jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yingying Wu | Yun Ou | Jinlin Peng | Chihou Lei
Source: Preprints.org (preprint, 2023-08-10)
Field: Materials Science — Materials Chemistry

Phase Structures, Electromechanical Responses and Electrocaloric Effects in K0.5Na0.5NbO3 Epitaxial Film Controlled by Non-Isometric Misfit Strain Environmentally friendly lead-free K1-xNaxNbO3 (KNN) ceramics possess electromechanical properties comparable to lead-based ferroelectric materials, but cannot meet the needs of device miniaturization, and the corresponding thin films lack of theoretical and experimental studies. To this end, we developed the nonlinear phenomenological theory for ferroelectric materials to study the effects of non-equiaxed misfit strain on the phase structure, electromechanical properties and electrical response of K0.5Na0.5NbO3 epitaxial films and constructed the in-plane misfit strain (u1-u2) phase diagrams. The results show that K0.5Na0.5NbO3 epitaxial film under non-equiaxed in-plane strain can exhibit abundant phase structures, including orthorhombic a1c, a2c and a1a2 phases, tetragonal a1, a2 and c phases, and monoclinic r12 phases. Moreover, in the vicinity of a2c-r12, a1c-c and a1a2-a2 phase boundaries, K0.5Na0.5NbO3 epitaxial films exhibit excellent dielectric constant ε11, while at a2c -r12 and a1c-c phase boundaries large piezoelectric coefficient d15 is observed. It was also found that high permittivity ε33 and piezoelectric coefficients d33 exist near the a2c - a2, r12- a1a2 and a1c-a1 phase boundaries due to the existence of polymorphic phase boundaries (PPB) in the KNN system, which makes it easy to polarize near the phase boundaries, and the polarizability changes suddenly, leading to electromechanical enhancement. In addition, the results show that the K0.5Na0.5NbO3 thin films possess a large electrocaloric response at the phase boundary at the r12-a1a2 and a1c-a1 phase boundaries. And the maximum adiabatic temperature change ∆T is about 3.62 K when the electric field change is 30 MV/m at room temperature, which is significantly enhanced compared with equiaxed strain. This study provides theoretical guidance for obtaining K1-xNaxNbO3 epitaxial thin films with excellent properties. Materials science ; Phase boundary ; Ferroelectricity ; Condensed matter physics ; Piezoelectricity ; Phase (matter) ; Perovskite (structure) ; Dielectric ; Composite material ; Optoelectronics ; Crystallography Ferroelectric and Piezoelectric Materials ; Multiferroics and related materials ; Acoustic Wave Resonator Technologies
Authors: Zhexi Yang | Tianqi Zhao | Peng Zhang | Chenxia Li | Bo Fang | Zhi Hong | Mingzhou Yu | X. M. Jing
Source: REVIEWS ON ADVANCED MATERIALS SCIENCE (article, 2023-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

All-dielectric tunable zero-refractive index metamaterials based on phase change materials Abstract We propose a tunable all-dielectric zero-index metamaterial based on phase change material. By constructing the alternating rod structure with phase change material and silica material, the zero-refractive index characteristic is realized. In order to verify the equivalent zero-index characteristic, we calculated the electromagnetic field distribution at center frequency. Based on phase invariance, it was found that this kind of metamaterial can achieve equivalent zero refractive index characteristics. To further achieve the polarization-insensitive zero refractive index, we propose a metamaterial structure based on the fishing nets structure. Based on the S-parameter inversion algorithm, the equivalent dielectric constant, equivalent permeability, equivalent impedance, and equivalent refractive index of the metamaterial were extracted. Based on the extracted equivalent parameters and the corresponding electromagnetic field distribution, the designed all-dielectric metamaterial has the tunable equivalent zero refractive index characteristic. Metamaterial ; Refractive index ; Materials science ; Dielectric ; Optics ; Polarization (electrochemistry) ; Zero (linguistics) ; Optoelectronics ; Physics Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Antenna Design and Analysis
Authors: Kuntal Ghosh | Timothy D. Loose | Gregory A. Voth
Source: ChemRxiv (preprint, 2023-09-18)
Field: Materials Science — Materials Chemistry

Can a Coarse-grained Water Model Capture the Key Physical Features of the Hydrophobic Effect? Coarse-grained (CG) Molecular Dynamics can be a powerful method for probing complex processes. However, most CG force fields use pairwise non-bonded interaction potentials sets, which can limit their ability to capture complex multi-body phenomena such as the hydrophobic effect. As the hydrophobic effect primarily manifests itself due to the non-polar solute affecting the nearby hydrogen bonding network in water, capturing such effects using a simple one CG site or “bead” water model is a challenge. In this work, we systematically test the ability of CG one site water models for capturing critical features of the solvent environment around a hydrophobe as well as the Potential of Mean Force (PMF) of neopentane association. We study two bottom-up water models: a Simple Pairwise (SP) Force-Matched water model constructed using the Multiscale Coarse-Graining method and the Bottom-Up Many-Body Projected Water (BUMPer) model, which has three-body correlations implicitly embedded into it. We also test the top-down monatomic (mW) and the Machine Learned mW (ML-mW) water models. We find that the top-down models perform well in capturing the structural correlation in the water functions near the hydrophobe, but not the energetics of the PMF. BUMPer outperforms the SP water model in capturing structural correlations, but also gives an accurate PMF in contrast to the two mW models. Our study highlights the importance of including three-body interactions in CG water models, either explicitly or implicitly, while in general highlighting the applicability of bottom-up CG water models for studying hydrophobic association effects in a quantitative fashion. Water model ; Potential of mean force ; Hydrophobe ; Biological system ; Molecular dynamics ; Pairwise comparison ; Hydrophobic effect ; Computer science ; Granularity ; Chemical physics ; Chemistry ; Statistical physics ; Physics ; Computational chemistry ; Artificial intelligence Machine Learning in Materials Science ; Advanced NMR Techniques and Applications ; Protein Structure and Dynamics
Authors: A. V. Sahakyan | M. K. Alenichev | A. D. Levin
Source: Industrial laboratory Diagnostics of materials (article, 2023-10-25)
Field: Chemistry — Analytical Chemistry

Characterization of cognacs and grape brandies by fluorescence spectra processed using machine learning methods A method for express characterization of cognacs and grape brandies is proposed in the case study of their classification by geographical origin. The method is based on the use of informative fragments of fluorescence spectra of samples of different geographic origin and their subsequent processing using machine learning algorithms. Three types of fluorescence spectra were selected, i.e., spectra of synchronous scanning at a wavelength difference of 50 nm, and emission spectra at an excitation wavelength of 250 and 280 nm. These spectra were measured for 43 samples of cognacs and grape brandies, which were divided into 3 classes according to their geographical origin, the regions of the Russian Federation (except for Dagestan), the Republic of Dagestan (Russian Federation), and the Republic of Armenia. A training set consisting of 33 samples and a test set consisting of 10 samples were formed from the samples under study. To train the models, an extreme gradient boosting, one of the modern machine learning algorithms, was chosen as suitable for a limited number of samples in the training set. The correctness of the sample recognition of the test set (consisting of 10 samples not used in training) was 100% for models based on emission spectra and spectra of synchronous scanning. The results obtained demonstrate the fundamental possibility of using informative fragments of fluorescence spectra in combination with machine learning to characterize cognacs and grape brandies, including their classification by the geographical origin. However, the use of this method in regulated procedures of the product control is possible only for cognacs and grape brandies with a protected geographical indication (designation of the origin). The above approach can also be used to classify other liquid food products (juices, honey, etc.). Artificial intelligence ; Pattern recognition (psychology) ; Correctness ; Spectral line ; Fluorescence ; Set (abstract data type) ; Computer science ; Mathematics ; Analytical Chemistry (journal) ; Algorithm ; Biological system ; Chemistry ; Physics ; Optics ; Chromatography ; Biology Spectroscopy and Chemometric Analyses ; Fermentation and Sensory Analysis ; Advanced Chemical Sensor Technologies
Authors: Kuntal Ghosh | Timothy D. Loose | Gregory A. Voth
Source: ChemRxiv (preprint, 2023-11-19)
Field: Materials Science — Materials Chemistry

Can a Coarse-grained Water Model Capture the Key Physical Features of the Hydrophobic Effect? Coarse-grained (CG) Molecular Dynamics can be a powerful method for probing complex processes. However, most CG force fields use pairwise non-bonded interaction potentials sets, which can limit their ability to capture complex multi-body phenomena such as the hydrophobic effect. As the hydrophobic effect primarily manifests itself due to the non-polar solute affecting the nearby hydrogen bonding network in water, capturing such effects using a simple one CG site or “bead” water model is a challenge. In this work, we systematically test the ability of CG one site water models for capturing critical features of the solvent environment around a hydrophobe as well as the Potential of Mean Force (PMF) of neopentane association. We study two bottom-up models: a Simple Pairwise (SP) Force-Matched water model constructed using the Multiscale Coarse-Graining method and the Bottom-Up Many-Body Projected Water (BUMPer) model, which has implicit three-body correlations. We also test the top-down monatomic (mW) and the Machine Learned mW (ML-mW) water models. The mW models perform well in capturing structural correlations, but not the energetics of the PMF. BUMPer outperforms SP in capturing structural correlations, but also gives an accurate PMF in contrast to the two mW models. Our study highlights the importance of including three-body interactions in CG water models, either explicitly or implicitly, while in general highlighting the applicability of bottom-up CG water models for studying hydrophobic effects in a quantitative fashion. This assertion comes with a caveat, however, regarding the accuracy of the enthalpy-entropy decomposition of the PMF of hydrophobe association. Water model ; Potential of mean force ; Molecular dynamics ; Hydrophobic effect ; Biological system ; Pairwise comparison ; Computer science ; Granularity ; Chemical physics ; Chemistry ; Statistical physics ; Physics ; Computational chemistry ; Artificial intelligence Machine Learning in Materials Science ; Protein Structure and Dynamics ; Spectroscopy and Quantum Chemical Studies
Authors: Florian A. Jung | Christine M. Papadakis
Source: Journal of Applied Crystallography (article, 2023-08-16)
Field: Materials Science — Surfaces, Coatings and Films

Strategy to simulate and fit 2D grazing-incidence small-angle X-ray scattering patterns of nanostructured thin films Grazing-incidence small-angle X-ray scattering (GISAXS) is a widely used method for the characterization of the nanostructure of supported thin films and enables time-resolved <i>in situ</i> measurements. The 2D scattering patterns contain detailed information about the nanostructures within the film and at its surface. However, this information is distorted not only by the reflection of the X-ray beam at the substrate-film interface and its refraction at the film surface but also by scattering of the substrate, the sample holder and other types of parasitic background scattering. In this work, a new, efficient strategy to simulate and fit 2D GISAXS patterns that explicitly includes these effects is introduced and demonstrated for (i) a model case nanostructured thin film on a substrate and (ii) experimental data from a microphase-separated block copolymer thin film. To make the protocol efficient, characteristic linecuts through the 2D GISAXS patterns, where the different contributions dominate, are analysed. The contributions of the substrate and the parasitic background scattering - which ideally are measured separately - are determined first and are used in the analysis of the 2D GISAXS patterns of the nanostructured, supported film. The nanostructures at the film surface and within the film are added step by step to the real-space model of the simulation, and their structural parameters are determined by minimizing the difference between simulated and experimental scattering patterns in the selected linecuts. Although in the present work the strategy is adapted for and tested with <i>BornAgain</i>, it can be easily used with other types of simulation software. The strategy is also applicable to grazing-incidence small-angle neutron scattering. Scattering ; Thin film ; Small-angle X-ray scattering ; Grazing-incidence small-angle scattering ; Optics ; Materials science ; Grazing ; Incidence (geometry) ; X-ray ; Small-angle scattering ; Nanotechnology ; Physics ; Small-angle neutron scattering Optical Coatings and Gratings ; Force Microscopy Techniques and Applications ; Nanofabrication and Lithography Techniques
Authors: Yichun Shi | Yu Pei | Nicholas Lamothe | Kirsten Macdonald | Sarah Jane Payne | Zhe She
Source: Electrochemical Science Advances (article, 2023-06-08)
Field: Chemistry — Electrochemistry

Electrochemical interference study of manganese and iron by multiplex method and the application for manganese analysis in drinking water Abstract Manganese is an emerging concern in drinking water, due to its potential health and aesthetic effects. Although accurate and sensitive, spectroscopic techniques for Mn 2+ detection are costly and not capable of rapid detection. Electrochemical methods, such as cathodic stripping voltammetry, have been intensively explored as portable low‐cost methods for Mn 2+ detection. Challenges of reliability and matrix interference are difficult to overcome with current electrochemical methods. Among the interference reagents, Fe 2+ is one of the biggest challenges for Mn 2+ detection. Herein, a new method based on multiplex chronoamperometry at potentials between 0.9 and 1.4 V by a multichannel potentiostat is explored for its ability for interference resistance and applicability for Mn 2+ detection in real drinking water samples. Compared to conventional one‐channel electrochemical techniques, the multiplex method generates a reliable pattern that is unique to the sample components. The interference between Mn 2+ and Fe 2+ is investigated and the results are promising even at 100:1 Fe 2+ :Mn 2+ concentrations. The detection limit determined for the multiplex method was 25.3 μM, and the optimum recovery rate in a real drinking water sample was 99.8%. Manganese ; Detection limit ; Chronoamperometry ; Multiplex ; Interference (communication) ; Electrochemistry ; Reagent ; Analytical Chemistry (journal) ; Cathodic protection ; Materials science ; Chemistry ; Electrode ; Computer science ; Chromatography ; Cyclic voltammetry ; Metallurgy ; Channel (broadcasting) ; Telecommunications ; Bioinformatics Electrochemical Analysis and Applications ; Analytical Chemistry and Sensors ; Advanced biosensing and bioanalysis techniques
Authors: Zhijin Zhang | Hang Zhang | Song Yue | Hao Wang
Source: Energies (article, 2023-03-21)
Field: Materials Science — Materials Chemistry

Contamination Deposit and Model of Insulator Insulators are widely used in transmission lines as insulating elements [...] Insulator (electricity) ; Contamination ; Electric power transmission ; Materials science ; Environmental science ; Engineering physics ; Electrical engineering ; Engineering ; Optoelectronics High voltage insulation and dielectric phenomena ; Thermal Analysis in Power Transmission ; Power Transformer Diagnostics and Insulation
Authors: Dhulipalla Ravindra Babu | R. C. Verma | Navneet Agrawal | Isha Suwalk
Source: Journal of Agricultural Engineering (India) (article, 2023-07-10)
Field: Chemistry — Analytical Chemistry

Classification of Defects in Potato Using Grey Level Co-Occurrence Matrix and Support Vector Machine An attempt was made to classify potato into five classes based on features of grey level co-occurrence matrix properties and support vector machine. Potato images were captured using the developed experimental set-up, which had a resolution of 0.22 mm per pixel. The four parameters of grey level co-occurrence matrix viz. variance, correlation, uniformity, and homogeneity were calculated from each image, and the data set was prepared. Sample sizes of crack, rotten, sprout, good, and skin-peel potatoes were 75, 39, 153, 96, and 635, respectively. Cracked, rotten, sprouted, good, and skin-peeled potato images were labelled as 1, 2, 3, 4, and 5, respectively. With addition of labels, the data set size increased to 998-by-five. Feature data set of 998-by-5 was fed into classification learner app in MATLAB. Combined parameters of grey level co-occurrence matrix of variance (contrast), correlation, uniformity (energy), and homogeneity were used in potato classifications. A cubic support vector machine had classification accuracy of 99.5 per cent. Quality parameters like true positive and positive predictive values were studied for cubic support vector machine model. True positive rates of 100, 97, 98, 99, and 100 were observed for crack, rotten, sprouted, good, and skin-peel categories, respectively. Crack and skin-peel categories had same number of potatoes in manual and cubic support vector machine classification. Mis-classified potatoes in cubic support vector machines were less as compared to manual classification by one, two, and two potatoes in rotten, sprouted, and good categories, respectively. Support vector machine ; Mathematics ; Homogeneity (statistics) ; Grey level ; Artificial intelligence ; Matrix (chemical analysis) ; Pattern recognition (psychology) ; Principal component analysis ; Statistics ; Horticulture ; Pixel ; Computer science ; Biology ; Materials science Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Identification and Quantification in Food
Authors: Aleksander Winski | Jan Ludwiczak | Małgorzata Orłowska | Rafał Madaj | Kamil Kamiński | Stanisław Dunin-Horkawicz
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2023-05-24)
Field: Chemistry — Spectroscopy

AlphaFold2 captures the conformational landscape of the HAMP signaling domain Abstract In this study, we present a conformational landscape of 5000 AlphaFold2 models of the HAMP domain, a short helical bundle that transduces signals from sensors to effectors in two-component signaling proteins such as sensory histidine kinases and chemoreceptors. The landscape reveals the conformational variability of the HAMP domain, including rotations, shifts, displacements, and tilts of helices, many combinations of which have not been observed in experimental structures. HAMP domains belonging to a single family tend to occupy a defined region of the landscape, even when their sequence similarity is low, suggesting that individual HAMP families have evolved to operate in a specific conformational range. The functional importance of this structural conservation is illustrated by poly-HAMP arrays, in which HAMP domains from families with opposite conformational preferences alternate, consistent with the rotational model of signal transduction. The only poly-HAMP arrays that violate this rule are predicted to be of recent evolutionary origin and structurally unstable. Finally, we identify a family of HAMP domains that are likely to be dynamic due to the presence of a conserved pi-helical bulge. All code associated with this work, including a tool for rapid sequence-based prediction of the rotational state in HAMP domains, is deposited at https://github.com/labstructbioinf/HAMPpred . HAMP ; HAMP domain ; Helix bundle ; Bundle ; Domain (mathematical analysis) ; Signaling proteins ; Biology ; Protein structure ; Biophysics ; Signal transduction ; Biochemistry ; Mathematics ; Binding site ; Binding domain ; Materials science Mass Spectrometry Techniques and Applications ; Microbial Natural Products and Biosynthesis ; Protein Kinase Regulation and GTPase Signaling
Authors: Gianluca Finocchio | Tatyana Krivobokova
Source: arXiv (Cornell University) (preprint, 2023-07-17)
Field: Chemistry — Analytical Chemistry

An extended latent factor framework for ill-posed linear regression In many applications, particularly in the natural sciences, the available high-dimensional set of features may contain variables that are not correlated with the response under consideration. Such irrelevant features can, in certain cases, hinder both the accurate estimation and meaningful interpretation of the effects of the relevant features on the response. At the same time, the relevant features may also be well-approximated within a low-dimensional linear subspace, rendering the problem ill-posed. These observations motivate an extension of the classical latent factor model for linear regression. In this extended framework, it is assumed that, up to an unknown orthogonal transformation, the feature set comprises two subsets: one relevant and one irrelevant to the response. A joint low-dimensionality is imposed solely on the relevant features and the response variable. This setting enables the analysis of arbitrary linear dimensionality reduction techniques under a random design setting. In particular, it is demonstrated why principal component regression (PCR) is generally unsuitable for most applications. The framework also allows for a comprehensive analysis of the partial least squares (PLS) algorithm under random design. High-probability convergence rates are established for the sample PLS estimator with respect to an oracle latent coefficient vector, along with the corresponding linear prediction risk. Additionally, it is shown that early stopping can be guided by the empirical condition numbers of the projected design matrix. The theoretical results are validated through numerical studies on both real and simulated datasets. Curse of dimensionality ; Estimator ; Latent variable ; Partial least squares regression ; Linear regression ; Mathematics ; Factor analysis ; Regression ; Projection (relational algebra) ; Computer science ; Algorithm ; Applied mathematics ; Mathematical optimization ; Statistics Spectroscopy and Chemometric Analyses ; Remote-Sensing Image Classification ; Statistical Methods and Inference