jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Alexei A. Yakushev | Alexei D. Averin | Olga A. Maloshitskaya | О. И. Койфман | Sergei A. Syrbu | I. P. Beletskaya
Source: Macroheterocycles (article, 2018-01-01)
Field: Materials Science — Materials Chemistry

Porphyrin-Containing Polymacrocycles: Synthesis and Evaluation as Fluorescent Detectors of Metal Cations Using Pd(0)-catalyzed amination reaction of zinc 5, 15-bis(4-bromophenyl)porphyrinate with diazatrioxamacrocycle -derivative of 3,3'-disubstituted biphenyl -a series of polymacrocyclic compounds was obtained. The investigation of their fluorescence in the presence of 18 metal cations revealed that two of them can act as molecular probes for Cu(II), Al(III) and Cr(III) by the fluorescence quenching. Tri-and tetramacrocyclic compounds of another structure were synthesized by the Pd(0)-catalyzed arylation of cryptands comprising central diazacrown ether moieties using zinc 5-(4-bromophenyl)porphyrinate. One of these compounds was characterized as a fluorescent chemosensor for Cu(II). Porphyrin ; Fluorescence ; Metal ; Photochemistry ; Chemistry ; Detector ; Combinatorial chemistry ; Inorganic chemistry ; Organic chemistry ; Physics ; Optics Porphyrin and Phthalocyanine Chemistry ; Molecular Sensors and Ion Detection ; Electrochemical sensors and biosensors
Authors: G. M. Hansford
Source: Journal of Applied Crystallography (article, 2018-10-25)
Field: Materials Science — Materials Chemistry

A prototype handheld X-ray diffraction instrument A conceptual design for a handheld X-ray diffraction (HHXRD) instrument is proposed. Central to the design is the application of energy-dispersive XRD (EDXRD) in a back-reflection geometry. This technique brings unique advantages which enable a handheld instrument format, most notably, insensitivity to sample morphology and to the precise sample position relative to the instrument. For fine-grained samples, including many geological specimens and the majority of common alloys, these characteristics negate sample preparation requirements. A prototype HHXRD device has been developed by minor modification of a handheld X-ray fluorescence instrument, and the performance of the prototype has been tested with samples relevant to mining/quarrying and with an extensive range of metal samples. It is shown, for example, that the mineralogical composition of iron-ore samples can be approximately quantified. In metals analysis, identification and quantification of the major phases have been demonstrated, along with extraction of lattice parameters. Texture analysis is also possible and a simple example for a phosphor bronze sample is presented. Instrument formats other than handheld are possible and online process control in metals production is a promising area. The prototype instrument requires extended measurement times but it is argued that a purpose-designed instrument can achieve data-acquisition times below one minute. HHXRD based on back-reflection EDXRD is limited by the low resolution of diffraction peaks and interference by overlapping fluorescence peaks and, for these reasons, cannot serve as a general-purpose XRD tool. However, the advantages of <i>in situ</i>, nondestructive and rapid measurement, tolerance of irregular surfaces, and no sample preparation requirement in many cases are potentially transformative. For targeted applications in which the analysis meets commercially relevant performance criteria, HHXRD could become the method of choice through sheer speed and convenience. Mobile device ; Sample (material) ; Diffraction ; X-ray fluorescence ; Materials science ; Computer science ; Optics ; Chemistry ; Physics ; Fluorescence X-ray Diffraction in Crystallography ; X-ray Spectroscopy and Fluorescence Analysis ; Nuclear Physics and Applications
Authors: Jiwei Shen | Zhiqing Wang | Xiaoxuan Wei | Jiawei Liu | Yinmao Wei
Source: RSC Advances (article, 2018-01-01)
Field: Materials Science — Materials Chemistry

Revealing the <i>in situ</i> NaF generation balance for user-friendly controlled synthesis of sub-10 nm monodisperse low-level Gd<sup>3+</sup>-doped β-NaYbF<sub>4</sub>:Er Herein, user-friendly control of the synthesis of sub-10 nm hexagonal (β-) NaYbF<sub>4</sub>:Er nanocrystals (NCs) with extremely low-level Gd<sup>3+</sup> doping (0%, 10 mol%) was achieved. We reveal for the first time that the effective sodium/fluoride levels during the formation of cubic (α-) nuclei are not only controlled by the sodium/fluoride to rare-earth precursor ratios used, but also sensitively restricted by the <i>in situ</i> NaF generation reaction in a sodium oleate-based solvothermal system. Excessive <i>in situ</i> NaF generation will lead to a respective sodium- and fluoride-deficient environment, delayed α-to-β transition and larger β-NCs. Based on these effects, sub-10 nm monodisperse low-level Gd<sup>3+</sup>-doped β-NaYbF<sub>4</sub>:Er was obtained with a user-friendly low fluoride dosage by finely balancing this NaF generation reaction and achieving an intrinsic optimized sodium-fluoride level for NC nucleation. Notably, our work represents the first example where the focus is on the competing <i>in situ</i> NaF generation reaction and its use for nucleation regulation, as well as for the user-friendly control of the solvothermal synthesis of sub-10 nm β-NaYbF<sub>4</sub>:Er. Fluoride ; Nucleation ; Sodium fluoride ; Dispersity ; Nanocrystal ; Doping ; In situ ; Inorganic chemistry ; Materials science ; Chemistry ; Chemical engineering ; Nanotechnology ; Optoelectronics ; Organic chemistry ; Polymer chemistry Luminescence Properties of Advanced Materials ; Inorganic Fluorides and Related Compounds ; Perovskite Materials and Applications
Authors: Xingshi Yang | Alexander E. Mayer | Ghislain Bournival | R.J. Pugh | Seher Ata
Source: Frontiers in Chemistry (article, 2018-08-14)
Field: Materials Science — Materials Chemistry

Experimental Technique to Study the Interaction Between a Bubble and the Particle-Laden Interface An experimental apparatus was developed based on the Langmuir-Blodgett trough design to investigate the compression of monolayers of micron size spherical glass particles at the air-water interface and the interaction of an air bubble with the monolayers. The setup modifies the regular Langmuir-Blodgett trough by using a deep and clear glass cell. The cell allowed both the optical observation of the particle monolayer and the insertion of a capillary to produce a bubble under the layer of particles. Surface pressure-area (Π-A) isotherms were measured while the particles rearranged at the interface during compression and expansion for different pH values and particle wettability. We also analyzed the motion of particles in the monolayer by the surface pressure and packing factor to gain further insights into the behavior of particles during the coalescence process. The results suggested that the coalescence of a bubble was dependent on the formation of a defect in the particle layer and the defect size was both strongly influenced by particle hydrophobicity and the pH of the subphase. Monolayer ; Bubble ; Coalescence (physics) ; Wetting ; Surface pressure ; Langmuir ; Particle (ecology) ; Particle size ; Materials science ; Capillary action ; Chemical physics ; Chemistry ; Chemical engineering ; Composite material ; Nanotechnology ; Adsorption ; Mechanics ; Physical chemistry Pickering emulsions and particle stabilization ; Minerals Flotation and Separation Techniques ; Micro and Nano Robotics
Authors: Patrick J. Willems | Alison Horne | Sofie Goormachtig | Ive De Smet | Alexander Botzki | Frank Van Breusegem | Kris Gevaert
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2018-09-13)
Field: Chemistry — Spectroscopy

The Plant PTM Viewer, a central resource exploring plant protein modifications. From site-seeing to protein function SUMMARY Posttranslational modifications (PTMs) of proteins are central in any kind of cellular signaling. Modern mass spectrometry technologies enable comprehensive identification and quantification of various PTMs. Given the increased number and types of mapped protein modifications, a database is necessary that simultaneouly integrates and compares site-specific information for different PTMs, especially in plants for which the available PTM data are poorly catalogued. Here, we present the Plant PTM Viewer ( http://www.psb.ugent.be/PlantPTMViewer ), an integrative PTM resource that comprises approximately 200,000 PTM sites for 17 types of protein modifications in plant proteins from five different species. The Plant PTM Viewer provides the user with a protein sequence overview in which the experimentally evidenced PTMs are highlighted together with functional protein domains or active site residues. The PTM sequence search tool can query PTM combinations in specific protein sequences, whereas the PTM BLAST tool searches for modified protein sequences to detect conserved PTMs in homologous sequences. Taken together, these tools facilitate to assume the role and potential interplay of PTMs in specific proteins or within a broader systems biology context. The Plant PTM Viewer is an open repository that allows submission of mass spectrometry-based PTM data to remain at pace with future PTM plant studies. Context (archaeology) ; Computational biology ; Protein function ; Resource (disambiguation) ; Function (biology) ; Biology ; Protein superfamily ; Computer science ; Genetics Advanced Proteomics Techniques and Applications ; Glycosylation and Glycoproteins Research ; Ubiquitin and proteasome pathways
Authors: Zhengtian Wu | Yang Zhang | Fuyuan Hu | Qing Gao | Xinyin Xu | Ronghao Zheng
Source: Journal of Nanomaterials (article, 2018-01-01)
Field: Materials Science — Materials Chemistry

Vibration Analysis of Bilayered Graphene Sheets for Building Materials in Thermal Environments Based on the Element-Free Method Graphene sheets are widely applied due to their unique and highly valuable properties, such as energy conservation materials in buildings. In this paper, we investigate the vibration behavior of double layer graphene sheets (DLGSs) in thermal environments which helps probe into the mechanism of energy conservation of graphene sheets in building materials. The nonlocal elastic theory and classical plate theory (CLPT) are used to derive the governing equations. The element-free method is employed to analyze the vibration behaviors of DLGSs embedded in an elastic medium. The accuracy of the solutions in this study is demonstrated by comparison with published results in the literature. Furthermore, a number of numerical results are presented to investigate the effects of various parameters (boundary conditions, nonlocal parameter, aspect ratio, side length, elastic foundation parameter, and temperature) on the frequency of DLGSs. Materials science ; Graphene ; Vibration ; Thermal ; Boundary value problem ; Finite element method ; Conservation of energy ; Foundation (evidence) ; Energy method ; Element (criminal law) ; Mechanics ; Composite material ; Structural engineering ; Nanotechnology ; Mathematical analysis ; Acoustics ; Thermodynamics ; Physics ; Mathematics Nonlocal and gradient elasticity in micro/nano structures ; Numerical methods in engineering ; Composite Structure Analysis and Optimization
Authors: Sergey V. Amitonov | Paul C. Spruijtenburg | Max W. S. Vervoort | Wilfred G. van der Wiel | Floris A. Zwanenburg
Source: Applied Physics Letters (article, 2018-01-08)
Field: Materials Science — Materials Chemistry

Depletion-mode quantum dots in intrinsic silicon We report the fabrication and electrical characterization of depletion-mode quantum dots in a two-dimensional hole gas (2DHG) in intrinsic silicon. We use fixed charge in a SiO2/Al2O3 dielectric stack to induce a 2DHG at the Si/SiO2 interface. Fabrication of the gate structures is accomplished with a single layer metallization process. Transport spectroscopy reveals regular Coulomb oscillations with charging energies of 10–15 meV and 3–5 meV for the few- and many-hole regimes, respectively. This depletion-mode design avoids complex multilayer architectures requiring precision alignment and allows us to adopt directly best practices already developed for depletion dots in other material systems. We also demonstrate a method to deactivate fixed charge in the SiO2/Al2O3 dielectric stack using deep ultraviolet light, which may become an important procedure to avoid unwanted 2DHG build-up in Si MOS quantum bits. Quantum dot ; Stack (abstract data type) ; Fabrication ; Dielectric ; Silicon ; Charge (physics) ; Coulomb ; Spectroscopy Silicon Nanostructures and Photoluminescence ; Semiconductor Quantum Structures and Devices ; Quantum and electron transport phenomena
Authors: Egor Kraev
Source: arXiv (Cornell University) (preprint, 2018-11-27)
Field: Materials Science — Materials Chemistry

Grammars and reinforcement learning for molecule optimization We seek to automate the design of molecules based on specific chemical properties. Our primary contributions are a simpler method for generating SMILES strings guaranteed to be chemically valid, using a combination of a new context-free grammar for SMILES and additional masking logic; and casting the molecular property optimization as a reinforcement learning problem, specifically best-of-batch policy gradient applied to a Transformer model architecture. This approach uses substantially fewer model steps per atom than earlier approaches, thus enabling generation of larger molecules, and beats previous state-of-the art baselines by a significant margin. Applying reinforcement learning to a combination of a custom context-free grammar with additional masking to enforce non-local constraints is applicable to any optimization of a graph structure under a mixture of local and nonlocal constraints. Reinforcement learning ; Computer science ; Transformer ; Artificial intelligence ; Margin (machine learning) ; Grammar ; Context (archaeology) ; Masking (illustration) ; Rule-based machine translation ; Theoretical computer science ; Machine learning ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: Mohammad Asif
Source: Organic and Medicinal Chemistry International Journal (article, 2018-03-07)
Field: Chemistry — Organic Chemistry

Review on Antimicrobial Agents Organic and Medicinal chemistry international Journal is an open access journal that is committed to publish the papers on various topics of chemistry, especially synthetic organic chemistry, and pharmacology and various other biological specialties Antimicrobial ; Medicine ; Microbiology ; Biology Synthesis and biological activity ; Essential Oils and Antimicrobial Activity ; Phenothiazines and Benzothiazines Synthesis and Activities
Authors: Keisuke Takahashi | Itsuki Miyazato | Shun Nishimura | Junya Ohyama
Source: ChemCatChem (article, 2018-08-13)
Field: Materials Science — Materials Chemistry

Unveiling Hidden Catalysts for the Oxidative Coupling of Methane based on Combining Machine Learning with Literature Data Abstract The front cover artwork for Issue 15/2018 is provided by National Institute for Materials Science; ICAT, Hokkaido University, Japan Advanced Institute of Science and Technology, and Nagoya University (all in Japan) as part of CREST: Catalysts Informatics Project. The image shows machine learning as a tool for catalyst design based on experimental catalysis data. See the Full Paper itself at https://doi.org/10.1002/cctc.201800310 . Front cover ; Catalysis ; Informatics ; Oxidative coupling of methane ; Cover (algebra) ; Materials informatics ; Computer science ; Chemistry ; Engineering ; Health informatics ; Political science ; Mechanical engineering ; Organic chemistry Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Topic Modeling
Authors: Helge S. Stein | Dan Guevarra | Paul F. Newhouse | Edwin Soedarmadji | John M. Gregoire
Source: ChemRxiv (preprint, 2018-07-11)
Field: Materials Science — Materials Chemistry

Machine Learning of Optical Properties of Materials - Predicting Spectra from Images and Images from Spectra As the materials science community seeks to capitalize on recent advancements in computer science, the sparsity of well-labelled experimental data and limited throughput by which it can be generated have inhibited deployment of machine learning algorithms to date. Several successful examples in computational chemistry have inspired further adoption of machine learning algorithms, and in the present work we present autoencoding algorithms for measured optical properties of metal oxides, which can serve as an exemplar for the breadth and depth of data required for modern algorithms to learn the underlying structure of experimental materials science data. Our set of 180,902 distinct materials samples spans 78 distinct composition spaces, includes 45 elements, and contains more than 80,000 unique quinary oxide and 67,000 unique quaternary oxide compositions, making it the largest and most diverse experimental materials set utilized in machine learning studies. The extensive dataset enabled training and validation of 3 distinct models for mapping between sample images and absorption spectra, including a conditional variational autoencoder that generates images of hypothetical materials with tailored absorption properties. The absorption patterns auto-generated from sample images capture the salient features of ground truth spectra, and direct band gap energies extracted from these auto-generated patterns are quite accurate with a mean absolute error of 240 meV, which is the approximate uncertainty from traditional extraction of the band gap energy from measurements of the full transmission and reflection spectra. Optical properties of materials are not only ubiquitous in materials applications but also emblematic of the confluence of underlying physical phenomena that yield the type of complex data relationships that merit and benefit from neural network-type modelling. Computer science ; Artificial intelligence ; Autoencoder ; Machine learning ; Spectral line ; Ground truth ; Set (abstract data type) ; Sample (material) ; Algorithm ; Artificial neural network ; Chemistry ; Physics Machine Learning in Materials Science ; Spectroscopy and Chemometric Analyses ; Geochemistry and Geologic Mapping
Authors: Felipe Monteiro | Isabelle C. Valim | R. De Siqueira | Flávio José de Abreu Moura | Andrea Grillo | Brenda Lohanny Passos Santos
Source: SHILAP Revista de lepidopterología (article, 2018-06-01)
Field: Chemistry — Analytical Chemistry

Application of Artificial Neural Networks for Identification of Catalysts Used in Thermogravimetry Lignocellulosic Biomass In recent decades there has been a growing need to develop efficient processes in economic and energetic terms for the sustainable production of fuels and chemical products. In this context, it is important to understand the thermal degradation behaviour of different biomass in an inert atmosphere to investigate the breakdown of polymer chains into smaller chains, which could be converted into new products. Catalysts can be used in these breaking processes, since they generate good effects in accelerating the degradation of organic structures and with this increases the yield of bio-oil production. In this work the fiber of crushed and sifted green coconut shell, submitted to the thermogravimetry analysis (TG), was used as biomass. Types of a catalyst were incorporated into the biomass, based on cobalt ferrite, Fe2CoO4. The analysis revealed distinct profiles of mass loss of the green coconut fiber with and without the catalysts. To detect possible instrumentation and/or preparation failures of the experimental samples, models were developed with the help of artificial intelligence. The strategy chosen was the use of artificial neural networks (ANN) feedforward because it is a set of mathematical procedures that seek an intelligent way (inspired in by the network of biological neurons) to manage and analyze systems. The RNA model was developed from the import of the TG data to the MATLAB R2017a software, identifying data inputs/outputs. The inputs were loss of mass (mg), derived from the loss of mass and temperature (°C). The training algorithms were: Levenberg-Marquardt (trainlm) and Levenberg-Marquardt with Bayesian regularization (trainbr) that avoids over-adjustment. The activation functions used were the hyperbolic tangent (tansig) and logistic (logsig) to activate the intermediate layers of the model. The results of the model involving mass loss information were satisfactory to identify the type of catalyst with a topology of 3-6-4-1. The evaluation of the SSE error rates of 0.882 and the MSE of 6.85E-04, confirmed the good fit of the neural network, since the values are close to zero. Thus, the model can be used in conjunction with TG analysis, preventing possible measurement failures. Thermogravimetry ; Artificial neural network ; Biomass (ecology) ; Biochemical engineering ; Context (archaeology) ; Process engineering ; Computer science ; Lignocellulosic biomass ; Environmental science ; Pulp and paper industry ; Biological system ; Biofuel ; Artificial intelligence ; Waste management ; Chemical engineering ; Engineering Spectroscopy and Chemometric Analyses ; Biodiesel Production and Applications ; Coconut Research and Applications
Authors: Keisuke Tanimoto | Yoshiteru Amemiya | Shin Yokoyama
Source: Japanese Journal of Applied Physics (article, 2018-02-28)
Field: Materials Science — Materials Chemistry

Optical waveguides with memory effect using photochromic material for neural network An optical neural network using a waveguide with a memory effect, a photodiode, CMOS circuits and LEDs was proposed. To realize the neural network, optical waveguides with a memory effect were fabricated using a cladding layer containing the photochromic material "diarylethene". The transmittance of green light was decreased by UV light irradiation and recovered by the passage of green light through the waveguide. It was confirmed that the transmittance versus total energy of the green light that passed through the waveguide well fit the universal exponential curve. Photochromism ; Artificial neural network ; Optoelectronics ; Materials science ; Computer science ; Nanotechnology ; Artificial intelligence Photochromic and Fluorescence Chemistry ; Photoreceptor and optogenetics research ; Neurobiology and Insect Physiology Research
Authors: Ilya I. Kiryanov | Arthur R. Tulyabaev | F. Kh. Mukminov | Л. М. Халилов
Source: Journal of Chemometrics (article, 2018-05-10)
Field: Materials Science — Materials Chemistry

Neural network for prediction of <sup>13</sup>C NMR chemical shifts of fullerene C<sub>60</sub> mono‐adducts Abstract Real‐valued models based on deep artificial neural networks were proposed to predict 13 C NMR chemical shifts of fullerene C 60 core carbon atoms for computer‐aided structure elucidation of complex fullerene C 60 mono‐adducts. We showed that parametric rectified linear units could be successfully used as activation functions in hidden layers of artificial neural networks for decision of complex physical‐chemical tasks. A total of 400 artificial neural networks were trained and tested in order to reveal the best‐fitted models. The best prediction accuracy of real‐valued models was achieved with MAEP = 1.83 ppm/RMSEP = 2.60 ppm using artificial neural network model which has 110 and 120 hidden units, respectively, with parametric rectified linear unit as activation function. Artificial neural network ; Fullerene ; Biological system ; Parametric statistics ; Artificial intelligence ; Adduct ; Chemical shift ; Computer science ; Pattern recognition (psychology) ; Chemistry ; Mathematics ; Physical chemistry ; Organic chemistry ; Statistics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Fullerene Chemistry and Applications