jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Madiha Batool
Source: Archives of Organic and Inorganic Chemical Sciences (article, 2018-10-04)
Field: Materials Science — Materials Chemistry

Kinetic Isotherm Studies of Azo Dyes by Metallic Oxide Nanoparticles Adsorbent We reported the synthesis of Cu4O3 nanoparticles fabricated by Camellia Sinensis (green tea) leaves extract as reducing and stabilizing agent and studied the azo dyes removal efficiency. The formation of copper oxide nanoparticles was confirmed after change in solution of salt and plant extract from green to pale yellow. Subsequently, the above said nanoparticles were characterized by SEM, XRD, FTIR, and UV spectrophotometer for size and morphology. The average particle size of copper oxide nanoparticle was found to be 17.26nm by XRD shrerrer equation, average grain diameter by SEM was calculated 8.5×10-2mm with spherical and oval shaped. UV spectroscopy range was between 200-400nm. These copper oxide nanoparticles were applied as azo dyes (Congo red and malachite green) degradation. Effect of reaction parameters were studied for optimum conditions. Kinetic models like Langmuir, Freundlich and elovich models were applied. Finally, concluded that these particles are effective degradation potential of azo dyes at about 70-75% from aqueous solution. Malachite green ; Congo red ; Nanoparticle ; Freundlich equation ; Aqueous solution ; Fourier transform infrared spectroscopy ; Nuclear chemistry ; Copper ; Oxide ; Adsorption ; Particle size ; Materials science ; Chemical engineering ; Metal ; Copper oxide ; Chemistry ; Inorganic chemistry ; Nanotechnology ; Organic chemistry ; Metallurgy Nanoparticles: synthesis and applications ; Copper-based nanomaterials and applications ; Advanced Nanomaterials in Catalysis
Authors: Eric A. Walker | D. Scott Mitchell | Gabriel Terejanu | Andreas Heyden
Source: ACS Catalysis (preprint, 2018-03-30)
Field: Materials Science — Materials Chemistry

Identifying Active Sites of the Water–Gas Shift Reaction over Titania Supported Platinum Catalysts under Uncertainty A comprehensive uncertainty quantification framework has been developed for integrating computational and experimental kinetic data and to identify active sites and reaction mechanisms in catalysis. Three hypotheses regarding the active site for the water–gas shift reaction on Pt/TiO2 catalysts, Pt(111), an edge interface site, and a corner interface site, are tested against experimental kinetic data from three research groups. Uncertainties associated with density functional theory (DFT) calculations and model errors of microkinetic models of the active sites are informed and verified using Bayesian inference and predictive validation. Significant evidence is found for the role of the oxide support in the mechanism. Positive evidence is found in support of the edge interface active site over the corner interface site. For the edge interface site, the CO-promoted redox mechanism is found to be the dominant pathway and only at temperatures above 573 K does the classical redox mechanism contribute significantly to the overall rate. At all reaction conditions, water and surface O–H bond dissociation steps at the Pt/TiO2 interface are the main rate controlling steps. Active site ; Catalysis ; Dissociation (chemistry) ; Chemistry ; Reaction mechanism ; Platinum ; Water-gas shift reaction ; Redox ; Interface (matter) ; Reaction rate ; Mechanism (biology) ; Chemical physics ; Physical chemistry ; Inorganic chemistry ; Adsorption ; Physics Catalytic Processes in Materials Science ; Electrocatalysts for Energy Conversion ; Catalysis and Oxidation Reactions
Authors: Pai Li | Zhenyu Li
Source: Chinese Science Bulletin (Chinese Version) (article, 2018-10-31)
Field: Materials Science — Materials Chemistry

Multiscale theoretical studies of graphene growth on copper surfaces <p indent="0mm">Similar simulation protocols are expected to be able to be used in studies of growth mechanisms of other 2D materials. Certainly, there are still many challenges in the study of two-dimensional material growth mechanism. For example, relaxation of the surface structure at very high temperatures may prohibit the use of lattice model in KMC simulations. Graphene ; Copper ; Materials science ; Nanotechnology ; Metallurgy Graphene research and applications ; Plasma Diagnostics and Applications ; nanoparticles nucleation surface interactions
Authors: А. Г. Фасхутдинов | I V Akhmetov | A. E. Musina | Irek Gubaydullin
Source:  (article, 2018-01-01)
Field: Materials Science — Materials Chemistry

Improvement of resource efficiency of the catalytic isomerization process by mathematical modeling The article examines main schemes of reaction networks of the catalytic isomerization of the pentane-hexane fraction. The selection of the kinetic model for the most complete description of the basic chemical processes based on the mechanism of the reactions proceeding is substantiated. The mathematical model is constructed on the basis of kinetic model. The foundations for the subsequent modeling of the entire chemical-technological process system are laid. Isomerization ; Process (computing) ; Pentane ; Mechanism (biology) ; Fraction (chemistry) ; Computer science ; Biochemical engineering ; Chemical reaction ; Process engineering ; Catalysis ; Resource (disambiguation) ; Chemical process ; Basis (linear algebra) ; Chemistry ; Computational chemistry ; Engineering ; Organic chemistry ; Mathematics ; Physics Polyoxometalates: Synthesis and Applications ; Catalysis and Hydrodesulfurization Studies ; Chemical Synthesis and Reactions
Authors: Lucian Chan | Geoffrey Hutchison | Garrett M. Morris
Source: ChemRxiv (preprint, 2018-10-22)
Field: Materials Science — Materials Chemistry

Bayesian Optimization for Conformer Generation Generating low-energy molecular conformers is a key task for many areas of computational chemistry, molecular modeling and cheminformatics. Most current conformer generation methods primarily focus on generating geometrically diverse conformers rather than finding the most probable or energetically lowest minima. Here, we present a new stochastic search method using Bayesian Optimization Algorithm (BOA) for finding the lowest energy conformation of a given molecule. We compare BOA with uniform random search, and systematic search as implemented in Confab, to determine which method finds the lowest energy. Energetic difference, root-mean-square deviation (RMSD), and torsion fingerprint deviation (TFD) are used to quantify differences between the conformer search algorithms. In general, we find BOA requires far fewer evaluations than systematic or uniform random search to find low-energy minima. For molecules with four or more rotatable bonds, Confab typically evaluates 104 (median) conformers in its search, while BOA only requires 102 energy evaluations to find top candidates. Despite evaluating fewer conformers, for many molecules, BOA finds lower-energy conformations than an exhaustive systematic Confab search. Conformational isomerism ; Maxima and minima ; Random search ; Cheminformatics ; Search algorithm ; Computer science ; Bayesian probability ; Statistical physics ; Molecule ; Mathematics ; Algorithm ; Computational chemistry ; Chemistry ; Artificial intelligence ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Maxim Ziatdinov | Artem Maksov | Ondrej Dyck | Stephen Jesse | Sergei V. Kalinin
Source: Microscopy and Microanalysis (article, 2018-08-01)
Field: Materials Science — Surfaces, Coatings and Films

Machine Learning for the Dynamic Scanning Transmission Electron Microscopy Experiment on Solid State Transformations An abstract is not available for this content so a preview has been provided. As you have access to this content, a full PDF is available via the 'Save PDF' action button. Solid-state ; Transmission electron microscopy ; Transmission (telecommunications) ; Computer science ; Content (measure theory) ; State (computer science) ; Materials science ; Nanotechnology ; Algorithm ; Physics ; Engineering physics ; Mathematics ; Telecommunications ; Mathematical analysis Electron and X-Ray Spectroscopy Techniques ; Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications
Authors: Fatma Yıldız Çavdar | Agah Uǧuz
Source: Mechanical Engineering Journal (article, 2018-12-23)
Field: Materials Science — Biomaterials

A comparative study of electrospinning process for two different collectors: The effect of the collecting method on the nanofiber diameters In this study, an experimental investigation of the electrospinning process has been conducted using two successive Taguchi experimental designs, namely L18 (35) and L9 (34) for the electrospinning setups with a rotating drum and a stationary plate, respectively. The processing parameters are; concentration of poly(vinyl alcohol) in aqueous solution, applied voltage, distance between the needle and the collector, flow rate of the polymer solution and the rotational speed of the collecting drum. The primary process setup is used to analyse the effects of the parameters to the diameter of nanofibers and its standard deviation (SD), through examinations of the range analysis and graphical interpretations. The secondary process set up is used to gather the data to verify the results and to compare the effects of the collecting methods on the process outputs. A short literature review of the publications on which parameter effects were analysed by statistical methods is also presented. Electrospinning ; Drum ; Taguchi methods ; Nanofiber ; Rotational speed ; Vinyl alcohol ; Materials science ; Design of experiments ; Orthogonal array ; Volumetric flow rate ; Process (computing) ; Mechanical engineering ; Polymer ; Composite material ; Process engineering ; Computer science ; Mathematics ; Engineering ; Mechanics ; Physics ; Statistics Electrospun Nanofibers in Biomedical Applications ; Conducting polymers and applications ; Advanced Sensor and Energy Harvesting Materials
Authors: Shuangyan Yang | Ying Hou | Yang Lingchun | Zhang Jianqiang | Liu Weijuan | Qiu Changgui | Ming Li | Yang Yanmei
Source: Journal of the Brazilian Chemical Society (article, 2018-01-01)
Field: Chemistry — Analytical Chemistry

Automatic Identification of Cigarette Brand Using Near-Infrared Spectroscopy and Sparse Representation Classification Algorithm A cigarette brand automatic classification method using near-infrared (NIR) spectroscopy and sparse representation classification (SRC) algorithm is put forward by the paper. Comparing with the traditional methods, it is more robust to redundancy because it uses non-negative least squares (NNLS) sparse coding instead of principal component analysis (PCA) for dimensionality reduction of the spectral data. The effectiveness of SRC algorithm is compared with PCA-linear discriminant analysis (LDA) and PCA-particle swarm optimization-support vector machine (PSO-SVM) algorithms. The results show that the classification accuracy of the proposed method is higher and is much more efficient. Identification (biology) ; Sparse approximation ; Representation (politics) ; Infrared ; Algorithm ; Pattern recognition (psychology) ; Infrared spectroscopy ; Computer science ; Artificial intelligence ; Chemistry ; Physics ; Organic chemistry ; Optics ; Biology Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Sajjad Esmaeili | Sepideh Maaref
Source: International Journal of Petrochemistry and Research (article, 2018-05-19)
Field: Chemistry — Analytical Chemistry

Predicting Asphaltene Precipitation from Heavy Oils Diluted by n-Heptane Using a Four-Parameter EOS International Journal of Petrochemistry and Research (IJPR) is a peer-reviewed scientific journal offers original papers and reviews of theoretical and experimental studies concerned with current problems of petro chemistry and processing such as chemical composition of crude oils and natural gas liquids; petroleum refining (cracking, hydrocracking, and catalytic reforming); catalysts for petrochemical processes (hydrogenation, isomerization, oxidation, hydroformylation, etc.); activation and catalytic transformation of hydrocarbons and other components of petroleum, natural gas, and other complex organic mixtures; new petrochemicals including lubricants and additives; environmental problems; and information on scientific meetings relevant to these areas. Asphaltene ; Heptane ; Precipitation ; Chromatography ; Environmental science ; Chemistry ; Environmental chemistry ; Organic chemistry ; Meteorology ; Physics Petroleum Processing and Analysis ; Enhanced Oil Recovery Techniques ; Hydrocarbon exploration and reservoir analysis
Authors: Önder EYECİOĞLU | Mehmet Kılıç | Zeynep Güven Özdemiir
Source: Gazi Üniversitesi Fen Bilimleri Dergisi Part C Tasarım ve Teknoloji (article, 2018-12-10)
Field: Materials Science — Polymers and Plastics

Polipropilen/Polianilin Kompozit Filmlerin Dielektrik Özelliklerinin Yapay Sinir Ağları Modeli İle Tahmini Bu çalışmada, polipropilen (PP) polimerinin frekansa bağlı kompleks dielektrik fonksiyonunun gerçek ve sanal bileşenlerinin, kütlece % 0,3, 0,4, 0,6 ve 0,7 polianilin (PANI) katkısına bağlı olarak değişimi dielektrik spektroskopisi yöntemiyle incelenmiştir. Dielektrik ölçümler, 100 Hz ile 15 MHz arasında değişen frekans aralığında oda sıcaklığında empedans analizör yardımıyla gerçekleştirilmiştir. Ayrıca, PP/PANI kompozitlerin kompleks dielektrik fonksiyonlarının gerçek ve sanal bileşenlerinin frekansa bağlılığının Yapay Sinir Ağları (YSA) modeli ile tahmin edilebileceği gösterilmiştir. Bu bağlamda, deneysel olarak hazırlanmamış farklı PP/PANI kompozitler için YSA modeliyle kütlece farklı PANI katkı yüzdeleri için (% 0,1, 0,2, 0,5, 0,8 ve 1), kompleks dielektrik fonksiyonun gerçek ve sanal bileşenlerin frekansa bağlı değişimleri hesaplanmıştır. YSA modeli ile elde edilen sonuçlar, kompleks dielektrik fonksiyonlarının gerçek ve sanal bileşenlerinin artan PANI katkısıyla lineer olmayan değişimiyle uyumlu olarak bulunmuştur. Physics ; Chemistry ; Molecular biology ; Biology Conducting polymers and applications ; Dielectric materials and actuators ; Polymer Nanocomposites and Properties
Authors: Matthew T. Moores | Kirsten Gracie | Jake Carson | Karen Faulds | Duncan Graham | Mark Girolami
Source: Strathprints: The University of Strathclyde institutional repository (University of Strathclyde) (article, 2018-01-24)
Field: Chemistry — Analytical Chemistry

Bayesian modelling and quantification of Raman spectroscopy Raman spectroscopy can be used to identify molecules such as DNA by the characteristic scattering of light from a laser. It is sensitive at very low concentrations and can accurately quantify the amount of a given molecule in a sample. The presence of a large, nonuniform background presents a major challenge to analysis of these spectra. To overcome this challenge, we introduce a sequential Monte Carlo (SMC) algorithm to separate each observed spectrum into a series of peaks plus a smoothly-varying baseline, corrupted by additive white noise. The peaks are modelled as Lorentzian, Gaussian, or pseudo-Voigt functions, while the baseline is estimated using a penalised cubic spline. This latent continuous representation accounts for differences in resolution between measurements. The posterior distribution can be incrementally updated as more data becomes available, resulting in a scalable algorithm that is robust to local maxima. By incorporating this representation in a Bayesian hierarchical regression model, we can quantify the relationship between molecular concentration and peak intensity, thereby providing an improved estimate of the limit of detection, which is of major importance to analytical chemistry. Bayesian probability ; Monte Carlo method ; Statistical physics ; Gaussian ; Algorithm ; Computer science ; Biological system ; Mathematics ; Physics ; Chemistry ; Artificial intelligence ; Statistics ; Computational chemistry Spectroscopy and Chemometric Analyses ; Advanced Statistical Process Monitoring ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Mathias Sawall | Stella Schmode | H. Schröder | Ralf Ludwig | Klaus Neymeyr
Source: RSC Advances (article, 2018-01-01)
Field: Chemistry — Analytical Chemistry

A chemometric study in the area of feasible solution of an acid–base titration of<i>N</i>-methyl-6-oxyquinolone Multivariate curve resolution methods aim at recovering the underlying chemical components from spectroscopic data on chemical reaction systems. In most cases the spectra and concentration profiles of the pure components cannot be uniquely determined from the given spectral data. Instead continua of possible factors exist. This fact is known as rotational ambiguity. The sets of all possible pure component factors can be represented in the so-called area of feasible solutions (AFS). This paper presents an AFS study of the pure component reconstruction problem for a series of UV/Vis spectra taken from an acid-base titration of <i>N</i>-methyl-6-oxyquinolone. Additional information on the equilibrium concentration profiles for a varying acid concentration is taken from fluorescence measurements. On this basis chemometric duality arguments lead to the construction of a unique final solution. Titration ; Principal component analysis ; Chemistry ; Component (thermodynamics) ; Chemometrics ; Base (topology) ; Spectral line ; Resolution (logic) ; Analytical Chemistry (journal) ; Biological system ; Physical chemistry ; Thermodynamics ; Mathematics ; Chromatography ; Computer science ; Statistics ; Physics ; Mathematical analysis ; Artificial intelligence Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: A. V. Korobeynikov | Yulia Kamalova | Mikhail Palabugin | Ivan Basov
Source: Instrumentation engineering, electronics and telecommunications (article, 2018-12-11)
Field: Chemistry — Analytical Chemistry

The use of convolutional neural network LeNet for pollen grains classification The convolutional neural network from the LenetMnistExample of the DeepLearning4j framework is described and applied for pollen grains classification. The selected basic topology of the LeNet neural network was not changed; the loading of images was modified, and the number of classes of the classification task (outputNum) as well as the subsample size of examples (batchSize) were changed. Training (1520 photos) and test (380 photos) samples of four classes of pollen grains were formed. The quality metrics values calculated according to the results of the test sample classification are: 1) Accuracy = 0.9289; 2) Precision = 0.9306; 3) Recall = 0.9266; 4) F1 Score = 0.9282. Convolutional neural network ; Artificial intelligence ; Pattern recognition (psychology) ; Computer science ; Pollen ; Artificial neural network ; Task (project management) ; Sample (material) ; Contextual image classification ; Image (mathematics) ; Botany ; Engineering ; Biology ; Physics Spectroscopy and Chemometric Analyses ; Advanced Scientific Research Methods
Authors: Jakub Juračka | Martin Šrejber | Michaela Melíková | Václav Bazgier | Karel Berka
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2018-11-19)
Field: Chemistry — Spectroscopy

MolMeDB: Molecules on Membranes Database Abstract Biological membranes act as barriers or reservoirs for many compounds within the human body. As such, they play an important role in pharmacokinetics and pharmacodynamics of drugs and other molecular species. Until now, most membrane/drug interactions have been inferred from simple partitioning between octanol and water phases. However, the observed variability in membrane composition and among compounds themselves stretches beyond such simplification as there are multiple drug-membrane interactions. Numerous experimental and theoretical approaches are used to determine the molecule-membrane interactions with variable accuracy, but there is no open resource for their critical comparison. For this reason, we have built Molecules on Membranes Database (MolMeDB), which gathers data about over 3600 compound-membrane interactions including partitioning, penetration, and positioning. The data have been collected from scientific articles published in peer-reviewed journals and complemented by inhouse calculations from high-throughput COSMOmic approach to set up a baseline for further comparison. The data in MolMeDB are fully searchable and browsable by means of name, SMILES, membrane, method, or dataset and we offer the collected data openly for further reuse and we are open to further additions. MolMeDB can be a powerful tool that could help researchers better understand the role of membranes and to compare individual approaches used for the study of molecule/membrane interactions. Database URL: http://molmedb.upol.cz . Membrane ; Computer science ; Database ; Set (abstract data type) ; Reuse ; Chemistry ; Computational biology ; Biology ; Biochemistry Analytical Chemistry and Chromatography ; Computational Drug Discovery Methods ; Molecular spectroscopy and chirality
Authors: Shakil Ahmed | Md. Kamruzzaman | Md. Masud Rana | Zobaer Akond | MNH Mollah
Source: Journal of Bio-Science (article, 2018-07-18)
Field: Materials Science — Biomaterials

In silico analyses of human collagen protein function prediction Collagen is the extracellular matrix protein in the several connective tissues in the human body. It is an important component for mediating cell-cell interactions and pathological conditions in human body. In this study we perform the analysis of physiochemical properties and investigate the functional characteristics of human collagen proteins. Also investigate the functional protein groups by the statistical analysis. The collagen protein family consisting 28 members in human which are involving in the complex structure of protein. The protein function, protein sequence properties, domain composition, phylogenetic and protein-protein interaction (PPI) networks analysis of human collagen alpha-1 protein sequences are implemented by the online bioinformatics tools which are currently available. Based on the PCA analysis amino acid composition, features of collagen protein sequences are divided into two supreme influential functional groups such as collagen 12, 14, 20 formed one group and the rest of others formed another group. The protein-protein interaction network study using STRING showed that top interacting score of functional group proteins 0.952, 0.939 and 0.929. The most common functional domain of collagen proteins are VWC, C4, LamG, VWA, KU, C1Q, TSPN and FN3. Physicochemical, functional and phylogenetic classification can give extensive information of protein’s structure and function. The depiction of alpha-1 chains of collagen protein family in human collagen 12, 14 and 20 as a prospective protein cluster. These three proteins are possess, low glycine and proline, very high aliphatic index and a close evolutionary relation in the human skin.J. bio-sci. 24: 55-65, 2016 Protein domain ; In silico ; Peptide sequence ; Extracellular matrix ; Protein structure ; Biology ; Biochemistry ; Chemistry ; Gene Collagen: Extraction and Characterization ; dental development and anomalies