jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Anna Bonfiglio | Lenka Pallova | Vincent César | Christophe Gourlaouen | Stéphane Bellemin‐Laponnaz | Chantal Daniel | Federico Polo | Matteo Mauro
Source: Chemistry - A European Journal (article, 2020-07-06)
Field: Chemistry — Organic Chemistry

Phosphorescent Cationic Heterodinuclear Ir<sup>III</sup>/M<sup>I</sup> Complexes (M=Cu<sup>I</sup>, Au<sup>I</sup>) with a Hybrid Janus‐Type N‐Heterocyclic Carbene Bridge A novel class of phosphorescent cationic heterobimetallic Ir<sup>III</sup> /M<sup>I</sup> complexes, where M<sup>I</sup> =Cu<sup>I</sup> (4) and Au<sup>I</sup> (5), is reported. The two metal centers are connected by the hybrid bridging 1,3-dimesityl-5-acetylimidazol-2-ylidene-4-olate (IMesAcac) ligand that combines both a chelating acetylacetonato-like and a monodentate N-heterocyclic carbene site coordinated onto an Ir<sup>III</sup> and a M<sup>I</sup> center, respectively. Complexes 4 and 5 have been prepared straightforwardly by a stepwise site-selective metalation with the zwitterionic [(IPr)M<sup>I</sup> (IMesAcac)] metalloproligand (IPr=1,3-(2,6-diisopropylphenyl)-2H-imidazol-2-ylidene) and they have been fully characterized by spectroscopic, electrochemical, and computational investigation. Complexes 4 and 5 display intense red emission arising from a low-energy excited state that is located onto the "Ir(C^N)" moiety featuring an admixed triplet ligand-centered/metal-to-ligand charge transfer (<sup>3</sup> IL/<sup>1</sup> MLCT) character. Comparison with the benchmark mononuclear complexes reveals negligible electronic coupling between the two distal metal centers at the electronic ground state. The bimetallic systems display enhanced photophysical properties in comparison with the parental congeners. Noteworthy, similar non-radiative rate constants have been determined along with a two-fold increase of radiative rate, yielding brightly red-emitting cyclometalating Ir<sup>III</sup> complexes. This finding is ascribed to the increased MLCT character of the emitting state in complexes 4 and 5 due to the smaller energy gap between the <sup>3</sup> IL and <sup>1</sup> MLCT manifolds, which mix via spin-orbit coupling. Phosphorescence ; Carbene ; Chemistry ; Cationic polymerization ; Excited state ; Denticity ; Ligand (biochemistry) ; Photochemistry ; Crystallography ; Moiety ; Stereochemistry ; Polymer chemistry ; Crystal structure ; Physics ; Fluorescence ; Atomic physics N-Heterocyclic Carbenes in Organic and Inorganic Chemistry ; Organic Light-Emitting Diodes Research ; Catalytic Cross-Coupling Reactions
Authors: Qian Xu | Dandan Liu | Chuchu Wang | Wangcheng Zhan | Yanglong Guo | Yun Guo | Li Wang | Qingping Ke | Minh Ngoc Ha
Source: Catalysts (article, 2020-10-08)
Field: Materials Science — Materials Chemistry

Sb-Containing Metal Oxide Catalysts for the Selective Catalytic Reduction of NOx with NH3 Sb-containing catalysts (SbZrOx (SbZr), SbCeOx (SbCe), SbCeZrOx (SbCeZr)) were prepared by citric acid method and investigated for the selective catalytic reduction (SCR) of NOx with NH3 (NH3-SCR). SbCeZr outperformed SbZr and SbCe and exhibited the highest activity with 80% NO conversion in the temperature window of 202–422 °C. Meanwhile, it also had good thermal stability and resistance against H2O and SO2. Various characterization methods, such as XRD, XPS, H2-TPR, NH3-TPD, and in situ diffuse reflectance infrared Fourier transform (DRIFT), were applied to understand their different behavior in NOx removal. The presence of Sb in the metal oxides led to the difference in acid distribution and redox property, which closely related with the NH3 adsorption and NO oxidation. Brønsted acid and Lewis acid were evenly distributed on SbCe, while Brønsted acid dominated on SbCeZr. Compared with Brønsted acid, Lewis acid was slightly active in NH3-SCR. The competition between NH3 adsorption and NO oxidation was dependent on SbOx and metal oxides, which were found on SbCe while not on SbCeZr. NOx ; Catalysis ; Selective catalytic reduction ; Chemistry ; Brønsted–Lowry acid–base theory ; Lewis acids and bases ; Inorganic chemistry ; Metal ; Oxide ; Thermal stability ; Adsorption ; Redox ; Citric acid ; Nuclear chemistry ; Organic chemistry Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Industrial Gas Emission Control
Authors: Lars Lilge | Alexander Reder | Frank Tippmann | Friedrich Morgenroth | Janice Grohmann | Dörte Becher | Katharina Riedel | Uwe Völker | Michael Hecker | Ulf Gerth
Source: Frontiers in Microbiology (article, 2020-05-12)
Field: Materials Science — Materials Chemistry

The Involvement of the McsB Arginine Kinase in Clp-Dependent Degradation of the MgsR Regulator in Bacillus subtilis Regulated ATP-dependent proteolysis is a common feature of developmental processes and plays also a crucial role during environmental perturbations such as stress and starvation. The <i>Bacillus subtilis</i> MgsR regulator controls a subregulon within the stress- and stationary phase σ<sup>B</sup> regulon. After ethanol exposition and a short time-window of activity, MgsR is ClpXP-dependently degraded with a half-life of approximately 6 min. Surprisingly, a protein interaction analysis with MgsR revealed an association with the McsB arginine kinase and an <i>in vivo</i> degradation assay confirmed a strong impact of McsB on MgsR degradation. <i>In vitro</i> phosphorylation experiments with arginine (R) by lysine (K) substitutions in McsB and its activator McsA unraveled all R residues, which are essentially needed for the arginine kinase reaction. Subsequently, site directed mutagenesis of the MgsR substrate was used to substitute all arginine residues with glutamate (R-E) to mimic arginine phosphorylation and to test their influence on MgsR degradation <i>in vivo</i>. It turned out, that especially the R33E and R94/95E residues (RRPI motif), the latter are adjacently located to the two redox-sensitive cysteines in a 3D model, have the potential to accelerate MgsR degradation. These results imply that selective arginine phosphorylation may have favorable effects for Clp dependent degradation of short-living regulatory proteins. We speculate that in addition to its kinase activity and adaptor function for the ClpC ATPase, McsB might also serve as a proteolytic adaptor for the ClpX ATPase in the degradation mechanism of MgsR. Bacillus subtilis ; Regulator ; Degradation (telecommunications) ; Arginine ; Kinase ; Microbiology ; Response regulator ; Chemistry ; Cell biology ; Arginine kinase ; Biology ; Bacteria ; Biochemistry ; Genetics ; Bacterial protein ; Gene ; Amino acid ; Computer science Enzyme Structure and Function ; Bacterial Genetics and Biotechnology ; RNA and protein synthesis mechanisms
Authors: D. Arabadzhieva | Plamen Tchoukov | Elena Mileva
Source: Colloids and Interfaces (article, 2020-11-13)
Field: Chemistry — Organic Chemistry

Impact of Adsorption Layer Properties on Drainage Behavior of Microscopic Foam Films: The Case of Cationic/Nonionic Surfactant Mixtures Aqueous mixtures of cationic hexadecyltrimethylammonium chloride (CTAC) and nonionic pentaethyleneglycol monododecyl ether (C12E5) are investigated. Adsorption layer properties are systematically studied within a wide concentration range for a 1:1 molar ratio of the surfactants. Surface tension and dilatational rheology measurements are conducted by profile analysis tensiometry. The interfacial data are juxtaposed to drainage kinetics and stability results for microscopic foam films, investigated by microinterferometric thin liquid film instrumentation. The obtained results give experimental evidence of synergistic interactions in the studied solutions, as compared to the corresponding single surfactant systems. Specific runs of dynamic and equilibrium surface tension curves are registered against the total surfactant quantity; the surface dilatational elasticities for the mixtures are systematically higher. A clear correlation is established between adsorption layer performance and foam film characteristics. The maxima of the film lifetimes are well outlined, and the respective values are shifted towards lower overall concentrations. The reported results substantiate the key role of the adsorption layers, and the surface dilatational properties in particular, for foam film drainage kinetics and stability. The well-expressed synergy observed in adsorption layer and foam film properties suggests the substantial benefits of using mixed surfactant systems in the design and fine-tuning of foam systems for innovative applications. Pulmonary surfactant ; Adsorption ; Surface tension ; Chemical engineering ; Aqueous solution ; Cationic polymerization ; Materials science ; Gibbs isotherm ; Kinetics ; Layer (electronics) ; Rheology ; Emulsion ; Chemistry ; Composite material ; Chromatography ; Thermodynamics ; Polymer chemistry ; Organic chemistry Surfactants and Colloidal Systems ; Enhanced Oil Recovery Techniques ; Pickering emulsions and particle stabilization
Authors: Nicolas Barth | Daniel George | Frédéric Bouyer | Aurélien Schwartz | Charles‐Henri Lambert | S. Ahzi | Yves Rémond
Source: Nuclear Engineering and Design (article, 2020-05-25)
Field: Materials Science — Materials Chemistry

An inverse method predicting thermal fluxes in nuclear waste glass canisters during vitrification and cooling Vitrification ; Radioactive waste ; High-level waste ; Decay heat ; Thermal ; Nuclear engineering ; Process (computing) ; Materials science ; Spent nuclear fuel ; Environmental science ; Waste management ; Thermodynamics ; Computer science ; Engineering ; Nuclear physics Graphite, nuclear technology, radiation studies ; Nuclear and radioactivity studies ; Soil and Unsaturated Flow
Authors: Amira G. Nawar | Abdelrahman Said | Alaa E. Elsayed | Mousa A. Abd‐Allah
Source: Egyptian Journal of Chemistry (article, 2020-07-06)
Field: Materials Science — Materials Chemistry

High Voltage Cross-Linked Polyethylene Insulator Characteristics Improvement Using Functionalized ZnO Nanoparticles This study aims to develop large-scale commercial Cross-Linked Polyethylene (XLPE) nanocomposites used for industrial applications with power cables insulations. To achieve this goal, XLPE/zinc oxide nanocomposites were prepared with four unique loadings of Zinc Oxid (ZnO) nanoparticles: 0.5, 2, 3.5 and 5 wt.%, in the presence of appropriate coupling agent which used to reduce the agglomeration of nanoparticles and to improve the compatibility inside the polymer matrix. Amino silane was the coupling agent used in this study and the nanocomposites preparation was done using the melt blending method. The morphology and the distribution of nanoparticles inside XLPE polymer were studied using Field Emission Scanning Electron Microscopy (FE-SEM). The mechanical properties were also evaluated. Using Differential Scanning Calorimetry (DSC) and Thermo Gravimetric Analysis (TGA), the thermal properties for nanocomposites were investigated. The dielectric properties of these prepared XLPE/ZnO nanocomposites were studied by measuring the loss tangent (tan δ) and the dielectric constant (ɛr) under frequencies ranging from 1 Hz to 1 MHz. The AC Breakdown Voltage (AC-BDV) was also measured using a controlled high voltage testing transformer (50 Hz) under the sphere-to-sphere field then; the AC dielectric strength was calculated using the Finite Element Method (FEM) technique. Materials science ; Differential scanning calorimetry ; Nanocomposite ; Dielectric ; Composite material ; Thermogravimetric analysis ; Nanoparticle ; Polyethylene ; Polymer nanocomposite ; Dissipation factor ; Polymer ; Chemical engineering ; Nanotechnology High voltage insulation and dielectric phenomena ; Dielectric materials and actuators ; Polymer Nanocomposites and Properties
Authors: Ralph Ivor Glastonbury | Johan P. Beukes | Pieter G. van Zyl | Merete Tangstad | Eli Ringdalen | Douglas Dall | J.D. Steenkamp | Masana Mushwana
Source: Metals (article, 2020-12-22)
Field: Materials Science — Materials Chemistry

Characterisation of a Real-World Søderberg Electrode Very little research on Søderberg electrodes has been published in the journal peer reviewed public domain. The main aim of this work is to characterise a Søderberg electrode that was cut off approximately 0.5 m below the contacts shoes of a submerged arc furnace. Additionally, the characterisation data can be used to verify if Søderberg electrode models accurately predict important electrode characteristics. The operational history (slipping, current, and paste levels) proved that the case study electrode was a representative specimen. The characterisation results indicated no significant electrical resistivity, degree of graphitisation (DOG), and bulk density changes from 0.7 to 2.7 m on the non-delta side (outward facing), while these characteristics changed relatively significantly on the delta side (inward facing) of the electrode. The area where the submerged arc would mostly like jump off the electrode had the lowest resistivity, as well as highest DOG and bulk density. No significant difference in porosity as a function of length below the contact shoes were observed; however, slight increases occurred near the perimeters. It was postulated that oxidation of carbon resulted in increased pore volumes near the electrode perimeter. No significant difference in compressive breaking strength was observed over the electrode area investigated. Electrode ; Slipping ; Materials science ; Electrical resistivity and conductivity ; Porosity ; Composite material ; Arc (geometry) ; Chemistry ; Geometry ; Electrical engineering ; Mathematics ; Engineering Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals ; Metal Extraction and Bioleaching
Authors: D.A. Burdin | D. V. Chashin | N. A. Ékonomov | В. Л. Преображенский | S. N. Gordeev | Y. K. Fetisov
Source: Physical Review Applied (article, 2020-05-27)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Parametric Generation of Subharmonics in a Composite Multiferroic Resonator Parametric generation of subharmonics in a composite multiferroic resonator is observed and investigated. The resonator has the form of a disk and contains two mechanically coupled layers, one of which is amorphous ferromagnet $\mathrm{Fe}\text{\ensuremath{-}}\mathrm{B}\text{\ensuremath{-}}\mathrm{Si}\text{\ensuremath{-}}\mathrm{C}$ and the other piezoelectric lead zirconate titanate. The resonator is placed inside two planar electromagnetic coils with orthogonal axes. A static magnetic field of 0--100 Oe is applied parallel to the plane of the resonator. The resonator is excited in the frequency range f = 9--10 kHz by either a harmonic magnetic field with an amplitude of up to 5 Oe generated by one of the coils, or a harmonic electric field with an amplitude of up to 500 V/cm applied to the piezoelectric layer. When the pump field is above a certain threshold, generation of a subharmonic of half-frequency (f/2) is observed for three different excitation methods. The first two employed either the direct magnetoelectric effect or the converse magnetoelectric effect, while in the third a transformer system is utilized. The subharmonic is generated in a limited range of pump frequencies and its amplitude is a nonlinear function of both the pump-field amplitude and the strength of static magnetic field. A theory of parametric generation of the subharmonic in a multiferroic resonator is developed, taking into account the magnetoacoustic nonlinearity of the ferromagnetic layer of the structure and excitation of acoustic resonances near the pump and subharmonic frequencies. The theory qualitatively describes the main characteristics of the subharmonic generation. Resonator ; Physics ; Excitation ; Condensed matter physics ; Amplitude ; Materials science ; Optics ; Quantum mechanics Multiferroics and related materials ; Seismic Waves and Analysis ; Underwater Acoustics Research
Authors: Kristof T. Schütt | Alexandre Tkatchenko | Klaus‐Robert Müller
Source: Lecture notes in physics (preprint, 2020-01-01)
Field: Materials Science — Materials Chemistry

Learning Representations of Molecules and Materials with Atomistic Neural Networks Intuition ; Computer science ; Chemical space ; Convolutional neural network ; Artificial neural network ; Variety (cybernetics) ; Artificial intelligence ; Space (punctuation) ; Theoretical computer science ; Cognitive science ; Chemistry ; Psychology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Topic Modeling
Authors: Rupayan Biswas | Richa Rashmi | Upakarasamy Lourderaj
Source: Resonance (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Machine Learning in Chemical Dynamics Computer science ; Simple (philosophy) ; Process (computing) ; Artificial intelligence ; Machine learning ; Dynamics (music) ; Chemical Dynamics ; Process dynamics ; Chemistry ; Physics Machine Learning in Materials Science ; Metabolomics and Mass Spectrometry Studies ; Mass Spectrometry Techniques and Applications
Authors: Rainier Barrett | Maghesree Chakraborty | Dilnoza B. Amirkulova | Heta A. Gandhi | Geemi P. Wellawatte | Andrew Dickson White
Source: The Journal of Open Source Software (article, 2020-07-28)
Field: Materials Science — Materials Chemistry

HOOMD-TF: GPU-Accelerated, Online Machine Learning in the HOOMD-blue Molecular Dynamics Engine Barrett et al., (2020). HOOMD-TF: GPU-Accelerated, Online Machine Learning in the HOOMD-blue Molecular Dynamics Engine. Journal of Open Source Software, 5(51), 2367, https://doi.org/10.21105/joss.02367 Computer science ; Software ; Open source ; Dynamics (music) ; Computer graphics (images) ; Operating system ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Topic Modeling
Authors: Serveh Kamrava | Pejman Tahmasebi | Muhammad Sahimi | Sepehr Arbabi
Source: Physical review. E (article, 2020-07-14)
Field: Materials Science — Materials Chemistry

Phase transitions, percolation, fracture of materials, and deep learning Percolation and fracture propagation in disordered solids represent two important problems in science and engineering that are characterized by phase transitions: loss of macroscopic connectivity at the percolation threshold p_{c} and formation of a macroscopic fracture network at the incipient fracture point (IFP). Percolation also represents the fracture problem in the limit of very strong disorder. An important unsolved problem is accurate prediction of physical properties of systems undergoing such transitions, given limited data far from the transition point. There is currently no theoretical method that can use limited data for a region far from a transition point p_{c} or the IFP and predict the physical properties all the way to that point, including their location. We present a deep neural network (DNN) for predicting such properties of two- and three-dimensional systems and in particular their percolation probability, the threshold p_{c}, the elastic moduli, and the universal Poisson ratio at p_{c}. All the predictions are in excellent agreement with the data. In particular, the DNN predicts correctly p_{c}, even though the training data were for the state of the systems far from p_{c}. This opens up the possibility of using the DNN for predicting physical properties of many types of disordered materials that undergo phase transformation, for which limited data are available for only far from the transition point. Percolation threshold ; Percolation (cognitive psychology) ; Phase transition ; Statistical physics ; Fracture (geology) ; Directed percolation ; Transition point ; Materials science ; Critical point (mathematics) ; Artificial neural network ; Poisson distribution ; Percolation theory ; Condensed matter physics ; Physics ; Computer science ; Conductivity ; Critical exponent ; Mathematics ; Artificial intelligence ; Thermodynamics ; Mathematical analysis ; Electrical resistivity and conductivity ; Quantum mechanics ; Statistics Machine Learning in Materials Science ; Theoretical and Computational Physics ; Neural Networks and Applications
Authors: Sergei V. Kalinin | Ondrej Dyck | Ayana Ghosh | Yongtao Liu | Roger Proksch | Bobby G. Sumpter | Maxim Ziatdinov
Source: arXiv (Cornell University) (preprint, 2020-10-18)
Field: Materials Science — Materials Chemistry

Unsupervised Machine Learning Discovery of Chemical and Physical\n Transformation Pathways from Imaging Data We show that unsupervised machine learning can be used to learn physical and\nchemical transformation pathways from the observational microscopic data, as\ndemonstrated for atomically resolved images in Scanning Transmission Electron\nMicroscopy (STEM) and ferroelectric domain structures in Piezoresponse Force\nMicroscopy (PFM). To enable this analysis in STEM, we assumed the existence of\natoms, a discreteness of atomic classes, and the presence of an explicit\nrelationship between the observed STEM contrast and the presence of atomic\nunits. In PFM, we assumed the uniquely-defined relationship between the\nmeasured signal and polarization distribution. With only these postulates, we\ndeveloped a machine learning method leveraging a rotationally-invariant\nvariational autoencoder (rVAE) that can identify the existing structural units\nobserved within a material. The approach encodes the information contained in\nimage sequences using a small number of latent variables, allowing the\nexploration of chemical and physical transformation pathways via the latent\nspace of the system. The results suggest that the high-veracity imaging data\ncan be used to derive fundamental physical and chemical mechanisms involved, by\nproviding encodings of the observed structures that act as bottom-up\nequivalents of structural order parameters. The approach also demonstrates the\npotential of variational (i.e., Bayesian) methods for physical sciences and\nwill stimulate the development of new ways to encode physical constraints in\nthe encoder-decoder architectures, and generative physical laws, topological\ninvariances, and causal relationships in the latent space of VAEs.\n Autoencoder ; Unsupervised learning ; Artificial intelligence ; Computer science ; Physical law ; Data-driven ; Generative model ; Invariant (physics) ; Physical system ; Pattern recognition (psychology) ; Machine learning ; Mathematics ; Generative grammar ; Physics ; Deep learning Machine Learning in Materials Science ; Cell Image Analysis Techniques
Authors: K.N. Nigussa
Source: Materials Chemistry and Physics (article, 2020-06-13)
Field: Materials Science — Materials Chemistry

Application of the training of density functional theory potentials within machine learning to adsorptions and reaction paths on Platinum surfaces Dissociation (chemistry) ; Density functional theory ; Adsorption ; Molecule ; Carbon monoxide ; Exothermic reaction ; Chemistry ; Hydrogen ; Chemical physics ; Ion ; Platinum ; Oxygen ; Physical chemistry ; Electrolyte ; Computational chemistry ; Atomic physics ; Catalysis ; Electrode ; Organic chemistry ; Physics Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Fuel Cells and Related Materials