jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Д. А. Агарков | М. А. Борик | G. M. Korableva | Aleksej V. Kulebyakin | Е. Е. Ломонова | Filipp Milovich | В. А. Мызина | П. А. Попов | N. Yu. Tabachkova
Source: Modern Electronic Materials (article, 2022-04-12)
Field: Materials Science — Ceramics and Composites

Thermal conductivity of single crystals zirconia stabilized by scandium, yttrium, gadolinium, and ytterbium oxides The phase composition and heat conductivity of (ZrO 2 ) 0.9 ( R 2 O 3 ) 0.1 solid solution single crystals have been studied, where R = (Gd, Yb, Sc, Y), (ZrO 2 ) 0.9 (Sc 2 O 3 ) 0.09 (Gd 2 O 3 ) 0.01 and (ZrO 2 ) 0.9 (Sc 2 O 3 ) 0.09 (Yb 2 O 3 ) 0.01 . Single crystals have been grown by directional melt crystallization in a cold skull. The phase composition of the crystals has been studied using X-ray diffraction and Raman spectroscopy. The heat conductivity of the crystals has been studied using the absolute steady-state technique of longitudinal heat flow in the 50–300 K range. We show that at a total stabilizing oxide concentration of 10 mol.% the phase composition of the crystals depends on the ionic radius of the stabilizing cation. The (ZrO 2 ) 0.9 (Sc 2 O 3 ) 0.1 crystals have the lowest heat conductivity in the 50–300 K range while the (ZrO 2 ) 0.9 (Gd 2 O 3 ) 0.1 solid solutions have the lowest heat conductivity at 300 K. Analysis of the experimental data suggests that the heat conductivity of the crystals depends mainly on the phase composition and ionic radius of the stabilizing cation. Phonon scattering caused by the difference in the weight of the co-doping oxide cation has a smaller effect on the heat conductivity. Materials science ; Analytical Chemistry (journal) ; Yttrium ; Ionic radius ; Thermal conductivity ; Ytterbium ; Scandium ; Crystallization ; Ionic conductivity ; Conductivity ; Raman spectroscopy ; Lutetium ; Oxide ; Crystallography ; Doping ; Ion ; Chemistry ; Physical chemistry ; Electrolyte ; Optics Advanced ceramic materials synthesis ; Glass properties and applications ; Nuclear materials and radiation effects
Authors: Silvia Goyanes
Source: Polymer Science Peer Review Journal (article, 2022-02-17)
Field: Materials Science — Polymers and Plastics

Conductive Particles in Anisotropic Conductive Films Polymer Science: Peer Review Journal Conductive Particles in Anisotropic Conductive Films Federico Trupp1, Roberto Cibils2 and Silvia Goyanes1* 1Physics Department, University of Buenos Aires, Argentina 2INVAP, Argentina *Corresponding author: Silvia Goyanes, Physics Department, University of Buenos Aires, Int Güiraldes 2160, Ciudad Autónoma de Buenos Aires, Argentina Submission: January 26, 2022;Published: February 17, 2022 DOI: 10.31031/PSPRJ.2022.03.000554 ISSN: 2770-6613 Volume3 Issue1 Electrical conductor ; Anisotropy ; Conductive polymer ; Engineering physics ; Materials science ; Humanities ; Physics ; Art ; Polymer ; Composite material ; Optics Textile materials and evaluations
Authors: Xiaoyi Huang | Haoran Xu | Jizheng Chu
Source: Mathematical Problems in Engineering (article, 2022-12-22)
Field: Chemistry — Analytical Chemistry

Nonlinear Model Order Selection: A GMM Clustering Approach Based on a Genetic Version of EM Algorithm Choosing an accurate model order is one of the important sections in system identification. Traditionally, the model order selection of a nonlinear system depends on a predetermined model. However, it requires excess calculation and is impossible to get rid of the trouble of the structural design of the model, once the specific model was determined. A false nearest neighbor (FNN) algorithm that only relies on input-output data to estimate the model order is proposed here. Due to the FNN algorithm is sensitive to its own threshold which is a crucial constant for evaluating the model structure, Gaussian mixture model (GMM) clustering based on a genetic version of the expectation-maximization (EM) algorithm and minimum description length (MDL) criterion is developed in this paper, where the order can be determined without relying on a specific model. The GMM clustering is proposed to calculate the threshold of the FNN. Then, the genetic algorithm and MDL criteria are embedded to optimize the calculation of the EM algorithm as reduce the influence of initial values and not prone to fall into local extreme values as well. Three examples are given here to indicate the superiority of this technique: simulation of a strongly nonlinear system, isothermal polymerization process, and Van der Vusse reaction in relevant reference. Finally, some typical modeling methods are conducted to confirm the validity of this approach. Mixture model ; Cluster analysis ; Expectation–maximization algorithm ; Minimum description length ; Genetic algorithm ; Model selection ; Nonlinear system ; Algorithm ; Selection (genetic algorithm) ; Computer science ; Process (computing) ; Mathematics ; Pattern recognition (psychology) ; Artificial intelligence ; Mathematical optimization ; Maximum likelihood ; Statistics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Thermodynamic properties of mixtures
Authors: Esmaeal Ghavanloo | S. Ahmad Fazelzadeh | Patrizia Trovalusci
Source: Archive of Applied Mechanics (article, 2022-10-29)
Field: Materials Science — Materials Chemistry

Mechanics of size-dependent materials Mechanics ; Classical mechanics ; Materials science ; Statistical physics ; Physics ; Mathematics Nonlocal and gradient elasticity in micro/nano structures ; Elasticity and Material Modeling ; Composite Structure Analysis and Optimization
Authors: Zhihao Lü | Zhang Zhong-fu | Xiaojun Zhang | Wanneng Yang | Longfu Zhu | Chenglong Huang
Source: Research Square (preprint, 2022-08-02)
Field: Chemistry — Analytical Chemistry

Intelligent identification on cotton verticillium wilt based on spectral and image feature fusion Abstract Background Verticillium wilt is the major disease of cotton, which would cause serious yield reduction and economic losses, and the identification of cotton verticillium wilt is of great significance to cotton research. However, the traditional method is still manual, which is subjective, inefficient, and labour-intensive, and therefore, this study has proposed a novel method for cotton verticillium wilt identification based on spectral and image feature fusion. The cotton hyper-spectral images have been collected, while the regions of interest (ROI) have been extracted as samples including 499 healthy leaves and 498 diseased leaves, and the average spectral information and RGB image of each sample were obtained. In spectral feature processing, the preprocessing methods including Savitzky-Golay smoothing (SG), multiplicative scatter correction (MSC), de-trending (DT) and mean normalization (MN) algorithms have been adopted, while the feature band extraction methods have adopted principal component analysis (PCA) and successive projections algorithm (SPA). In RGB image feature processing, the EfficientNet was applied to build classification model and 16 image features have been extracted from the last convolutional layer. And then, the obtained spectral and image features were fused, while the classification model was established by support vector machine (SVM) and back propagation neural network (BPNN). Additionally, the full spectrum and feature band spectrum were used as comparison for SVM and BPNN classification respectively. Result The results showed that the average accuracy of EfficientNet network for cotton verticillium wilt identification was 93.00%. By full spectrum, SG-MSC-BPNN model obtained the better performance with classification accuracy of 93.78%. By feature bands, SG-MN-SPA-BPNN model obtained the better performance with classification accuracy of 93.57%. By spectral and image fused features, SG-MN-SPA-FF-BPNN model obtained the best performance with classification accuracy of 98.80%. Conclusions The study demonstrated that it was feasible and effective to use fused spectral and image features based on hyper-spectral imaging to improve identification accuracy of cotton verticillium wilt. The study provided theoretical basis and methods for non-destructive and accurate identification of cotton verticillium wilt. Artificial intelligence ; Pattern recognition (psychology) ; Feature extraction ; Support vector machine ; Computer science ; Feature (linguistics) ; RGB color model ; Convolutional neural network ; Principal component analysis ; Mathematics Spectroscopy and Chemometric Analyses ; Industrial Vision Systems and Defect Detection
Authors: Manuel Athènes | Gilles Adjanor | Jérôme Creuze
Source: Physical Review Materials (article, 2022-01-27)
Field: Materials Science — Materials Chemistry

Estimating linear mass transport coefficients in solid solutions via correlation splitting and a law of total diffusion Directly computing linear mass transport coefficients in stochastic models entails integrating over time the equilibrium correlations between atomic displacements. Here, we show how to improve the accuracy of kinetic Monte Carlo simulations via correlation splitting and conditioning, which statistically amounts to estimating the mass transport coefficients through a law of total diffusion. We illustrate the approach with kinetic path sampling simulations of atomic diffusion in a random alloy model in which percolating solute clusters trap the mediating vacancy. There, Green functions serve to generate first-passage paths escaping the traps and to propagate the long-time dynamics. When they also serve to estimate mean-squared displacements via conditioning, colossal reductions of statistical errors are achieved. Statistical physics ; Diffusion ; Monte Carlo method ; Vacancy defect ; Kinetic energy ; Materials science ; Thermodynamics ; Physics ; Mathematics ; Statistics ; Condensed matter physics ; Classical mechanics Thermal properties of materials ; Nuclear Materials and Properties ; High Temperature Alloys and Creep
Authors: Kang Ding | Shiqiu Yin | Zhongwei Li | Shiju Jiang | Yang Yang | Wenbiao Zhou | Yingsheng Zhang | Bo Huang
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2022-01-25)
Field: Chemistry — Physical and Theoretical Chemistry

Observing non-covalent interactions in experimental electron density for macromolecular systems: A novel perspective for protein–ligand interaction research ABSTRACT We report for the first time the use of experimental electron density (ED) in the Protein Data Bank for modeling non-covalent interactions (NCIs) for protein–ligand complexes. Our methodology is based on the reduced electron density gradient (RDG) theory describing intermolecular NCI by ED and its first derivative. We established a database called the Experimental NCI Database (ExptNCI; http://ncidatabase.stonewise.cn/#/nci ) containing ED saddle points, indicating ~200,000 NCIs from over 12,000 protein–ligand complexes. We also demonstrated the use of the database for depicting amide–π interactions in a protein–ligand binding system. In summary, the database provides details on experimentally observed NCIs for protein–ligand complexes and can support future studies, including studies on rarely documented NCIs and the development of artificial intelligent models for protein–ligand binding prediction. Ligand (biochemistry) ; Intermolecular force ; Macromolecule ; Protein ligand ; Covalent bond ; Chemistry ; Electron density ; Saddle ; Computer science ; Electron ; Biochemistry ; Physics ; Molecule ; Mathematics ; Receptor ; Organic chemistry Crystallography and molecular interactions ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Shiwei Chen | Jinliang Zhang | Huilong Liu | Tongyue Qiu | Haoxiang Tang | Zunmin Zhang
Source: Molecules (article, 2022-11-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Dissipative Particle Dynamics Simulation of the Sensitive Anchoring Behavior of Smectic Liquid Crystals at Aqueous Phase Rational design of thermotropic liquid crystal (LC)-based sensors utilizing different mesophases holds great promise to open up novel detection modalities for various chemical and biological applications. In this context, we present a dissipative particle dynamics study to explore the unique anchoring behavior of nematic and smectic LCs at amphiphile-laden aqueous-LC interface. By increasing the surface coverage of amphiphiles, two distinct anchoring sequences, a continuous planar-tilted-homeotropic transition and a discontinuous planar-to-homeotropic transition, can be observed for the nematic and smectic LCs, respectively. More importantly, the latter occurs at a much lower surface coverage of amphiphiles, demonstrating an outstanding sensitivity for the smectic-based sensors. The dynamics of reorientation further reveals that the formation of homeotropic smectic anchoring is mainly governed by the synchronous growth of smectic layers through the LCs, which is significantly different from the mechanism of interface-to-bulk ordering propagation in nematic anchoring. Furthermore, the smectic LCs have also been proven to possess a potential selectivity in response to a subtle change in the chain rigidity of amphiphiles. These simulation findings are promising and would be valuable for the development of novel smectic-based sensors. Homeotropic alignment ; Liquid crystal ; Anchoring ; Thermotropic crystal ; Dissipative particle dynamics ; Chemical physics ; Materials science ; Phase transition ; Micelle ; Amphiphile ; Planar ; Phase (matter) ; Nanotechnology ; Aqueous solution ; Chemistry ; Condensed matter physics ; Organic chemistry ; Liquid crystalline ; Polymer ; Physics ; Optoelectronics ; Copolymer ; Composite material Liquid Crystal Research Advancements ; Lipid Membrane Structure and Behavior ; Nonlinear Dynamics and Pattern Formation
Authors: Zachariah Baird | Vahur Oja
Source: Oil Shale (article, 2022-02-22)
Field: Chemistry — Analytical Chemistry

Multivariate models based on infrared spectra as a substitute for oil property correlations to predict thermodynamic properties: evaluated on the basis of the narrow-boiling fractions of Kukersite retort oil This article investigates a potential for using models based on infrared spectra to predict basic thermodynamic properties of narrow boiling range oil fractions or pseudocomponents. The work took advantage of the simultaneous availability of a property database of narrow boiling range fractions of Kukersite oil shale retort oil (from the industrial retorting process) together with infrared spectra of these fractions. The work was based on the hypothesis that the models based on infrared spectra could potentially be used to reduce experimental data when developing other predictive methods, or even as a substitute for other prediction methods. In this study four basic oil properties, which are often used to predict other thermodynamic properties, were predicted from infrared spectra using support vector regression. These were specific gravity, refractive index parameter, average boiling point and molecular weight. According to bulk property prediction approach these selected properties can be grouped into energy parameters (two former) and size parameters (two latter). It was found that, for distillation fractions with varying compositions, both the energy parameters (specific gravity, refractive index) as well as the size parameters (molecular weight, average boiling point) can be predicted from Fourier transform infrared (FTIR) spectra, and that the accuracy of the predictions based on infrared spectra was comparable with the accuracies of petroleum bulk property correlations. Thus, infrared spectra can provide a convenient alternative in the thermodynamic property prediction field because they can be easily measured and correlated to a wide variety of properties. Boiling point ; Infrared spectroscopy ; Retort ; Infrared ; Chemistry ; Boiling ; Spectral line ; Organic chemistry ; Optics ; Physics Spectroscopy and Chemometric Analyses ; Petroleum Processing and Analysis ; Phase Equilibria and Thermodynamics
Authors: Steve P. Meisburger | David A. Case | Nozomi Ando
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2022-08-22)
Field: Materials Science — Materials Chemistry

Robust total X-ray scattering workflow to study correlated motion of proteins in crystals Abstract The breathing motions of proteins are thought to play a critical role in function. However, current techniques to study key collective motions are limited to spectroscopy and computation. We present a high-resolution experimental approach based on the total scattering from protein crystals at room temperature (TS/RT-MX) that captures both structure and collective motions. To reveal the scattering signal from protein motions, we developed a general workflow that enables robust subtraction of lattice disorder. The workflow introduces two new methods: GOODVIBES, a detailed and refinable lattice disorder model based on the rigid-body vibrations of a crystalline elastic network; and DISCOBALL, an independent method of validation that estimates the displacement covariance between proteins in the lattice in real space. We demonstrate the robustness of this workflow and further demonstrate how it can be interfaced with MD simulations towards obtaining high-resolution insight into functionally important protein motions. Computation ; Workflow ; Lattice (music) ; Scattering ; Robustness (evolution) ; Physics ; Statistical physics ; Raman scattering ; Algorithm ; Computer science ; Biological system ; Computational physics ; Crystallography ; Chemistry ; Raman spectroscopy ; Acoustics ; Optics ; Biology Enzyme Structure and Function ; Protein Structure and Dynamics ; Advanced NMR Techniques and Applications
Authors: Issar Arab | William E. Fondrie | Kris Laukens | Wout Bittremieux
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2022-10-03)
Field: Chemistry — Spectroscopy

Semi-supervised machine learning for sensitive open modification spectral library searching Abstract A key analysis task in mass spectrometry proteomics is matching the acquired tandem mass spectra to their originating peptides by sequence database searching or spectral library searching. Machine learning is an increasingly popular post-processing approach to maximize the number of confident spectrum identifications that can be obtained at a given false discovery rate threshold. Here, we have integrated semi-supervised machine learning in the ANN-SoLo tool, an efficient spectral library search engine that is optimized for open modification searching to identify peptides with any type of post-translational modification. We show that machine learning rescoring boosts the number of spectra that can be identified for both standard searching and open searching, and we provide insights into relevant spectrum characteristics harnessed by the machine learning model. The semi-supervised machine learning functionality has now been fully integrated into ANN-SoLo, which is available as open source under the permissive Apache 2.0 license on GitHub at https://github.com/bittremieux/ANN-SoLo . Computer science ; Artificial intelligence ; Machine learning ; License ; Key (lock) ; Machine translation ; Matching (statistics) ; Supervised learning ; Artificial neural network Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Ikhsan Rahman Husein | Minarni Shiddiq | Dewi Laila Sari | Annisa Putri
Source: Science, Technology, and Communication Journal. (article, 2022-06-30)
Field: Chemistry — Analytical Chemistry

Wavelength Dependence of Optical Electronic Nose for Ripeness Detection of Oil Palm Fresh Fruits Electronic noses have been developed as an artificial sense to imitate the human nose based on volatile gases. They have been used in agriculture to monitor and predict fruit qualities such as ripeness and chemical contents. Electronic noses with semiconductor gas sensors have a limitation of volatile gases detected. Therefore, optical electronic noses using an output coupler become an alternative due to the wavelength dependency of the gas types. The ripeness of oil palm fresh fruit bunches (FFBs) is one of the main factors in determining the quality of crude palm oil. Electronic detection is preferable to substitute the manual methods for ripeness detection. This study built an optical electronic nose and analyzed the wavelength dependence on the detection performance. The electronic nose consisted of an infrared LED and a photodiode enclosed in a chamber, a microcontroller, and a sample chamber. We tested four infrared LEDs with 760, 780, 840, and 910 nm wavelengths. The samples were fruitlets taken from oil palm FFBs, previously categorized as unripe, ripe, and overripe. The fruits were grounded, inserted into the sample chamber, and preheated to increase the volatile gas concentration. Trapezoid areas represented the time-varying output voltages for each LED. The results showed that overripe fruits had slightly higher trapezoid areas. LED of 840 nm wavelength obtained higher trapezoid areas. LED of 780 nm was the best candidate for the electronic nose due to linearity in increasing trapezoid areas. The results showed the potential of the optical electronic nose for oil palm fruits. Ripeness ; Palm oil ; Electronic nose ; Palm ; Wavelength ; Horticulture ; Optoelectronics ; Food science ; Materials science ; Physics ; Chemistry ; Biology ; Nanotechnology ; Ripening Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Garrett M. Ginell | Aidan J. Flynn | Alex S. Holehouse
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2022-09-19)
Field: Chemistry — Spectroscopy

SHEPHARD: a modular and extensible software architecture for analyzing and annotating large protein datasets ABSTRACT The emergence of high-throughput experiments and high-resolution computational predictions has led to an explosion in the quality and volume of protein sequence annotations at proteomic scales. Unfortunately, integrating and analyzing complex sequence annotations remains logistically challenging. Here we present SHEPHARD, a software package that makes large-scale integrative protein bioinformatics trivial. SHEPHARD is provided as a stand-alone package and with a pre-compiled set of human annotations in a Google Colab notebook. Computer science ; Modular design ; Software ; Set (abstract data type) ; Extensibility ; Software engineering ; Volume (thermodynamics) ; Sequence (biology) ; Data mining ; Computational biology ; Programming language ; Biology Advanced Proteomics Techniques and Applications ; Genomics and Phylogenetic Studies ; Bioinformatics and Genomic Networks