jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Cahit Benel | Thomas Reisinger | Robert Kruk | Horst Hahn
Source: Advanced Materials (review, 2018-12-27)
Field: Materials Science — Materials Chemistry

Cluster‐Assembled Nanocomposites: Functional Properties by Design The unique functional properties of nanocomposites meet many of the material requirements sought after in numerous applications of today's high-tech industry. This, in turn, inspires material scientists to devise new methods that can further expand the palette of available nanocomposites. Precise control over the chemistry, morphology, and microstructure of nanocomposites' constituents promises the eventual ability to design any composite material for any specific requirement. However, today's synthesis methods still lack the ability to simultaneously control all chemical, morphological, and microstructural features of nanocomposites in a one-step process. Here, an alternative approach to fabricate fully tailorable nanocomposites under well-defined conditions is described. In particular, this progress report focuses on the combination of cluster ion beam and thin-film deposition technologies to fabricate cluster-assembled nanocomposites via codeposition of cluster ions and matrix materials. Emphasis is given to the state-of-the-art cluster deposition system, designed and built by our research group, as well as to its unique abilities. Moreover, case studies on two cluster-assembled nanocomposite material systems (Fe/Ag<sub>m</sub> and Fe/Cr<sub>m</sub> ) prepared with this method are presented. Finally, an outlook on research directions for cluster-assembled nanocomposites is discussed. Nanocomposite ; Materials science ; Cluster (spacecraft) ; Nanotechnology ; Palette (painting) ; Microstructure ; Composite number ; Deposition (geology) ; Composite material ; Computer science Copper-based nanomaterials and applications ; Ion-surface interactions and analysis ; Nanomaterials for catalytic reactions
Authors: Lauren Sfakis | Tim Kamaldinov | Alexander Khmaladze | Zeinab Hosseini | Deirdre A. Nelson | Melinda Larsen | James Castracane
Source: International Journal of Molecular Sciences (article, 2018-03-29)
Field: Materials Science — Biomaterials

Mesenchymal Cells Affect Salivary Epithelial Cell Morphology on PGS/PLGA Core/Shell Nanofibers Engineering salivary glands is of interest due to the damaging effects of radiation therapy and the autoimmune disease Sjögren's syndrome on salivary gland function. One of the current problems in tissue engineering is that in vitro studies often fail to predict in vivo regeneration due to failure of cells to interact with scaffolds and of the single cell types that are typically used for these studies. Although poly (lactic co glycolic acid) (PLGA) nanofiber scaffolds have been used for in vitro growth of epithelial cells, PLGA has low compliance and cells do not penetrate the scaffolds. Using a core-shell electrospinning technique, we incorporated poly (glycerol sebacate) (PGS) into PLGA scaffolds to increase the compliance and decrease hydrophobicity. PGS/PLGA scaffolds promoted epithelial cell penetration into the scaffold and apical localization of tight junction proteins, which is necessary for epithelial cell function. Additionally, co-culture of the salivary epithelial cells with NIH3T3 mesenchymal cells on PGS/PLGA scaffolds facilitated epithelial tissue reorganization and apical localization of tight junction proteins significantly more than in the absence of the mesenchyme. These data demonstrate the applicability of PGS/PLGA nanofibers for epithelial cell self-organization and facilitation of co-culture cell interactions that promote tissue self-organization in vitro. PLGA ; Cell biology ; Mesenchymal stem cell ; Tissue engineering ; Chemistry ; Nanofiber ; Regeneration (biology) ; Cell culture ; Cell ; In vitro ; Biology ; Biomedical engineering ; Materials science ; Nanotechnology ; Biochemistry ; Medicine Electrospun Nanofibers in Biomedical Applications ; Mesenchymal stem cell research ; Salivary Gland Disorders and Functions
Authors: Kaleem Ullah | Braulio García‐Cámara | Muhammad Habib | Xuefeng Liu | Alex Krasnok | Sergey Lepeshov | Jingjing Hao | Juan Liu | Nagendra Prasad Yadav
Source: Journal of Quantitative Spectroscopy and Radiative Transfer (article, 2018-01-11)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Chiral all-dielectric trimer nanoantenna Trimer ; Dielectric ; Optical rotation ; Circular dichroism ; Chirality (physics) ; Polarization (electrochemistry) ; Light scattering ; Ray ; Circular polarization ; Near-field optics ; Optics ; Materials science ; Optical force ; Molecular physics ; Scattering ; Physics ; Optoelectronics ; Optical microscope ; Chemistry ; Optical tweezers ; Nuclear magnetic resonance ; Dimer ; Quantum mechanics Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Photonic Crystals and Applications
Authors: Saad Touqeer | Laura Castoldi | Thierry Langer | Wolfgang Hölzer | Vittorio Pace
Source: Chemical Communications (article, 2018-01-01)
Field: Chemistry — Organic Chemistry

Homologation of halostannanes with carbenoids: a convenient and straightforward one-step access to α-functionalized organotin reagents A direct, single synthetic homologative transformation of halostannanes into mono- or di-substituted methyl analogues is documented. Critical for the success of the operation is the excellent nucleophilicity of carbenoid-like methyllithium reagents (LiCHXY, X, Y = halogen, OR, and CN): by simply individuating the reagents' substitution pattern, the desired functionalized fragment is delivered to the electrophile. The wide scope of the protocol is evidenced also in the case of analogous halogermanium compounds. The tandem homologation-quenching with nucleophiles and the use of α-chloroallyllithium is also discussed. Reagent ; One-Step ; Combinatorial chemistry ; Two step ; Chemistry ; Organic chemistry ; Chemical engineering ; Engineering Organometallic Complex Synthesis and Catalysis ; Synthetic Organic Chemistry Methods ; Catalytic Cross-Coupling Reactions
Authors: Ryuta Yagi | Taiki Hirahara | Ryoya Ebisuoka | Tomoaki Nakasuga | Shingo Tajima | Kenji Watanabe | Takashi Taniguchi
Source: Scientific Reports (article, 2018-08-23)
Field: Materials Science — Materials Chemistry

Low-energy band structure and even-odd layer number effect in AB-stacked multilayer graphene How atoms acquire three-dimensional bulk character is one of the fundamental questions in materials science. Before addressing this question, how atomic layers become a bulk crystal might give a hint to the answer. While atomically thin films have been studied in a limited range of materials, a recent discovery showing how to mechanically exfoliate bulk crystals has opened up the field to study the atomic layers of various materials. Here, we show systematic variation in the band structure of high mobility graphene with one to seven layers by measuring the quantum oscillation of magnetoresistance. The Landau fan diagram showed distinct structures that reflected differences in the band structure, as if they were finger prints of multilayer graphene. In particular, an even-odd layer number effect was clearly observed, with the number of bands increasing by one for every two layers and a Dirac cone observed only for an odd number of layers. The electronic structure is significantly influenced by the potential energy arising from carrier screening associated with a gate electric field. Graphene ; Electronic band structure ; Band diagram ; Dirac (video compression format) ; Electronic structure ; Crystal (programming language) ; Band gap ; Range (aeronautics) ; Semimetal Graphene research and applications ; 2D Materials and Applications ; Topological Materials and Phenomena
Authors: Jian-Huang She | Michael J. Lawler | Eun-Ah Kim
Source: Physical Review Letters (article, 2018-12-07)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Quantum Spin Liquid Intertwining Nematic and Superconducting Order in Fese Despite its seemingly simple composition and structure, the pairing mechanism of FeSe remains an open problem due to several striking phenomena. Among them are nematic order without magnetic order, nodeless gap and unusual inelastic neutron spectra with a broad continuum, and gap anisotropy consistent with orbital selection of unknown origin. Here we propose a microscopic description of a nematic quantum spin liquid that reproduces key features of neutron spectra. We then study how the spin fluctuations of the local moments lead to pairing within a spin-fermion model. We find the resulting superconducting order parameter to be nodeless $s\ifmmode\pm\else\textpm\fi{}d$ wave within each domain. Further we show that orbital dependent Kondo-like coupling can readily capture observed gap anisotropy. Our prediction calls for inelastic neutron scattering in a detwinned sample. Condensed matter physics ; Pairing ; Liquid crystal ; Physics ; Superconductivity ; Inelastic neutron scattering ; Spin (aerodynamics) ; Anisotropy ; Neutron scattering ; Quantum spin liquid ; Order (exchange) ; Neutron ; Spin polarization ; Quantum mechanics ; Electron Iron-based superconductors research ; Advanced Condensed Matter Physics ; Physics of Superconductivity and Magnetism
Authors: Snehashish Chakraverty | Subrat Kumar Jena
Source: Curved and Layered Structures (article, 2018-11-01)
Field: Materials Science — Materials Chemistry

Free Vibration of Single Walled Carbon Nanotube Resting on Exponentially Varying Elastic Foundation Abstract In this article, free vibration of SingleWalled Carbon Nanotube (SWCNT) resting on exponentially varying Winkler elastic foundation is investigated by using Differential Quadrature Method (DQM). Euler-Bernoulli beam theory is considered in conjunction with the nonlocal elasticity theory of Eringen. Step by step procedure is included and MATLAB code has been developed to obtain the numerical results for different scaling parameters as well as for four types of edge conditions. Obtained results are validated with known results in special cases showing good agreement. Further, numerical as well as graphical results are illustrated to show the effects of nonuniform parameter, nonlocal parameter, aspect ratio,Winkler modulus parameter and edge conditions on the frequency parameters. Vibration ; Carbon nanotube ; Timoshenko beam theory ; Nyström method ; Materials science ; Quadrature (astronomy) ; Euler's formula ; Elasticity (physics) ; Scaling ; Mechanics ; Mathematical analysis ; Mathematics ; Physics ; Composite material ; Acoustics ; Geometry ; Boundary value problem ; Optics Nonlocal and gradient elasticity in micro/nano structures ; Composite Structure Analysis and Optimization ; Numerical methods in engineering
Authors: Imene Mitiche | Gordon Morison | A. Nesbitt | Michael Hughes-Narborough | Brian Stewart | Philip Boreham
Source: Sensors (article, 2018-09-14)
Field: Materials Science — Materials Chemistry

Imaging Time Series for the Classification of EMI Discharge Sources In this work, we aim to classify a wider range of Electromagnetic Interference (EMI) discharge sources collected from new power plant sites across multiple assets. This engenders a more complex and challenging classification task. The study involves an investigation and development of new and improved feature extraction and data dimension reduction algorithms based on image processing techniques. The approach is to exploit the Gramian Angular Field technique to map the measured EMI time signals to an image, from which the significant information is extracted while removing redundancy. The image of each discharge type contains a unique fingerprint. Two feature reduction methods called the Local Binary Pattern (LBP) and the Local Phase Quantisation (LPQ) are then used within the mapped images. This provides feature vectors that can be implemented into a Random Forest (RF) classifier. The performance of a previous and the two new proposed methods, on the new database set, is compared in terms of classification accuracy, precision, recall, and F-measure. Results show that the new methods have a higher performance than the previous one, where LBP features achieve the best outcome. EMI ; Random forest ; Feature extraction ; Pattern recognition (psychology) ; Artificial intelligence ; Computer science ; Electromagnetic interference ; Classifier (UML) ; Data mining High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Power Quality and Harmonics
Authors: P.S. Noori Banu | S. Devaki Rani
Source: Computational Materials Science (article, 2018-03-23)
Field: Materials Science — Materials Chemistry

Artificial neural network based optimization of prerequisite properties for the design of biocompatible titanium alloys Materials science ; Ultimate tensile strength ; Titanium alloy ; Titanium ; Biocompatibility ; Young's modulus ; Elastic modulus ; Ternary operation ; Elasticity (physics) ; Modulus ; Artificial neural network ; Alloy ; Composite material ; Metallurgy ; Computer science ; Machine learning Titanium Alloys Microstructure and Properties ; Orthopaedic implants and arthroplasty ; Bone Tissue Engineering Materials
Authors: Rushikesh Nalla | Rajdeep Pinge | Manish Narwaria | Bhaskar Chaudhury
Source:  (article, 2018-01-11)
Field: Chemistry — Analytical Chemistry

Priority based functional group identification of organic molecules using machine learning Functional groups in organic compounds determine the properties of the compounds/molecules. When multiple functional groups are present, the dominant functional group determines majority of the properties of the compound. Hence priority based identification of functional groups is an important problem in chemistry. Fourier-transform Infrared spectroscopy (FTIR) is a commonly used spectroscopic method for identifying the presence or absence of functional groups within a compound, and the current approach for this task mainly relies on visual inspection and analysis of the FTIR spectral data. However, such visual identification process by humans is error prone, especially when patterns in the FTIR spectrum overlap, resulting in loss of uniqueness of features which help in identification of different functional groups in the unknown sample. Therefore, the main goal of this paper is to develop a machine-learning based classification system which can perform priority based functional group identification of organic molecules. To the best of our knowledge, this is the first effort to address this problem using machine learning (ML), and a unique aspect of our study is the incorporation of domain specific information into the process of classification by employing a set of priority rules generated from expert knowledge. We have carried out extensive study on real IR spectral data, first using a rule based approach and then using ML in an effort to improve the classification accuracy. Our analysis indicates that the basic rule based method is reasonably effective in predicting the presence (or absence) of functional groups. However, such approach is practically not accurate enough for the more challenging problem of priority based identification, and ML based classification offers much higher identification accuracies in this case. The primary reason is that ML algorithm can adaptively exploit data patterns to classify the functional group unlike the rule-based approach which uses a fixed set of rules for the said purpose. Finally, we have also carried out extensive statistical analysis of the results by using confidence intervals and permutation tests, in an effort to gain more descriptive information about the learning process, and not simply treat it as a black box. Computer science ; Artificial intelligence ; Identification (biology) ; Pattern recognition (psychology) ; Machine learning ; Set (abstract data type) ; Process (computing) ; Functional group ; Fourier transform ; Data mining ; Mathematics ; Chemistry Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Computational Drug Discovery Methods
Authors: Baichuan Sun | Héctor Barrón | George Opletal | Amanda S. Barnard
Source: The Journal of Physical Chemistry C (article, 2018-11-29)
Field: Materials Science — Materials Chemistry

From Process to Properties: Correlating Synthesis Conditions and Structural Disorder of Platinum Nanocatalysts Understanding the complicated relationship between various synthetic processing parameters and the functional properties or performance of nanoparticles is one of the goals of computational materials design, and is an ideal problem for materials informatics. In this study, we use machine learning to build predictions of how bulk and surface disorder is controlled by the growth time, atom deposition rate, and temperature, and how they can impact some indicators of the functional properties of platinum nanoparticles used in catalysis, including a range of structural features. We have used an ensemble of 690 unique nanoparticles generated from molecular dynamics trajectories that sample a large variety of different temperature/growth rate combinations, and genetic algorithms providing a prediction accuracy over 85% for all models. We have developed reliable and insightful predictions of structure/property and process/structure relationships (even when the polydispersed structures are almost entirely disordered), but our results suggest that the transcendent process/property relationship requires more detailed descriptors than those traditionally available from conventional computational studies. Nanomaterial-based catalyst ; Materials informatics ; Materials science ; Nanoparticle ; Platinum nanoparticles ; Computer science ; Nanotechnology ; Process (computing) ; Platinum ; Catalysis ; Chemistry ; Health informatics Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalytic Processes in Materials Science
Authors: Parham Soltani | Esmaeil Hadavandi
Source: Soft Computing (article, 2018-11-15)
Field: Materials Science — Polymers and Plastics

A monarch butterfly optimization-based neural network simulator for prediction of siro-spun yarn tenacity Yarn ; Tenacity (mineralogy) ; Linear density ; Fiber ; Weaving ; Spinning ; Artificial neural network ; Fineness ; Process (computing) ; Computer science ; Materials science ; Composite material ; Artificial intelligence Textile materials and evaluations ; Industrial Vision Systems and Defect Detection ; Material Properties and Processing
Authors: T.T. Dele‐Afolabi | Azmah Hanim Mohamed Ariff | Norkhairunnisa Mazlan | Shafreeza Sobri | R. Calin | Nur Ismarrubie Zahari
Source: Materials Characterization (article, 2018-05-17)
Field: Materials Science — Ceramics and Composites

Agro-waste shaped porous Al2O3/Ni composites: Corrosion resistance performance and artificial neural network modelling Materials science ; Corrosion ; Porosity ; Husk ; Composite material ; Composite number ; Bagasse ; Ceramic Advanced ceramic materials synthesis ; Concrete and Cement Materials Research ; Recycling and utilization of industrial and municipal waste in materials production
Authors: Laura Escorihuela | Benjamí Martorell | Robert Ralló | Alberto Fernández
Source: Environmental Science Nano (article, 2018-01-01)
Field: Materials Science — Materials Chemistry

Toward computational and experimental characterisation for risk assessment of metal oxide nanoparticles <italic>In silico</italic>methods provide an alternative and reliable route for the toxicity evaluation of metal oxide nanoparticles. Nanoparticle ; In silico ; Oxide ; Metal ; Nanotechnology ; Risk assessment ; Materials science ; Computer science ; Chemistry ; Metallurgy ; Computer security Nanoparticles: synthesis and applications ; Electrochemical Analysis and Applications
Authors: Yogesh W. More | Sachin D. Padghan | Rajesh S. Bhosale | Rajendra P. Pawar | Avinash L. Puyad | Sidhanath V. Bhosale | Sheshanath V. Bhosale
Source: Sensors (article, 2018-10-12)
Field: Chemistry — Organic Chemistry

Proton Triggered Colorimetric and Fluorescence Response of a Novel Quinoxaline Compromising a Donor-Acceptor System Quinoxaline-based novel acid-responsive probe <b>Q1</b> was designed on the basis of a conjugated donor-acceptor (D-A) subunit. <b>Q1</b> shows colorimetric and fluorometric changes through protonation and deprotonation in dichloromethane. With the addition of the trifluoroacetic acid (TFA), UV-vis absorption spectral changes in peak intensity of <b>Q1</b> was observed. Moreover, the appearance of a new peaks at 284 nm 434 nm in absorption spectra with the addition of TFA indicating protonation of quinoxaline nitrogen and form <b>Q1.H⁺</b> and <b>Q1.2H⁺</b>. The emission spectra display appearance of new emission peak at 515 nm. The optical property variations were supported by time resolved fluorescence studies. The energy band gap was calculated by employing cyclic voltammetry and density functional calculations. Upon addition of triethylamine (TEA) the fluorescence emission spectral changes of <b>Q1</b> are found to be reversible. <b>Q1</b> shows color changes from blue to green in basic and acidic medium, respectively. The paper strip test was developed for making <b>Q1</b> a colorimetric and fluorometric indicator. Quinoxaline ; Trifluoroacetic acid ; Chemistry ; Protonation ; Fluorescence ; Photochemistry ; Absorption (acoustics) ; Deprotonation ; Analytical Chemistry (journal) ; Absorption spectroscopy ; Conjugated system ; Cyclic voltammetry ; Proton ; Physical chemistry ; Organic chemistry ; Materials science ; Ion Synthesis and Biological Evaluation ; Molecular Sensors and Ion Detection ; Luminescence and Fluorescent Materials
Authors: Djamel Ghernaout
Source: World Journal of Applied Chemistry (article, 2018-01-01)
Field: Chemistry — Analytical Chemistry

Decolorization of BF Cibacete Blue (CB) and Red Solophenyle 3BL (RS) Using Aluminum Sulfate and Ferric Chloride Coagulation process is largely applied for dye removal from textile wastewater treatment. This research aims to focus on implied mechanisms through coagulation of such wastewater. In this work, jar tests are performed to assess the coagulation process as a technique to treat synthetic solutions containing two textile dyes: BF cibacete blue (CB) and red solophenyle 3BL (RS). The effects of operational parameters such as coagulant type (FeCl<sub>3</sub> and Al<sub>2</sub> (SO<sub>4</sub>)<sub>3</sub>. 18H<sub>2</sub>O (alum)) and dose, initial pH, and dye concentration are studied. For a fixed 15 mg/L concentration of the two dyes, the FeCl3 optimal doses are found to 80 and 20 mg/L for RS and CB with removal rates of 65 and 89%, respectively. Regarding alum, discoloration is achieved at 44 and 77% for CB and RS with optimal doses of 80 and 40 mg/L, respectively. Similar efficiency trends are also obtained for a 50 mg/L concentration of the two dyes. The involved coagulation mechanisms are: charge neutralization followed by precipitation of the insoluble dye-coagulant complexes, and their adsorption into the Al or Fe hydroxides. It would be interesting to follow a real effluent application on the textile industry possibly containing the two dyes tested in this work. Alum ; Ferric ; Chemistry ; Coagulation ; Wastewater ; Effluent ; Nuclear chemistry ; Aluminium sulfate ; Sulfate ; Red mud ; Adsorption ; Chloride ; Flocculation ; Aluminium ; Pulp and paper industry ; Inorganic chemistry ; Environmental engineering ; Organic chemistry ; Environmental science Dye analysis and toxicity ; Advanced oxidation water treatment ; Dyeing and Modifying Textile Fibers