliminate the requirement of measuring droplet width from the top view experimental image. The volume of the droplet can thus be predicted from its side profile alone when utilized in conjunction with the theoretical model. Further, we developed an ANN model which predicts the CAH of the droplet by considering the length scales of the droplet. The developed ANN models performed a very good prediction with an R-value of &gt; 0.98. Ellipsoid ; Volume (thermodynamics) ; Materials science ; Drop (telecommunication) ; Mechanics ; Polydimethylsiloxane ; Spherical cap ; Substrate (aquarium) ; Optics ; Composite material ; Geometry ; Thermodynamics ; Physics ; Mathematics ; Computer science ; Geology Surface Modification and Superhydrophobicity ; Electrowetting and Microfluidic Technologies ; Interactive and Immersive Displays
Authors: Brenda Andrade | Ashley Chen | Michael K. Gilson
Source: ChemRxiv (preprint, 2023-12-13)
Field: Chemistry — Spectroscopy

Host-Guest Systems for the SAMPL9 Blinded Prediction Challenge: Phenothiazine as a Privileged Scaffold for Binding to Cyclodextrins Model systems are widely used in biology and chemistry to gain insight into more complex systems. In the field of computational chemistry, researchers use host-guest systems, relatively simple exemplars of noncovalent binding, to train and test the computational methods used in drug discovery. Indeed, host-guest systems have been developed to support the community-wide blinded SAMPL prediction challenges for over a decade. While seeking new host-guest systems for the recent SAMPL9 binding prediction challenge, which is the focus of the present PCCP Themed Collection, we identified phenothiazine as a privileged scaffold for guests of β cyclodextrin (βCD) and its derivatives. Building on this observation, we used calorimetry and NMR spectroscopy to characterize the noncovalent association of native βCD and three methylated derivatives of βCD with five phenothiazine drugs. The strongest association observed, that of thioridazine and one of the methyl derivatives, exceeds the well-known high affinity of rimantidine with βCD. Intriguingly, however, methylation of βCD at the 3 position abolished detectible binding for all of the drugs studied. The dataset has a clear pattern of entropy-enthalpy compensation. The NMR data show that all of the drugs position at least one aromatic proton at the secondary face of the CD, and most also show evidence of deep penetration of the binding site. The results of this study were used in the SAMPL9 blinded binding affinity-prediction challenge, which are detailed in accompanying papers of the present Themed Collection. These data also open the phenothiazines and, potentially, chemically similar drugs, such as the tricyclic antidepressants, as relatively potent binders of βCD, setting the stage for future SAMPL challenge datasets and for possible applications as drug reversal agents. Phenothiazine ; Chemistry ; Non-covalent interactions ; Combinatorial chemistry ; Computational biology ; Hydrogen bond ; Molecule ; Pharmacology ; Organic chemistry ; Biology Analytical Chemistry and Chromatography ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Zhenghua Xin | Liusong Wang | Cunchuan Liu | Haifeng Xu | Zhenggao Pan | Lin Cui | Mingdong Li | Chao Wang | Liangyi Hu
Source: Research Square (preprint, 2023-02-28)
Field: Chemistry — Analytical Chemistry

Detection of infection degree of Botrytis disease in apple by fusing spectra and image features from hyperspectral imaging Abstract Apples are vulnerable to fungal infection for the duration of storage, transportation and processing. In this study, we explore hyperspectral imaging and deep learning networks to detect the degree of apple infection caused by Botrytis disease. Savitzky-Golay smoothing and multiplicative scatter correction are used to preprocess the raw spectra from HSI images, and principal component analysis is used to screen effective variables through correlation analysis. Then, the textural features are extracted from images of important wavelengths selected by competitive adaptive reweighted sampling using a gray-level gradient co-occurrence matrix. With the above features, a random forest, K-nearest neighbor, support vector machines, convolutional neural network (CNN) and input-preprocessed CNN (IPCNN) are used to develop identification models of the apple fungal infection degree. Classification results of the fusion of efficient spectra and textural features are much better than those using a single feature, and IPCNN has the best accuracy of the training and prediction sets, at 100% and 92.8%, respectively. Hence, the proposed method provides a technical basis for the investigation of fruit qualities. Hyperspectral imaging ; Pattern recognition (psychology) ; Artificial intelligence ; Computer science ; Smoothing ; Convolutional neural network ; Principal component analysis ; Feature (linguistics) ; Random forest ; Mathematics ; Computer vision Spectroscopy and Chemometric Analyses ; Essential Oils and Antimicrobial Activity ; Smart Agriculture and AI
Authors: Romain Menegaux | Emmanuel Jehanno | Margot Selosse | Julien Mairal
Source: arXiv (Cornell University) (preprint, 2023-04-21)
Field: Materials Science — Materials Chemistry

Self-Attention in Colors: Another Take on Encoding Graph Structure in Transformers We introduce a novel self-attention mechanism, which we call CSA (Chromatic Self-Attention), which extends the notion of attention scores to attention _filters_, independently modulating the feature channels. We showcase CSA in a fully-attentional graph Transformer CGT (Chromatic Graph Transformer) which integrates both graph structural information and edge features, completely bypassing the need for local message-passing components. Our method flexibly encodes graph structure through node-node interactions, by enriching the original edge features with a relative positional encoding scheme. We propose a new scheme based on random walks that encodes both structural and positional information, and show how to incorporate higher-order topological information, such as rings in molecular graphs. Our approach achieves state-of-the-art results on the ZINC benchmark dataset, while providing a flexible framework for encoding graph structure and incorporating higher-order topology. Computer science ; Graph ; Theoretical computer science ; Chromatic scale ; Lattice graph ; Attention network ; Topology (electrical circuits) ; Transformer ; Algorithm ; Line graph ; Artificial intelligence ; Voltage graph ; Mathematics ; Combinatorics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks