jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Savka Janković | Dragana Milisavić | Tanja Okolić | Dijana Jelić
Source: Contemporary Materials (article, 2018-01-10)
Field: Materials Science — Materials Chemistry

PREPARATION AND CHARACTERIZATION OF ZnO NANOPARTICLES BY SOLVENT FREE METHOD ZnO nanoparticles have numerous applications as photo catalysts, gas sensors, UV lasers, as optoelectronic and microelectronic devices or in cosmetic field. ZnO nanoparticles were synthesized by solvent free method using zinc nitrate hexahydrate as precursor and glycerol as dispersant, without solvent present. This method proved to be very simple, economic and ecofriendly. Zinc nitrate and glycerol were mixed in different ratio in order to avoid and overcome a possibility of agglomeration. Characterization of samples was performed by UV/VIS and FTIR spectrophotometry. The strongest absorption appeared at wavelength 206 nm. Using combination of UV/VIS spectrophotometry and hyperbolic band model (HBM) particles size of ZnO particles were evaluated to 2.06 nm. Additionally, using Tauc plot, a band gap energy was determined. Band gap energy of ZnO nanoparticles amounted to 5.00 eV. IR spectrum showed existence of ZnO in interval 600- 400 cm-1. Materials science ; Zinc ; Nanoparticle ; Spectrophotometry ; Solvent ; Zinc nitrate ; Band gap ; Fourier transform infrared spectroscopy ; Absorption (acoustics) ; Dispersant ; Characterization (materials science) ; Analytical Chemistry (journal) ; Chemical engineering ; Nuclear chemistry ; Nanotechnology ; Dispersion (optics) ; Chemistry ; Chromatography ; Optoelectronics ; Organic chemistry ; Optics ; Metallurgy ZnO doping and properties ; Gas Sensing Nanomaterials and Sensors ; Copper-based nanomaterials and applications
Authors: Cheng Shang | Sida Huang | Zhi‐Pan Liu
Source: Journal of Computational Chemistry (article, 2018-11-10)
Field: Materials Science — Materials Chemistry

Massively parallelization strategy for material simulation using high‐dimensional neural network potential The potential energy surface (PES) calculation is the bottleneck for modern material simulation. The high-dimensional neural network (HDNN) technique emerged recently appears to be a problem solver for fast and accurate PES computation. The major cost of the HDNN lies at the computation of the structural descriptors that capture the geometrical environment of atoms. Here, we introduce a massive parallelization strategy optimized for our recently developed power-type structural descriptor. The method involves three-levels: from the top to the bottom the parallelization is over atoms first, then, over structural descriptors and finally over the n-body functions. We illustrate the parallelization method in a boron crystal system and show that the parallelization efficiency is maximally 100%, 58%, and 34% at each level. © 2018 Wiley Periodicals, Inc. Bottleneck ; Massively parallel ; Computer science ; Parallel computing ; Computation ; Solver ; Artificial neural network ; Molecular dynamics ; Computational science ; Automatic parallelization ; Algorithm ; Computational chemistry ; Artificial intelligence ; Chemistry ; Compiler Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Protein Structure and Dynamics
Authors: Lauri Himanen | Patrick Rinke | Adam S. Foster
Source: npj Computational Materials (article, 2018-09-05)
Field: Materials Science — Materials Chemistry

Materials structure genealogy and high-throughput topological classification of surfaces and 2D materials Abstract Automated and verifiable structural classification for atomistic structures is becoming necessary to cope with the vast amount of information stored in various computational materials databases. Here we present a general recursive scheme for the structural classification of atomistic systems and introduce a structural materials map that can be used to organize the materials structure genealogy. We also introduce our implementation for the automatic classification of two-dimensional structures, especially focusing on surfaces and 2D materials. This classification procedure can automatically determine the dimensionality of a structure, further categorize the structure as a surface or a 2D material, return the underlying unit cell and also identify the outlier atoms, such as adsorbates. The classification scheme does not require explicit search patterns and works even in the presence of defects and dislocations. The classification is tested on a wide variety of atomistic structures and provides a high-accuracy determination for all of the returned structural properties. A software implementation of the classification algorithm is freely available with an open-source license. Computer science ; Curse of dimensionality ; Categorization ; Software ; Classification scheme ; Outlier ; MIT License ; Data mining ; Theoretical computer science ; Artificial intelligence ; Machine learning Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Surface Chemistry and Catalysis
Authors: Piia Kokkonen | David Bednář | Veronika Dockalova | Zbyněk Prokop | Jiřı́ Damborský
Source: Journal of Biological Chemistry (article, 2018-06-01)
Field: Materials Science — Materials Chemistry

Conformational changes allow processing of bulky substrates by a haloalkane dehalogenase with a small and buried active site Haloalkane dehalogenases catalyze the hydrolysis of halogen-carbon bonds in organic halogenated compounds and as such are of great utility as biocatalysts. The crystal structures of the haloalkane dehalogenase DhlA from the bacterium from <i>Xanthobacter autotrophicus</i> GJ10, specifically adapted for the conversion of the small 1,2-dichloroethane (DCE) molecule, display the smallest catalytic site (110 Å<sup>3</sup>) within this enzyme family. However, during a substrate-specificity screening, we noted that DhlA can catalyze the conversion of far bulkier substrates, such as the 4-(bromomethyl)-6,7-dimethoxy-coumarin (220 Å<sup>3</sup>). This large substrate cannot bind to DhlA without conformational alterations. These conformational changes have been previously inferred from kinetic analysis, but their structural basis has not been understood. Using molecular dynamic simulations, we demonstrate here the intrinsic flexibility of part of the cap domain that allows DhlA to accommodate bulky substrates. The simulations displayed two routes for transport of substrates to the active site, one of which requires the conformational change and is likely the route for bulky substrates. These results provide insights into the structure-dynamics function relationships in enzymes with deeply buried active sites. Moreover, understanding the structural basis for the molecular adaptation of DhlA to 1,2-dichloroethane introduced into the biosphere during the industrial revolution provides a valuable lesson in enzyme design by nature. Active site ; Dehalogenase ; Chemistry ; Stereochemistry ; Biochemistry ; Catalysis ; Enzyme Enzyme Structure and Function ; Protein Structure and Dynamics ; Microbial bioremediation and biosurfactants
Authors: Olga A. Kogtenkova | Boris B. Straumal | Anna Korneva | T. Czeppe | Anna Wierzbicka-Miernik | M. Faryna | P. Zięba
Source: Metals (article, 2018-12-21)
Field: Materials Science — Materials Chemistry

Grain Boundary Complexions and Phase Transformations in Al- and Cu-Based Alloys High-pressure torsion has been used to obtain the ultra-fine grained (UFG) state with a high specific area of grain boundaries (GBs) in Al-Zn, Al-Mg, Cu-Ag, Cu-Co, and Cu-Ni solid solutions with face-centered cubic (fcc) lattices. The UFG samples were heated in a differential scanning calorimeter (DSC). Small endothermic peaks in the DSC curves were observed in the one-phase solid-solution area of the respective phase diagrams, i.e., far away from the bulk solidus and solvus lines. A possible explanation of these endothermic peaks is based on the hypothesis of phase transformations between GB complexions. This hypothesis has been supported by observations with transmission electron microscopy and electron backscattering diffraction. The new lines of GB phase transformations have been constructed in the Al-Zn, Al-Mg, Cu-Ag, Cu-Co, and Cu-Ni bulk phase diagrams. Solvus ; Solidus ; Grain boundary ; Endothermic process ; Materials science ; Differential scanning calorimetry ; Phase diagram ; Phase (matter) ; Phase boundary ; Solid solution ; Transmission electron microscopy ; Crystallography ; Diffraction ; Analytical Chemistry (journal) ; Metallurgy ; Thermodynamics ; Chemistry ; Microstructure ; Alloy ; Physical chemistry ; Optics ; Physics ; Nanotechnology ; Superalloy Microstructure and mechanical properties ; Aluminum Alloy Microstructure Properties ; Aluminum Alloys Composites Properties
Authors: Qingwei Ma | Eric Adua | Mary C. Boyce | Xingang Li | Guang Ji | Wei Wang
Source: OMICS A Journal of Integrative Biology (article, 2018-11-01)
Field: Chemistry — Spectroscopy

IMass Time: The Future, in Future! Joseph John Thomson discovered and proved the existence of electrons through a series of experiments. His work earned him a Nobel Prize in 1906 and initiated the era of mass spectrometry (MS). In the intervening time, other researchers have also been awarded the Nobel Prize for significant advances in MS technology. The development of soft ionization techniques was central to the application of MS to large biological molecules and led to an unprecedented interest in the study of biomolecules such as proteins (proteomics), metabolites (metabolomics), carbohydrates (glycomics), and lipids (lipidomics), allowing a better understanding of the molecular underpinnings of health and disease. The interest in large molecules drove improvements in MS resolution and now the challenge is in data deconvolution, intelligent exploitation of heterogeneous data, and interpretation, all of which can be ameliorated with a proposed IMass technology. We define IMass as a combination of MS and artificial intelligence, with each performing a specific role. IMass will offer advantages such as improving speed, sensitivity, and analyses of large data that are presently not possible with MS alone. In this study, we present an overview of the MS considering historical perspectives and applications, challenges, as well as insightful highlights of IMass. Glycomics ; Data science ; Computer science ; Lipidomics ; Proteomics ; Nanotechnology ; Metabolomics ; Computational biology ; Chemistry ; Bioinformatics ; Biology Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Advanced Proteomics Techniques and Applications
Authors: Mónica Cristina García | Rubén H. Manzo | Alvaro F. Jimenez‐Kairuz
Source: International Journal of Pharmaceutics (article, 2018-05-07)
Field: Chemistry — Organic Chemistry

Polysaccharides-based multiparticulated interpolyelectrolyte complexes for controlled benznidazole release Benznidazole ; Chitosan ; Chemistry ; Mucoadhesion ; Gelatin ; Polysaccharide ; Dosage form ; Granulation ; Polyelectrolyte ; Drug delivery ; Controlled release ; Alginic acid ; Pharmacology ; Materials science ; Drug carrier ; Chromatography ; Nanotechnology ; Organic chemistry ; Biochemistry ; Medicine ; Polymer ; Trypanosoma cruzi ; Composite material Carbohydrate Chemistry and Synthesis ; Trypanosoma species research and implications ; Advanced Drug Delivery Systems
Authors: Eric D. Raftery | Eric G. Gharkhanian | Nicole G. Ricapito | J. McNamara | Timothy J. Deming
Source: Chemistry - An Asian Journal (article, 2018-09-17)
Field: Materials Science — Biomaterials

Influence of Sulfur‐Containing Diamino Acid Structure on Covalently Crosslinked Copolypeptide Hydrogels Biologically occurring non-canonical di-α-amino acids were converted into new di-N-carboxyanhydride (di-NCA) monomers in reasonable yields with high purity. Five different di-NCAs were separately copolymerized with tert-butyl-l-glutamate NCA to obtain covalently crosslinked copolypeptides capable of forming hydrogels with varying crosslinker density. Comparison of hydrogel properties with residue structure revealed that different di-α-amino acids were not equivalent in crosslink formation. Notably, l-cystine was found to produce significantly weaker hydrogels compared to l-homocystine, l-cystathionine, and l-lanthionine, suggesting that l-cystine may be a sub-optimal choice of di-α-amino acid for preparation of copolypeptide networks. The di-α-amino acid crosslinkers also provided different chemical stability, where disulfide crosslinks were readily degraded by reduction, and thioether crosslinks were stable against reduction. This difference in response may provide a means to fine tune the reduction sensitivity of polypeptide biomaterial networks. Lanthionine ; Self-healing hydrogels ; Cystine ; Covalent bond ; Monomer ; Amino acid ; Chemistry ; Thioether ; Polymer chemistry ; Copolymer ; Residue (chemistry) ; Imino acid ; Organic chemistry ; Polymer ; Cysteine ; Biochemistry ; Enzyme Supramolecular Self-Assembly in Materials ; Hydrogels: synthesis, properties, applications ; Chemical Synthesis and Analysis
Authors: Wout Bittremieux | Damon May | Jeffrey A. Bilmes | William Stafford Noble
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2018-11-29)
Field: Chemistry — Spectroscopy

A learned embedding for efficient joint analysis of millions of mass spectra Abstract Computational methods that aim to exploit publicly available mass spectrometry repositories primarily rely on unsupervised clustering of spectra. Here, we propose to train a deep neural network in a supervised fashion based on previous assignments of peptides to spectra. The network, called “GLEAMS,” learns to embed spectra into a low-dimensional space in which spectra generated by the same peptide are close to one another. We use GLEAMS as the basis for a large-scale spectrum clustering, detecting groups of unidentified, proximal spectra representing the same peptide, and we show how to use these clusters to explore the dark proteome of repeatedly observed yet consistently unidentified mass spectra. We provide a software implementation of our approach, along with a tool to quickly embed additional spectra using a pre-trained model, to facilitate large-scale analyses. Computer science ; Mass spectrum ; Cluster analysis ; Embedding ; Spectral line ; Artificial intelligence ; Spectral clustering ; Pattern recognition (psychology) ; Joint (building) ; Artificial neural network ; Exploit ; Scale (ratio) ; Data mining ; Mass spectrometry ; Chemistry ; Physics ; Engineering Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Ying Tian | Xinyu You | Xiuhui Huang
Source: Symmetry (article, 2018-12-18)
Field: Chemistry — Analytical Chemistry

SDAE-BP Based Octane Number Soft Sensor Using Near-infrared Spectroscopy in Gasoline Blending Process As the most important properties in the gasoline blending process, octane number is difficult to be measured in real time. To address this problem, a novel deep learning based soft sensor strategy, by using the near-infrared (NIR) spectroscopy obtained in the gasoline blending process, is proposed. First, as a network structure with hidden layer as symmetry axis, input layer and output layer as symmetric, the denosing auto-encoder (DAE) realizes the advanced expression of input. Additionally, the stacked DAE (SDAE) is trained based on unlabeled NIR and the weights in each DAE is recorded. Then, the recorded weights are used as the initial parameters of back propagation (BP) with the reason that the SDAE trained initial weights can avoid local minimums and realizes accelerate convergence, and the soft sensor model is achieved with labeled NIR data. Finally, the achieved soft sensor model is used to estimate the real time octane number. The performance of the method is demonstrated through the NIR dataset of gasoline, which was collected from a real gasoline blending process. Compared with PCA-BP (the dimension of datasets of BP reduced by principal component analysis) soft sensor model, the prediction accuracy was improved from 86.4% of PCA-BP to 94.8%, and the training time decreased from 20.1 s to 16.9 s. Therefore, SDAE-BP is proposed as a novel method for rapid and efficient determination of octane number in the gasoline blending process. Gasoline ; Soft sensor ; Octane rating ; Principal component analysis ; Octane ; Process (computing) ; Near-infrared spectroscopy ; Biological system ; Computer science ; Artificial intelligence ; Pattern recognition (psychology) ; Materials science ; Chemistry ; Optics ; Physics Spectroscopy and Chemometric Analyses ; Fault Detection and Control Systems ; Water Quality Monitoring and Analysis
Authors: Muhammad Irfan Said | Basran Burhan | T. Tensi | H. Haerati
Source: Journal of the Indonesian Tropical Animal Agriculture (article, 2018-08-23)
Field: Materials Science — Biomaterials

Synthesis of collagen from Bali cattle's hide using a combination of acid and alkali on the extracting process The process of pre-extraction is an important stage in the process of collagen synthesis. This stage increases the sensitivity of collagen molecule chains that can affect production yields and collagen properties. The objectives of the study were to synthesize and evaluate halal collagen from Bali cattle’s hide on different -extracting processes. A total of 5 treatments applied in this study, namely: T1 = Ca(OH)2 5% (b/v) ; T2= Ca(OH)2 15% (b/v) ; T3= Ca(OH)2 5% + CH3COOH 5%(b/v) ; T4= Ca(OH)2 15% + CH3COOH 5% (b/v). Each treatment was repeated 4 times. Data were analyzed in a variety of ways using SPSS program. The observed parameters consisted of: 1) yield, 2) viscosity and 3) pH. The results showed that the difference in real pre-extracting process increased the yield and viscosity, but not the pH value. The application of T4 treatment provided the best results compared to other treatments as well as controls to increase the yield and also improve the collagen's physical properties and pH. Yield (engineering) ; Extraction (chemistry) ; Viscosity ; Alkali metal ; Process (computing) ; Chemistry ; Molecule ; Nuclear chemistry ; Materials science ; Chromatography ; Organic chemistry ; Composite material ; Computer science Collagen: Extraction and Characterization
Authors: Taku Ogura | Takaaki Sato | Masahiko Abe | Tomomichi Okano
Source: Journal of Oleo Science (article, 2018-01-01)
Field: Chemistry — Organic Chemistry

Small Angle X-ray Scattering and Electron Spin Resonance Spectroscopy Study on Fragrance Infused Cationic Vesicles Modeling Scent-Releasing Fabric Softeners Industrially relevant systems for household and personal-care products often involve a large number of components. Such multiple component formulations are indispensable and effective for functionalization of the products, but may simultaneously provide more complex structural features compared to those in ideal systems comprising a smaller number of highly pure substances. Using cryogenic transmission electron microscopy (cryo-TEM), small angle X-ray scattering (SAXS), and electron spin resonance (ESR) spectroscopy, we have investigated effects of fragrance-incorporation into cationic vesicles on their bilayer structures and membrane-membrane interactions. Cationic vesicles were prepared from TEQ surfactant, whose major component was di(alkyl fatty ester) quaternary ammonium methosulfate, and fragrance components, l-menthol, linalool, and d-limonene, were infused into the vesicle membranes to model scent-releasing fabric softeners. The cryo-TEM images confirm formation of multilamellar vesicles (MLVs). Generalized indirect Fourier transformation (GIFT) analysis of the SAXS intensities based on the modified Caillé structure factor model reveals that incorporation of a more hydrophobic fragrance component leads to a more pronounced increase of the surface separation (water layer thickness). Furthermore, the fragrance-infused systems show longer-range order of the bilayer correlations and enhanced undulation fluctuation of the membranes than those in the TEQ alone system. The spin-label ESR results indicate different restricted molecular motions in the TEQ bilayers depending on the labeled position and their marked changes upon addition of the fragrance components, suggesting different mixing schemes and solubilization positions of the fragrance molecules in the TEQ bilayers. The present data have demonstrated how the infused fragrance molecules having different hydrophobicity and molecular architectures into the cationic vesicles affect the membrane structures and the intermembrane interactions, which may provide useful information for precisely controlling a fragrance-releasing property. Small-angle X-ray scattering ; Bilayer ; Vesicle ; Chemistry ; Cationic polymerization ; Membrane ; Alkyl ; Chemical engineering ; Crystallography ; Scattering ; Organic chemistry ; Optics Surfactants and Colloidal Systems ; Luminescence and Fluorescent Materials ; Polydiacetylene-based materials and applications
Authors: Durga Paudel | Raymond Atta‐Fynn | D. A. Drabold | Stephen R. Elliott | Parthapratim Biswas
Source: Physical review. B./Physical review. B (article, 2018-05-02)
Field: Materials Science — Ceramics and Composites

Small-angle x-ray scattering in amorphous silicon: A computational study We present a computational study of small-angle x-ray scattering (SAXS) in amorphous silicon ($a$-Si) with particular emphasis on the morphology and microstructure of voids. The relationship between the scattering intensity in SAXS and the three-dimensional structure of nanoscale inhomogeneities or voids is addressed by generating large high-quality $a$-Si networks with 0.1%--0.3% volume concentration of voids, as observed in experiments using SAXS and positron annihilation spectroscopy. A systematic study of the variation of the scattering intensity in the small-angle scattering region with the size, shape, number density, and the spatial distribution of the voids in the networks is presented. Our results suggest that the scattering intensity in the small-angle region is particularly sensitive to the size and the total volume fraction of the voids, but the effect of the geometry or shape of the voids is less pronounced in the intensity profiles. A comparison of the average size of the voids obtained from the simulated values of the intensity, using the Guinier approximation and Kratky plots, with that of the same from the spatial distribution of the atoms in the vicinity of void surfaces is presented. Small-angle X-ray scattering ; Scattering ; Materials science ; Void (composites) ; Intensity (physics) ; Silicon ; Amorphous silicon ; Amorphous solid ; Small-angle scattering ; Volume fraction ; Optics ; Biological small-angle scattering ; Crystallography ; Molecular physics ; Small-angle neutron scattering ; Crystalline silicon ; Physics ; Composite material ; Chemistry ; Neutron scattering ; Optoelectronics Glass properties and applications ; Metallic Glasses and Amorphous Alloys ; Thin-Film Transistor Technologies