jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Stanislav S. Fedotov | Dmitry A. Aksyonov | Aleksandr Sh. Samarin | Olesia M. Karakulina | Joke Hadermann | Keith J. Stevenson | Nellie R. Khasanova | Artem M. Abakumov | Evgeny V. Antipov
Source: European Journal of Inorganic Chemistry (article, 2019-08-07)
Field: Chemistry — Inorganic Chemistry

Tuning the Crystal Structure of A<sub>2</sub>CoPO<sub>4</sub>F (A = Li, Na) Fluoride‐Phosphates: A New Layered Polymorph of LiNaCoPO<sub>4</sub>F Co‐containing fluoride‐phosphates are of interest in sense of delivering high electrode potentials and attractive specific energy values as positive electrode materials for rechargeable batteries. In this paper we report on a new Co‐based fluoride‐phosphate, LiNaCoPO 4 F, with a layered structure (2D), which was Rietveld‐refined based on X‐ray powder diffraction data [ P 2 1 / c, a = 6.83881(4) Å, b = 11.23323(5) Å, c = 5.07654(2) Å, β = 90.3517(5) °, V = 389.982(3) Å 3 ] and validated by electron diffraction and high‐resolution scanning transmission electron microscopy. The differential scanning calorimetry measurements revealed that 2D‐LiNaCoPO 4 F forms in a narrow temperature range of 520–530 °C and irreversibly converts to the known 3D‐LiNaCoPO 4 F modification ( Pnma ) above 530 °C. The non‐carbon‐coated 2D‐LiNaCoPO 4 F shows reversible electrochemical activity in Li‐ion cell in the potential range of 3.0–4.9 V vs. Li/Li + with an average potential of ≈ 4.5 V and in Na‐ion cell in the range of 3.0–4.5 V vs. Na/Na + exhibiting a plateau profile centered around 4.2 V, in agreement with the calculated potentials by density functional theory. The energy barriers for both Li + and Na + migration in 2D‐LiNaCoPO 4 F amount to 0.15 eV along the [001] direction rendering 2D‐LiNaCoPO 4 F as a viable electrode material for high‐power Li‐ and Na‐ion rechargeable batteries. The discovery and stabilization of the 2D‐LiNaCoPO 4 F polymorph indicates that temperature influence on the synthesis of A 2 MPO 4 F fluoride‐phosphates needs more careful examination with perspective to unveil new structures. Chemistry ; Electrochemistry ; Ion ; Fluoride ; Crystallography ; Rietveld refinement ; Crystal structure ; Powder diffraction ; Electrode ; Differential scanning calorimetry ; Transmission electron microscopy ; Analytical Chemistry (journal) ; Inorganic chemistry ; Nanotechnology ; Physical chemistry ; Materials science Inorganic Fluorides and Related Compounds ; Advancements in Battery Materials ; Chemical Synthesis and Characterization
Authors: Marie Hoarau | Karl J. Koebke | Zhan Chen | E. Neil G. Marsh
Source: Frontiers in Molecular Biosciences (article, 2019-08-27)
Field: Chemistry — Spectroscopy

Probing Metal Ion Discrimination in a Protein Designed to Bind Uranyl Cation With Femtomolar Affinity The design of metal-binding sites in proteins that combine high affinity with high selectivity for the desired metal ion remains a challenging goal. Recently, a protein designed to display femtomolar affinity for <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msubsup><mml:mrow><mml:mtext>UO</mml:mtext></mml:mrow> <mml:mrow><mml:mn>2</mml:mn></mml:mrow> <mml:mrow><mml:mn>2</mml:mn> <mml:mo>+</mml:mo></mml:mrow> </mml:msubsup> </mml:math> , dubbed "Super Uranyl-binding Protein" (SUP), was described, with potential applications for removing <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msubsup><mml:mrow><mml:mtext>UO</mml:mtext></mml:mrow> <mml:mrow><mml:mn>2</mml:mn></mml:mrow> <mml:mrow><mml:mn>2</mml:mn> <mml:mo>+</mml:mo></mml:mrow> </mml:msubsup> </mml:math> in water. Although it discriminated most metal ions present in seawater, the protein showed a surprisingly high affinity for Cu<sup>2+</sup> ions. Here, we have investigated Cu<sup>2+</sup> binding to SUP using a combination of electron paramagnetic resonance, fluorescence and circular dichroism spectroscopies. Our results provide evidence for two Cu<sup>2+</sup> binding sites on SUP that are distinct from the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msubsup><mml:mrow><mml:mtext>UO</mml:mtext></mml:mrow> <mml:mrow><mml:mn>2</mml:mn></mml:mrow> <mml:mrow><mml:mn>2</mml:mn> <mml:mo>+</mml:mo></mml:mrow> </mml:msubsup> </mml:math> binding site, but one of which interferes with <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msubsup><mml:mrow><mml:mtext>UO</mml:mtext></mml:mrow> <mml:mrow><mml:mn>2</mml:mn></mml:mrow> <mml:mrow><mml:mn>2</mml:mn> <mml:mo>+</mml:mo></mml:mrow> </mml:msubsup> </mml:math> binding. They further suggest that in solution the protein's secondary structure changes significantly in response to binding <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msubsup><mml:mrow><mml:mtext>UO</mml:mtext></mml:mrow> <mml:mrow><mml:mn>2</mml:mn></mml:mrow> <mml:mrow><mml:mn>2</mml:mn> <mml:mo>+</mml:mo></mml:mrow> </mml:msubsup> </mml:math> ; in contrast, the crystal structures of the apo- and holo-protein are almost superimposable. These results provide insights for further improving the selectivity of SUP for <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msubsup><mml:mrow><mml:mtext>UO</mml:mtext></mml:mrow> <mml:mrow><mml:mn>2</mml:mn></mml:mrow> <mml:mrow><mml:mn>2</mml:mn> <mml:mo>+</mml:mo></mml:mrow> </mml:msubsup> </mml:math> , paving the way toward protein-based biomaterials for decontamination and/or recovery of uranium. Uranyl ; Chemistry ; Ion ; Metal ; Biophysics ; Biology ; Organic chemistry Mass Spectrometry Techniques and Applications ; Radioactive element chemistry and processing ; Analytical chemistry methods development
Authors: Ivan Shtepliuk | Ivan G. Ivanov | N. Pliatsikas | N. Ben Sédrine | Olof Andersson | Tihomir Iakimov | Andreas Jamnig | Kostas Sarakinos | Rositsa Yakimova
Source: Surface and Coatings Technology (article, 2019-11-28)
Field: Materials Science — Materials Chemistry

Interplay between thin silver films and epitaxial graphene Graphene ; Materials science ; Epitaxy ; Raman spectroscopy ; Sputter deposition ; van der Waals force ; Thin film ; Optoelectronics ; Context (archaeology) ; Nanotechnology ; Sputtering ; Layer (electronics) ; Condensed matter physics ; Optics ; Molecule Graphene research and applications ; ZnO doping and properties ; Quantum and electron transport phenomena
Authors: Huayan Pu | Rongqing Xie | Yan Peng | Yang Yang | Shiyang He | Jun Luo | Yi Sun | Shaorong Xie
Source: Advances in Manufacturing (article, 2019-08-26)
Field: Materials Science — Materials Chemistry

Accelerating sample preparation of graded thermoelectric materials using an automatic powder feeding system In recent years, the development of techniques for the controlled preparation of functional graded materials (FGMs) has become a vigorous research field. In this study, to improve the efficiency and accuracy of sample preparations, an automated feeding system based on gravimetric principles for dry powder with three dosing feeders is designed and realized. The feeding rate and accuracy can be regulated by coordinating the protruded length L (mm) and rotational speed V (r/min) of the feeder stirrer. To demonstrate this automatic sample preparation system, the well-known thermoelectric material BixSb2−xTe3 (x = 0.3, 0.4, 0.5, 0.6, 0.7 and 0.8) is selected and prepared by the developed system, and the composition distribution of the functional graded material is characterized. Experimental results show that the BixSb2−xTe3 (x = 0.3–0.8) functionally graded material crystalizes in the rhombohedral phase after hot-pressing sintering and annealing and the prepared sample has a good gradient composition distribution. This verifies the reliability and accuracy of the feeding system. The concept of samples with a gradient component and application of the automatic powder feeding system could considerably accelerate the research and development of new materials. Thermoelectric effect ; Materials science ; Sintering ; Gravimetric analysis ; Annealing (glass) ; Pressing ; Thermoelectric materials ; Composite material ; Chemistry ; Physics Advanced Thermoelectric Materials and Devices
Authors: Péter Vancsó | Imre Hagymási | Pauline Castenetto | Philippe Lambin
Source: Physical Review Materials (article, 2019-09-10)
Field: Materials Science — Materials Chemistry

Stability of edge magnetism against disorder in zigzag <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi>MoS</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:math> nanoribbons Molybdenum disulfide nanoribbons with zigzag edges show ferromagnetic and metallic properties based on previous ab initio calculations. The investigation of the role of disorder on the magnetic properties is, however, still lacking due to the computational costs of these methods. In this work we fill this gap by studying the magnetic and electronic properties of several-nanometer-long ${\mathrm{MoS}}_{2}$ zigzag nanoribbons using tight-binding and Hubbard Hamiltonians. Our results reveal that proper tight-binding parameters for the edge atoms are crucial to obtain quantitatively the metallic states and the magnetic properties of ${\mathrm{MoS}}_{2}$ nanoribbons. With the help of the fine-tuned parameters, we perform large-scale calculations and predict the spin domain-wall energy along the edges, which is found to be significantly lower compared to that of the zigzag graphene nanoribbons. The tight-binding approach allows us to address the effect of edge disorder on the magnetic properties. Our results open the way for investigating electron-electron effects in realistic-size nanoribbon devices in ${\mathrm{MoS}}_{2}$ and also provide valuable information for spintronic applications. Zigzag ; Spintronics ; Magnetism ; Ferromagnetism ; Graphene nanoribbons ; Enhanced Data Rates for GSM Evolution ; Ab initio ; Graphene ; Spin (aerodynamics) 2D Materials and Applications ; Heusler alloys: electronic and magnetic properties ; Surface and Thin Film Phenomena
Authors: Branko Koprivica | Krzysztof Chwastek
Source: Acta Physica Polonica A (article, 2019-11-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Verification of Bertotti's Loss Model for Non-Standard Excitation This paper focuses on the possibility to use the Bertotti loss theory to describe energy losses in a toroidal core made of grain-oriented steel sheet under non-standard excitation conditions. The temporal H clas (t) and Hexc (t) dependences are computed using a simple numerical scheme, which may be easily implemented in a spreadsheet. Excitation ; Toroid ; Simple (philosophy) ; Physics ; Core (optical fiber) ; Energy (signal processing) ; Scheme (mathematics) ; Computational physics ; Statistical physics ; Computer science ; Mechanics ; Mathematical analysis ; Mathematics ; Nuclear physics ; Optics ; Quantum mechanics ; Plasma Magnetic Properties and Applications ; Non-Destructive Testing Techniques ; Lightning and Electromagnetic Phenomena
Authors: Muhammad Taha Sultan | Valentin‐Adrian Maraloiu | Ionel Stavarache | Jón Tómas Guðmundsson | Andrei Manolescu | V. S. Teodorescu | Magdalena Lidia Ciurea | Halldór Guðfinnur Svavarsson
Source: Beilstein Journal of Nanotechnology (article, 2019-09-17)
Field: Materials Science — Materials Chemistry

Fabrication and characterization of Si <sub>1−</sub> <i> <sub>x</sub> </i> Ge <i> <sub>x</sub> </i> nanocrystals in as-grown and annealed structures: a comparative study Multilayer structures comprising of SiO<sub>2</sub>/SiGe/SiO<sub>2</sub> and containing SiGe nanoparticles were obtained by depositing SiO<sub>2</sub> layers using reactive direct current magnetron sputtering (dcMS), whereas, Si and Ge were co-sputtered using dcMS and high-power impulse magnetron sputtering (HiPIMS). The as-grown structures subsequently underwent rapid thermal annealing (550-900 °C for 1 min) in N<sub>2</sub> ambient atmosphere. The structures were investigated using X-ray diffraction, high-resolution transmission electron microscopy together with spectral photocurrent measurements, to explore structural changes and corresponding properties. It is observed that the employment of HiPIMS facilitates the formation of SiGe nanoparticles (2.1 ± 0.8 nm) in the as-grown structure, and that presence of such nanoparticles acts as a seed for heterogeneous nucleation, which upon annealing results in the periodically arranged columnar self-assembly of SiGe core-shell nanocrystals. An increase in photocurrent intensity by more than an order of magnitude was achieved by annealing. Furthermore, a detailed discussion is provided on strain development within the structures, the consequential interface characteristics and its effect on the photocurrent spectra. Materials science ; High-power impulse magnetron sputtering ; Photocurrent ; Annealing (glass) ; Sputter deposition ; Nucleation ; Nanocrystal ; Transmission electron microscopy ; Silicon ; Sputtering ; Nanoparticle ; Optoelectronics ; Nanotechnology ; Thin film ; Composite material ; Chemistry Silicon Nanostructures and Photoluminescence ; Semiconductor materials and devices ; Ion-surface interactions and analysis
Authors: Katlyn Hughes Burriss | Amber L. Mosley
Source: Methods (review, 2019-03-19)
Field: Chemistry — Spectroscopy

Methods review: Mass spectrometry analysis of RNAPII complexes RNA Polymerase II (RNAPII) is responsible for transcribing multiple RNA species throughout eukaryotes. A variety of protein-protein interactions occur throughout the transcription cycle for coordinated regulation of transcription initiation, elongation, and/or termination. Taking a proteomics approach to study RNAPII transcription thereby offers a comprehensive view of both RNAPII biology and the variety of proteins that regulate the process itself. This review will focus on how mass spectrometry (MS) methods have expanded understanding of RNAPII and its transcription-regulatory interaction partners. The application of affinity purification mass spectrometry has led to the discovery of a number of novel groups of proteins that regulate an array of RNAPII biology ranging from nuclear import to regulation of phosphorylation state. Additionally, a number of methods have been developed using mass spectrometry to measure protein subunit stoichiometry within and across protein complexes and to perform various types of architectural analysis using structural proteomics approaches. The key methods that we will focus on related to RNAPII mass spectrometry analyses include: affinity purification mass spectrometry, protein post-translational modification analysis, crosslinking mass spectrometry, and native mass spectrometry. Mass spectrometry ; Proteomics ; RNA polymerase II ; Chemistry ; Protein subunit ; Transcription (linguistics) ; Phosphorylation ; Computational biology ; Proteome ; Cell biology ; Biology ; Biochemistry ; Chromatography ; Gene ; Promoter ; Gene expression Advanced Proteomics Techniques and Applications ; RNA and protein synthesis mechanisms ; Mass Spectrometry Techniques and Applications
Authors: Nishanthi Sunthrasakaran | Nor Akmal Mohd Jamail | Rahisham Abd Rahman | Md Nor Ramdon Bin Baharom | Qamarul Ezani Kamarudin | Mohamad Farid Sies | W. Muhammad W. N. Azrina
Source: Indonesian Journal of Electrical Engineering and Computer Science (article, 2019-08-01)
Field: Materials Science — Polymers and Plastics

Factors that affects the dielectric properties of natural fibre reinforced hybrid biocomposite &lt;span&gt;Almost all research on natural fibre reinforced composite has focused on the mechanical properties of the composite. Drawing on the important factors that effect the end product of composite, this paper reviews electrical properties of hybrid biocomposite when fibre content and surface treatment is varied. The compilation of dielectric constant, dissipation factor, loss factor and moisture absorption reported by researchers has been presented in this paper. The intrinsic characteristic of natural fibre which is hydrophilic is the main drawback that affects the electrical properties of the composite. Alkaline treatment on natural fibre has proven to overcome the hydrophilic characteristic and improves the adhesion between matrix and fibre. Besides that, the percentage of fibre content plays an important role in producing a good natural fibre reinforced hybrid composite. The moisture uptake, dielectric constant, dissipation factor and loss factor of the composite decreases with decreased fibre content.&lt;/span&gt; Biocomposite ; Dissipation factor ; Materials science ; Composite material ; Composite number ; Dielectric ; Loss factor ; Dielectric loss ; Dissipation ; Moisture Natural Fiber Reinforced Composites
Authors: Jakub Eichler | Miroslav Novák | Miloslav Košek
Source: Acta Physica Polonica A (article, 2019-11-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Experimental Determination of the Preisach Model for Grain Oriented Steel The full material characteristics in the Preisach model of hysteresis is a two-dimensional weighting function. It can be determined experimentally from systematic measurement of partial hysteresis loops followed by derivation of their decreasing parts. Because of measurement errors, the derivation is not correct. Nevertheless, basic material features can be obtained either from incomplete measurement that uses the Preisach triangle respecting the measurement errors. The non-uniform grid in the Preisach model is used in order to get approximately the same steps in changes of the flux density. Simulation shows that this withdrawal of some rows and columns does not change the hysteresis loop considerably, only the steps of the flux density may be higher. Preisach model of hysteresis ; Hysteresis ; Weighting ; Flux (metallurgy) ; Row ; Materials science ; Function (biology) ; Magnetic hysteresis ; Mechanics ; Physics ; Condensed matter physics ; Computer science ; Magnetization ; Acoustics Magnetic Properties and Applications ; Solidification and crystal growth phenomena ; Microstructure and Mechanical Properties of Steels
Authors: Xiaoxuan Zhang | Krishna Garikipati
Source: arXiv (Cornell University) (preprint, 2019-12-31)
Field: Materials Science — Materials Chemistry

Machine learning materials physics: Multi-resolution neural networks learn the free energy and nonlinear elastic response of evolving microstructures Many important multi-component crystalline solids undergo mechanochemical spinodal decomposition: a phase transformation in which the compositional redistribution is coupled with structural changes of the crystal, resulting in dynamically evolving microstructures. The ability to rapidly compute the macroscopic behavior based on these detailed microstructures is of paramount importance for accelerating material discovery and design. Here, our focus is on the macroscopic, nonlinear elastic response of materials harboring microstructure. Because of the diversity of microstructural patterns that can form, there is interest in taking a purely computational approach to predicting their macroscopic response. However, the evaluation of macroscopic, nonlinear elastic properties purely based on direct numerical simulations (DNS) is computationally very expensive, and hence impractical for material design when a large number of microstructures need to be tested. A further complexity of a hierarchical nature arises if the elastic free energy and its variation with strain is a small-scale fluctuation on the dominant trajectory of the total free energy driven by microstructural dynamics. To address these challenges, we present a data-driven approach, which combines advanced neural network (NN) models with DNS to predict the homogenized, macroscopic, mechanical free energy and stress fields arising in a family of multi-component crystalline solids that develop microstructure. The hierarchical structure of the free energy's evolution induces a multi-resolution character to the machine learning paradigm: We construct knowledge-based neural networks (KBNNs) with either pre-trained fully connected deep neural networks (DNNs), or pre-trained convolutional neural networks (CNNs) that describe the dominant characteristic of the data to fully represent the hierarchically evolving free energy. Nonlinear system ; Statistical physics ; Elastic energy ; Artificial intelligence ; Artificial neural network ; Microstructure ; Computer science ; Convolutional neural network ; Materials science ; Physics Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Microstructure and mechanical properties
Authors: Steven Kauwe | Jake Graser | Ryan Murdock | Taylor D. Sparks
Source: ChemRxiv (preprint, 2019-08-09)
Field: Materials Science — Materials Chemistry

Can Machine Learning Find Extraordinary Materials? One of the most common criticisms of machine learning is an assumed inability for models to extrapolate, i.e. to identify extraordinary materials with properties beyond those present in the training data set. To investigate whether this is indeed the case, this work takes advantage of density functional theory calculated properties (bulk modulus, shear modulus, thermal conductivity, thermal expansion, band gap and Debye temperature) to investigate whether machine learning is truly capable of predicting materials with properties that extend beyond previously seen values. We refer to these materials as extraordinary, meaning they represent the top 1% of values in the available data set. Interestingly, we show that even when machine learning is trained on a fraction of the bottom 99% we can consistently identify 3/4 of the highest performing compositions for all considered properties with a precision that is typically above 0.5. Moreover, we investigate a few different modeling choices and demonstrate how a classification approach can identify an equivalent amount of extraordinary compounds but with significantly fewer false positives than a regression approach. Finally, we discuss cautions and potential limitations in implementing such an approach to discover new record-breaking materials. Machine learning ; Computer science ; Artificial intelligence ; Debye model ; Shear modulus ; Set (abstract data type) ; Physics ; Thermodynamics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: W. Gerrard | Lars Andersen Bratholm | Martin J. Packer | Adrian J. Mulholland | David R. Glowacki | Craig P. Butts
Source: Chemical Science (preprint, 2019-11-20)
Field: Materials Science — Materials Chemistry

IMPRESSION – prediction of NMR parameters for 3-dimensional chemical structures using machine learning with near quantum chemical accuracy The IMPRESSION (Intelligent Machine PREdiction of Shift and Scalar information Of Nuclei) machine learning system provides an efficient and accurate method for the prediction of NMR parameters from 3-dimensional molecular structures. Here we demonstrate that machine learning predictions of NMR parameters, trained on quantum chemical computed values, can be as accurate as, but computationally much more efficient (tens of milliseconds per molecular structure) than, quantum chemical calculations (hours/days per molecular structure) starting from the same 3-dimensional structure. Training the machine learning system on quantum chemical predictions, rather than experimental data, circumvents the need for the existence of large, structurally diverse, error-free experimental databases and makes IMPRESSION applicable to solving 3-dimensional problems such as molecular conformation and stereoisomerism. Quantum chemical ; Impression ; Computer science ; Molecule ; Scalar (mathematics) ; Quantum ; Chemical shift ; Mean squared prediction error ; Artificial intelligence ; Algorithm ; Machine learning ; Biological system ; Mathematics ; Quantum mechanics ; Physics ; Geometry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics