jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ivan Sukuba | Lei Chen | Michael Probst | Alexander Kaiser
Source: Molecular Simulation (article, 2018-12-27)
Field: Materials Science — Materials Chemistry

A neural network interface for DL_POLY and its application to liquid water After a general discussion of neural networks potential energy functions and their standing within the various approaches of representing the potential energy function of a system, we describe a new interface between the open source atomistic library aenet of Artrith and Urban and the DL_POLY 4 code. As an application example, the training of a neural network for liquid water is described and the network is used in a molecular dynamics simulation. The resulting thermodynamic properties are compared with those from a reference simulation with the same SPC/E model that has been used in the training. Artificial neural network ; Interface (matter) ; Molecular dynamics ; Energy (signal processing) ; Computer science ; Function (biology) ; Code (set theory) ; Liquid water ; Biological system ; Statistical physics ; Computational science ; Materials science ; Chemistry ; Thermodynamics ; Artificial intelligence ; Physics ; Computational chemistry ; Parallel computing ; Programming language Machine Learning in Materials Science ; Protein Structure and Dynamics ; Mass Spectrometry Techniques and Applications
Authors: Ross Darnell | Jagger Harvey | Glen Fox | Mary T. Fletcher | James M. Wainaina | Immaculate Wanjuki | W. J. Turner
Source: Australian Journal of Chemistry (article, 2018-09-12)
Field: Chemistry — Analytical Chemistry

NIRS Calibration of Aflatoxin in Maize The aim of this study is to determine the value of near-infrared spectroscopy (NIRS) as a diagnostic tool for aflatoxin contamination, specifically to rapidly predict levels of aflatoxin, either quantitatively or qualitatively, in ground maize. Maize was collected from inoculated field trials conducted across four sites in Kenya. Inoculated and uninoculated maize ears were harvested, milled, and prepared for NIRS scanning and wet chemistry-based aflatoxin quantification. Several statistical and machine learning techniques were compared. Absorbance at a single bandwidth explained 34 % of the variation in levels of aflatoxin using a regression model while a partial least-squares (PLS) method showed that NIR measurements could explain 42 % of the variation in aflatoxin levels. To compare various methods for their ability to classify samples with high (&amp;gt;100 ppb) levels of aflatoxin, various additional procedures were applied. The k-nearest neighbour classification method yielded sensitivity and specificity values of 0.75 and 0.52 respectively, compared with the support vector machine method with estimates of 0.81 and 0.68, whereas PLS could achieve values of 0.82 and 0.72 respectively. The corresponding false positive and false negative values are still unacceptable for NIRS to be used with confidence, as ~18 % of contaminated ground maize samples would be accepted and 28 % of good maize would be discarded or declared contaminated or downgraded. However, such calibrations could be useful in breeding programs without access to wet chemistry analysis, seeking to rank entries semiquantitatively. Aflatoxin ; Partial least squares regression ; Absorbance ; Contamination ; Chemistry ; Mathematics ; Food science ; Chromatography ; Biology ; Statistics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Mycotoxins in Agriculture and Food
Authors: Tomohiro Yonezu | Tomoyuki Tamura | Ichiro Takeuchi | Masayuki Karasuyama
Source: Physical Review Materials (article, 2018-11-20)
Field: Materials Science — Materials Chemistry

Knowledge-transfer-based cost-effective search for interface structures: A case study on fcc-Al [110] tilt grain boundary Determining the atomic configuration of an interface is one of the most important issues in materials science research. Although theoretical simulations are effective tools, an exhaustive search is computationally prohibitive due to the high degrees of freedom of the interface structure. In the interface structure search, multiple energy surfaces created by a variety of orientation angles need to be explored, and the necessary computational costs for different angles vary substantially owing to significant variations in the supercell sizes. In this paper, we introduce two machine-learning concepts, called transfer learning and cost-sensitive search, to the interface-structure search. As a case study, we demonstrate the effectiveness of our method, called cost-sensitive multitask Bayesian optimization, using the fcc-Al [110] tilt grain boundary. Four microscopic parameters, the three-dimensional rigid body translation, and the number of atomic columns, are optimized by transferring knowledge of energy surfaces among different orientation angles. We show that transferring knowledge of different energy surfaces can accelerate the structure search, and that considering the cost variations further improves the total efficiency. Tilt (camera) ; Interface (matter) ; Orientation (vector space) ; Grain boundary ; Supercell ; Energy (signal processing) ; Boundary (topology) Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Advanced Materials Characterization Techniques
Authors: Mengyao Chen | Kaixia Xiao | Tanfeng Zhao | Yanmei Zhou | Qingyou Zhang | João Aires‐de‐Sousa
Source: Journal of Molecular Liquids (article, 2018-02-19)
Field: Chemistry — Spectroscopy

Exploration of automatic learning to establish relationships between the molecular structure of chiral ionic liquids and the specific optical rotation Optical rotation ; Specific rotation ; Rotation (mathematics) ; Ionic liquid ; Enantiomer ; Chemistry ; Artificial neural network ; Sign (mathematics) ; Steric effects ; Biological system ; Computational chemistry ; Chemical physics ; Computer science ; Artificial intelligence ; Mathematics ; Stereochemistry ; Organic chemistry Molecular spectroscopy and chirality ; Analytical Chemistry and Chromatography ; Spectroscopy and Chemometric Analyses
Authors: Hania Al-Saddik | Anthony Laybros | Jean–Claude Simon | Frédéric Cointault
Source: Methods in molecular biology (article, 2018-10-25)
Field: Chemistry — Analytical Chemistry

Protocol for the Definition of a Multi-Spectral Sensor for Specific Foliar Disease Detection: Case of “Flavescence Dorée” Protocol (science) ; Computer science ; Medicine ; Pathology Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Leaf Properties and Growth Measurement
Authors: Akhiruddin Maddu | A. Muhammad Afdhal Saputra | Noor Intan Ayuningtiyas | Annisa Tsalsabila | Nurhalim Nurhalim
Source: International Journal of Renewable Energy Research (article, 2018-01-01)
Field: Materials Science — Materials Chemistry

Synthesis of MnO2/Carbon Dots Nanocomposite Derived From Rice Husk for Supercapacitor Electrodes This article reports the use of carbon dots (C-dots) derived from rice husk that was composited with MnO 2 to be applied as supercapacitor electrodes. Carbon dots were synthesized by acid hydrolysis method with the addition of nitrogen doping. Carbon dots produced were well dispersed in water and show a blue-green emission under UV light exposure. Spectra of fluorescence emission were taken using a violet laser (405 nm) as excitation source that resulted in broad spectra with the peak wavelength at 497 nm. The average particle size (APS) of carbon dots measured by using particle size analyzer (PSA) was found about 11 nm. MnO 2 /carbon dots (MnO 2 /C-dots) nanocomposites were made by mixing MnO 2 and carbon dots solution. The nanocomposite samples were characterized their crystal structure, morphology, and chemical composition. X-ray Diffraction (XRD) and Energy Dispersive X-ray Spectroscopy (EDXS) analysis confirmed the presence of any element that forming the nanocomposites. The nanocomposite electrodes were fabricated by coating the produced composites onto the glassy graphite electrode, then measured their electrochemical characteristics from which can be determined the specific capacitance of the electrodes. Based on cyclic voltammetry curves, it found the best specific capacitance value is 308 F/g. Materials science ; Nanocomposite ; Supercapacitor ; Carbon fibers ; Cyclic voltammetry ; Quantum dot ; Electrode ; Analytical Chemistry (journal) ; Nanoparticle ; Graphite ; Chemical engineering ; Capacitance ; Electrochemistry ; Nanotechnology ; Composite material ; Composite number ; Chemistry ; Organic chemistry Carbon and Quantum Dots Applications ; Supercapacitor Materials and Fabrication ; Graphene and Nanomaterials Applications
Authors: Ibrahim A. Amar | A. Sharif | M. M. Alkhayali | M. A. Jabji | Fatima Altohami | Muhammad Abdul Qadir | Mohammed M. Ahwidi
Source: Iranica Journal of Energy and Environment (article, 2018-01-01)
Field: Chemistry — Organic Chemistry

Adsorptive Removal of Methylene Blue Dye from Aqueous Solutions Using CoFe1.9Mo0.1O4 Magnetic Nanoparticles In this study, the adsorption properties of spinel ferrite-based adsorbent, CoFe1.9Mo0.1O4 (CFMo), for removal of methylene blue (MB) from aqueous solution have been investigated. Sol-gel process was successfully employed to prepare CoFe1.9Mo0.1O4 magnetic nanoparticles. The synthesized adsorbent was characterized by Fourier transform infrared (FTIR), scanning electron microscope (SEM) and X-ray diffraction (XRD). The adsorption experiments were carried out at various operational conditions (solution pH, initial dye concentration, contact time, adsorbent dosage and temperature) to evaluate the potential adsorption property of CFMo magnetic nanoparticles. The results showed that, under the optimum adsorption parameters, approximately 95 % of MB dye can be removed. The adsorption data were better described by Langmuir isotherm model and the maximum amount of MB adsorbed was about 20.45 mg/g. Several adsorption kinetic models and thermodynamic parameters (∆Gº, ∆Hº, ∆Sº) were used to fit the adsorption experimental data. The adsorption kinetics followed the pseudo-second-order model (PSO), while the thermodynamic parameters indicate that the proposed adsorption process was endothermic and spontaneous in nature. The obtained results suggest that CFMo is promising adsorbent material for removal of very toxic dyes from aqueous solutions. Adsorption ; Aqueous solution ; Methylene blue ; Endothermic process ; Langmuir adsorption model ; Fourier transform infrared spectroscopy ; Materials science ; Magnetic nanoparticles ; Nanoparticle ; Scanning electron microscope ; Nuclear chemistry ; Kinetics ; Chemical engineering ; Spinel ; Chemistry ; Organic chemistry ; Nanotechnology ; Composite material ; Photocatalysis ; Catalysis Nanomaterials for catalytic reactions ; Adsorption and biosorption for pollutant removal ; Magnetic Properties and Synthesis of Ferrites
Authors: Scott M. Woodley | Tomas Lazauskas | Malcolm Illingworth | Adam Carter | Alexey A. Sokol
Source: Faraday Discussions (article, 2018-01-01)
Field: Materials Science — Materials Chemistry

What is the best or most relevant global minimum for nanoclusters? Predicting, comparing and recycling cluster structures with WASP@N To address the question posed in the title, we have created, and now report details of, an open-access database of cluster structures with a web-assisted interface and toolkit as part of the WASP@N project. The database establishes a map of connectivities within each structure, the information about which is coded and kept as individual labels, called hashkeys, for the nanoclusters. These hashkeys are the basis for structure comparison within the database, and for establishing a map of connectivities between similar structures (topologies). The database is successfully used as a key element in a data-mining study of (MX)12 clusters of three binary compounds (LiI, SrO and GaAs) of which the database has no prior knowledge. The structures are assessed on the energy landscapes determined by the corresponding bulk interatomic potentials. Global optimisation, using a Lamarckian genetic algorithm, is used to search for low lying minima on the same energy landscape to confirm that the data-mined structures form a representative sample of the landscapes, with only very few structures missing from the close energy neighbourhood of the respective global minima. Maxima and minima ; Nanoclusters ; Cluster (spacecraft) ; Computer science ; Data mining ; Neighbourhood (mathematics) ; Binary number ; Database ; Materials science ; Nanotechnology ; Mathematics Machine Learning in Materials Science ; Quantum Dots Synthesis And Properties ; nanoparticles nucleation surface interactions
Authors: José M. Abad | Álvaro Y. Tesio | Emiliano Martínez‐Periñán | F. Pariente | Encarnación Lorenzo
Source: Nano Research (article, 2018-02-14)
Field: Chemistry — Electrochemistry

Imaging resolution of biocatalytic activity using nanoscale scanning electrochemical microscopy Scanning electrochemical microscopy represents a powerful tool for electro(chemical) characterization of surfaces, but its applicability has been limited in most cases at microscale spatial resolution, and the greatest challenge has been the scaling down to the nanoscale for fabrication and the use of nanometer-sized tips. Here, Pt nanoelectrodes with nanometer electroactive area were fabricated and employed for imaging a distribution of gold nanoparticles (AuNPs) and bioelectrocatalytic activity of a redox-active enzyme immobilized on gold surfaces. Microscale chemistry ; Nanotechnology ; Nanoscopic scale ; Materials science ; Nanometre ; Scanning electrochemical microscopy ; Characterization (materials science) ; Colloidal gold ; Electrochemistry ; Microscopy ; Scanning probe microscopy ; Resolution (logic) ; Fabrication ; Nanoparticle ; Scanning thermal microscopy ; Chemistry ; Electrode ; Optics Electrochemical Analysis and Applications ; Force Microscopy Techniques and Applications ; Electrochemical sensors and biosensors
Authors: Andrew Pedlow | G. McCullough | Alexandre Goguet | Óscar García-Afonso | David Sellick
Source: Emission Control Science and Technology (article, 2018-02-14)
Field: Materials Science — Materials Chemistry

The Development and Application of an Evolutionary Algorithm for the Determination of Kinetic Parameters in Automotive Aftertreatment Models Accurate mathematical models are an essential tool in the development of aftertreatment systems, as they can provide detailed information on the impact of design changes while simultaneously reducing development costs and the time between the initiation and completion of a catalytic process development. Identifying the set of kinetic parameters that achieves a perceived acceptable level of accuracy may require significant time and effort. Optimisation techniques can be used to speed up the tuning process; however, these techniques can require a large computation time, and may not produce a satisfactory answer. This invariably leads to questioning regarding the accuracy of such automated approaches. In this paper, the performance of a number of optimisation algorithms including a GA, nPSO and a hybrid algorithm has been explored with respect to their applicability to kinetic parameters optimisation in the context of aftertreatment modelling. The focus is therefore on the assessment of the optimisers’ performance, rather than the characterisation of catalytic reactions. The different algorithms were applied to the optimisation of parameters in a number of mathematical functions and theoretical aftertreatment systems. The optimisation algorithms were tested on theoretical aftertreatment systems since these have known absolute solutions thereby allowing the optimisers’ performance to be assessed in the absence of any other external source of inaccuracy such as model structure and experimental error. The results obtained demonstrate that such optimisation approaches facilitate the determination of kinetic parameters with suitable accuracy. The proposed hybrid optimisation algorithm achieved excellent performance in considerably shorter computation time than the GA or nPSO optimisers. Computation ; Context (archaeology) ; Automotive industry ; Computer science ; Process (computing) ; Algorithm ; Design of experiments ; Mathematical optimization ; Mathematics ; Engineering Catalytic Processes in Materials Science ; Biodiesel Production and Applications ; Advanced Combustion Engine Technologies
Authors: Claudio Corgnale | Martin Sulic
Source: Preprints.org (preprint, 2018-06-04)
Field: Materials Science — Materials Chemistry

Techno-Economic Analysis of High-Pressure Metal Hydride Compressor Systems Traditional high pressure mechanical compressors account for over half of the car station&amp;rsquo;s cost, have insufficient reliability and are not feasible for a large-scale fuel cell market. An alternative technology, employing a two-stage, hybrid system based on electrochemical and metal hydride compression technologies, represents an excellent alternative to conventional compressors. The high-pressure stage, operating at 100-875 bar, is based on a metal hydride thermal system. A techno-economic analysis of the metal hydride system is presented and discussed. A model of the metal hydride system was developed, integrating a lumped parameter mass and energy balance model with an economic model. A novel metal hydride heat exchanger configuration is also presented, based on mini-channel heat transfer systems, allowing for effective high-pressure compression. Several metal hydrides were analyzed and screened, demonstrating that one selected material, namely (Ti0.97Zr0.03)1.1Cr1.6Mn0.4, is likely the best candidate material to be employed for high-pressure compressors under the specific conditions. System efficiency and costs were assessed based on the properties of currently available materials at industrial levels. Results show that the system can reach pressures on the order of 875 bar with thermal power provided at approximately 150 &amp;deg;C. The system cost is comparable with the current mechanical compressors and can be reduced in several ways as discussed in the paper. Gas compressor ; Hydride ; Heat exchanger ; Process engineering ; Nuclear engineering ; Mechanical engineering ; Materials science ; Engineering ; Metal ; Metallurgy Hydrogen Storage and Materials ; Catalytic Processes in Materials Science ; Nuclear Materials and Properties
Authors: Huihui Wang | Kunlun Wang | Biyao Wang | Yan Lv | Xueheng Tao | Xu Zhang | Mingqian Tan
Source: Journal of Food Quality (article, 2018-05-08)
Field: Chemistry — Analytical Chemistry

Integration of Artificial Neural Network Modeling and Hyperspectral Data Preprocessing for Discrimination of<i> Colla Corii Asini</i> Adulteration The study of hyperspectral imaging in tandem with spectral preprocessing and neural network techniques was conducted to realize Colla Corii Asini (CCA, E’jiao) adulteration discrimination. CCA was adulterated with pig skin gelatin (PSG) in the range of 5–95% (w/w) at 5% increments. Three methods were used to pretreat the original spectra, which are multiplicative scatter correction (MSC), Savitzky-Golay (SG) smoothing, and the combination of MSC and SG (MSC-SG). SPA was employed to select the characteristic wavelengths (CWs) to reduce the high dimension. Colour and texture features of CWs were extracted as input of prediction model. Two kinds of artificial neural network (ANN) with three spectral preprocessing methods were applied to establish the prediction models. The prediction model of generalized regression neural network (GRNN) in tandem with the MSC-SG preprocessed method presented satisfactory performance with the correct classification rate value of 92.5%. The results illustrated that the integration of preprocessing methods, hyperspectral imaging features, and ANN modeling had a great potential and feasibility for CCA adulteration discrimination. Hyperspectral imaging ; Preprocessor ; Artificial intelligence ; Pattern recognition (psychology) ; Artificial neural network ; Smoothing ; Data pre-processing ; Computer science ; Computer vision Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Advanced Chemical Sensor Technologies
Authors: Mohd Nazrul Effendy Mohd Idrus | Kim Seng Chia
Source: MATEC Web of Conferences (article, 2018-01-01)
Field: Chemistry — Analytical Chemistry

Partial Least Square with Savitzky Golay Derivative in Predicting Blood Hemoglobin Using Near Infrared Spectrum Near infrared spectroscopy (NIRS) is a reliable technique that widely used in medical fields. Partial least square was developed to predict blood hemoglobin concentration using NIRS. The aims of this paper are (i) to develop predictive model for near infrared spectroscopic analysis in blood hemoglobin prediction, (ii) to establish relationship between blood hemoglobin and near infrared spectrum using a predictive model, (iii) to evaluate the predictive accuracy of a predictive model based on root mean squared error (RMSE) and coefficient of determination r p 2 . Partial least square with first order Savitzky Golay (SG) derivative preprocessing (PLS-SGd1) showed the higher performance of predictions with RMSE = 0.7965 and r p 2 = 0.9206 in K-fold cross validation. Optimum number of latent variable (LV) and frame length ( f ) were 32 and 27 nm, respectively. These findings suggest that the relationship between blood hemoglobin and near infrared spectrum is strong, and the partial least square with first order SG derivative is able to predict the blood hemoglobin using near infrared spectral data. Partial least squares regression ; Hemoglobin ; Mean squared error ; Mathematics ; Near-infrared spectroscopy ; Second derivative ; Analytical Chemistry (journal) ; Chemistry ; Statistics ; Chromatography ; Physics ; Mathematical analysis ; Optics ; Biochemistry Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Metabolomics and Mass Spectrometry Studies
Authors: Yuanxiang Zhou | Xin Huang | Jianwen Huang | Ling Zhang | Zhongliu Zhou
Source: Molecules (article, 2018-06-21)
Field: Materials Science — Materials Chemistry

Predicting the Dielectric Properties of Nanocellulose-Modified Presspaper Based on the Multivariate Analysis Method Nanocellulose-modified presspaper is a promising solution to achieve cellulose insulation with better performance, reducing the risk of electrical insulation failures of a converter transformer. Predicting the dielectric properties will help to further design and improvement of presspaper. In this paper, a multivariable method was adopted to determine the effect of softwood fiber on the macroscopic performance of presspaper. Based on the parameters selected using the optimum subset method, a multiple linear regression was built to model the relationship between the fiber properties and insulating performance of presspaper. The results show that the fiber width and crystallinity had an obvious influence on the mechanical properties of presspaper, and fiber length, fines, lignin, and nanocellulose had a significant impact on the breakdown properties. The proposed models exhibit a prediction accuracy of higher than 90% when verified with the experimental results. Finally, the effect of nanocellulose on the breakdown strength of presspaper was taken into account and new models were derived. Nanocellulose ; Materials science ; Composite material ; Crystallinity ; Dielectric ; Multivariate statistics ; Cellulose ; Fiber ; Softwood ; Lignin ; Computer science ; Chemical engineering ; Optoelectronics ; Machine learning ; Chemistry High voltage insulation and dielectric phenomena ; Advanced Cellulose Research Studies ; Power Transformer Diagnostics and Insulation