jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Andrea Carrà | Veronica Macaluso | Peter W. Villalta | Riccardo Spezia | Silvia Balbo
Source: Journal of the American Society for Mass Spectrometry (article, 2019-11-06)
Field: Chemistry — Spectroscopy

Fragmentation Spectra Prediction and DNA Adducts Structural Determination In this work, chemical dynamics simulations were optimized and used to predict fragmentation mass spectra for DNA adduct structural determination. O<sup>6</sup>-methylguanine (O<sup>6</sup>-Me-G) was used as a simple model adduct to calculate theoretical spectra for comparison with measured high-resolution fragmentation data. An automatic protocol was established to consider the different tautomers accessible at a given energy and obtain final theoretical spectra by insertion of an initial tautomer. In the work reported here, the most stable tautomer was chosen as the initial structure, but in general, any structure could be considered. Allowing for the formation of the various possible tautomers during simulation calculations was found to be important to getting a more complete fragmentation spectrum. The calculated theoretical results reproduce the experimental peaks such that it was possible to determine reaction pathways and product structures. The calculated tautomerization network was crucial to correctly identifying all the observed ion peaks, showing that a mobile proton model holds not only for peptide fragmentation but also for nucleobases. Finally, first principles results were compared to simple machine learning fragmentation models. Tautomer ; Fragmentation (computing) ; Chemistry ; Nucleobase ; Adduct ; Spectral line ; Computational chemistry ; Molecular dynamics ; Mass spectrum ; Mass spectrometry ; DNA ; Stereochemistry ; Chromatography ; Physics ; Organic chemistry ; Quantum mechanics Mass Spectrometry Techniques and Applications ; Enzyme Structure and Function ; Advanced Chemical Physics Studies
Authors: Lan Sun | Chang Hsiung | Valton Smith
Source: Molecules (article, 2019-05-24)
Field: Chemistry — Analytical Chemistry

Investigation of Direct Model Transferability Using Miniature Near-Infrared Spectrometers Recent developments in compact near infrared (NIR) instruments, including both handheld and process instruments, have enabled easy and affordable deployment of multiple instruments for various field and online or inline applications. However, historically, instrument-to-instrument variations could prohibit success when applying calibration models developed on one instrument to additional instruments. Despite the usefulness of calibration transfer techniques, they are difficult to apply when a large number of instruments and/or a large number of classes are involved. Direct model transferability was investigated in this study using miniature near-infrared (MicroNIR™) spectrometers for both classification and quantification problems. For polymer classification, high cross-unit prediction success rates were achieved with both conventional chemometric algorithms and machine learning algorithms. For active pharmaceutical ingredient quantification, low cross-unit prediction errors were achieved with the most commonly used partial least squares (PLS) regression method. This direct model transferability is enabled by the robust design of the MicroNIR™ hardware and will make deployment of multiple spectrometers for various applications more manageable. Transferability ; Partial least squares regression ; Calibration ; Spectrometer ; Software deployment ; Computer science ; Field (mathematics) ; Near-infrared spectroscopy ; Mobile device ; Machine learning ; Mathematics ; Statistics ; Optics ; Software engineering ; Physics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Water Quality Monitoring and Analysis
Authors: Naveena Yanamala | Ishika C. Desai | William E. Miller | Vamsi Kodali | Girija Syamlal | Jenny R. Roberts | Aaron Erdely
Source: Nanotoxicology (article, 2019-07-08)
Field: Materials Science — Materials Chemistry

Grouping of carbonaceous nanomaterials based on association of patterns of inflammatory markers in BAL fluid with adverse outcomes in lungs Carbonaceous nanomaterials (CNMs) are universally being used to make commodities, as they present unique opportunities for development and innovation in the fields of engineering, biotechnology, etc. As technology advances to incorporate CNMs in industry, the potential exposures associated with these particles also increase. CNMs have been found to be associated with substantial pulmonary toxicity, including inflammation, fibrosis, and/or granuloma formation in animal models. This study attempts to categorize the toxicity profiles of various carbon allotropes, in particular, carbon black, different multi-walled carbon nanotubes, graphene-based materials, and their derivatives. Statistical and machine learning-based approaches were used to identify groups of CNMs with similar pulmonary toxicity responses from a panel of proteins measured in bronchoalveolar lavage (BAL) fluid samples and with similar pathological outcomes in the lungs. Thus, grouped particles, based on their pulmonary toxicity profiles, were used to select a small set of proteins that could potentially identify and discriminate between the toxicity profiles associated within each group. Specifically, MDC/CCL22 and MIP-3β/CCL19 were identified as common protein markers associated with both toxicologically distinct groups of CNMs. In addition, the persistent expression of other selected protein markers in BAL fluid from each group suggested their ability to predict toxicity in the lungs, i.e. fibrosis and microgranuloma formation. The advantages of such approaches can have positive implications for further research in toxicity profiling. Toxicity ; Bronchoalveolar lavage ; Pulmonary toxicity ; Toxicogenomics ; Fibrosis ; Lung ; Medicine ; Chemistry ; Pathology ; Internal medicine ; Biochemistry ; Gene expression Nanoparticles: synthesis and applications ; Graphene and Nanomaterials Applications ; Occupational and environmental lung diseases
Authors: Yifei Li | Akshay Singh | Sergiy Krylyuk | Albert V. Davydov | Rafael Jaramillo
Source:  (article, 2019-09-09)
Field: Materials Science — Materials Chemistry

Near-infrared photonic phase-change properties of transition metal ditellurides We use the (Mo,W)Te<sub>2</sub> system to explore the potential of transition metal dichalcogenides (TMDs) as phase-change materials for integrated photonics. We measure the complex optical constant of MoTe<sub>2</sub> in both the 2H and 1T’ phases by spectroscopic ellipsometry. We find that both phases have large refractive index, which is good for confined lightmatter interaction volume. The change &Delta;<i>n</i> between phases is of &sigma;(1), which is large and comparable to established phase-change materials. However, both phases have large optical loss, which limits to figure of merit throughout the measured range. We further measure the NIR reflectivity of MoTe<sub>2</sub> and Mo<sub>0.91</sub>W<sub>0.09</sub>Te<sub>2</sub>, in both the 2H and 1T’ phases. The data show that the strong optical contrast between the 2H and 1T’ structures persists even as the thermodynamic barrier between them is reduced by alloying. This bodes well for alloy design of phase-change materials. Infrared ; Photonics ; Phase change ; Materials science ; Phase transition ; Optoelectronics ; Transition metal ; Condensed matter physics ; Chemistry ; Optics ; Physics ; Engineering physics Phase-change materials and chalcogenides ; Nonlinear Optical Materials Studies ; 2D Materials and Applications
Authors: Joshua Olusegun Okeniyi | Jacob Olumuyiwa Ikotun | Esther T. Akinlabi | Elizabeth Toyin Okeniyi
Source: The Scientific World JOURNAL (article, 2019-08-19)
Field: Materials Science — Materials Chemistry

Anticorrosion Behaviour of<i>Rhizophora mangle</i>L. Bark-Extract on Concrete Steel-Rebar in Saline/Marine Simulating-Environment This paper investigates anticorrosion behaviour of the bark-extract from <i>Rhizophora mangle</i> L. on steel-rebar in concrete slabs in 3.5% NaCl medium of immersion (for simulating saline/marine environment). Corrosion-rate, corrosion-current, and corrosion-potential were measured from the NaCl-immersed steel-reinforced concrete cast with admixture of different plant-extract concentrations and from positive control concrete immersed in distilled water. Analyses indicate excellent mathematical-correlation between the corrosion-rate, concentration of the bark-extract admixture, and electrochemical noise-resistance (ratio of the corrosion-potential standard deviation to that of corrosion-current). The 0.4667% <i>Rhizophora mangle</i> L. bark-extract admixture exhibited optimal corrosion-inhibition performance, <i>η</i> = 99.08±0.11% (experimental) or <i>η</i> = 97.89±0.24% (correlation), which outperformed the positive control specimens, experimentally. Both experimental and correlated results followed Langmuir adsorption isotherm which suggests prevalent physisorption mechanism by the plant-extract on the reinforcing-steel corrosion-protection. These findings support <i>Rhizophora mangle</i> L. bark-extract suitability for corrosion-protection of steel-rebar in concrete structure designed for immersion in the saline/marine environmental medium. Corrosion ; Rhizophora mangle ; Rebar ; Materials science ; Distilled water ; Electrochemistry ; Metallurgy ; Chemistry ; Mangrove ; Chromatography ; Electrode ; Biology Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability ; Smart Materials for Construction
Authors: Igor Shikhov | Donald S. Thomas | Christoph H. Arns
Source: Energy & Fuels (article, 2019-07-26)
Field: Chemistry — Analytical Chemistry

On the Optimum Aging Time: Magnetic Resonance Study of Asphaltene Adsorption Dynamics in Sandstone Rock Wettability is a key factor defining ultimate hydrocarbon recovery. Extensive experimentation is required to replicate the wetting state of reservoir rocks. This involves a rock sample wettability restoration procedure, including an aging step and a concept of an optimum aging time, in which asphaltene chemistry plays a major role. There are numerous reports on significance of various crude oil components and elements of asphaltene structure to their tendency to interact with the solid phase, though subject evidence is contradictory. We investigate a possible relationship between the composition of oil, kinetics of an aging process, and a change of sandstone rock wettability. Wettability state of the cores was monitored using low-field NMR relaxometry. The composition and key components of accumulated deposits were established by matching 1H solution-state NMR and X-band EPR spectra of deposits to the spectra of SARA (saturates-aromatics-resins-asphaltenes) fractions of aging fluids. We determined adsorption rate as a function of aging fluid type, monitored free radical and vanadyl content of deposits, and wettability state (through surface relaxivity) over extended aging time interval. EPR data suggest no correlation between the concentration of free radicals in deposits and wettability of the cores. We observe two distinctive periods in the aging process: (i) early-time adsorption best described by a pseudo-second-order kinetic model similar for all aging fluids used in this study, suggesting a common reaction-limited process; (ii) a late-time adsorption process of a faster rate proportional to a bulk diffusion coefficient of aging fluids approximated by an intraparticle diffusion kinetic model. The transition time interval between the two can be used as a definition of an “optimum aging time” in special core analysis. Results provide a link between oil chemistry and wettability phenomena in rocks and may contribute to the development of a model predicting wettability reversal and more accurate reservoir modeling. Asphaltene ; Wetting ; Adsorption ; Diffusion ; Chemistry ; Electron paramagnetic resonance ; Chemical engineering ; Relaxometry ; Hydrocarbon ; Kinetics ; Mineralogy ; Thermodynamics ; Organic chemistry ; Nuclear magnetic resonance Petroleum Processing and Analysis ; Hydrocarbon exploration and reservoir analysis ; NMR spectroscopy and applications
Authors: Jiwoong Kang | Ning Lu | Issac Loo | Nancy Senabulya | Ashwin J. Shahani
Source: Integrating materials and manufacturing innovation (article, 2019-07-18)
Field: Materials Science — Materials Chemistry

PolyProc: A Modular Processing Pipeline for X-ray Diffraction Tomography Abstract Direct imaging of three-dimensional microstructure via X-ray diffraction-based techniques gives valuable insight into the crystallographic features that influence materials properties and performance. For instance, X-ray diffraction tomography provides information on grain orientation, position, size, and shape in a bulk specimen. As such techniques become more accessible to researchers, demands are placed on processing the datasets that are inherently “noisy,” multi-dimensional, and multimodal. To fulfill this need, we have developed a one-of-a-kind function package, PolyProc, that is compatible with a range of data shapes, from planar sections to time-evolving and three-dimensional orientation data. Our package comprises functions to import, filter, analyze, and visualize the reconstructed grain maps. To accelerate the computations in our pipeline, we harness computationally efficient approaches: for instance, data alignment is done via genetic optimization; grain tracking through the Hungarian method; and feature-to-feature correlation through k -nearest neighbors algorithm. As a proof-of-concept, we test our approach in characterizing the grain texture, topology, and evolution in a polycrystalline Al–Cu alloy undergoing coarsening. Pipeline (software) ; Modular design ; Orientation (vector space) ; Computer science ; Computation ; Feature (linguistics) ; Planar ; Position (finance) ; Texture (cosmology) ; Algorithm ; Artificial intelligence ; Computational science ; Mathematics ; Geometry ; Image (mathematics) ; Computer graphics (images) Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications
Authors: Umberto Michelucci | Francesca Venturini
Source: Applied Sciences (article, 2019-11-07)
Field: Materials Science — Materials Chemistry

Multi-Task Learning for Multi-Dimensional Regression: Application to Luminescence Sensing The classical approach to non-linear regression in physics is to take a mathematical model describing the functional dependence of the dependent variable from a set of independent variables, and then using non-linear fitting algorithms, extract the parameters used in the modeling. Particularly challenging are real systems, characterized by several additional influencing factors related to specific components, like electronics or optical parts. In such cases, to make the model reproduce the data, empirically determined terms are built in the models to compensate for the difficulty of modeling things that are, by construction, difficult to model. A new approach to solve this issue is to use neural networks, particularly feed-forward architectures with a sufficient number of hidden layers and an appropriate number of output neurons, each responsible for predicting the desired variables. Unfortunately, feed-forward neural networks (FFNNs) usually perform less efficiently when applied to multi-dimensional regression problems, that is when they are required to predict simultaneously multiple variables that depend from the input dataset in fundamentally different ways. To address this problem, we propose multi-task learning (MTL) architectures. These are characterized by multiple branches of task-specific layers, which have as input the output of a common set of layers. To demonstrate the power of this approach for multi-dimensional regression, the method is applied to luminescence sensing. Here, the MTL architecture allows predicting multiple parameters, the oxygen concentration and temperature, from a single set of measurements. Artificial neural network ; Set (abstract data type) ; Variable (mathematics) ; Variables ; Power (physics) ; Regression Machine Learning in Materials Science ; Neural Networks and Reservoir Computing ; Air Quality Monitoring and Forecasting
Authors: Maria Betânia de Freitas Marques | Bárbara Caroline Rodrigues de Araujo | Christian Fernandes | María Irene Yoshida | Wagner da Nova Mussel | Rita C. O. Sebastião
Source: Journal of the Brazilian Chemical Society (article, 2019-09-03)
Field: Materials Science — Materials Chemistry

Kinetics of Lumefantrine Thermal Decomposition Employing Isoconversional Models and Artificial Neural Network Thermal analysis can be used to determine shelf-life and kinetic parameters in pharmaceutical systems. This work investigates the kinetic of lumefantrine thermal decomposition, an antimalarial, using non-isothermal and isothermal experimental data. The non-isothermal conditions are analyzed applying Vyazovkin method, while isothermal conditions employ models fitting procedure and artificial neural network. Lumefantrine was characterized by powder X-ray diffraction and Fourier transform infrared spectroscopy. The initial stage of lumefantrine thermal decomposition, about 5% of conversion, corresponds to the loss of chlorine and hydroxyl, being correctly predicted by the neural network as a complex event. At room temperature, the D3 model is appropriate to describe the process, once the half-life time is 19 months, in agreement with manufacturer. Isoconversional model determined the activation energy along the whole process while isothermal methodology determined the global value considering the entire process. The results provide important information for the pharmaceutical industry to assay levels of acceptable lumefantrine contents. Thermal decomposition ; Kinetics ; Artificial neural network ; Decomposition ; Materials science ; Chemical engineering ; Chemistry ; Computer science ; Artificial intelligence ; Engineering ; Physics ; Organic chemistry Thermal and Kinetic Analysis ; Chemical Thermodynamics and Molecular Structure
Authors: Chen Zhang | Kenta Yamanaka | Huakang Bian | Akihiko Chiba
Source: npj Materials Degradation (article, 2019-08-27)
Field: Materials Science — Materials Chemistry

Corrosion-resistant carbide-reinforced martensitic steel by Cu modification Abstract Carbide-reinforced martensitic steels, known as high-speed steels (HSSs), have been used as tool materials in various industries because of their high hardness and wear resistance. Nonetheless, such steels show severe degradation when used in a corrosive environment because typical Cr 2 O 3 films, which generally realise passivity in these alloys, do not often work effectively. Here, we demonstrate that the corrosion resistance of a high-carbon-containing Fe–Cr–W-based alloy in a sulfuric acid solution can be significantly improved by the addition of trace Cu. The enrichment of Cu at the surface of the alloy as corrosion proceeds is key to inhibiting further corrosion. A theoretical model for a micro corrosion cell, which should be applicable to any material employed under the same corrosion conditions in fields such as the chemical and energy industries, was developed to interpret the experimental observations. Corrosion ; Materials science ; Metallurgy ; Carbide ; Martensite ; Alloy ; Sulfuric acid ; Passivity ; Microstructure Metal Alloys Wear and Properties ; Microstructure and Mechanical Properties of Steels ; Advanced materials and composites
Authors: Mohamed A. Gebril | Mohd Zaidi Omar | Intan Fadhlina Mohamed | Norinsan Kamil Othman
Source: Metals (article, 2019-03-08)
Field: Materials Science — Materials Chemistry

Microstructural Evaluation and Corrosion Resistance of Semisolid Cast A356 Alloy Processed by Equal Channel Angular Pressing As-cast and semisolid casting using a cooling slope A356 alloy were processed by equal channel angular pressing (ECAP) for Si and grain refinement. The ECAP was conducted at room temperature in a mold, with a channel angle of 120°, and this resulted in a significant size reduction of grain and Si particles from 170.5 and 4.22 to 23.12 and 0.71 µm, respectively, after six passes of heat-treated cooling slope casting, using the ECAP process. The hardness increased with ECAP processing, from 61 HV, for the as-cast alloy, to 134 Hv, after six passes of heat-treated cooling slope casting. The corrosion resistance of the alloy improved, from 0.042 to 0.0012 mmy−1, after the ECAP process. In this work both the strength and corrosion resistance of the ECAPed A356 alloys were improved with the application of the cooling slope process than without (i.e., from the as-cast condition). Materials science ; Metallurgy ; Pressing ; Alloy ; Mold ; Casting ; Corrosion ; Grain size ; Composite material Microstructure and mechanical properties ; Aluminum Alloy Microstructure Properties ; Aluminum Alloys Composites Properties
Authors: I Owunna | Aniekan Ikpe
Source: Nigerian Journal of Technology (article, 2019-01-16)
Field: Materials Science — Metals and Alloys

Modelling and prediction of the mechanical properties of TIG welded joint for AISI 4130 low carbon steel plates using Artificial Neural Network (ANN) approach The mechanical properties (Ultimate Tensile Strength (UTS), modulus of elasticity (E), elongation and strain (e)) for twenty samples of AISI 4130 Low carbon steel plate were studied in this paper. Statistical design of experiment (DOE) using the central composite design method (CCD) was employed in Design Expert 7.01 software to generate DOE for twenty (20) experimental runs as input variables (current, voltage and gas flowrate) which were used in predicting and optimizing the output parameters (maximum UTS and maximum modulus of elasticity with corresponding elongation and strain). One out of the 20 welding runs was found to be optimum using the Artificial Neural Network (ANN) optimization approach. The same twenty (20) predicted variables were subjected to TIG welding experimentation which showed close proximity between the predicted and experimental values. Optimized ANN predicted output parameters were UTS of 421 MPa, modulus of elasticity of 793 MPa, strain of 0.61 and elongation of 61% while experimental values using the optimized input variables produced output parameters of 427 MPa for UTS of 421 MPa, 806 MPa for modulus of elasticity, strain of 0.62 and 62% elongation. Visuals of the weldment obtained from Scanning Electron Microscopy with Energy Dispersive Spectroscopy (SEM/EDS) revealed a uniformly distributed grain sizes in the weldment primarily composing of iron (Fe), chromium (Cr), molybdenum (Mo), and nickel (Ni). To save time, energy and resources required for welding experimentation processes, conventional software such as ANN can be used to obtain accurate results.Keywords: Modelling, Prediction, Low carbon steel, TIG welding, Welding variables Materials science ; Welding ; Gas tungsten arc welding ; Ultimate tensile strength ; Composite material ; Artificial neural network ; Young's modulus ; Elongation ; Carbon steel ; Structural engineering ; Metallurgy ; Corrosion ; Computer science ; Engineering ; Arc welding ; Machine learning Hydrogen embrittlement and corrosion behaviors in metals ; Welding Techniques and Residual Stresses ; Non-Destructive Testing Techniques