jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: May Ali | Suraya Abdul Rashid | Mohd Nizar Hamidon | Faizah Md Yasin
Source: BULLETIN OF CHEMICAL REACTION ENGINEERING AND CATALYSIS (article, 2018-02-18)
Field: Materials Science — Materials Chemistry

Facile Synthesis and Characterization of Multi-Layer Graphene Growth on Co-Ni Oxide/Al2O3 Substrate Using Chemical Vapour Deposition The synthesis and characterization of multilayer graphene (MLG) growth on bimetallic Co-Ni oxide/Al2O3 substrate using chemical vapour deposition (CVD) were investigated. The synthesis of MLG was performed at a temperature range of 700-900 oC. Characterization was carried out using TGA, XRD, FESEM, HRTEM, EDX, XPS, FTIR, and Raman spectroscopy. The MLG growth on the bimetallic substrate was confirmed by XRD, FESEM, and HRTEM analysis. TGA and Raman spectroscopy analyses indicate the formation of thermally stable and high-quality MLG. The kinetic growth of MLG was investigated by varying the reaction temperature and monitoring the partial pressure of the ethanol (C2H5OH) as well as that of hydrogen. The data obtained were fitted to the Langmuir-Hinshelwood kinetic model for the estimation of the reaction rate constants at different temperatures. The results showed that the reaction rate constant increased with temperature and the apparent activation energy of 13.72 kJ.mol-1 was obtained indicating a relatively fast rate of MLG growth. The parity plot obtained for the comparison of the predicted and observed rate of C2H5OH consumptions showed an excellent agreement. This study is important for understanding the growth kinetics of MLG in order to develop appropriate measures that can control the production of MLG thin films for use in the electronic industries. Bimetallic strip ; High-resolution transmission electron microscopy ; Graphene ; Chemical vapor deposition ; Raman spectroscopy ; X-ray photoelectron spectroscopy ; Oxide ; Substrate (aquarium) ; Activation energy ; Analytical Chemistry (journal) ; Reaction rate constant ; Materials science ; Chemical engineering ; Catalysis ; Chemistry ; Physical chemistry ; Nanotechnology ; Kinetics ; Metallurgy ; Transmission electron microscopy ; Organic chemistry Graphene research and applications ; Molecular Junctions and Nanostructures ; Advancements in Battery Materials
Authors: Trung Hai Nguyen | Ariën S. Rustenburg | S.G. Krimmer | Hexi Zhang | John D. Clark | Paul Novick | Kim Branson | Vijay S. Pande | John D. Chodera | David D. L. Minh
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2018-05-21)
Field: Chemistry — Physical and Theoretical Chemistry

Bayesian analysis of isothermal titration calorimetry for binding thermodynamics Abstract Isothermal titration calorimetry (ITC) is the only technique able to determine both the enthalpy and entropy of noncovalent association in a single experiment. The standard data analysis method based on nonlinear regression, however, provides unrealistically small uncertainty estimates due to its neglect of dominant sources of error. Here, we present a Bayesian framework for sampling from the posterior distribution of all thermodynamic parameters and other quantities of interest from one or more ITC experiments, allowing uncertainties and correlations to be quantitatively assessed. For a series of ITC measurements on metal:chelator and protein:ligand systems, the Bayesian approach yields uncertainties which represent the variability from experiment to experiment more accurately than the standard data analysis. In some datasets, the median enthalpy of binding is shifted by as much as 1.5 kcal/mol. A Python implementation suitable for analysis of data generated by MicroCal instruments (and adaptable to other calorimeters) is freely available online. Isothermal titration calorimetry ; Bayesian probability ; Enthalpy ; Thermodynamics ; Chemistry ; Isothermal process ; Gibbs sampling ; Calorimetry ; Statistical physics ; Statistics ; Mathematics ; Physical chemistry ; Physics thermodynamics and calorimetric analyses ; Computational Drug Discovery Methods ; Field-Flow Fractionation Techniques
Authors: Frank Schoeneman | Varun Chandola | Nils Napp | Olga Wodo | Jarosław Żola
Source:  (preprint, 2018-12-01)
Field: Chemistry — Analytical Chemistry

Entropy-Isomap: Manifold Learning for High-dimensional Dynamic Processes Scientific and engineering processes deliver massive high-dimensional data sets that are generated as non-linear transformations of an initial state and few process parameters. Mapping such data to a low-dimensional manifold facilitates better understanding of the underlying processes, and enables their optimization. In this paper, we first show that off-the-shelf non-linear spectral dimensionality reduction methods, e.g., Isomap, fail for such data, primarily due to the presence of strong temporal correlations. Then, we propose a novel method, Entropy-Isomap, to address the issue. The proposed method is successfully applied to large data describing a fabrication process of organic materials. The resulting low-dimensional representation correctly captures process control variables, allows for low-dimensional visualization of the material morphology evolution, and provides key insights to improve the process. Isomap ; Nonlinear dimensionality reduction ; Dimensionality reduction ; Computer science ; Curse of dimensionality ; Visualization ; Process (computing) ; High dimensional ; Manifold (fluid mechanics) ; Artificial intelligence ; Entropy (arrow of time) ; Representation (politics) ; Clustering high-dimensional data ; Pattern recognition (psychology) ; Data mining ; Cluster analysis ; Engineering ; Physics Spectroscopy and Chemometric Analyses ; Industrial Vision Systems and Defect Detection ; Face and Expression Recognition
Authors: Marwa Ammar | Walid Oueslati
Source: Advances in Civil Engineering (article, 2018-01-01)
Field: Materials Science — Biomaterials

Crystalline Swelling Process of Mg‐Exchanged Montmorillonite: Effect of External Environmental Solicitation This work reports characterization of the possible effects that might distress the hydration properties of Mg‐exchanged low‐charge montmorillonite (SWy‐2) when it undergoes external environmental solicitation. This perturbation was created by an alteration of relative humidity rates (i.e., RH%) over two hydration‐dehydration cycles with different sequence orientations. Structural characterization is mainly based on the X‐ray diffraction (XRD) profile‐modeling approach achieved by comparing the “in situ” obtained experimental 00l reflections with other ones calculated from theoretical models. This method allows assessing the evolution of the interlayer water retention mechanism and the progress of diverse hydration state’s contributions versus external strain. Obtained results prove that the hydration behavior of the studied materials is strongly dependent on the RH sequence orientation which varied over cycles. The interlayer organization of Mg‐exchanged montmorillonite (i.e., SWy‐2‐Mg) is characterized by a heterogeneous hydration behavior, which is systematically observed at different stages of both cycles. By comparing the interlayer water process evolution of Mg‐exchanged montmorillonite with the observed SWy‐2‐Ni sample hydration behaviors, a same hysteresis thickness characterized by obvious fluctuations of interlayer water molecule abundances is observed. Nevertheless, in the case of Hg and Ba‐saturated montmorillonite, the retention water process versus the applied cycles was steadier comparing with Mg ions. Montmorillonite ; Swelling ; Process (computing) ; Materials science ; Chemical engineering ; Composite material ; Computer science ; Engineering Clay minerals and soil interactions ; Concrete and Cement Materials Research ; CO2 Sequestration and Geologic Interactions
Authors: Karthik Kuchangi Jothi Prakash
Source: ARROW@Dublin Institute of Technology (Dublin Institute of Technology) (article, 2018-01-01)
Field: Chemistry — Analytical Chemistry

Spoilage Detection in Raspberry Fruit Based on Spectral Imaging Using Convolutional Neural Networks Effective spoilage detection of perishable food items like fruits and vegetables is essential for retailers who stock and sell large quantities of these items. This research is aimed at developing a non-destructive, rapid and accurate method which is based on Spectral Imaging (SI) used in tandem with Convolutional Neural Network (CNN) to predict whether the fruit is fresh or rotten. The study also aims to determine the number of days before which the fruit rots. This research employs a primary, quantitative and inductive methods to investigate the Deep Learning based approach to detect fruit spoilage. Raspberry fruit in particular has been chosen for the experiment. Baskets of raspberries from three different stores were bought and stored in the refrigerator at four-degree Celsius. Images of these baskets was captured on a daily basis using an RGB digital camera until all the baskets of fruits were rotten. The study employs a Supervised learning-based classification approach where-by the data is labelled based on the physical appearance of fruits in the basket. The results show that a Spectral imaging technique used along with a CNN yields a good accuracy of 86% with the F1 score of 0.82 to classify the fruits as Good or Bad but does not fare well in estimating the number of days before the fruit actually rots. The ability of CNN to process and identify patterns in a SI to detect spoilage in fruits would help fruit retail operators to optimize their business chain. Convolutional neural network ; Food spoilage ; Computer science ; Artificial intelligence ; Remote sensing ; Horticulture ; Environmental science ; Biology ; Geography Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Advanced Chemical Sensor Technologies
Authors: Johannes Anastasiadis | Fernando Puente León
Source: tm - Technisches Messen (article, 2018-09-01)
Field: Chemistry — Analytical Chemistry

Detektion von Stoffen in Lebensmitteln mit Hilfe von 3D-Faltungsautoencodern / Detection of substances in food with 3D convolutional autoencoders Zusammenfassung Hyperspektrale Bilder beinhalten dank ihrer hohen spektralen Auflösung wertvolle Informationen. Sie können beispielsweise zur berührungslosen Untersuchung von Lebensmitteln verwendet werden. Um diese mit Hilfe konvolutionaler neuronaler Netze verarbeiten zu können, werden jedoch große Lernstichproben benötigt. Dies gilt insbesondere, wenn die Daten nicht vorverarbeitet werden und deshalb eine hohe Dimension besitzen. Allerdings existieren nur verhältnismäßig wenige hyperspektrale Datensätze. Um dieses Problem zu umgehen, kann ein Vortraining mit einem Autoencoder durchgeführt werden. Dieser komprimiert das Bild und rekonstruiert es im Anschluss wieder. Der Autoencoder wird trainiert, indem der Rekonstruktionsfehler minimiert wird. Bei der Komprimierung entstehen so aussagekräftige Merkmale. In diesem Beitrag wird am Beispiel von Gewürzmischungen untersucht, ob einzelne Komponenten in Mischungen detektiert werden können. Dabei wird mit einem kleinen Datensatz ein neuronales Netz mit Hilfe eines 3D-Faltungsautoencoders trainiert. Physics ; Humanities ; Molecular biology ; Gynecology ; Art ; Biology ; Medicine Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies
Authors: Aditya Kamath | Sergei Manzhos
Source: Preprints.org (preprint, 2018-09-28)
Field: Chemistry — Spectroscopy

Inverse Multiquadratic Functions as Basis for Rectangular Collocation Method to Solve the Vibrational Schrödinger Equation We explore the use of inverse multiquadratic (IMQ) functions as basis functions when solving the vibrational Schrödinger equation with the rectangular collocation method. The quality of the vibrational spectrum of formaldehyde (in six dimensions) is compared to that obtained using Gaussian basis functions when using different numbers of width-optimized IMQ functions. The effects of the ratio of the number of collocation points to the number of basis functions and of the choice of the IMQ exponent are studied. We show that the IMQ basis can be used with parameters where the IMQ function is not integrable. We find that the quality of the spectrum with IMQ basis functions is somewhat lower that that with a Gaussian basis when the basis size is large and for a range of IMQ exponents. The IMQ functions are, however, advantageous when a small number of functions is used or with a small number of collocation points e.g. when using square collocation. Basis (linear algebra) ; Collocation (remote sensing) ; Basis function ; Mathematics ; Gaussian ; Inverse ; Spectrum (functional analysis) ; Mathematical analysis ; Function (biology) ; Quantum mechanics ; Physics ; Computer science ; Geometry Spectroscopy and Laser Applications ; Spectroscopy and Quantum Chemical Studies ; Photonic and Optical Devices
Authors: Ali Yahyaee | Mina Nazifi | Mohsen Kianpour | Kurosh Tabar Heidar
Source: American Journal of Analytical Chemistry (article, 2018-01-01)
Field: Chemistry — Spectroscopy

Experimental Investigation and Modeling of Activity Coefficient at Infinite Dilution of Solutes Using Dicationic Solvent Based on Pyrrolidinium as a New Stationary Phase in Gas Chromatography Activity coefficients at infinite dilution, i, were calculated for 12 solutes, with organic solutes including linear alcohols (methanol, ethanol, propanol), linear alkanes (heptane, octane), benzene, toluene, cyclohexane, 1, 2-dichloroethane, trichloroethylene, acetonitrile and carbon tetrachloride. The values of i were determined via either thermodynamic or artificial neural network modelling at different temperatures. A comparison between extracted results from these two methods confirmed that experimental and predicted results are roughly the same. The accuracy of predicted results proves this model is fully compatible with a wide range of solutes, and it can readily be used as an alternative to conventional gas-liquid chromatography for the measurements of activity coefficient at infinite dilution. Chemistry ; Cyclohexane ; Activity coefficient ; Dilution ; Toluene ; Solvent ; Gas chromatography ; Methanol ; Acetonitrile ; Benzene ; Heptane ; Chromatography ; Partition coefficient ; Octane ; Organic chemistry ; Thermodynamics ; Aqueous solution Analytical Chemistry and Chromatography ; Advanced Chemical Sensor Technologies ; Chemical Thermodynamics and Molecular Structure
Authors: Zhihui Li | Zhen Tong | Yuhua Zhu
Source: Instrumentation Mesure Métrologie (article, 2018-12-30)
Field: Chemistry — Analytical Chemistry

Identification of pests hidden in wheat kernels based on support vector machine classifier The identification of pests hidden in stored wheats, essential to grain storage safety, is a key difficulty in the research of target detection. This paper introduces the support vector machine (SVM) classifier to identify the pests hidden in wheat kernels, and selects the proper kernel function and parameters to classify various samples. It is verified that the proposed method could accurately detect the pests in wheat kernels. This research provides new insights into the application of pattern recognition in bio-photon detection of pests in stored grains. Support vector machine ; Classifier (UML) ; Artificial intelligence ; Identification (biology) ; Structured support vector machine ; Pattern recognition (psychology) ; Margin classifier ; Computer science ; Quadratic classifier ; Machine learning ; Biology ; Botany Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Advanced Algorithms and Applications
Authors: Xuping Feng | Haijun Yin | Chu Zhang | Cheng Peng | Yong He
Source: Spanish Journal of Agricultural Research (article, 2018-06-19)
Field: Chemistry — Analytical Chemistry

Screening of transgenic maize using near infrared spectroscopy and chemometric techniques The applicability of near infrared (NIR) spectroscopy combined with chemometrics was examined to develop fast, low-cost and non-destructive spectroscopic methods for classification of transgenic maize plants. The transgenic maize plants containing both cry1Ab/cry2Aj-G10evo proteins and their non-transgenic parent were measured in the NIR diffuse reflectance mode with the spectral range of 700–1900 nm. Three variable selection algorithms, including weighted regression coefficients, principal component analysis -loadings and second derivatives were used to extract sensitive wavelengths that contributed the most discrimination information for these genotypes. Five classification methods, including K-nearest neighbor, Soft Independent Modeling of Class Analogy, Naive Bayes Classifier, Extreme Learning Machine (ELM) and Radial Basis Function Neural Network were used to build discrimination models based on the preprocessed full spectra and sensitive wavelengths. The results demonstrated that ELM had the best performance of all methods, even though the model’s recognition ability decreased as the variables in the training of neural networks were reduced by using only the sensitive wavelengths. The ELM model calculated on the calibration set showed classification rates of 100% based on the full spectrum and 90.83% based on sensitive wavelengths. The NIR spectroscopy combined with chemometrics offers a powerful tool for evaluating large number of samples from maize hybrid performance trials and breeding programs. Chemometrics ; Principal component analysis ; Pattern recognition (psychology) ; Biological system ; Artificial intelligence ; Near-infrared spectroscopy ; Spectroscopy ; Computer science ; Extreme learning machine ; Artificial neural network ; Classifier (UML) ; Partial least squares regression ; Feature selection ; Linear discriminant analysis ; Mathematics ; Machine learning ; Biology ; Physics ; Optics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Smart Agriculture and AI
Authors: Yerowo Ogbekene | Pratik Shukla | Yu Zhang | Xiaojun Shen | S. Prabhakaran | S. Kalainathan | Kiran Gulia | J. Lawrence
Source: Pure (Coventry University) (article, 2018-11-30)
Field: Materials Science — Materials Chemistry

Laser Cleaning of Grey Cast Iron Automotive Brake Disc: Rust Removal and Improvement in Surface Integrity There is a great need for removal of rust and surface damage from corroded engineering parts. This enables the retention of strength and increased longevity of metals and alloys in general. The use of lasers for cleaning, polishing and ablation has proven to be effective and promising overtime. This research is focused on a parametric study of laser cleaning a corroded grey cast iron brake disc. A continuous wave CO2 laser having a wavelength of 10.6μm was used for the study. A systematic approach was employed for the experiments where one parameter was changed while other parameters remained constant. Additional effects of laser cleaning were predicted by a Gaussian process regression approach. The results revealed that the best parameters which cleanly removed the rust were 60W of laser power, 900mm/s traverse speed, and a spot size of 722μm. The enhancement of surface microhardness of laser cleaned specimen was 37% compared to the rusted specimen surfaces. The roughness of the laser cleaned surface was, 1.29μm while the rusted surface comprised of 55.45μm (Ra). Microstructural analysis showed a presence of randomly distributed graphite flakes surrounded by a pearlitic matrix containing ferrite and cementite after laser cleaning. This was similar to that of the un-rusted surface. The hardness, roughness and microstructural content were in close relation with the respective properties of the unrusted automotive brake disc. This showed that the mechanical and physical properties of the brake disc were not altered negatively during the laser cleaning process. Implementation of the laser-cleaning technique in automotive and manufacturing industries should be embraced as it provides a faster, safer and cheaper way of enhancing the surface integrity of components and also paves way for other surface enhancement methodologies to be applied such as blast cleaning or laser shock cleaning for inducing extra strength, by beneficial residual stresses. Cast iron ; Automotive industry ; Surface integrity ; Metallurgy ; Rust (programming language) ; Materials science ; Brake ; Engineering ; Computer science ; Machining Metal Alloys Wear and Properties ; High-Temperature Coating Behaviors ; Surface Treatment and Coatings
Authors: Paul Cueva | Elliot Padget | David A. Muller
Source: Microscopy and Microanalysis (article, 2018-08-01)
Field: Materials Science — Materials Chemistry

A Natural Basis for Unsupervised Machine Learning on Scanning Diffraction Data An abstract is not available for this content so a preview has been provided. As you have access to this content, a full PDF is available via the 'Save PDF' action button. Basis (linear algebra) ; Natural (archaeology) ; Computer science ; Unsupervised learning ; Artificial intelligence ; Materials science ; Geology ; Mathematics ; Geometry X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Cultural Heritage Materials Analysis
Authors: Jeffrey Brender | Shun Kishimoto | Hellmut Merkle | Galen D. Reed | Ralph E. Hurd | Albert P. Chen | Jan Henrik Ardenkjær‐Larsen | Jeeva Munasinghe | Keita Saito | Tomohiro Seki | Nobu Oshima | Kazu Yamamoto | Peter L. Choyke | John C. Mitchell | Murali C. Krishna
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2018-02-15)
Field: Chemistry — Spectroscopy

PET by MRI: Glucose Imaging by <sup>13</sup> C-MRS without Dynamic Nuclear Polarization by Noise Suppression through Tensor Decomposition Rank Reduction Abstract Metabolic reprogramming is one of the defining features of cancer and abnormal metabolism is associated with many other pathologies. Molecular imaging techniques capable of detecting such changes have become essential for cancer diagnosis, treatment planning, and surveillance. In particular, 18 F-FDG (fluorodeoxyglucose) PET has emerged as an essential imaging modality for cancer because of its unique ability to detect a disturbed molecular pathway through measurements of glucose uptake. However, FDG-PET has limitations that restrict its usefulness in certain situations and the information gained is limited to glucose uptake only. 13 C magnetic resonance spectroscopy theoretically has certain advantages over FDG-PET, but its inherent low sensitivity has restricted its use mostly to single voxel measurements. We show here a new method of imaging glucose metabolism in vivo that relies on a simple, but robust and efficient, post-processing procedure by the higher dimensional analog of singular value decomposition, tensor decomposition. Using this procedure, we achieve an order of magnitude increase in signal to noise in both dDNP and non-hyperpolarized non-localized experiments without sacrificing accuracy. In CSI experiments an approximately 30-fold increase was observed, enough that the glucose to lactate conversion indicative of the Warburg effect can be imaged without hyper-polarization with a time resolution of 12 s and an overall spatial resolution that compares favorably to 18 F-FDG PET. Magnetic resonance spectroscopic imaging ; Nuclear magnetic resonance ; Warburg effect ; Diffusion MRI ; Chemistry ; Magnetic resonance imaging ; Nuclear medicine ; Physics ; Medicine ; Glycolysis ; Radiology Advanced NMR Techniques and Applications ; Advanced MRI Techniques and Applications ; NMR spectroscopy and applications