jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Bhagoji Bapurao Sul | K. Dhanalakshami
Source: Soft Computing (article, 2022-03-08)
Field: Materials Science — Materials Chemistry

RETRACTED ARTICLE: Machine learning-based self-sensing of the stiffness of shape memory coil actuator Stiffness ; Shape-memory alloy ; Soft robotics ; Artificial intelligence ; Artificial neural network ; Actuator ; Computer science ; Electromagnetic coil ; Robotics ; Engineering ; Robot ; Structural engineering ; Electrical engineering Shape Memory Alloy Transformations
Authors: Yaofeng Ji | Qingbo Lu | Qingyu Yao
Source: Mechanical sciences (article, 2022-03-23)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Short communication: A case study of stress monitoring with non-destructive stress measurement and deep learning algorithms Abstract. Non-destructive stress measurement is necessary to provide safety maintenance in some extreme machining environments. This paper reports a case study that reveals the potential application of automatic metal stress monitoring with the aid of the magnetic Barkhausen noise (MBN) signal and deep learning algorithms (convolutional neural network, CNN, and long short-term memory, LSTM). Specifically, we applied the experimental magnetic signals from steel samples to validate the feasibility and efficiency of two deep learning models for stress prediction. The results indicate that the CNN model possesses a faster training speed and a better test accuracy (91.4 %), which confirms the feasibility of automatic stress monitoring applications. Convolutional neural network ; Stress (linguistics) ; Deep learning ; Computer science ; Noise (video) ; Artificial intelligence ; Barkhausen effect ; Artificial neural network ; Machining ; Algorithm ; Machine learning ; Engineering ; Mechanical engineering Magnetic Properties and Applications ; Non-Destructive Testing Techniques ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Loukas Petrou | Julius Georgiou
Source: IEEE Access (article, 2022-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

An ASIC Architecture With Inter-Chip Networking for Individual Control of Adaptive-Metamaterial Cells In this paper, the architecture of an application-specific integrated circuit for adaptive metasurfaces is presented. The architecture allows scalable networking over large metasurfaces and reconfiguration of each unit-cell with unique complex impedance settings, for adjusting metasurface electromagnetic performance. The one-chip-design metasurface array, includes self-initialization/addressing, configuration-packet routing, and network adaptation for fault-tolerant dynamic metasurfaces. An asynchronous design is adopted for power efficiency and high scalability, whereas speed is enhanced through multilevel pipelining. The ability to dynamically form reconfigurable networks in conjunction with the clockless operation helps the metasurface to cover arbitrary physical shapes, implemented on both rigid or flexible substrates. The architecture is validated through transistor-level simulations of the complete design implemented in a commercially available process. Then, a variety of scalable and/or flexible networks are presented, exploiting the strengths offered by the architecture, to demonstrate its capabilities and estimated performance. The demonstrated results of the architecture and its networking capabilities, allow the realization of chip-to-chip programmability of metasurfaces with up to 2<sup>18</sup> ASICs. The individual ASICs can be reconfigured in 2 &#x03BC;s and consume only 342 &#x03BC;W of static power. Computer science ; Control reconfiguration ; Scalability ; Application-specific integrated circuit ; Computer architecture ; Embedded system ; Chip ; Telecommunications Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Antenna Design and Analysis
Authors: Ling Qin
Source: Applied Mathematics and Nonlinear Sciences (article, 2022-07-15)
Field: Materials Science — Polymers and Plastics

Optimization in Mathematics Modeling and Processing of New Type Silicate Glass Ceramics Abstract This paper applies the Gaussian random field to the mathematical modeling of new-type silicate glass-ceramic trachoma detection. The article established a three-dimensional numerical model of trachoma structure based on the anisotropic random field. Then, the open and closed operations in mathematical morphology are used to obtain the strongly connected boundaries of the new-type silicate glass-ceramic trachoma image. At the same time, the connected domain detection in binary morphology is used to remove the noise to obtain the target image of the silicate glass-ceramic pore. The study found that this method can better meet the requirements of silicate glass-ceramic trachoma measurement than the classic edge detection operator. Ceramic ; Silicate ; Trachoma ; Materials science ; Gaussian ; Glass-ceramic ; Cordierite ; Computer science ; Composite material ; Physics ; Engineering ; Chemical engineering Textile materials and evaluations
Authors: Chong Wen | Xue Zhao | Ge Mu | Menglu Chen | Xin Tang
Source: Coatings (article, 2022-06-22)
Field: Materials Science — Materials Chemistry

Simulation and Design of HgSe Colloidal Quantum-Dot Microspectrometers In recent years, colloidal quantum dots (CQD) have been intensively studied in various fields due to their excellent optical properties, such as size-tunable absorption features and wide spectral tunability. Therefore, CQDs are promising infrared materials to become alternatives for epitaxial semiconductors, such as HgCdTe, InSb, and type II superlattices. Here, we report a simulation study of a microspectrometer fabricated by integrating an intraband HgSe CQD detector with a distributed Bragg reflector (DBR). Intraband HgSe CQDs possess unique narrowband absorption and optical response, which makes them an ideal material platform to achieve high-resolution detection for infrared signatures, such as molecular vibration. A microspectrometer with a center wavelength of 4 µm is studied. The simulation results show that the optical absorption rate of the HgSe CQD detector can be increased by 300%, and the full-width-at-half-maximum (FWHM) is narrowed to 30%, realizing precise regulation of the absorption wavelength. The influence of the incident angle of light waves on the microspectrometer is also simulated, and the results show that the absorption rate of the HgSe quantum dot detector is increased 2–3 times within the incident angle of 0–23 degrees, reaching a spectral absorption rate of more than 80%. Therefore, we believe that HgSe CQDs are a promising material for realizing practical HgSe microspectrometers. Quantum dot ; Full width at half maximum ; Absorption (acoustics) ; Materials science ; Optoelectronics ; Detector ; Wavelength ; Infrared ; Narrowband ; Optics ; Superlattice ; Spectral width ; Physics Quantum Dots Synthesis And Properties ; Chalcogenide Semiconductor Thin Films ; Advanced Semiconductor Detectors and Materials
Authors: Nugi Asmara | Adhi Harmoko Saputro
Source: Advances in biological sciences research/Advances in Biological Sciences Research (article, 2022-01-01)
Field: Chemistry — Analytical Chemistry

Animal and Plant-Based Milk Identification System Using Hyperspectral Imaging and Convolutional Neural Network Milk is a beverage that completes human nutrition. It is produced by cows and goats and can be obtained by plants such as soy and coconut. The nutrition composition contained in kinds of milk is different from one another. The differences in nutrition composition have their identification potential, such as the processing, nutrition differences, purity, quality, etc. Hence, it is necessary to build a system that can identify milk types with a non-destructive method utilizing hyperspectral images and a Deep Learning algorithm. This research used a hyperspectral camera at a Visible and Near-Infrared (VNIR) range of light (400 -1000 nm). We used Convolutional Neural Network (CNN) as its image classification algorithm. Milk sample was collected from cow, goat, soy, and coconut and obtained exactly 1920 data. After the data was collected, we created datasets based on the type of classification tested. The category includes milk types with classes of animal-based and plant-based milk, the organisms that produce the milk with classes of coconut, cow, goat, and soy, and the processing method with classes of fresh and Ultra High Temperature (UHT). The tested algorithms of CNN architecture are GoogleNet, AlexNet, and Proposed CNN. The highest accuracy for 480 data was 100% reached by processing method classification of soy milk, and the computation took only 20 seconds. Meanwhile, the highest accuracy for 1920 data was 99.9% achieved by Proposed CNN architecture, and the calculation took only 78 seconds. These results showed that hyperspectral imaging and CNN algorithm are suitable for classifying types of milk. Hyperspectral imaging ; Convolutional neural network ; Computer science ; Artificial intelligence ; Identification (biology) ; Pattern recognition (psychology) ; Computer vision ; Biology ; Botany Spectroscopy and Chemometric Analyses ; Food Supply Chain Traceability ; Advanced Chemical Sensor Technologies
Authors: Yoichi Hosokawa | Ayako Oshima | Takaaki Hatanaka | Nobuhiro Ishida
Source: Minerals (article, 2022-01-25)
Field: Chemistry — Spectroscopy

Improved Recovery and Selectivity of Lanthanide-Ion-Binding Cyclic Peptide Hosts by Changing the Position of Acidic Amino Acids The development of an effective host molecule to separate lanthanide (Ln) ions and a method for predicting its guest recognition/self-assembly behavior based on primary chemical structures are highly sought after in both academia and industry. Herein, we report the improvement of one-pot Ln ion recovery and a performance prediction method for four new cyclic peptide hosts that differ in the position of acidic amino acids. These cyclic peptide hosts could recognize Ln3+ directly through a 1:1 complexation–precipitation process and exhibited high Lu3+ selectivity in spite of similar ion size and electronegativity when the positions of the acidic amino acids were changed. This unpredictable selectivity was explained by considering the dipole moment, lowest unoccupied molecular orbital, and cohesion energy. In addition, a semi-empirical function using these parameters was proposed for screening the sequence and estimating the isolated yields without long-time molecular dynamics calculations. The insights obtained from this study can be employed for the development of high-performance peptides for the selective recovery of Ln and other metal ions, as well as for the construction of diverse supramolecular recognition systems. Selectivity ; Peptide ; Lanthanide ; Electronegativity ; Ion ; Chemistry ; Amino acid ; Metal ions in aqueous solution ; Supramolecular chemistry ; Dipole ; Molecule ; Combinatorial chemistry ; Computational chemistry ; Chemical physics ; Organic chemistry ; Biochemistry Molecular Sensors and Ion Detection ; Supramolecular Chemistry and Complexes ; Supramolecular Self-Assembly in Materials
Authors: Marco Medves | Daniele Toffoli | Mauro Stener | Luca Sementa | Alessandro Fortunelli
Source: The Journal of Physical Chemistry A (article, 2022-08-24)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Coupling between Plasmonic and Molecular Excitations: TDDFT Investigation of an Ag-Nanorod/BODIPY–Dye Interaction A time-dependent density functional theory (TDDFT) computational approach is employed to study the optical coupling between a plasmonic system (a Ag<sub>50</sub> nanorod) and a fluorescent dye (BODIPY). It is found that the BODIPY dye can interact with a plasmonic system in a rather different and selective way according to the mutual orientation of the fragments. Indeed, (i) the plasmon excitation turns out to be sensitive to the presence of the BODIPY transition and (ii) this can lead to amplify or suppress the resonance accordingly to the relative orientation of the corresponding transition dipoles. To understand the coupling mechanism, we analyze the shape of the induced density in real space and the Individual Component Map of the Oscillator Strength (ICM-OS) plots and achieve a simple rationalization and insight on the origin and features of the coupling. The resulting possibility of understanding plasmon/fluorophore interactions by simple qualitative arguments opens the way to a rational design of hybrid (plasmon + dye) systems with the desired optical behavior. BODIPY ; Time-dependent density functional theory ; Plasmon ; Nanorod ; Fluorophore ; Materials science ; Surface plasmon resonance ; Coupling (piping) ; Chemical physics ; Bathochromic shift ; Density functional theory ; Photochemistry ; Molecular physics ; Fluorescence ; Nanotechnology ; Chemistry ; Optoelectronics ; Computational chemistry ; Physics ; Nanoparticle ; Optics Gold and Silver Nanoparticles Synthesis and Applications ; Nanocluster Synthesis and Applications ; Plasmonic and Surface Plasmon Research
Authors: Jiewei Ding | Ho-Kin Tang | Wing Chi Yu
Source: SciPost Physics (article, 2022-09-09)
Field: Materials Science — Materials Chemistry

Rapid detection of phase transitions from Monte Carlo samples before equilibrium We found that Bidirectional LSTM and Transformer can classify different phases of condensed matter models and determine the phase transition points by learning features in the Monte Carlo raw data before equilibrium. Our method can significantly reduce the time and computational resources required for probing phase transitions as compared to the conventional Monte Carlo simulation. We also provide evidence that the method is robust and the performance of the deep learning model is insensitive to the type of input data (we tested spin configurations of classical models and green functions of a quantum model), and it also performs well in detecting Kosterlitz-Thouless phase transitions. Monte Carlo method ; Statistical physics ; Quantum Monte Carlo ; Hybrid Monte Carlo ; Dynamic Monte Carlo method ; Phase transition ; Monte Carlo molecular modeling ; Monte Carlo method in statistical physics ; Computer science ; Algorithm ; Physics ; Markov chain Monte Carlo ; Condensed matter physics ; Mathematics ; Statistics Machine Learning in Materials Science ; Quantum many-body systems ; Statistical Mechanics and Entropy
Authors: Ákos Godó | K. Aoki | Atsushi Nakagawa | Yasushi Yagi
Source: IEEE Access (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Residue Assignment in Crystallographic Protein Electron Density Maps With 3D Convolutional Networks This work proposes a neural network architecture, called 3D FC-DenseNet, for assigning amino acid labels to X-ray crystallographic electron density maps without relying on the amino acid sequence of proteins. The 3DFC-DenseNet is able to treat the task as a 3D semantic segmentation problem, assigning amino acid labels directly to protein electron density maps. By creating dedicated data sets and models for high, medium and low resolution samples, our method matches the performance of crystallographic toolkits for primary structure assignment at high resolutions. Furthermore, it outperforms them at medium resolution and functions at low resolutions where current toolkits and human ability fails. Convolutional neural network ; Computer science ; Resolution (logic) ; Segmentation ; Electron density ; Protein structure ; Artificial intelligence ; Pattern recognition (psychology) ; Crystallography ; Topology (electrical circuits) ; Electron ; Chemistry ; Combinatorics ; Mathematics ; Physics ; Biochemistry Enzyme Structure and Function ; Machine Learning in Materials Science ; Genomics and Phylogenetic Studies
Authors: Xiaochen Ma | Kai Jiang | Cheng Zhou | Yanfen Xue | Yanhe Ma
Source: Microbial Biotechnology (article, 2022-01-05)
Field: Materials Science — Materials Chemistry

Identification and characterization of a novel GNAT superfamily N <sup>α</sup> ‐acetyltransferase from <i>Salinicoccus halodurans</i> H3B36 N<sup>α</sup> -acetyl-α-lysine was found as a new type of compatible solutes that acted as an organic cytoprotectant in the strain of Salinicoccus halodurans H3B36. A novel lysine N<sup>α</sup> -acetyltransferase gene (shkat), encoding an enzyme that catalysed the acetylation of lysine exclusively at α position, was identified from this moderate halophilic strain and expressed in Escherichia coli. Sequence analysis indicated ShKAT contained a highly conserved pyrophosphate-binding loop (Arg-Gly-Asn-Gly-Asn-Gly), which was a signature of the GNAT superfamily. ShKAT exclusively recognized free amino acids as substrate, including lysine and other basic amino acids. The enzyme showed a wide range of optimal pH value and was tolerant to high-alkali and high-salinity conditions. As a new member of the GNAT superfamily, the ShKAT was the first enzyme recognized free lysine as substrate. We believe this work gives an expanded perspective of the GNAT superfamily, and reveals great potential of the shkat gene to be applied in genetic engineering for resisting extreme conditions. Lysine ; Biochemistry ; Biology ; Enzyme ; Gene ; Amino acid ; Escherichia coli ; Peptide sequence ; Genetics Enzyme Structure and Function ; Biochemical and Molecular Research ; Peptidase Inhibition and Analysis
Authors: Hannah L. Reeves | Lee‐Ping Wang
Source: Glycobiology (article, 2022-11-02)
Field: Chemistry — Spectroscopy

The impact of conformational sampling on first-principles calculations of vicinal COCH J-couplings in carbohydrates Dihedral angles in organic molecules and biomolecules are vital structural parameters that can be indirectly probed by nuclear magnetic resonance (NMR) measurements of vicinal J-couplings. The empirical relations that map the measured couplings to dihedral angles are typically determined by fitting using static structural models, but this neglects the effects of thermal fluctuations at the finite temperature conditions under which NMR measurements are often taken. In this study, we calculate ensemble-averaged J-couplings for several structurally rigid carbohydrate derivatives using first-principles molecular dynamics simulations to sample the thermally accessible conformations around the minimum energy structure. Our results show that including thermal fluctuation effects significantly shifts the predicted couplings relative to single-point calculations at the energy minima, leading to improved agreement with experiments. This provides evidence that accounting for conformational sampling in first-principles calculations can improve the accuracy of NMR-based structure determination for structurally complex carbohydrates. Dihedral angle ; Vicinal ; Maxima and minima ; Chemistry ; Biomolecule ; J-coupling ; Molecule ; Computational chemistry ; Thermal ; Thermal fluctuations ; Molecular dynamics ; Sampling (signal processing) ; Chemical physics ; Molecular physics ; Nuclear magnetic resonance spectroscopy ; Physics ; Thermodynamics ; Stereochemistry ; Optics Molecular spectroscopy and chirality ; Spectroscopy and Quantum Chemical Studies ; Analytical Chemistry and Chromatography
Authors: Tianbai Huang | Stephan Kupfer | Martin Richter | Stefanie Gräfe | Robert Geitner
Source: ChemCatChem (article, 2022-07-22)
Field: Chemistry — Organic Chemistry

Bidentate Rh(I)‐Phosphine Complexes for the C−H Activation of Alkanes: Computational Modelling and Mechanistic Insight Abstract The C−H activation and subsequent carbonylation mediated by metal complexes, i. e., Rh(I) complexes, has drawn considerable attention in the past. To extend the mechanistic insight from Rh complexes featuring monodentate ligands like P(Me) 3 towards more active bisphosphines (PLP), a computationally derived fully conclusive mechanistic picture of the Rh(I)‐catalyzed C−H activation and carbonylation is presented here. Depending on the nature of the bisphosphine ligand, the highest lying transition state (TS) is associated either to the initial C−H activation in [Rh(PLP)(CO)(Cl)] or to the rearrangement of the chloride in [Rh(PLP)(H)(R)(Cl)]. The chloride rearrangement was found to play a key role in the subsequent carbonylation. A set of 20 complexes of different architectures was studied, in order to fine tune the C−H activation in a knowledge‐driven approach. The computational analysis suggests that a flexible ligand architecture with aromatic rings can potentially increase the performance of Rh‐based catalysts for the C−H activation. Carbonylation ; Chemistry ; Denticity ; Phosphine ; Ligand (biochemistry) ; Catalysis ; Rhodium ; Stereochemistry ; Medicinal chemistry ; Chloride ; Transition metal ; Metal ; Carbon monoxide ; Organic chemistry ; Receptor Catalytic C–H Functionalization Methods ; Asymmetric Hydrogenation and Catalysis ; Carbon dioxide utilization in catalysis
Authors: Shirin Nasr-Esfahani | Venkatesan Muthukumar | Emma Regentova | Kazem Taghva | Mohamed B. Trabia
Source: IEEE Access (article, 2022-01-01)
Field: Chemistry — Analytical Chemistry

Detection of Pits in Olive Using Hyperspectral Imaging Data The unknowing presence of pits in olives is an undesirable and even dangerous experience that affects overall quality of products contain olives or serving dishes in salad bars, airline meals, and cocktail lounges. Therefore, detection for removal of pits and pit fragments are very important for maintaining a high quality of products/services. The contributions of the paper are a) new hyperspectral data captured in a specific range of the wavelengths that are suitable for detecting olives with pits inside; b) developing a classification method, that is the CNN classifier that yields a high accuracy of pit detection. We have collected spectral responses of Manzanilla (black and green) and Gemlik (black) olives in the 400 nm- 900 nm spectrum. Olives have been classified using 1D convolutional neural networks (1D-CNN) with 99.5% and 97.69% of classification accuracies of pitted and whole olives for training and test sets, respectively. Further boosting of the accuracy up to 98.27% on the test dataset has been attained by CNN with a dropout layer. As expected, the CNN attained a better performance than those of the Support Vector Machine (SVM), k-Nearest Neighbors (KNN), Random Forest (RF), and Logistic Regression (LA) classifiers. Hyperspectral imaging ; Convolutional neural network ; Support vector machine ; Random forest ; Artificial intelligence ; Pattern recognition (psychology) ; Computer science ; Boosting (machine learning) ; Classifier (UML) ; Dropout (neural networks) ; Machine learning Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Spectroscopy Techniques in Biomedical and Chemical Research