jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Carlos José Boluda | Mariana Macías | Joaquín G. Marrero
Source: Ciencia Ingenierías y Aplicaciones (article, 2019-12-12)
Field: Chemistry — Analytical Chemistry

La complejidad química de las gasolinas de automoción Utilizadas principalmente como combustibles en motores de combustión interna, las gasolinas son mezclas complejas de hidrocarburos que incluyen en su composición distintos aditivos para mejorar su estabilidad y prestaciones. Su composición química es no solo diversa, sino también variable, dependiendo del crudo de procedencia, del proceso de obtención y de las normativas existentes. Estas fijan el contenido máximo permitido de determinados compuestos que, por su toxicidad, deben quedar limitados. Las gasolinas se obtienen por destilación fraccionada del petróleo y también a partir de fracciones más pesadas del mismo, mediante craqueo térmico o catalítico, procesos que junto al reformado catalítico van a tener una incidencia directa en su composición. El objetivo de esta revisión es abordar la compleja composición que presentan las gasolinas, así como algunos procesos básicos del refinado de petróleo. Estos procesos determinan en buena medida la naturaleza química de los componentes de este combustible, algunos de los cuales se relacionan con la emisión de sustancias nocivas para la salud y el medio ambiente. De esta forma, proporcionamos un contexto más exacto sobre los agentes contaminantes de la gasolina, a la vez que ponemos de manifiesto la necesidad de contar con normativas avanzadas que regulen la composición de los combustibles. Humanities ; Physics ; Philosophy Petroleum Processing and Analysis ; Biodiesel Production and Applications
Authors: Cormac Toher | Corey Oses | David Hicks | Stefano Curtarolo
Source: npj Computational Materials (preprint, 2019-07-10)
Field: Materials Science — Materials Chemistry

Unavoidable disorder and entropy in multi-component systems Abstract The need for improved functionalities is driving the search for more complicated multi-component materials. Despite the factorially increasing composition space, ordered compounds with four or more species are rare. Here, we unveil the competition between the gain in enthalpy and entropy with increasing number of species by statistical analysis of the AFLOW data repositories. A threshold in the number of species is found where entropy gain exceeds enthalpy gain. Beyond that, enthalpy can be neglected, and disorder—complete or partial—is unavoidable. Enthalpy ; Entropy (arrow of time) ; Component (thermodynamics) ; Thermodynamics ; Statistical physics ; Computer science ; Physics Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices ; Chalcogenide Semiconductor Thin Films
Authors: Ahmad Abi Sodik | Nely Ana Mufarida | Kosjoko Kosjoko
Source: J-Proteksion Jurnal Kajian Ilmiah dan Teknologi Teknik Mesin (article, 2019-02-28)
Field: Materials Science — Materials Chemistry

PENGARUH PENERAPAN WPS (WELDING PROCEDURE SPECIFICATION) AL 6005 TIPE BUTT JOINT TERHADAP KEKUATAN SAMBUNGAN LAS AL 6061 Dokumen WPS ( welding procedure specification ) merupakan dokumen yang sangat penting dalam pengelasan masal di suatu industri. Maka dari itu penelitian ini bertujuan untuk mencari pengaruh penerapan WPS aluminium 6005 tipe butt joint terhadap pengelasan sambungan aluminium 6061 dengan posisi pengelasan 1G ( flat ) dan 3G ( vertikal ) serta pada ketebalan 3 mm, 5 mm, dan 8 mm dan menggunakan filler metal ER 5356. Pengelasan aluminium 6061 ini dilakukan oleh welder bersertifikat dengan menggunakan las GTAW ( gas tungsten arc welding ) atau TIG ( tungsten inert gas ) dengan detail sambungan pengelasan single V- butt joint sudut 60o. pengujian material dilakukan dengan menggunakan pengujian kekuatan tarik, bending dan juga pengujian cacat dengan menggunakan radiography dan dye penetrant . Hasil penelitian ini menunjukan bahwa pengelasan aluminium 6061 dengan menggunakan WPS aluminium 6005 dengan posisi 1G menghasilkan las yang lebih baik dibandingkan posisi 3G dari hasil pengujian cacat luar maupun dalam. Cacat porositas terjadi pada posisi pengelasan 1G dengan jumlah dan ukuran yang sangat kecil sedangkan pada posisi pengelasan 3G terjadi cacat porositas dan incomplete fusion dengan ukuran dan jumlah di luar syarat penerimaan pengujian berdasarkan ISO. Selain pengujian cacat las, hasil dari pengujian kekuatan material las yang dilakukan dengan menggunakan uji tarik dan uji tekuk juga menunjukan kekuatan yang signifikan terjadi pada posisi pengelasan 1G baik material tebal 3 mm, 5 mm maupun tebal 8 mm. Gas tungsten arc welding ; Metallurgy ; Materials science ; Gas metal arc welding ; Welding ; Composite material ; Physics ; Arc welding Material Selection and Properties ; Welding Techniques and Residual Stresses ; Advanced Welding Techniques Analysis
Authors: Сергій Пічугін | Viktor Chichulin | Ksenia Chichulina
Source: International Journal for Computational Civil and Structural Engineering (article, 2019-09-24)
Field: Materials Science — Materials Chemistry

DETERMINATION OF THE ELEMENTS SIGNIFICANCE IN THE RELIABILITY OF REDUNDANT FRAMES The paper attacks the problem of steel redundant structures reliability. In calculations the probabilistic method of limit equilibrium is applied. All possible mechanisms of structural failure are considered. The influence of each section on the work of the frame as a whole is taken into account. Stochastic strength and load characteristics are used in the calculations. The proposed method of calculation allows to obtain structures with a given reliability. The calculation provides an opportunity to take into account the existing reserves of frames. The numerical example uses the logic of probabilistic transformations. The graphs of specific contributions of individual sections and the most probable mechanisms of destruction are presented. The probabilistic method takes into account the correlation between the individual mechanisms of destruction. The developed method determines the limiting moments, but it is allowed to take into account the action of the longitudinal force. In this example, the task was to align the impact of the frame sections without reducing the specified reliability, but it is possible to obtain a design with the same specific contributions, which is most economically justified. Specific contributions are increased or decreased as necessary to obtain a design with equal probability of failure. In the design, the influence of destruction individual mechanisms is used, because the cross sections of the beam span or floor column do not change from the design conditions. The method provides an opportunity to obtain more optimal designs and the use of modern software systems for static calculation. Recommendations for the design of these structures have been developed. It is proposed to use the reliability coefficient of redundant steel structures. Probabilistic logic ; Reliability (semiconductor) ; Frame (networking) ; Computer science ; Limit (mathematics) ; Probabilistic design ; Reliability engineering ; Probabilistic method ; Mathematical optimization ; Structural reliability ; Structural engineering ; Mathematics ; Engineering design process ; Engineering ; Mechanical engineering Structural mechanics and materials
Authors: Osman Mamun | Kirsten T. Winther | Jacob R. Boes | Thomas Bligaard
Source: ChemRxiv (preprint, 2019-11-25)
Field: Materials Science — Materials Chemistry

A Bayesian Framework for Adsorption Energy Prediction on Bimetallic Alloy Catalysts For high-throughput screening of materials for heterogeneous catalysis, scaling relations provides an efficient scheme to estimate the chemisorption energies of hydrogenated species. However, conditioning on a single descriptor ignores the model uncertainty and leads to sub optimal prediction of the chemisorption energy. In this paper, we extend the single descriptor linear scaling relation to a multi descriptor linear regression models to leverage the correlation between adsorption energy of any two pair of adsorbates. With a large dataset, we use Bayesian Information Criteria (BIC) as the model evidence to select the best linear regression model that are derived from non-informative priors. Furthermore, Gaussian Process Regression (GPR) based on the meaningful convolution of physical properties of the metal-adsorbate complex can be used to predict the baseline residual of the selected model. This integrated Bayesian model selection and Gaussian process regression, dubbed as residual learning, can achieve performance comparable to standard DFT error (0.1 eV) for most adsorbate system. For sparse and small datasets, we propose an ad hoc Bayesian Model Averaging (BMA) approach to make a robust prediction. With this Bayesian framework, we significantly reduce the model uncertainty and improve the prediction accuracy. The possibilities of the framework for high-throughput catalytic materials exploration in a realistic setting is illustrated using large and small sets of both dense and sparse simulated dataset generated from a public database of bimetallic alloys available in Catalysis-Hub.org. Computer science ; Bimetallic strip ; Gaussian process ; Bayesian probability ; Prior probability ; Leverage (statistics) ; Kriging ; Pooling ; Bayesian inference ; Residual ; Gaussian ; Linear regression ; Scaling ; Chemisorption ; Artificial intelligence ; Machine learning ; Algorithm ; Chemistry ; Mathematics ; Adsorption ; Computational chemistry ; Catalysis ; Physical chemistry Machine Learning in Materials Science ; Fuel Cells and Related Materials
Authors: Osman Mamun | Kirsten T. Winther | Jacob R. Boes | Thomas Bligaard
Source: ChemRxiv (preprint, 2019-11-25)
Field: Materials Science — Materials Chemistry

A Bayesian Framework for Adsorption Energy Prediction on Bimetallic Alloy Catalysts For high-throughput screening of materials for heterogeneous catalysis, scaling relations provides an efficient scheme to estimate the chemisorption energies of hydrogenated species. However, conditioning on a single descriptor ignores the model uncertainty and leads to sub optimal prediction of the chemisorption energy. In this paper, we extend the single descriptor linear scaling relation to a multi descriptor linear regression models to leverage the correlation between adsorption energy of any two pair of adsorbates. With a large dataset, we use Bayesian Information Criteria (BIC) as the model evidence to select the best linear regression model that are derived from non-informative priors. Furthermore, Gaussian Process Regression (GPR) based on the meaningful convolution of physical properties of the metal-adsorbate complex can be used to predict the baseline residual of the selected model. This integrated Bayesian model selection and Gaussian process regression, dubbed as residual learning, can achieve performance comparable to standard DFT error (0.1 eV) for most adsorbate system. For sparse and small datasets, we propose an ad hoc Bayesian Model Averaging (BMA) approach to make a robust prediction. With this Bayesian framework, we significantly reduce the model uncertainty and improve the prediction accuracy. The possibilities of the framework for high-throughput catalytic materials exploration in a realistic setting is illustrated using large and small sets of both dense and sparse simulated dataset generated from a public database of bimetallic alloys available in Catalysis-Hub.org. Gaussian process ; Bimetallic strip ; Computer science ; Bayesian probability ; Kriging ; Bayesian inference ; Prior probability ; Chemisorption ; Residual ; Leverage (statistics) ; Linear regression ; Gaussian ; Scaling ; Artificial intelligence ; Machine learning ; Algorithm ; Chemistry ; Mathematics ; Adsorption ; Computational chemistry ; Catalysis ; Physical chemistry Machine Learning in Materials Science
Authors: Santiago E. Faraj | Rolando Rossi | Mónica R. Montes
Source: Journal of Biological Education (article, 2019-10-22)
Field: Chemistry — Physical and Theoretical Chemistry

How to distinguish ligand-binding mechanisms: an example of <i>conformational selection</i> disguised as an <i>induced fit</i> This report describes the implementation of a laboratory exercise for an advanced biochemistry or enzyme kinetics class at the undergraduate or graduate level, designed to improve understanding of protein conformational changes associated with the binding of a ligand. Students measure the fluorescence changes induced by the conformational transition of a glycoprotein (the Na,K-ATPase) upon addition of different ligands (Pi and BeF3−) and analyse the results in order to determine the mechanism of the process. The results show that Pi and BeF3− present opposite effects on the observed rate constants (kobs) with ligand concentration: kobs decreases with [Pi] and increases with [BeF3−]. This observation, together with the frequently used assumption that binding occurs under rapid equilibrium, led to propose different models for ligand-induced conformational transitions: a conformational selection for Pi and an induced fit for BeF3−. In this paper, we show that if the rapid-equilibrium approximation for ligand binding is not assumed, a conformational selection mechanism can account for the effects of both ligands. This active-learning exercise serves as the basis for discussing the consequences of not being extremely cautious when invoking approximations about not-very-well-known systems and the importance of a correct understanding of models assigned to chemical processes. Ligand (biochemistry) ; Chemistry ; Conformational change ; Mechanism (biology) ; Selection (genetic algorithm) ; Kinetics ; Pi ; Stereochemistry ; Computer science ; Physics ; Philosophy ; Biochemistry ; Epistemology ; Artificial intelligence Various Chemistry Research Topics ; Cancer and biochemical research ; Protein Structure and Dynamics
Authors: Teresa Portone | John Niederhaus | Jason Sanchez | Laura Swiler
Source:  (article, 2019-04-14)
Field: Materials Science — Materials Chemistry

Bayesian model selection for metal yield models in high-velocity impact Abstract There are multiple candidate models for the von Mises yield stress in elastoplastic models that are used to simulate material deformation in high-velocity impact. Previous work has studied the effects of such models on quantities of interest in high-strain-rate deformation, in order to select the most appropriate model in comparison to experimental data [1-2]. This work focuses on selecting between three such models, based on their ability to reproduce time-varying depth of penetration data of a tungsten-alloy rod impacting a hardened steel plate at high velocity, measured by Anderson, Hohler et al. [3]. Novel in the present study is the systematic treatment of uncertainty in the process, and automation of the process. The three models considered are the Johnson-Cook (JC), Zerilli-Armstrong (ZA), and Steinberg-Guinan-Lund (SGL) flow stress models [4-6]. von Mises yield criterion ; Flow stress ; Deformation (meteorology) ; Materials science ; Viscoplasticity ; Model selection ; Work (physics) ; Mechanics ; Strain rate ; Constitutive equation ; Mathematics ; Structural engineering ; Engineering ; Metallurgy ; Mechanical engineering ; Finite element method ; Composite material ; Physics ; Statistics High-Velocity Impact and Material Behavior ; Nuclear Materials and Properties ; Laser-Plasma Interactions and Diagnostics
Authors: Sheshera Mysore | Zach Jensen | Edward Kim | Kevin Huang | Haw-Shiuan Chang | Emma Strubell | Jeffrey Flanigan | Andrew McCallum | Elsa Olivetti
Source: arXiv (Cornell University) (preprint, 2019-05-16)
Field: Materials Science — Materials Chemistry

The Materials Science Procedural Text Corpus: Annotating Materials\n Synthesis Procedures with Shallow Semantic Structures Materials science literature contains millions of materials synthesis\nprocedures described in unstructured natural language text. Large-scale\nanalysis of these synthesis procedures would facilitate deeper scientific\nunderstanding of materials synthesis and enable automated synthesis planning.\nSuch analysis requires extracting structured representations of synthesis\nprocedures from the raw text as a first step. To facilitate the training and\nevaluation of synthesis extraction models, we introduce a dataset of 230\nsynthesis procedures annotated by domain experts with labeled graphs that\nexpress the semantics of the synthesis sentences. The nodes in this graph are\nsynthesis operations and their typed arguments, and labeled edges specify\nrelations between the nodes. We describe this new resource in detail and\nhighlight some specific challenges to annotating scientific text with shallow\nsemantic structure. We make the corpus available to the community to promote\nfurther research and development of scientific information extraction systems.\n Computer science ; Semantics (computer science) ; Graph ; Information extraction ; Knowledge graph ; Resource (disambiguation) ; Natural language processing ; Data science ; Information retrieval ; Artificial intelligence ; Programming language ; Theoretical computer science Machine Learning in Materials Science ; Software Engineering Research ; Topic Modeling
Authors: Swarn S. Warshaneyan | Prachi Srivastava
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2019-09-20)
Field: Chemistry — Organic Chemistry

Analysis and subsequent molecular docking of selected phytochemicals with SLC6A3 and SLC6A4 as potential therapeutic agents for Obsessive-Compulsive Disorder (OCD) ABSTRACT Obsessive-Compulsive Disorder (OCD) is one of the most problematic disorders of the brain. It lies in the category of moodomics disorders and can be identified by the presence of recurring intrusive thoughts (“compulsions”) and urges to do certain things repeatedly (“obsessions”), with a definite lack of control on these tendencies and their intensity affecting the patient’s daily life. It is known to be associated with not only anxiety disorder but also depression, with its cause being unknown, although possessing genetic components. Several separate methods are being attempted to develop an effective therapy for OCD but this field of research requires more focus. In the present course of research, there is an attempt to signify the molecular analysis approach by testing potential drugs computationally with previously identified targets, i. e. dopamine active transporter (DAT or SLC6A3) and sodium-dependent serotonin transporter (SERT or 5-HTT or SLC6A4) – two proteins which have been observed playing a major role in the manifestation of OCD symptoms. Their 3D structure prediction has already been done in the previous course of study. In continuation to the previous study, there is an attempt to screen out the potentially useful phytochemicals against this moodomics disorder. Several potentially useful phytochemicals are known to associate with SLC6A3 and SLC6A4, which have been identified through the text mining of various publications. First of all, these phytochemicals are analysed through the use of Molinspiration software followed by selection of the ones suitable as drug candidates using Lipinski’s Rule of five and finally, best docking configurations identified using AutoDock 4 in order to obtain potential therapeutics for OCD patients. Nuciferine, Epicatechin (EC), Retinal, Aphidicolin and Salvinorin A are showing good docking scores but among all these phytochemicals, Nuciferin, an extract of Indian Lotus plant, appears to be the one most suitable compound on the basis of binding energy calculations. Hence, we can say this could be a good revealing therapeutic agent for this disorder. Serotonin transporter ; Dopamine transporter ; Obsessive compulsive ; Anxiety ; Lipinski's rule of five ; AutoDock ; Psychology ; Psychiatry ; Medicine ; Bioinformatics ; Dopamine ; Neuroscience ; Biology ; Serotonin ; Genetics ; In silico ; Internal medicine ; Gene Chemical synthesis and alkaloids ; Neurotransmitter Receptor Influence on Behavior ; Computational Drug Discovery Methods
Authors: Shuang Yang | Zeren Yang | Perry G. Wang
Source: Bioanalysis (article, 2019-06-01)
Field: Chemistry — Spectroscopy

Bioanalysis for Precision Medicine In comparison to the 'one-size-fits all' treatment (or same treatment for all patients who have similar diseases), precision medicine (PM) has become an important strategy to identify improved diagnostic and targeted therapeutic treatment " Bioanalysis ; Precision medicine ; Chemistry ; Nanotechnology ; Chromatography ; Computer science ; Biochemical engineering ; Medicine ; Engineering ; Materials science Advanced Proteomics Techniques and Applications ; Biosimilars and Bioanalytical Methods ; Cell Image Analysis Techniques
Authors: Kenion H. Blakeman
Source: Carolina Digital Repository (University of North Carolina at Chapel Hill) (article, 2019-08-12)
Field: Chemistry — Spectroscopy

Development of High Pressure Mass Spectrometry for Handheld Instruments This work describes the development of microscale ion traps intended for high pressure mass spectrometry in handheld mass spectrometers. By operating at pressures approaching 1 Torr, the major size, weight and power (SWaP) contributor, the turbopump can be eliminated. Unlike other mass analyzers, ion traps can successfully operate at higher pressures by reducing the trap size and operating at higher RF drive frequencies. HPMS was first demonstrated with helium buffer gas with volatile organic compounds (VOCs) and a ro = 500 µm cylindrical ion trap (CIT). RF frequencies up to 10 MHz minimized mass resolution loss with pressure. HPMS with nitrogen and air were then explored as field available buffer gases to eliminate helium tanks in the field, reducing instrument size and weight. Peak widths at 1.0 Torr were 0.7 Da for helium and 5 Da for nitrogen and air were observed at RF drive frequencies of 10 MHz. As peak widths widen at high pressures due to more frequent collisions, the RF frequency was increased along with a reduction in trap size to regain mass resolution. Five CITs were operated at 1 Torr in air with RF drive frequencies between 6.14 MHz to 59.44 MHz resulting in peak widths improving from 5.5 Da to 0.8 Da. Stretched length ion traps (SLITs) and 7-element CIT arrays improved sensitivity over the single element traps by factors of 6 and 7. Operating at ambient air pressures between 250 mTorr and 1.0 Torr with RF frequencies between 30 MHz and 60 MHz reduced peak widths to sub-0.6 Da. Finally, printed circuit board (PCB) and silicon ion traps were developed as alternative materials to metal ion traps. Both PCB and Si traps lower trap capacitance reducing RF power needs and improving instrument SWaP. PCB traps had a factor of two lower sensitivity and peak widths within 10% of metal traps while silicon ion traps had twice the signal intensity of metal traps and better peak widths by up to 30 %. In terms of fabrication, PCB traps can be mass produced while Si traps can be produced with higher dimensional fidelity than metal ion traps. Mobile device ; Mass spectrometry ; Computer science ; Chemistry ; Chromatography ; World Wide Web Mass Spectrometry Techniques and Applications ; Analytical Chemistry and Chromatography ; Analytical chemistry methods development