jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: N. Barbero | S. Holenstein | Tian Shang | Z. Shermadini | Felix Lochner | Ilya Eremin | Chuanhe Wang | Guang‐Han Cao | R. Khasanov | H. R. Ott | J. Mesot | T. Shiroka
Source: Physical review. B./Physical review. B (article, 2018-04-13)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Pressure effects on the electronic properties of the undoped superconductor ThFeAsN The recently synthesized ThFeAsN iron pnictide superconductor exhibits a ${T}_{c}$ of 30 K, the highest of the 1111-type series in the absence of chemical doping. To understand how pressure affects its electronic properties, we carried out microscopic investigations up to 3 GPa via magnetization, nuclear magnetic resonance, and muon-spin rotation experiments. The temperature dependence of the $^{75}\mathrm{As}$ Knight shift, the spin-lattice relaxation rates, and the magnetic penetration depth suggest a multiband ${s}^{\ifmmode\pm\else\textpm\fi{}}$-wave gap symmetry in the dirty limit, whereas the gap-to-${T}_{c}$ ratio $\mathrm{\ensuremath{\Delta}}/{k}_{\mathrm{B}}{T}_{c}$ hints at a strong-coupling scenario. Pressure modulates the geometrical parameters, thus reducing ${T}_{c}$ as well as ${T}_{m}$, the temperature where magnetic-relaxation rates are maximized, both at the same rate of approximately $\ensuremath{-}1.1\phantom{\rule{0.28em}{0ex}}\mathrm{K}/\mathrm{GPa}$. This decrease in ${T}_{c}$ with pressure is consistent with band-structure calculations, which relate it to the deformation of the Fe $3{d}_{{z}^{2}}$ orbitals. Condensed matter physics ; Knight shift ; Superconductivity ; Muon spin spectroscopy ; Pnictogen ; Physics ; Magnetization ; Atomic orbital ; Materials science ; Electron ; Magnetic field ; Quantum mechanics Iron-based superconductors research ; Corporate Taxation and Avoidance
Authors: A.D. Crişan | F. Vasiliu | Ionel Mercioniu | Cristina Bartha | Monica Enculescu | O. Crisan
Source: Metals (article, 2018-06-19)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Annealing-Induced High Ordering and Coercivity in Novel L10 CoPt-Based Nanocomposite Magnets A novel class of quaternary intermetallic alloys based on CoPt is investigated in view of their interesting magnetic properties induced by the presence of hard magnetic L10 phase. A Co48Pt28Ag6B18 alloy has been prepared by rapid solidification from the melt and subjected to various isothermal annealing procedures. The structure and magnetism of both as-cast and annealed samples as well as the phase evolution with temperature are investigated by means of thermal analysis, X-ray, and selected area electron diffraction, scanning and high-resolution electron microscopy, and magnetic measurements. The X-ray diffraction (XRD) analysis shows that both the as-cast alloy and the sample annealed at 400 °C (673 K) have a nanocrystalline structure where fcc CoPt phase predominates. Annealing at 473 °C promotes the formation of L10 phase triggered by the disorder-order phase transformation as documented in the differential scanning calorimetry results. The sample annealed at 670 °C (943 K) shows full formation of L10 CoPt as revealed by XRD. Magnetic measurements showed coercivity values ten times increased compared to the as-cast state. This confirms the full formation of L10 CoPt in the annealed samples. Moreover, detailed atomic resolution HREM images and SAED patterns show the occurrence of the rarely seen (003) superlattice peaks, which translated into a high ordering of the L10 CoPt superlattice. Such results spur more interest in finding novel classes of nanocomposite magnets based on L10 phase. Materials science ; Annealing (glass) ; Intermetallic ; Coercivity ; Alloy ; Nanocrystalline material ; Superlattice ; Nanocomposite ; Crystallography ; Scanning electron microscope ; Analytical Chemistry (journal) ; Metallurgy ; Condensed matter physics ; Composite material ; Nanotechnology ; Chemistry ; Optoelectronics Magnetic Properties of Alloys ; Magnetic properties of thin films ; Magnetic Properties and Applications
Authors: S. Vangelista | Rossella Piagge | Satu Ek | Alessio Lamperti
Source: Beilstein Journal of Nanotechnology (article, 2018-03-15)
Field: Materials Science — Materials Chemistry

Effect of annealing treatments on CeO<sub>2</sub> grown on TiN and Si substrates by atomic layer deposition In this work, we investigate the effect of thermal treatment on CeO<sub>2</sub> films fabricated by using atomic layer deposition (ALD) on titanium nitride (TiN) or on silicon (Si) substrates. In particular, we report on the structural, chemical and morphological properties of 25 nm thick ceria oxide with particular attention to the interface with the substrate. The annealing treatments have been performed in situ during the acquisition of X-Ray diffraction patterns to monitor the structural changes in the film. We find that ceria film is thermally stable up to annealing temperatures of 900 °C required for the complete crystallization. When ceria is deposited on TiN, the temperature has to be limited to 600 °C due to the thermal instability of the underlying TiN substrate with a broadening of the interface, while there are no changes detected inside the CeO<sub>2</sub> films. As-deposited CeO<sub>2</sub> films show a cubic fluorite polycrystalline structure with texturing. Further, after annealing at 900 °C an increase of grain dimensions and an enhanced preferential (200) orientation are evidenced. These findings are a strong indication that the texturing is an intrinsic property of the system more than a metastable condition due to the ALD deposition process. This result is interpreted in the light of the contributions of different energy components (surface energy and elastic modulus) which act dependently on the substrate properties, such as its nature and structure. Materials science ; Annealing (glass) ; Tin ; Atomic layer deposition ; Crystallization ; Crystallite ; Titanium nitride ; Thin film ; Chemical engineering ; Silicon ; Nitride ; Oxide ; Nanotechnology ; Layer (electronics) ; Composite material ; Metallurgy Catalytic Processes in Materials Science ; Semiconductor materials and devices ; Electronic and Structural Properties of Oxides
Authors: Jia Guan | Snežana D. Zarić | Edward N. Brothers | Michael B. Hall
Source: International Journal of Quantum Chemistry (article, 2018-02-28)
Field: Chemistry — Organic Chemistry

Recent computational studies on transition‐metal carbon–hydrogen bond activation of alkanes Abstract This review on computational studies of transition‐metal promoted CH activation of light linear alkanes will cover computational work published since 2010, following upon seminal reviews by Niu and Hall (Chem. Rev. 2000, 100, 353), Vastine and Hall (Coord. Chem. Rev. 2009, 253, 1202), and Balcells et al. (Chem. Rev. 2010, 110, 749). The computational studies are surveyed in terms of the mechanistic nature of the CH activation step (oxidative addition, σ‐bond metathesis, 1,2 addition, or electrophilic activation), the type of CH bond being activated (primary or secondary), and the effect of metal, ligand, and alkane size on the reaction process. In addition to the primary focus on theoretical mechanistic investigations via calculated thermodynamics and kinetics, this review aims to bridge the computational and experimental observations and to highlight the insights that computational chemistry delivers to understanding the nature of CH activation of linear alkanes mediated by transition metals. Chemistry ; Transition metal ; Alkane ; Metathesis ; Electrophile ; Carbon–hydrogen bond activation ; Computational chemistry ; Thermodynamics ; Catalysis ; Organic chemistry ; Physics Catalytic C–H Functionalization Methods ; Asymmetric Hydrogenation and Catalysis ; Carbon dioxide utilization in catalysis
Authors: Dorothee Wasserberg | Jordi Cabanas‐Danés | Vinod Subramaniam | Jurriaan Huskens | Pascal Jonkheijm
Source: Chemical Communications (article, 2018-01-01)
Field: Materials Science — Biomaterials

Orthogonal supramolecular protein assembly on patterned bifunctional surfaces We report successful and selective dual protein assembly on patterned bifunctional βCD-Ni(ii)NTA surfaces, using red fluorescent protein variants with hexahistidine-tags and teal fluorescent protein variants conjugated with a peptide containing three adamantyl groups. We show that dual protein patterns can only be assembled, when opposing supramolecular interactions have been optimized and nonspecific interactions have been sufficiently suppressed. Bifunctional ; Supramolecular chemistry ; Supramolecular assembly ; Chemistry ; Materials science ; Nanotechnology ; Crystallography ; Organic chemistry ; Crystal structure Supramolecular Self-Assembly in Materials ; Polymer Surface Interaction Studies ; Chemical Synthesis and Analysis
Authors: A. Jabri | Yacine Belkhodja | Yann Berger | Isabelle Kleiner | Pierre Asselin
Source: Journal of Molecular Spectroscopy (article, 2018-03-30)
Field: Chemistry — Spectroscopy

High resolution rovibrational analysis of dimethyl sulfide spectrum in the 10 µm atmospheric window combining supersonic jet-quantum cascade laser and FTIR spectroscopies Rotational–vibrational spectroscopy ; Quantum cascade laser ; Carbonyl sulfide ; Infrared ; Atomic physics ; Materials science ; Rotational spectroscopy ; Physics ; Excited state ; Laser ; Spectral line ; Optics Spectroscopy and Laser Applications ; Atmospheric Ozone and Climate ; Molecular Spectroscopy and Structure
Authors: Behrouz Raftari | Neil Budko | Kees Vuik
Source: AIP Advances (article, 2018-01-01)
Field: Materials Science — Surfaces, Coatings and Films

A modified and calibrated drift-diffusion-reaction model for time-domain analysis of charging phenomena in electron-beam irradiated insulators This paper presents a modified self-consistent drift-diffusion-reaction model suitable for the analysis of electron-beam irradiated insulators at both short and long time scales. A novel boundary condition is employed that takes into account the reverse electron current and a fully dynamic trap-assisted generation-recombination mechanism is implemented. Sensitivity of the model with respect to material parameters is investigated and a calibration procedure is developed that reproduces experimental yield-energy curves for uncharged insulators. Long-time charging and yield variations are analyzed for stationary defocused and focused beams as well as moving beams dynamically scanning composite insulators. Irradiation ; Calibration ; Sensitivity (control systems) ; Yield (engineering) ; Boundary value problem ; Electron ; Electron beam processing Electron and X-Ray Spectroscopy Techniques ; Radiation Effects in Electronics ; High voltage insulation and dielectric phenomena
Authors: Kumar Vijaya | Rapolu Sridhar | D. Ravinder
Source: Processing and Application of Ceramics (article, 2018-01-01)
Field: Materials Science — Materials Chemistry

Magnetic and electrical properties of Cr substituted Ni nano ferrites Nano-ferrites with composition NiCrxFe2-xO4 (where x = 0.1, 0.3, 0.5, 0.7, 0.9, 1.0) were synthesized through citrate-gel auto combustion technique at moderately low temperature. X-ray analysis shows cubic spinel structure single phase without any impurity peak and average crystallite size in the range 8.5-10.5 nm. Magnetic properties were measured using a vibrating sample magnetometer at room temperature in the applied field of ?6 KOe. The obtained M-H loop area is very narrow, hence the synthesized nano ferrites are soft magnetic materials with small coercivity. Magnetic parameters such as saturation magnetization (Ms), coercivity (Hc), remanent magnetization (Mr) and residual magnetization were measured and discussed with regard to Cr3+ ion concentration. Electrical properties were measured using two probe method from room temperature to well beyond transition temperature. The DC resistivity variation with temperature shows the semiconductor nature. Resistivity, drift mobility and activation energy values are measured and discussed with regard to composition. The Curie temperature was determined using DC resistivity data and Loria-Sinha method. The observed results can be explained in detail on the basis of composition. Coercivity ; Materials science ; Curie temperature ; Electrical resistivity and conductivity ; Magnetization ; Crystallite ; Remanence ; Analytical Chemistry (journal) ; Spinel ; Condensed matter physics ; Nuclear magnetic resonance ; Magnetic field ; Ferromagnetism ; Metallurgy ; Chemistry Magnetic Properties and Synthesis of Ferrites ; Magneto-Optical Properties and Applications
Authors: Emilia Ciupan | Mihai Ciupan | Daniela Corina Jucan
Source: International Journal of Engineering Trends and Technology (article, 2018-12-25)
Field: Materials Science — Polymers and Plastics

Determining the Mechanical Properties of a New Composite Material using Artificial Neural Networks The paper studies the possibility of using artificial neural networks (ANN) to determine certain mechanical properties of a new composite material. This new material is obtained by a mixture of hemp and polypropylene fibres. The material was developed for the industry of upholstered furniture. Specifically, it is intended for the making of elements of the support structure of some upholstered goods (chairs, armchairs, sofa sides) with the objective of replacing wood. The paper aims to calculate the following mechanical properties: maximum tensile strength and maximum elongation. Ultimate tensile strength ; Composite number ; Artificial neural network ; Polypropylene ; Materials science ; Elongation ; Composite material ; Process engineering ; Mechanical engineering ; Computer science ; Engineering ; Artificial intelligence Textile materials and evaluations
Authors: Farag K. Abo‐Elyousr | L. S. Nassrat
Source: TELKOMNIKA (Telecommunication Computing Electronics and Control) (article, 2018-04-01)
Field: Materials Science — Materials Chemistry

Evaluation of Flashover Voltage Levels of Contaminated Hydrophobic Polymer Insulators Using Regression Trees, Neural Networks, and Adaptive Neuro-Fuzzy Polluted insulators at high voltages has acquired considerable importance with the rise of voltage transmission lines. The contamination may lead to flashover voltage. As a result, flashover voltage could lead to service outage and affects negatively the reliability of the power system. This paper presents a dynamic model of ac 50Hz flashover voltages of polluted hydrophobic polymer insulators. The models are constructed using the regression tree method, artificial neural network (ANN), and adaptive neuro-fuzzy (ANFIS). For this purpose, more than 2000 different experimental testing conditions were used to generate a training set. The study of the ac flashover voltages depends on silicone rubber (SiR) percentage content in ethylene propylene diene monomer (EPDM) rubber. Besides, water conductivity (μS/cm), number of droplets on the surface, and volume of water droplet (ml) are considered. The regression tree model is obtained and the performance of the proposed system with other intelligence methods is compa ed. It can be concluded that the performance of the least squares regression tree model outperforms the other intelligence methods, which gives the proposed model better generalization ability. Arc flash ; Voltage ; Silicone rubber ; Artificial neural network ; Adaptive neuro fuzzy inference system ; Biological system ; Materials science ; Computer science ; Neuro-fuzzy ; Composite material ; Environmental science ; Artificial intelligence ; Fuzzy logic ; Insulator (electricity) ; Engineering ; Electrical engineering ; Fuzzy control system High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Lightning and Electromagnetic Phenomena
Authors: Dmitrij Rappoport | Alán Aspuru‐Guzik
Source: ChemRxiv (preprint, 2018-01-01)
Field: Materials Science — Materials Chemistry

Predicting Feasible Organic Reaction Pathways Using Heuristically Aided Quantum Chemistry Studying organic reaction mechanisms using quantum chemical methods requires from the researcher an extensive knowledge of both organic chemistry and first-principles computation. The need for empirical knowledge arises because any reasonably complete exploration of the potential energy surfaces (PES) of organic reactions is computationally prohibitive. We have previously introduced the Heuristically-Aided Quantum Chemistry (HAQC) approach to modeling complex chemical reactions, which abstracts the empirical knowledge in terms of chemical heuristics—simple rules guiding the PES exploration—and combines them with structure optimizations using quantum chemical methods. The HAQC approach makes use of heuristic kinetic criteria for selecting reaction paths that are not only plausible, that is, consistent with the empirical rules of organic reactivity, but also feasible under the reaction conditions. In this work, we develop heuristic kinetic feasilibity criteria, which correctly predict feasible reaction pathways for a wide range of simple polar (substitutions, additions, and eliminations) and pericyclic organic reactions (cyclizations, sigmatropic shifts, and cycloadditions). In contrast to knowledge-based reaction mechanism prediction methods, the same kinetic heuristics are successful in classifying reaction pathways as feasible or infeasible across this diverse set of reaction mechanisms. We discuss the energy profiles of HAQC and their potential applications in machine learning of chemical reactivity.&lt;br&gt; Pericyclic reaction ; Heuristics ; Heuristic ; Computer science ; Chemistry ; Computational chemistry ; Quantum chemistry ; Quantum ; Biochemical engineering ; Quantum chemical ; Reactivity (psychology) ; Biological system ; Reaction mechanism ; Artificial intelligence ; Organic chemistry ; Molecule ; Physics ; Catalysis Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: João Marcelo Lamim Ribeiro | Pablo Bravo Collado | Yihang Wang | Pratyush Tiwary
Source: The Journal of Chemical Physics (preprint, 2018-05-04)
Field: Materials Science — Materials Chemistry

Reweighted autoencoded variational Bayes for enhanced sampling (RAVE) Here we propose the reweighted autoencoded variational Bayes for enhanced sampling (RAVE) method, a new iterative scheme that uses the deep learning framework of variational autoencoders to enhance sampling in molecular simulations. RAVE involves iterations between molecular simulations and deep learning in order to produce an increasingly accurate probability distribution along a low-dimensional latent space that captures the key features of the molecular simulation trajectory. Using the Kullback-Leibler divergence between this latent space distribution and the distribution of various trial reaction coordinates sampled from the molecular simulation, RAVE determines an optimum, yet nonetheless physically interpretable, reaction coordinate and optimum probability distribution. Both then directly serve as the biasing protocol for a new biased simulation, which is once again fed into the deep learning module with appropriate weights accounting for the bias, the procedure continuing until estimates of desirable thermodynamic observables are converged. Unlike recent methods using deep learning for enhanced sampling purposes, RAVE stands out in that (a) it naturally produces a physically interpretable reaction coordinate, (b) is independent of existing enhanced sampling protocols to enhance the fluctuations along the latent space identified via deep learning, and (c) it provides the ability to easily filter out spurious solutions learned by the deep learning procedure. The usefulness and reliability of RAVE is demonstrated by applying it to model potentials of increasing complexity, including computation of the binding free energy profile for a hydrophobic ligand-substrate system in explicit water with dissociation time of more than 3 min, in computer time at least twenty times less than that needed for umbrella sampling or metadynamics. Computer science ; Bayes' theorem ; Deep learning ; Artificial intelligence ; Spurious relationship ; Posterior probability ; Sampling (signal processing) ; Algorithm ; Divergence (linguistics) ; Latent variable ; Reaction coordinate ; Bayesian probability ; Machine learning ; Filter (signal processing) ; Computer vision ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Protein Structure and Dynamics ; Gaussian Processes and Bayesian Inference
Authors: Hebbar Gautama | Grynal D’Mello | P. Srinivasa Pai
Source: Materials Today Proceedings (article, 2018-01-01)
Field: Materials Science — Biomaterials

Surface Roughness Optimization in Machining of Biodegradable Magnesium Alloys Materials science ; Surface roughness ; Machining ; Surface integrity ; Magnesium alloy ; Magnesium ; Surface finish ; Metallurgy ; Mechanical engineering ; Composite material ; Engineering Magnesium Alloys: Properties and Applications ; Aluminum Alloys Composites Properties ; Metal and Thin Film Mechanics
Authors: Bao-guo Yao | Yun-liang Peng | Yun-juan Yang
Source: Polymers (article, 2018-03-27)
Field: Materials Science — Polymers and Plastics

Mechanical Measurement System and Precision Analysis for Tactile Property Evaluation of Porous Polymeric Materials Tactile properties are one of the most important attributes of porous polymeric materials such as textiles, comprising a subjective evaluation index for textile materials and functional clothing, primarily affecting the sensation of comfort during the wearing of a garment. A new test method was proposed, and a mechanical measurement system was developed to objectively characterize the tactile properties of porous polymeric materials by simulating the dynamic contact processes during human skin contact with the materials and in consideration of different aspects of tactile sensations. The measurement system can measure the bending, compression, friction, and thermal transfer properties in one apparatus, and is capable of associating the objective measurements with the subjective tactile sensations. The test and evaluation method, the components of the mechanical measurement system, the definition and grading method of the evaluation indices, and the neural network prediction model from objective test results to subjective sensations of tactile properties were presented. The experiments were conducted for the objective tests and correlation tests. Seven types of porous polymeric sheet materials from seven categories for the tactile properties were cut to a size of 200 mm × 200 mm and tested. Each index of tactile properties was significantly different (<i>P</i> < 0.05) between different sheet materials. The correlations of bending, compression, friction, and thermal transfer properties with Kawabata KES test methods were analyzed. An intra-laboratory test was conducted and an analysis of the variance was performed to determine the critical differences of within laboratory precisions of the measurement system. This mechanical measurement system provides a method and system for objective measurement and evaluation of tactile properties of porous polymeric sheet materials in industrial application. Materials science ; Porosity ; Property (philosophy) ; Composite material Textile materials and evaluations ; Additive Manufacturing and 3D Printing Technologies ; Advanced Sensor and Energy Harvesting Materials