jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zhang Chun-xia | Yuxiao He | Weiguo Li | Xiaoming Guo | Chunyan Xiao | Tongqian Zhao
Source: Water (article, 2022-05-17)
Field: Materials Science — Biomaterials

High-Throughput Sequencing of Diatom Community, Its Spatial and Temporal Variation and Interrelationships with Physicochemical Factors in Danjiangkou Reservoir, China Diatoms constitute an important part of the phytoplankton community in lakes and reservoirs and play a significant role in regulating ecological balance. Danjiangkou Reservoir is the water source area of the middle route of China’s South-to-North Water Diversion project. In order to explore the spatial and temporal distribution and know the governing factors of the diatom community, 18srRNA sequencing was carried out from seven sampling sites of the reservoir. At the same time, the concentration of nutrients present in the collected sample water was also determined. The results showed that a total of 51 genera and 96 species were thriving the community of diatoms in Danjiangkou Reservoir. Discostella was dominant in summer and autumn, accounting for 98.84% and 62.71% of the diatom abundance, respectively. Aulacoseira was dominant in spring and winter, accounting for 60.62% and 60.90%, respectively. Discostella and Aulacoseira showed significant differences in seasonal variation (p &lt; 0.05). The colinear network of diatoms changed significantly with the seasons, mainly consisting of Aulacoseira, Discostella, and Stephanodiscus. RDA redundancy analysis showed that water temperature (WT), total nitrogen (TN), NH4+-N, pH, and electrical conductivity (Cond) were the main environmental factors driving the changes in diatom community structure. Diatom ; Nutrient ; Community structure ; Environmental science ; Ecology ; Phytoplankton ; Abundance (ecology) ; Hydrology (agriculture) ; Seasonality ; Water quality ; Biology ; Geology Diatoms and Algae Research ; Microbial Community Ecology and Physiology ; Aquatic Invertebrate Ecology and Behavior
Authors: Abdelghani Yahiou | Hacène Mellah | Abdelhafid Bayadi
Source: International Journal of Engineering (article, 2022-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Inrush Current Reduction by a Point-on-wave Energization Strategy and Sequential Phase Shifting in Three-Phase Transformer The main goal of this work is the mitigation of inrush current in a three-phase transformer. This inrush current appears when energizing a no-load or lightly loaded transformer. It can reach very high values and can cause failures in the electrical system. The control strategy is achieved by considering the value of the residual flux when the transformer is de-energized as well as by respecting the phase shifting between the three phases. To measure the inrush current, an experimental configuration with a data acquisition system using dSPACE 1104 card was developed and is presented in this paper. A technique to control the circuit breaker for energizing a 2 kVA three-phase transformer without the appearance of inrush current was also tested and applied in the experimental setup. The specific contribution of this work is that this technique is applied in the measurements with a thorough investigation of the residual flux. The proposed technique achieved complete elimination of the inrush current. Inrush current ; Circuit breaker ; Transformer ; Current transformer ; Engineering ; Three-phase ; Electrical engineering ; Control theory (sociology) ; Computer science ; Voltage ; Control (management) Magnetic Properties and Applications ; Power Transformer Diagnostics and Insulation ; Power Systems Fault Detection
Authors: Xiyang Luo | Wout Bittremieux | Johannes Griss | Eric W. Deutsch | Timo Sachsenberg | Lev I. Levitsky | Mark V. Ivanov | Julia A. Bubis | Ralf Gabriels | Henry Webel | Aniel Sánchez | Mingze Bai | Lukas Käll | Yasset Pérez‐Riverol
Source: Journal of Proteome Research (article, 2022-05-13)
Field: Chemistry — Spectroscopy

A Comprehensive Evaluation of Consensus Spectrum Generation Methods in Proteomics Spectrum clustering is a powerful strategy to minimize redundant mass spectra by grouping them based on similarity, with the aim of forming groups of mass spectra from the same repeatedly measured analytes. Each such group of near-identical spectra can be represented by its so-called consensus spectrum for downstream processing. Although several algorithms for spectrum clustering have been adequately benchmarked and tested, the influence of the consensus spectrum generation step is rarely evaluated. Here, we present an implementation and benchmark of common consensus spectrum algorithms, including spectrum averaging, spectrum binning, the most similar spectrum, and the best-identified spectrum. We have analyzed diverse public data sets using two different clustering algorithms (spectra-cluster and MaRaCluster) to evaluate how the consensus spectrum generation procedure influences downstream peptide identification. The BEST and BIN methods were found the most reliable methods for consensus spectrum generation, including for data sets with post-translational modifications (PTM) such as phosphorylation. All source code and data of the present study are freely available on GitHub at https://github.com/statisticalbiotechnology/representative-spectra-benchmark. Cluster analysis ; Benchmark (surveying) ; Computer science ; Similarity (geometry) ; Data mining ; Spectrum (functional analysis) ; Identification (biology) ; Algorithm ; Broad spectrum ; Artificial intelligence ; Chemistry ; Physics ; Biology Advanced Proteomics Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Mass Spectrometry Techniques and Applications
Authors: Said el Bouhaddani | Hae‐Won Uh | Geurt Jongbloed | Jeanine J. Houwing‐Duistermaat
Source: Journal of the Royal Statistical Society Series C (Applied Statistics) (article, 2022-08-16)
Field: Chemistry — Analytical Chemistry

Statistical Integration of Heterogeneous Omics Data: Probabilistic Two-Way Partial Least Squares (PO2PLS) Abstract The availability of multi-omics data has revolutionized the life sciences by creating avenues for integrated system-level approaches. Data integration links the information across datasets to better understand the underlying biological processes. However, high dimensionality, correlations and heterogeneity pose statistical and computational challenges. We propose a general framework, probabilistic two-way partial least squares (PO2PLS), that addresses these challenges. PO2PLS models the relationship between two datasets using joint and data-specific latent variables. For maximum likelihood estimation of the parameters, we propose a novel fast EM algorithm and show that the estimator is asymptotically normally distributed. A global test for the relationship between two datasets is proposed, specifically addressing the high dimensionality, and its asymptotic distribution is derived. Notably, several existing data integration methods are special cases of PO2PLS. Via extensive simulations, we show that PO2PLS performs better than alternatives in feature selection and prediction performance. In addition, the asymptotic distribution appears to hold when the sample size is sufficiently large. We illustrate PO2PLS with two examples from commonly used study designs: a large population cohort and a small case–control study. Besides recovering known relationships, PO2PLS also identified novel findings. The methods are implemented in our R-package PO2PLS. Partial least squares regression ; Probabilistic logic ; Data integration ; Omics ; Computer science ; Statistical model ; Data mining ; Statistics ; Computational biology ; Data science ; Mathematics ; Machine learning ; Biology ; Artificial intelligence ; Bioinformatics Spectroscopy and Chemometric Analyses ; Gene expression and cancer classification ; Sensory Analysis and Statistical Methods
Authors: Jayasubba Reddy Yarava | Lokeswara Rao Potnuru | Bholanath Pahari | Srinu Tothadi | K. V. Ramanathan
Source: Journal of Magnetic Resonance Open (article, 2022-03-23)
Field: Chemistry — Spectroscopy

Supramolecular Synthon Identification in Azelaic Acid – Isonicotinamide Supramolecular synthons are the smallest structural units that contain the information inherent in building large molecular assemblies. Here, we apply solid-state NMR methods for the identification of the synthon in a cocrystal of Azelaic acid [(CH2)7(COOH)2]– Isonicotinamide [C6H6N2O]. It is observed that the spin-lattice relaxation time of protons in the cocrystal is long, implying that the choice of experiments, particularly those that involve low-sensitivity nuclei, could be time consuming. It is noticed that the two dimensional 1H double quantum (DQ) – 1H single quantum (SQ) correlation experiment at very fast MAS (60 kHz) is able to provide information on proton-proton proximities that leads to an understanding of the synthon structure. Experiments such as the CP-Reverse CP and 1H–13C HETCOR have been used to obtain spectral assignment which was additionally confirmed by first-principle GIPAW NMR chemical shift calculations. Present study indicates the basic unit to be a heterosynthon in Azelaic acid (AZA) – Isonicotinamide (INIC) cocrystal formed by two different modes of intermolecular connectivity, viz. acid–pyridine and acid–amide hydrogen bonding interactions. The presence of a CH…O type bond involving a hydrogen of INIC is also indicated from the observation of a larger chemical shift of the corresponding proton. The experimental protocol presented in this paper is expected to be a useful tool for the estimation of the basic structural arrangement of the synthons in a rapid fashion. Synthon ; Cocrystal ; Isonicotinamide ; Azelaic acid ; Supramolecular chemistry ; Chemistry ; Hydrogen bond ; Crystallography ; Crystal engineering ; Amide ; Proton ; Stereochemistry ; Computational chemistry ; Molecule ; Crystal structure ; Organic chemistry ; Physics Advanced NMR Techniques and Applications ; Crystallography and molecular interactions ; Solid-state spectroscopy and crystallography
Authors: Helena Lopes | Susana P. Silva | João Paulo Carvalho | José Machado
Source: Scientific Reports (article, 2022-05-14)
Field: Materials Science — Polymers and Plastics

A new modelling approach for predicting process evolution of cork-rubber composites slabs vulcanization Abstract In order to predict the evolution of the vulcanization process of cork-rubber slabs, a numerical approach was developed combining heat transfer by conduction and kinetics models. A one-dimensional model was applied to predict the evolution of temperature and degree of cure at different stages of the vulcanization of a cork-rubber composite. Also, due to the degradation verified by the compound, an existent reversion model was added to the problem. Based on rheometer data, cure and reversion parameters were determined. Experimental data were used to determine the thermal properties of the compound, assuming a constant value or according to its degree of cure and temperature. The results obtained by simulation showed a good correspondence with experimental results, even when assuming constant thermal properties. The application of the proposed methodology provides information about the optimum process parameters for each thickness slab, without compromising the homogeneity and characteristics of the final product, which can be a valuable tool during the development and product stages of cork-rubber composites. Vulcanization ; Cork ; Natural rubber ; Materials science ; Composite material ; Rheometer ; Slab ; Composite number ; Structural engineering Natural Fiber Reinforced Composites ; Polymer Nanocomposites and Properties ; Plant Surface Properties and Treatments
Authors: Ryo Nakayama | Ryota Shimizu | Taishi Haga | Takefumi Kimura | Yasunobu Ando | Shigeru Kobayashi | Nobuaki Yasuo | Masakazu Sekijima | Taro Hitosugi
Source: Science and Technology of Advanced Materials Methods (article, 2022-04-19)
Field: Materials Science — Materials Chemistry

Tuning of Bayesian optimization for materials synthesis: simulation of the one-dimensional case Materials exploration requires the optimization of a multidimensional space including the chemical composition and synthesis parameters such as temperature and pressure. Bayesian optimization has attracted attention as a method for efficient multidimensional optimization. Appropriate choices of the acquisition function and initial values of the hyperparameters of the kernel functions are essential for the Bayesian optimization of synthesis conditions in a small number of experiments. However, to date, there has been little discussion on how to tune Bayesian optimization for materials exploration, and no guidelines have been provided for materials scientists. In this study, we investigated the optimum initial values of the hyperparameters in Bayesian optimization using one-dimensional model functions that mimic actual materials syntheses. The optimal lengthscale and variance for different process windows of materials synthesis were investigated. It was shown that the use of an appropriate acquisition function and suitable initial values of the hyperparameters of the kernel functions enable the optimization of synthesis conditions in a small number of trials. These results provide insight for enabling fully automated and autonomous materials synthesis using Bayesian optimization and robotics for materials exploration. Bayesian optimization ; Hyperparameter ; Computer science ; Bayesian probability ; Kernel (algebra) ; Artificial intelligence ; Process optimization ; Kriging ; Machine learning ; Mathematical optimization ; Mathematics ; Engineering Machine Learning in Materials Science ; Quality Function Deployment in Product Design ; Advanced Multi-Objective Optimization Algorithms
Authors: Junpei Oba | Seiji Kajita
Source: Physical Review Materials (article, 2022-02-08)
Field: Materials Science — Materials Chemistry

Crystal morphing: Structural interpolation including crystal invariances Material data are used to accelerate the development of materials. For the crystalline solid domain, however, informatics schemes are challenged by the encoding of crystal structures because embedding the invariances with respect to translation, rotation, and unit-cell choice is intractable by data augmentation schemes. We propose an efficient search space in which identical structures are reduced given such crystal operators. This crystal-invariant search space is created by morphing between the topologies of known crystal structures in accordance with the gradient of a smooth overlap of atomic positions descriptor formulated in reciprocal space as a metric. This crystal morphing is examined using an identification task of the structures from an x-ray diffraction spectrum. By applying Bayesian optimization on the search space, good initial structures for the Rietveld analysis are achieved via automatic determination without expert operations. Our method is applicable in the autonomous search of crystals, various structural analyses, and design of metamaterials. Morphing ; Reciprocal lattice ; Materials science ; Crystal (programming language) ; Algorithm ; Embedding ; Computer science ; Diffraction ; Artificial intelligence ; Optics ; Physics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Crystallization and Solubility Studies
Authors: Maryam Razavipour | M. Cabecera Gonzalez | Naveen K. Singh | Çağla Eren Çimenci | Nicole Chu | Emilio I. Alarcón | Julio Villafuerte | B. Jodoin
Source: Journal of Thermal Spray Technology (review, 2022-01-01)
Field: Chemistry — Organic Chemistry

Biofilm Inhibition and Antiviral Response of Cold Sprayed and Shot Peened Copper Surfaces: Effect of Surface Morphology and Microstructure Biofilm ; Copper ; Microstructure ; Materials science ; Scanning electron microscope ; Metallurgy ; Shot peening ; Bacteria ; Chemical engineering ; Composite material ; Biology Antimicrobial agents and applications ; Bacterial biofilms and quorum sensing ; Water Treatment and Disinfection
Authors: Arturo Sauza‐de la Vega | Leonardo J. Duarte | Arnaldo F. Silva | Jonathan M. Skelton | Tomás Rocha‐Rinza | Paul L. A. Popelier
Source: Physical Chemistry Chemical Physics (article, 2022-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

Towards an atomistic understanding of polymorphism in molecular solids Understanding and controlling polymorphism in molecular solids is a major unsolved problem in crystal engineering. While the ability to calculate accurate lattice energies with atomistic modelling provides valuable insight into the associated energy scales, existing methods cannot connect energy differences to the delicate balances of intra- and intermolecular forces that ultimately determine polymorph stability ordering. We report herein a protocol for applying Quantum Chemical Topology (QCT) to study the key intra- and intermolecular interactions in molecular solids, which we use to compare the three known polymorphs of succinic acid including the recently-discovered γ form. QCT provides a rigorous partitioning of the total energy into contributions associated with topological atoms, and a quantitative and chemically intuitive description of the intra- and intermolecular interactions. The newly-proposed Relative Energy Gradient (REG) method ranks atomistic energy terms (steric, electrostatic and exchange) by their importance in constructing the total energy profile for a chemical process. We find that the conformation of the succinic acid molecule is governed by a balance of large and opposing electrostatic interactions, while the H-bond dimerisation is governed by a combination of electrostatics and sterics. In the solids, an atomistic energy balance emerges that governs the contraction, towards the equilibrium geometry, of a molecular cluster representing the bulk crystal. The protocol we put forward is as general as the capabilities of the underlying quantum-mechanical model and it can provide novel perspectives on polymorphism in a wide range of chemical systems. Intermolecular force ; Crystal structure prediction ; Steric effects ; Electrostatics ; Lattice energy ; Chemical physics ; Polymorphism (computer science) ; Chemistry ; Molecule ; Molecular solid ; Crystal engineering ; Crystal (programming language) ; Molecular dynamics ; Energy landscape ; Statistical physics ; Computational chemistry ; Crystal structure ; Physics ; Thermodynamics ; Crystallography ; Hydrogen bond ; Physical chemistry ; Computer science ; Stereochemistry ; Organic chemistry Crystallography and molecular interactions ; X-ray Diffraction in Crystallography ; Advanced Chemical Physics Studies
Authors: Thorbjørn Skovhus | Thomas Olsen | H. M. Rønnow
Source: Physical Review Materials (article, 2022-05-04)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Influence of static correlation on the magnon dynamics of an itinerant ferromagnet with competing exchange interactions: First-principles study of MnBi We present first-principles calculations of the dynamic susceptibility in strained and doped ferromagnetic MnBi using time-dependent density functional theory. In spite of being a metal, MnBi exhibits signatures of strong correlation and a proper description in the framework of density functional theory requires Hubbard corrections to the $\mathrm{Mn}\phantom{\rule{0.16em}{0ex}}d$ orbitals. To permit calculations of the dynamic susceptibility with Hubbard corrections applied to the ground-state electronic structure, we use a consistent rescaling of the exchange-correlation kernel maintaining the delicate balance between the magnon dispersion and the Stoner continuum. We find excellent agreement with the experimentally observed magnon dispersion for pristine MnBi and show that the material undergoes a phase transition to helical order under application of either doping or strain. The presented methodology paves the way for future linear response time-dependent density functional theory studies of magnetic phase transitions, also for the wide range of materials with pronounced static correlation effects that are not accounted for at the local density approximation level. Magnon ; Condensed matter physics ; Ferromagnetism ; Density functional theory ; Materials science ; Hybrid functional ; Physics ; Quantum mechanics Magnetic Properties of Alloys ; Magnetic properties of thin films ; Magnetic and transport properties of perovskites and related materials
Authors: Nitin Lokachari | Goutham Kukkadapu | Brian D. Etz | Gina M. Fioroni | Seonah Kim | Mathias Steglich | András Bödi | Patrick Hemberger | С. С. Матвеев | Anna Thomas | Hwasup Song | Guillaume Vanhove | Kuiwen Zhang | Guillaume Dayma | Maxence Lailliau | Zeynep Serinyel | Alexander A. Konnov | Philippe Dagaut | William J. Pitz | Henry J. Curran
Source: Combustion and Flame (article, 2022-12-13)
Field: Chemistry — Organic Chemistry

A comprehensive experimental and kinetic modeling study of di-isobutylene isomers: Part 2 A wide variety of high temperature experimental data obtained in this study complement the data on the oxidation of the two di-isobutylene isomers presented in Part I and offers a basis for an extensive validation of the kinetic model developed in this study. Due to the increasing importance of unimolecular decomposition reactions in high-temperature combustion, we have investigated the di-isobutylene isomers in high dilution utilizing a pyrolysis microflow reactor and detected radical intermediates and stable products using vacuum ultraviolet (VUV) synchrotron radiation and photoelectron photoion coincidence (PEPICO) spectroscopy. Additional speciation data at oxidative conditions were also recorded utilizing a plug flow reactor at atmospheric pressure in the temperature range 725–1150 K at equivalence ratios of 1.0 and 3.0 and at residence times of 0.35 s and 0.22 s, respectively. Combustion products were analyzed using gas chromatography (GC) and mass spectrometry (MS). Ignition delay time measurements for di-isobutylene were performed at pressures of 15 and 30 bar at equivalence ratios of 0.5, 1.0, and 2.0 diluted in ‘air’ in the temperature range 900–1400 K using a high-pressure shock-tube facility. New measurements of the laminar burning velocities of di-isobutylene/air flames are also presented. The experiments were performed using the heat flux method at atmospheric pressure and initial temperatures of 298–358 K. Moreover, data consistency was assessed with the help of analysis of the temperature and pressure dependencies of laminar burning velocity measurements, which was interpreted using an empirical power-law expression. Electronic structure calculations were performed to compute the energy barriers to the formation of many of the product species formed. The predictions of the present mechanism were found to be in adequate agreement with the wide variety of experimental measurements performed. Chemistry ; Isobutylene ; Atmospheric pressure ; Shock tube ; Laminar flow ; Combustion ; Analytical Chemistry (journal) ; Isoprene ; Atmospheric temperature range ; Kinetic energy ; Thermodynamics ; Shock wave ; Physical chemistry ; Organic chemistry Chemical Thermodynamics and Molecular Structure ; Advanced Combustion Engine Technologies ; Free Radicals and Antioxidants