jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Uzma Hira | Jan‐Willem G. Bos | Alexander Missyul | François Fauth | Nini Pryds | Falak Sher
Source: Inorganic Chemistry (article, 2021-11-08)
Field: Materials Science — Materials Chemistry

Ba<sub>2–<i>x</i></sub>Bi<sub><i>x</i></sub>CoRuO<sub>6</sub> (0.0 ≤ <i>x</i> ≤ 0.6) Hexagonal Double-Perovskite-Type Oxides as Promising p-Type Thermoelectric Materials A new series of Ba2–xBixCoRuO6 (0.0 ≤ x ≤ 0.6) hexagonal double perovskite oxides have been synthesized by a solid-state reaction method by substituting Ba with Bi. The polycrystalline materials are structurally characterized by the laboratory X-ray diffraction, synchrotron X-ray, and neutron powder diffraction. The lattice parameters are found to increase with increasing Bi doping despite the smaller ionic radius of Bi3+ compared to Ba2+. The expansion is attributed to the reduction of Co/Ru-site cations. Scanning electron microscopy further shows that the grain size increases with the Bi content. All Ba2–xBixCoRuO6 (0.0 ≤ x ≤ 0.6) samples exhibit p-type behavior, and the electrical resistivity (ρ) is consistent with a small polaron hopping model. The Seebeck coefficient (S) and thermal conductivity (κ) are improved significantly with Bi doping. High values of the power factor (PF ∼ 6.64 × 10–4 W/m·K2) and figure of merit (zT ∼ 0.23) are obtained at 618 K for the x = 0.6 sample. These results show that Bi doping is an effective approach for enhancing the thermoelectric properties of hexagonal Ba2–xBixCoRuO6 perovskite oxides. Chemistry ; Hexagonal crystal system ; Crystallography ; Perovskite (structure) ; X-ray crystallography ; Diffraction ; Physics ; Optics Advanced Thermoelectric Materials and Devices ; Perovskite Materials and Applications ; Magnetic and transport properties of perovskites and related materials
Authors: Ramona Achermann | Viktoria Wiedmeyer | Mercedeh Sadat Hosseinalipour | Selin Güngör | Marco Mazzotti
Source: Process Safety and Environmental Protection (article, 2021-07-08)
Field: Materials Science — Materials Chemistry

Model-based design of pressure-driven product removal from stirred suspensions Achieving “mixed-product removal” in a mixed-suspension mixed-product removal crystallizer (MSMPRC), operating with a pressure-driven suspension transfer, is challenging. In this work, single and multiphase computational fluid dynamics (CFD) simulations were used to guide the choice of a suitable withdrawal position, which was found to be that of an immersed tube close to the vessel wall. The chosen withdrawal position was then tested and compared to other withdrawal positions experimentally. It performed as predicted, whereas for the other positions, the degree of dilution (size-independent classification) and of sieving (size-dependent classification) of the product removed increased. Furthermore, the influence of a non-representative withdrawal on an MSMPRC steady-state was investigated using a population balance equation (PBE) model. Suspension (topology) ; Work (physics) ; Computational fluid dynamics ; Dilution ; Position (finance) ; Population ; Chemistry ; Mechanics ; Population balance equation ; Chromatography ; Thermodynamics ; Materials science ; Mathematics ; Physics Crystallization and Solubility Studies ; Calcium Carbonate Crystallization and Inhibition ; Surfactants and Colloidal Systems
Authors: Laura Vinnenberg | Stefanie Böck | Petra Hundehege | Tobias Ruck | Sven G. Meuth
Source: Cellular Physiology and Biochemistry (article, 2021-05-27)
Field: Chemistry — Organic Chemistry

Impact of Diverse Ion Channels on Regulatory T Cell Functions The population of regulatory T cells (Tregs) is critical for immunological self-tolerance and homeostasis. Proper ion regulation contributes to Treg lineage identity, regulation, and effector function. Identified ion channels include Ca<sup>2+</sup> release-activated Ca<sup>2+</sup>, transient receptor potential, P2X, volume-regulated anion and K<sup>+</sup> channels Kv1.3 and KCa3.1. Ion channel modulation represents a promising therapeutic approach for the treatment of autoimmune diseases such as rheumatoid arthritis and multiple sclerosis. This review summarizes studies with gene-targeted mice and pharmacological modulators affecting Treg number and function. Furthermore, participation of ion channels is illustrated and the power of future research possibilities is discussed. Ion channel ; Transient receptor potential channel ; Function (biology) ; Regulatory T cell ; Effector ; Population ; Cell biology ; Receptor ; Immunology ; Biology ; T cell ; Medicine ; Chemistry ; Neuroscience ; IL-2 receptor ; Internal medicine ; Immune system Synthesis and Biological Evaluation ; Immune Cell Function and Interaction ; Adenosine and Purinergic Signaling
Authors: Mohsen Hafez-Torbati | Davide Bossini | Frithjof B. Anders | Götz S. Uhrig
Source: Physical Review Research (article, 2021-12-30)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Magnetic blue shift of Mott gaps enhanced by double exchange A substantial energy gap of charge excitations induced by strong correlations is the characteristic feature of Mott insulators. We study how the Mott gap is affected by long-range antiferromagnetic order. Our key finding is that the Mott gap is increased by the magnetic ordering: A magnetic blue shift (MBS) occurs. Thus the effect is proportional to the exchange coupling in the leading order in the Hubbard model. In systems with additional localized spins the double-exchange mechanism induces an additional contribution to the MBS which is proportional to the hopping in the leading order. The coupling between spin and charge degrees of freedom bears the potential to enable spin-to-charge conversion in Mott systems on extreme time scales determined by hopping and exchange only, since a spin-orbit-mediated transfer of angular momentum is not involved in the process. In view of spintronic and magnonic applications, it is highly promising to observe that several entire classes of compounds show exchange and double-exchange effects. Exemplarily, we show that the magnetic contribution to the band-gap blue shift observed in the optical conductivity of $\ensuremath{\alpha}$-MnTe is correctly interpreted as the MBS of a Mott gap. Mott insulator ; Spintronics ; Condensed matter physics ; Antiferromagnetism ; Spins ; Spin (aerodynamics) ; Physics ; Band gap ; Hubbard model ; Exchange interaction ; Strongly correlated material ; Charge (physics) ; Ferromagnetism ; Quantum mechanics ; Electron Magnetic and transport properties of perovskites and related materials ; Physics of Superconductivity and Magnetism ; Advanced Condensed Matter Physics
Authors: Jinghao Li | Fangyuan Sun | Yuhao Jin | Yun Da Chua | Kian Hua Tan | Satrio Wicaksono | Carlo Sirtori | Soon Fatt Yoon | Qi Jie Wang
Source: Optics Express (article, 2021-12-09)
Field: Chemistry — Spectroscopy

Widely tunable single-mode slot waveguide quantum cascade laser array We report designs and experimental demonstrations of a widely tunable single-mode quantum cascade laser array based on slot waveguide structures in the mid-infrared region. The laser array device realized a continuous tuning range of 71 cm<sup>-1</sup> from 9.66 μm to 10.37 μm at 300 K only using the current tuning without any external heatsink temperature adjustments, in good agreement with the design. Stable single-mode operations free of undesired mode-hops have been obtained over the whole tuning range. Another slot waveguide QCL array with a 41 cm<sup>-1</sup> continuous tuning range around 7.3 μm has also been realized with the same design principle, demonstrating the universal applicability of the array design. The broadly continuous tuning with simple processing makes the array device a suitable candidate for mid-infrared sensing and spectroscopy application. Quantum cascade laser ; Cascade ; Optics ; Waveguide ; Laser ; Single-mode optical fiber ; Continuous wave ; Optoelectronics ; Materials science ; Semiconductor laser theory ; Physics Spectroscopy and Laser Applications ; Atmospheric Ozone and Climate ; Semiconductor Lasers and Optical Devices
Authors: Arthur V. Dolmatov | Sergey S. Maklakov | Polina A. Zezyulina | Alexey V. Osipov | Dmitry A. Petrov | Andrey Naboko | Viktor I. Polozov | Sergey A. Maklakov | S. N. Starostenko | Andrey N. Lagarkov
Source: Sensors (article, 2021-07-06)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Deposition of a SiO2 Shell of Variable Thickness and Chemical Composition to Carbonyl Iron: Synthesis and Microwave Measurements Protective SiO<sub>2</sub> coating deposited to iron microparticles is highly demanded both for the chemical and magnetic performance of the latter. Hydrolysis of tetraethoxysilane is the crucial method for SiO<sub>2</sub> deposition from a solution. The capabilities of this technique have not been thoroughly studied yet. Here, two factors were tested to affect the chemical composition and the thickness of the SiO<sub>2</sub> shell. It was found that an increase in the hydrolysis reaction time thickened the SiO<sub>2</sub> shell from 100 to 200 nm. Moreover, a decrease in the acidity of the reaction mixture not only thickened the shell but also varied the chemical composition from SiO<sub>3.0</sub> to SiO<sub>8.6</sub>. The thickness and composition of the dielectric layer were studied by scanning electron microscopy and energy-dispersive X-ray analysis. Microwave permeability and permittivity of the SiO<sub>2</sub>-coated iron particles mixed with a paraffin wax matrix were measured by the coaxial line technique. An increase in thickness of the silica layer decreased the real quasi-static permittivity. The changes observed were shown to agree with the Maxwell Garnett effective medium theory. The new method developed to fine-tune the chemical properties of the protective SiO<sub>2</sub> shell may be helpful for new magnetic biosensor designs as it allows for biocompatibility adjustment. Microwave ; Shell (structure) ; Deposition (geology) ; Carbonyl iron ; Chemical composition ; Composition (language) ; Chemical engineering ; Materials science ; Chemistry ; Analytical Chemistry (journal) ; Organic chemistry ; Composite material ; Computer science ; Telecommunications ; Engineering ; Geology Electromagnetic wave absorption materials ; Microwave Dielectric Ceramics Synthesis ; Microwave-Assisted Synthesis and Applications
Authors: Norhan Nady | Noha Salem | Marwa A. A. Mohamed | Sherif Kandil
Source: Nanomaterials (article, 2021-11-12)
Field: Materials Science — Materials Chemistry

Iron-Nickel Alloy with Starfish-like Shape and Its Unique Magnetic Properties: Effect of Reaction Volume and Metal Concentration on the Synthesized Alloy Iron-nickel alloy is an example of bimetallic nanostructures magnetic alloy, which receives intensive and significant attention in recent years due to its desirable superior ferromagnetic and mechanical characteristics. In this work, a unique starfish-like shape of an iron-nickel alloy with unique magnetic properties was presented using a simple, effective, high purity, and low-cost chemical reduction. There is no report on the synthesis of such novel shape without complex precursors and/or surfactants that increase production costs and introduce impurities, so far. The synthesis of five magnetic iron-nickel alloys with varying iron to nickel molar ratios (10-50% Fe) was undertaken by simultaneously reducing Fe(II) and Ni(II) solution using hydrazine hydrate as a reducing agent in strong alkaline media for 15 min at 95-98 °C. The effect of reaction volume and total metal concentration on the properties of the synthesized alloys was studied. Alloy morphology, chemical composition, crystal structure, thermal stability, and magnetic properties of synthesized iron-nickel alloys were characterized by means of SEM, TEM, EDX, XRD, DSC and VSM. ImageJ software was used to calculate the size of the synthesized alloys. A deviation from Vegard's law was recorded for iron molar ration higher than 30%., in which superstructure phase of FeNi<sub>3</sub> was formed and the presence of defects in it, as well as the dimensional effects of nanocrystals. The saturation magnetization (Ms), coercivity (Hc), retentivity (Mr), and squareness are strongly affected by the molar ratio of iron and nickel and reaction volume as well as the total metal concentration. Alloy ; Materials science ; Coercivity ; Nickel ; Chemical engineering ; Impurity ; Metallurgy ; Metal ; Chemistry Magnetic Properties and Synthesis of Ferrites ; Magnetic properties of thin films ; Magnetic Properties and Applications
Authors: Demian Abreu | Washington Martins da Silva | Miguel Angel Narvaez Ardila | José Daniel Biasoli de Mello
Source: Materials Research (article, 2021-11-19)
Field: Materials Science — Metals and Alloys

Tribocorrosion in Ferritic Stainless Steels: an Improved Methodological Approach This study aimed to improve a potentiodynamic-based methodological approach for characterising the tribocorrosion of ferritic stainless steel. Synergistic effects in tribocorrosion systems have been widely investigated and debated under potentiostatic conditions. Because potentiostatic tests can ignore essential phenomena that are typically noticeable in potentiodynamic tests (potential scanning), such as variations in the friction coefficient that are motivated by potential scanning, e.g., during active, passive, and transpassive domains, a potentiodynamic-technique-based methodology was enhanced to analyse tribocorrosion in ferritic stainless steels. The proposed method facilitates the analysis of all essential parameters for tribocorrosion phenomena based on a single figure directly associating the average friction coefficient curves from sliding tests and tribocorrosion tests. It consists of performing sliding tribological tests to evaluate mechanical wear accurately, potentiodynamic corrosion tests to determine corrosion resistance in the absence of mechanical wear, and tribocorrosion tests to associate mechanical wear and corrosion degradation caused by chemical/electrochemical effects. Validation of the methodology consisted of its application to an 11% Cr ferritic stainless steel. The results demonstrate that tribocorrosion intensifies the material loss, but only by a minimal amount. The results demonstrate that the average passivation current density in the tribocorrosive tests was significantly higher. Apparently, the corrosion products generated during the tribocorrosion tests acted as solid lubricants inducing a lower friction coefficient in the tribocorrosive tests. Finally, tribocorrosion intensifies the material loss, but only by a minimal amount. Tribocorrosion ; Corrosion ; Materials science ; Tribology ; Metallurgy ; Passivation ; Electrochemistry ; Composite material ; Electrode ; Layer (electronics) ; Chemistry Hydrogen embrittlement and corrosion behaviors in metals ; Corrosion Behavior and Inhibition ; Mechanical stress and fatigue analysis
Authors: Rui Yao | Jun Li | Meng Hui | Lin Bai | Qisheng Wu
Source: IEEE Access (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Pattern Recognition for Partial Discharge Using Multi-Feature Combination Adaptive Boost Classification Model This paper proposes a multi-feature combination adaptive boost classification model, considering the difference and complementarity of the three different single feature sets for partial discharge pattern recognition. First, eight types of physical models are designed. Then, an UHF measurement system is used to collect partial discharge data. Second, three kinds of single feature sets extracted from the Phase Resolved Pulse Sequence (PRPS) data are combined with pairs and three to construct new feature sets. The final optimal feature set is selected from the single feature set and the combined feature set as the input of the classification model. Finally, using the boosting algorithm in combination learning to process the training data set, taking the support vector machine as the base classifier, and measuring the inconsistency between one base classifier and other base classifiers by using the “unpaired” diversity index based on information entropy. By this method, a series of various SVM-based classifiers with moderate accuracy are obtained, and finally an adaptive boost classification model based on the multi-feature combination method is obtained. For each defect, 25 samples were obtained at the same test voltage level, and a total of 150 samples were obtained at 6 voltage levels through multiple experiments. The proposed method was compared with the traditional methods using these data sets. The results revealed that the proposed method successfully identified the types of partial discharge insulation defects. Computer science ; Partial discharge ; Pattern recognition (psychology) ; Feature (linguistics) ; Artificial intelligence ; Feature extraction ; Voltage ; Engineering High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Electrostatic Discharge in Electronics
Authors: Juhwan Kim | Geun Ho Gu | Juhwan Noh | Seongun Kim | Suji Gim | Jaesik Choi | Yousung Jung
Source: Chemical Science (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Predicting potentially hazardous chemical reactions using an explainable neural network Predicting potentially dangerous chemical reactions is a critical task for laboratory safety. However, a traditional experimental investigation of reaction conditions for possible hazardous or explosive byproducts entails substantial time and cost, for which machine learning prediction could accelerate the process and help detailed experimental investigations. Several machine learning models have been developed which allow the prediction of major chemical reaction products with reasonable accuracy. However, these methods may not present sufficiently high accuracy for the prediction of hazardous products which particularly requires a low false negative result for laboratory safety in order not to miss any dangerous reactions. In this work, we propose an explainable artificial intelligence model that can predict the formation of hazardous reaction products in a binary classification fashion. The reactant molecules are transformed into substructure-encoded fingerprints and then fed into a convolutional neural network to make the binary decision of the chemical reaction. The proposed model shows a false negative rate of 0.09, which can be compared with 0.47-0.66 using the existing main product prediction models. To provide explanations for what substructures of the given reactant molecules are important to make a decision for target hazardous product formation, we apply an input attribution method, layer-wise relevance propagation, which computes the contributions of individual inputs per input data. The computed attributions indeed match some of the existing chemical intuitions and mechanisms, and also offer a way to analyze possible data-imbalance issues of the current predictions based on relatively small positive datasets. We expect that the proposed hazardous product prediction model will be complementary to existing main product prediction models and experimental investigations. Hazardous waste ; Artificial neural network ; Computer science ; Artificial intelligence ; Biochemical engineering ; Engineering ; Waste management Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Fault Detection and Control Systems
Authors: Christopher Karpovich | Zach Jensen | Vineeth Venugopal | Elsa Olivetti
Source: arXiv (Cornell University) (preprint, 2021-12-17)
Field: Materials Science — Materials Chemistry

Inorganic Synthesis Reaction Condition Prediction with Generative Machine Learning Data-driven synthesis planning with machine learning is a key step in the design and discovery of novel inorganic compounds with desirable properties. Inorganic materials synthesis is often guided by chemists' prior knowledge and experience, built upon experimental trial-and-error that is both time and resource consuming. Recent developments in natural language processing (NLP) have enabled large-scale text mining of scientific literature, providing open source databases of synthesis information of synthesized compounds, material precursors, and reaction conditions (temperatures, times). In this work, we employ a conditional variational autoencoder (CVAE) to predict suitable inorganic reaction conditions for the crucial inorganic synthesis steps of calcination and sintering. We find that the CVAE model is capable of learning subtle differences in target material composition, precursor compound identities, and choice of synthesis route (solid-state, sol-gel) that are present in the inorganic synthesis space. Moreover, the CVAE can generalize well to unseen chemical entities and shows promise for predicting reaction conditions for previously unsynthesized compounds of interest. Autoencoder ; Computer science ; Generative grammar ; Generative model ; Calcination ; Chemical space ; Key (lock) ; Artificial intelligence ; Machine learning ; Chemistry ; Artificial neural network ; Catalysis ; Organic chemistry ; Drug discovery Machine Learning in Materials Science ; Topic Modeling ; Expert finding and Q&A systems
Authors: Soo Hyun Kim | Leslie Ching Ow Tiong | Dong-Hun Kim | Sang Soo Han
Source: The Journal of Physical Chemistry Letters (article, 2021-08-26)
Field: Materials Science — Materials Chemistry

Deep Learning-Based Prediction of Material Properties Using Chemical Compositions and Diffraction Patterns as Experimentally Accessible Inputs We report a deep learning (DL) model that predicts various material properties while accepting directly accessible inputs from routine experimental platforms: chemical compositions and diffraction data, which can be obtained from the X-ray or electron-beam diffraction and energy-dispersive spectroscopy, respectively. These heterogeneous forms of inputs are treated simultaneously in our DL model, where the novel chemical composition vector is proposed by developing element embedding with the normalized composition matrix. With 1524 binary samples available in the Materials Project database, the model predicts formation energies and band gaps with mean absolute errors of 0.29 eV/atom and 0.66 eV, respectively. According to the weighing test between these two inputs, the properties tend to be more influenced by the chemical composition than the crystal structure. This work intentionally avoids using inputs that are not directly accessible (e.g., atomic coordinates) in experimental platforms, and thus is expected to substantially improve the practical use of DL models. Diffraction ; Chemical composition ; Embedding ; Matrix (chemical analysis) ; Work (physics) ; Crystal (programming language) ; Binary number ; Atom (system on chip) ; Biological system ; Materials science ; X-ray photoelectron spectroscopy ; Computer science ; Computational physics ; Chemistry ; Mathematics ; Thermodynamics ; Artificial intelligence ; Physics ; Optics ; Chemical engineering ; Composite material ; Engineering Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Kai Lin Woon | Zhao Xian Chong | Azhar Ariffin | Chee Seng Chan
Source: Journal of Molecular Graphics and Modelling (article, 2021-03-13)
Field: Materials Science — Materials Chemistry

Relating molecular descriptors to frontier orbital energy levels, singlet and triplet excited states of fused tricyclics using machine learning HOMO/LUMO ; Molecular orbital ; Singlet state ; Excited state ; Artificial neural network ; Molecular descriptor ; Artificial intelligence ; Molecule ; Chemistry ; Machine learning ; Computer science ; Computational chemistry ; Topology (electrical circuits) ; Physics ; Mathematics ; Atomic physics ; Quantum mechanics ; Quantitative structure–activity relationship ; Combinatorics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Molecular Junctions and Nanostructures