jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mohamed S. Hamdy | Otto van den Berg | Guido Mul
Source: Journal of Molecular Structure (article, 2019-09-23)
Field: Materials Science — Materials Chemistry

Size-tunable TiO2 nanoparticles in mesoporous silica: Size-dependent performance in selective photo-oxidation Photocatalysis ; Chemistry ; Nanoparticle ; Mesoporous material ; Propane ; Selectivity ; Ultraviolet ; Chemical engineering ; Hydrothermal circulation ; Sorption ; Nanotechnology ; Catalysis ; Adsorption ; Organic chemistry ; Materials science Catalytic Processes in Materials Science ; TiO2 Photocatalysis and Solar Cells ; Catalysis and Oxidation Reactions
Authors: Natascha Breuer | Irina Gruber | Christoph Janiak | Thomas J. J. Müller
Source: Beilstein Journal of Organic Chemistry (article, 2019-11-12)
Field: Materials Science — Materials Chemistry

Emission solvatochromic, solid-state and aggregation-induced emissive α-pyrones and emission-tuneable 1<i>H</i>-pyridines by Michael addition–cyclocondensation sequences Starting from substituted alkynones, α-pyrones and/or 1<i>H</i>-pyridines were generated in a Michael addition-cyclocondensation with ethyl cyanoacetate. The peculiar product formation depends on the reaction conditions as well as on the electronic substitution pattern of the alkynone. While electron-donating groups furnish α-pyrones as main products, electron-withdrawing groups predominantly give the corresponding 1<i>H</i>-pyridines. Both heterocycle classes fluoresce in solution and in the solid state. In particular, dimethylamino-substituted α-pyrones, as donor-acceptor systems, display remarkable photophysical properties, such as strongly red-shifted absorption and emission maxima with daylight fluorescence and fluorescence quantum yields up to 99% in solution and around 11% in the solid state, as well as pronounced emission solvatochromism. Also a donor-substituted α-pyrone shows pronounced aggregation-induced emission enhancement. Solvatochromism ; Chemistry ; Fluorescence ; Photochemistry ; Michael reaction ; Aggregation-induced emission ; Polar effect ; Solid-state ; Absorption (acoustics) ; Emission spectrum ; Organic chemistry ; Physical chemistry ; Molecule ; Spectral line ; Materials science Luminescence and Fluorescent Materials ; Synthesis and Properties of Aromatic Compounds ; Catalytic C–H Functionalization Methods
Authors: Aamir Iqbal Waidha | Hongbin Zhang | Maren Lepple | Supratik Dasgupta | Lambert Alff | Peter R. Slater | A. Dominic Fortes | Oliver Clemens
Source: Chemical Communications (article, 2019-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

BaCoO<sub>2+δ</sub>: a new highly oxygen deficient perovskite-related phase with unusual Co coordination obtained by high temperature reaction with short reaction times A new highly oxygen deficient metastable modification of perovskite-related BaCoO2+δ (δ ∼ 0.01-0.02) has been prepared using high temperature reactions with short heating times. This defect rich compound has at least partially square planar coordination of the Co2+ ions, a highly unusual coordination environment for Co. Low temperature neutron powder diffraction showed a G-type antiferromagnetic ordering, confirmed by SQUID magnetic measurements, which indicate a high Néel temperature of 220 K. This work shows how novel defective phases can be synthesized by exploiting short reaction times in solid state synthesis, thus offering an alternative route for new materials synthesis. Metastability ; Antiferromagnetism ; Perovskite (structure) ; Neutron diffraction ; Phase (matter) ; Squid ; Crystallography ; Oxygen ; Planar ; Ion ; Materials science ; Chemistry ; Crystal structure ; Organic chemistry ; Condensed matter physics Magnetic and transport properties of perovskites and related materials ; Perovskite Materials and Applications ; Advanced Condensed Matter Physics
Authors: Caterina Viglianisi | Leonardo Di Pietro | Valentina Meoni | Riccardo Amorati | Stefano Menichetti
Source: ARKIVOC (article, 2019-08-29)
Field: Chemistry — Organic Chemistry

From simple phenols to potent chain-breaking antioxidants by transposition of benzo[1,4]oxathiines to benzo[b]thiophenes Simple phenols, including food stock recycled derivatives, were used for the synthesis of 1,4-benzooxathiine intermediates, with limited or no antioxidant activity. Depending upon their substitution pattern, these compounds, through an acid promoted transposition, can be converted into o-hydroxydihydrobenzo[b]thiophenes or o-hydroxy-benzo[b]thiophenes as a new class of potent chain-breaking antioxidants, showing, in the reaction with alkyl peroxyl radicals, kinetic rate constants (k inh ) comparable to that of tocopherol, the more potent natural lipophilic phenolic antioxidant. Chemistry ; Transposition (logic) ; Phenols ; Stereochemistry ; Organic chemistry Free Radicals and Antioxidants
Authors: Sachin Babu | Jeong‐Bong Lee
Source: Sensors (article, 2019-11-28)
Field: Materials Science — Surfaces, Coatings and Films

Axially-Anisotropic Hierarchical Grating 2D Guided-Mode Resonance Strain-Sensor Guided-mode resonance strain sensors are planar binary gratings that have fixed resonance positions and quality factors decided by material properties and grating parameters. If one is restricted by material choices, the quality factor can only be improved by adjusting the grating parameters. We report a new method to improve quality factor by applying a slotting design rule to a grating design. We investigate this design rule by first providing a theoretical analysis on how it works and then applying it to a previously studied 2D solid-disc guided-mode resonance grating strain sensor design to create a new slotted-disc guided-mode resonance grating design. We then use finite element analysis to obtain reflection spectrum results that show the new design produces resonances with at least a 6-fold increase in quality factor over the original design and more axially-symmetric sensitivities. Lastly, we discuss the applicability of the slotting design rule to binary gratings in general as a means of improving grating performance while retaining both material and resonance position choices. Grating ; Resonance (particle physics) ; Guided-mode resonance ; Q factor ; Optics ; Quality (philosophy) ; Axial symmetry ; Diffraction grating ; Planar ; Materials science ; Acoustics ; Computer science ; Physics ; Engineering ; Structural engineering Optical Coatings and Gratings ; Photonic and Optical Devices ; Advanced Fiber Optic Sensors
Authors: P. Potera | I.S. Virt | Bogumił Cieniek | G. Wisz
Source: Ukrainian Journal of Physical Optics (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

Optical properties of ZnO:TM (TM = Cr, Mn and Co) layers obtained by pulsed laser deposition technique We study the optical properties of zinc-chromium (ZnCrO), zincmanganese (ZnMnO) and zinc-cobalt (ZnCoO) oxide layers grown on sapphire substrates, using a pulsed laser deposition technique. The influence of target composition and post-growth annealing on the optical transmission of the films and their bandgap energy are analyzed. Materials science ; Pulsed laser deposition ; Deposition (geology) ; Laser ; Optoelectronics ; Analytical Chemistry (journal) ; Thin film ; Optics ; Nanotechnology ; Chemistry ; Environmental chemistry ZnO doping and properties
Authors: Nathan M George | Ryan Sweet | Jeffrey J. Powers | Andrew Worrall | Kurt A. Terrani | Brian D. Wirth | G. Ivan Maldonado
Source: Annals of Nuclear Energy (article, 2019-05-13)
Field: Materials Science — Materials Chemistry

Full-core analysis for FeCrAl enhanced accident tolerant fuel in boiling water reactors Cladding (metalworking) ; Materials science ; Zirconium alloy ; Boiling ; Nuclear engineering ; Burnup ; Light-water reactor ; Boiling water reactor ; Composite material ; Control rod ; Bundle ; Alloy ; Mechanical engineering ; Engineering ; Chemistry Nuclear Materials and Properties ; Nuclear reactor physics and engineering ; Fusion materials and technologies
Authors: Yuki Ishikura | Jun Imaoka | Mostafa Noah | Masayoshi Yamamoto
Source: IEEJ Journal of Industry Applications (article, 2019-04-30)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Comparative Evaluation of Electrical and Calorimetric Methods to Estimate Core Loss in Powder Cores The utilization of powder cores for many power electronic applications has drawn a significant amount of attention owing to their attractive magnetic properties. However, because of their low relative permeability characteristics, a precise measurement of the core loss is very difficult to obtain. Imprecise estimation of the core loss may lead to an inaccurate thermal design of power converters. Measuring and calculating the accurate value of the core loss allows the circuit designer to design a more efficient system. This paper discusses the revalidation of electrical measurement by comparing the electrical method with the calorimetric method in powder cores. The calorimetric method is one of the most promising methods to accurately measure the core loss; however, it is time consuming. A difference less than 10% has been reported for the core loss measurement error while comparing the electrical method and the calorimetric method in various conditions. These results indicate that the electrical method using the B-H analyzer is effective for measuring the core losses in some conditions for powder cores. Along with the theoretical discussion, simulation and experimental tests are also conducted. Core (optical fiber) ; Materials science ; Computer science ; Process engineering ; Nuclear engineering ; Composite material ; Engineering Magnetic Properties and Applications ; Silicon Carbide Semiconductor Technologies ; Advanced DC-DC Converters
Authors: Guofa Sun | Yaming Xu
Source: Complexity (article, 2019-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Finite‐Time Observer‐Based Adaptive Control of Switched System with Unknown Backlash‐Like Hysteresis This work investigates a finite‐time observer problem for a class of uncertain switched nonlinear systems in strict‐feedback form, preceded by unknown hysteresis. By using a finite‐time performance function, a finite‐time switched state observer (FTSO) is derived using radial basis function neural networks (RBFNNs) to estimate the unmeasured states. An adaptive feedback neural network tracking control is derived based on the backstepping technique, which guarantees that all the signals of the closed‐loop system are bounded, the output tracking error converges to zero, and the observer error converges to a prescribed arbitrarily small region within a finite‐time interval. In addition, two simulation studies and an experiment test are provided to verify the feasibility and effectiveness of the theoretical finding in this study. Backlash ; Control theory (sociology) ; Observer (physics) ; Hysteresis ; Computer science ; Adaptive control ; Control (management) ; Physics ; Artificial intelligence Magnetic Properties and Applications ; Piezoelectric Actuators and Control ; Iterative Learning Control Systems
Authors: Debasis Nayak | K. Chandra Babu Naidu | D. Ravinder
Source: SN Applied Sciences (article, 2019-02-15)
Field: Materials Science — Materials Chemistry

Effect of chromium on structural, morphological and electrical properties of lithium ferrite nanoparticles Spinel ; Dielectric ; Materials science ; Transmission electron microscopy ; Lattice constant ; Scanning electron microscope ; Nanoparticle ; Dielectric loss ; Ferrite (magnet) ; Analytical Chemistry (journal) ; Chromium ; Diffraction ; Chemistry ; Nanotechnology ; Composite material ; Metallurgy ; Optics ; Optoelectronics ; Chromatography Magnetic Properties and Synthesis of Ferrites ; Advancements in Battery Materials ; Electromagnetic wave absorption materials
Authors: Mrinal C. Saha
Source: ODU Digital Commons (Old Dominion University) (article, 2019-10-18)
Field: Materials Science — General Materials Science

Elastic Properties, Strength and Damage Tolerance of Pultruded Composites Pultruded composites are candidate materials for civil engineering infrastructural applications due their higher corrosion resistance and lower life cycle cost. Efficient use of materials like structural members requires thorough understanding of the mechanism that affects their response. The present investigation addresses the modeling and characterization of E-glass fiber/polyester resin matrix pultruded composites in the form of sheets of various thicknesses. The elastic constants were measured using static, vibration and ultrasonic methods. Two types of piezoelectric crystals were used in ultrasonic measurements. Finally, the feasibility of using a single specimen, in the form of a circular disk, was shown in measuring all the elastic constants using ultrasonic technique. The effects of stress gradient on tensile strength were investigated. A large number of specimens, parallel and transverse to the pultrusion direction, were tested in tension, 3-point flexure, and 4-point flexure. A 2-parameter Weibull model was applied to predict the tensile strength from the flexure tests. The measured and Weibull-predicted ratios did not show consistent agreement. Microstructural observations suggested that the flaw distribution in the material was not uniform, which appears to be a basic requirement for the Weibull distribution. Compressive properties were measured using a short-block compression test specimen of 44.4-mm long and 25.4-mm wide. Specimens were tested at 0°, 30°, 45°, 60° and 90° orientations. The compression test specimen was modeled using 4-noded isoparametric layered plate and shell elements. The predicted elastic properties for the roving layer and the continuous strand mat layer was used for the finite element study. The damage resistance and damage tolerance were investigated experimentally. Using a quasi-static indentation loading, damage was induced at various incrementally increased force levels to investigate the damage growth process. Damage parameters were measured in the form of dent depth, back surface crack length, and damage area. The compression tests were performed, using an end-gripped compression test fixture, on both the damaged specimens and open-hole specimens. A relationship between the compressive strength and hole-diameter was established. The compressive strength of damaged specimens was compared to determine the “equivalent-hole-diameter.” A correlation between damage parameters and the “equivalent-hole-diameter” was established to find a parameter that could be used as a measure of damage resistance and damage tolerance of pultruded composite structures. Composite material ; Pultrusion ; Materials science ; Damage tolerance ; Advanced composite materials ; Composite number Material Properties and Applications ; Additive Manufacturing and 3D Printing Technologies ; Innovations in Concrete and Construction Materials
Authors: Anton V. Sinitskiy | Vijay S. Pande
Source: arXiv (Cornell University) (preprint, 2019-08-01)
Field: Materials Science — Materials Chemistry

Physical machine learning outperforms "human learning" in Quantum Chemistry Two types of approaches to modeling molecular systems have demonstrated high practical efficiency. Density functional theory (DFT), the most widely used quantum chemical method, is a physical approach predicting energies and electron densities of molecules. Recently, numerous papers on machine learning (ML) of molecular properties have also been published. ML models greatly outperform DFT in terms of computational costs, and may even reach comparable accuracy, but they are missing physicality - a direct link to Quantum Physics - which limits their applicability. Here, we propose an approach that combines the strong sides of DFT and ML, namely, physicality and low computational cost. By generalizing the famous Hohenberg-Kohn theorems, we derive general equations for exact electron densities and energies that can naturally guide applications of ML in Quantum Chemistry. Based on these equations, we build a deep neural network that can compute electron densities and energies of a wide range of organic molecules not only much faster, but also closer to exact physical values than current versions of DFT. In particular, we reached a mean absolute error in energies of molecules with up to eight non-hydrogen atoms as low as 0.9 kcal/mol relative to CCSD(T) values, noticeably lower than those of DFT (down to ~3 kcal/mol on the same set of molecules) and ML (down to ~1.5 kcal/mol) methods. A simultaneous improvement in the accuracy of predictions of electron densities and energies suggests that the proposed approach describes the physics of molecules better than DFT functionals developed by "human learning" earlier. Thus, physics-based ML offers exciting opportunities for modeling, with high-theory-level quantum chemical accuracy, of much larger molecular systems than currently possible. Density functional theory ; Range (aeronautics) ; Molecule ; Quantum ; Statistical physics ; Artificial neural network ; Quantum chemistry ; Electron ; Quantum mechanics ; Physics ; Computational chemistry ; Computer science ; Chemistry ; Machine learning ; Materials science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Moatasem M. Kh
Source: Journal of Petroleum and Mining Engineering (article, 2019-12-02)
Field: Materials Science — Materials Chemistry

Use of artificial neural network for prediction of mechanical properties of Al-Si alloys synthesized by stir casting Mechanical testing plays an important role in evaluating the fundamental properties of engineering materials as well as, in developing new materials. The use of conventional mathematical models in analytical calculating of the mechanical properties in most materials is very complex. In the current study Al-Si alloys were synthesized using the stir casting method. The mechanical properties as the tensile strength, Brinell hardness and wear property for the produced Al-Si alloys were investigated. Then, the obtained experimental results were used to train the artificial neural network (ANN). The neural network model is used to predict the effect of silicon content on the tensile strength, the hardness test, and wear loss for the prepared Al-Si alloys. Three neural networks were used in this study and the percent of silicon content variable was used as the ANN's input for each. Tensile test is used as ANN's output and the training function used is (traincgp) in first neural network. Also, hardness test is used as ANN's output and the training function used is (traincgf) in second neural network and wear loss test is used as ANN's output and the training function used is (traincgf) in third neural network. The obtained outcomes showed that predictions data in the applied neural networks were closer to the experimental results. The optimum mean square error (MSE) for ANNs during the tensile test, the hardness test and the wear loss test equal to 0.0335, 0.0023, 0.014 respectively and these results were satisfactory. Brinell scale ; Artificial neural network ; Ultimate tensile strength ; Materials science ; Casting ; Tensile testing ; Test data ; Vickers hardness test ; Mean squared error ; Composite material ; Computer science ; Microstructure ; Mathematics ; Machine learning ; Statistics Material Selection and Properties ; Metal Forming Simulation Techniques ; Aluminum Alloy Microstructure Properties
Authors: Ehsan Moharreri | Maryam Pardakhti | Ranjan Srivastava | Steven L. Suib
Source: ACS Combinatorial Science (article, 2019-08-07)
Field: Materials Science — Materials Chemistry

Energy–Geometry Dependency of Molecular Structures: A Multistep Machine Learning Approach There is growing interest in estimating quantum observables while circumventing expensive computational overhead for facile in silico materials screening. Machine learning (ML) methods are implemented to perform such calculations in shorter times. Here, we introduce a multistep method based on machine learning algorithms to estimate total energy on the basis of spatial coordinates and charges for various chemical structures, including organic molecules, inorganic molecules, and ions. This method quickly calculates total energy with 0.76 au in root-mean-square error (RMSE) and 1.5% in mean absolute percent error (MAPE) when tested on a database of optimized and unoptimized structures. Using similar molecular representations, experimental thermochemical properties were estimated, with MAPE as low as 6% and RMSE of 8 cal/mol·K for heat capacity in a 10-fold cross-validation. Mean squared error ; Chemistry ; Mean absolute percentage error ; Quantum ; Dependency (UML) ; Overhead (engineering) ; Observable ; Energy (signal processing) ; Molecule ; Root mean square ; Algorithm ; Biological system ; Statistical physics ; Artificial intelligence ; Statistics ; Computer science ; Quantum mechanics ; Mathematics ; Organic chemistry ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Kristin DeWitt
Source:  (article, 2019-05-14)
Field: Chemistry — Analytical Chemistry

Machine learning for better trace chemical detection One of the biggest challenges facing standoff chemical detection is the ability to accurately predict spectral influences on chemical signatures that are caused by factors such as particle size, particle shape, deposition thickness, or mesoscale crystallinity and morphology. Currently, most chemical detection algorithms treat each substrate/sorbate combination as a separate library entry that must be empirically measured. Physics-based or semi-empirical models work fairly well for fitting spectra and qualitatively mapping features and trends, but are much less accurate for quantitatively predictions, and are too computationally intensive for real-time development of field libraries for practical instruments. IARPA’s MORGOTH’S CROWN prize challenge was a crowdsourced effort to encourage new approaches to infrared spectral modeling to quantitatively predict trace spectra on surfaces from bulk reflectance spectra. Challenge participants were given a training set which included reflectance spectra of sample coupons with trace chemical residues on them, including examples with different particle sizes, crystal structures, and mass loadings. Participants were then asked to generate an algorithm to predict what the spectra of different combinations of chemicals and substrates would look like. The results of the MORGOTH’S CROWN challenge showed that machine-learning based algorithms were better able to quantitatively predict new spectra than physics-based models. The article will describe the execution of the MORGOTH’S CROWN challenge, and discuss the “bigger picture” of what the MORGOTH’S CROWN results mean, and where we “go from here”. TRACE (psycholinguistics) ; Computer science ; Machine learning ; Artificial intelligence Analytical chemistry methods development ; Electrochemical Analysis and Applications ; Water Quality Monitoring and Analysis