jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ercan Aydoğmuş | Muhammet Aydın | Hasan Arslanoğlu
Source: Petroleum Science and Technology (article, 2022-11-29)
Field: Materials Science — Materials Chemistry

Production and characterization of microsphere reinforced polyester composite: Modeling of thermal decomposition with ANN and optimization studies by RSM In this research, hollow inorganic microsphere (Q-cel) reinforced unsaturated polyester composite is produced, and its thermophysical properties have been characterized. The thermal decomposition kinetics of the composite obtained at different heating rates and various compositions are modeled using artificial neural networks. Also, the production optimization of the polyester composite has been evaluated using response surface methodology. The study has been repeated for certain heating rates (5, 10, and 20 K/min). Activation energies of polyester composites have been calculated using thermogravimetric data and kinetic methods. Changes in activation energy during the thermal decomposition (4 wt.% Q-cel, 94 wt.% UP, 580–660 K, and 20 K/min) of the composite are compared using FWO (129.4 kJ/mol), KAS (127.6 kJ/mol), and CR (126.5 kJ/mol). According to the results, it is seen that the activation energy goes up as the temperature and Q-cel ratio by the mass increase. When the amount of the filler in the polyester composite increases, the thermal conductivity coefficient also rises. As well as, it is determined that as Q-cel ratio in the mixture raises, the density of the composite decreases and Shore D hardness goes up.HIGHLIGHTSA new and original composite has been produced with Qcel-reinforced unsaturated polyester.The density of the composite is low, its thermal stability is high, and its easy processability will provide a great convenience.Modeling the Qcel ratio of the composite with RSM and the activation energy with ANN have been provided fast, efficient and optimum results.With the composite obtained, both lightweight and thermally stable polymer materials will be produced. Composite number ; Materials science ; Thermal decomposition ; Activation energy ; Polyester ; Response surface methodology ; Composite material ; Thermogravimetric analysis ; Shore durometer ; Decomposition ; Thermal stability ; Chemical engineering ; Chemistry ; Organic chemistry ; Chromatography Thermal and Kinetic Analysis ; Injection Molding Process and Properties ; Additive Manufacturing and 3D Printing Technologies
Authors: Xuan Wang | Jiangping Yang | Xue Yang | Xin Hu
Source: International Journal of Energy Research (article, 2022-03-24)
Field: Materials Science — Materials Chemistry

A novel study on activated carbon production based on artificial neural network model: An experimental and artificial intelligence method approach There are many factors affecting the activated carbon (AC) production process. The process simulation and product prediction methods of activated carbon are still lacking. In this work, the production of activated carbon was conducted on a tubular reactor. The influence of activation temperature, time, steam/char ratio and CO2/char ratio were investigated. Further, a novel artificial neural network (ANN) model was built to predict the production of activated carbon. Different raw material samples were used and in a total of nine influencing variables are used in the input layer. A total of 113 groups of datasets were collected and used for ANN model training, testing and model validation. Results showed that logsig was more suitable to be used as the transfer function in the hidden layer and the optimized neuron number was fifteen, where the MSE value and R-Squared value were 0.010967 and 0.967 respectively. Back propagation(BP)-ANN model could be used for the prediction of the AC yield and the specific surface area. This method is effective in predicting both physical activation and chemical activation. The prediction of AC yield in the process of steam activation is highly accurate. Although there is a certain deviation in the absolute value of the predicted data, the predicted results show a good consistency with the experiment in the variation trend for CO2 activation and KOH activation process. Impact analysis of input variables on outputs indicated that for both AC yield and specific surface area, the oxygen content, activation temperature and CO2/char ratio were the three major influencing variables. Activated carbon ; Char ; Activation function ; Artificial neural network ; Yield (engineering) ; Mean squared error ; Response surface methodology ; Biological system ; Materials science ; Process engineering ; Chemistry ; Mathematics ; Engineering ; Computer science ; Chemical engineering ; Statistics ; Artificial intelligence ; Pyrolysis ; Composite material ; Organic chemistry Catalytic Processes in Materials Science ; Carbon Dioxide Capture Technologies ; Catalysis and Oxidation Reactions
Authors: Roshan Kumar | Trina Dhara | Han Hu | Monojit Chakraborty
Source: Energy and AI (article, 2022-09-17)
Field: Chemistry — Electrochemistry

Visualization-based prediction of dendritic copper growth in electrochemical cells using convolutional long short-term memory Electrodeposition in electrochemical cells is one of the leading causes of its performance deterioration. The prediction of electrodeposition growth demands a good understanding of the complex physics involved, which can lead to the fabrication of a probabilistic mathematical model. As an alternative, a convolutional Long short-term memory architecture-based image analysis approach is presented herein. This technique can predict the electrodeposition growth of the electrolytes, without prior detailed knowledge of the system. The captured images of the electrodeposition from the experiments are used to train and test the model. A comparison between the expected output image and predicted image on a pixel level, percentage mean squared error, absolute percentage error, and pattern density of the electrodeposit are investigated to assess the model accuracy. The randomness of the electrodeposition growth is outlined by investigating the fractal dimension and the interfacial length of the electrodeposits. The trained model predictions show a significant promise between all the experimentally obtained relevant parameters with the predicted one. It is expected that this deep learning-based approach for predicting random electrodeposition growth will be of immense help for designing and optimizing the relevant experimental scheme in near future without performing multiple experiments. Randomness ; Computer science ; Fractal dimension ; Pixel ; Artificial intelligence ; Convolutional neural network ; Probabilistic logic ; Deep learning ; Algorithm ; Pattern recognition (psychology) ; Fractal ; Mathematics ; Statistics Electrochemical Analysis and Applications ; Conducting polymers and applications ; Electrodeposition and Electroless Coatings
Authors: John M. Simmie
Source: The Journal of Physical Chemistry A (article, 2022-02-03)
Field: Chemistry — Spectroscopy

C <sub>2</sub> H <sub>5</sub> NO Isomers: From Acetamide to 1,2-Oxazetidine and Beyond This work documents the properties of a number of isomers of molecular formula C<sub>2</sub>H<sub>5</sub>NO from the most stable, acetamide, through 1,2-oxazetidine and including even higher energy species largely of a dipolar nature. Only two of the isomers have been detected in emissions from the interstellar medium (ISM); possible further candidates are identified, and the likelihood of their being detectable is considered. In general, hardly any of these compounds have been discussed in the existing chemical literature, so this work represents an important contribution extending the canon of chemical bonding which can contribute to machine learning, providing a more exacting test of AI applications. The presence in the ISM of acetamide, CH<sub>3</sub>C(O)NH<sub>2</sub>, is the subject of current debate with no clear and obvious paths to its formation; it is shown that a 1,3-[H]-transfer from (<i>E,Z</i>)-ethanimidic acid, CH<sub>3</sub>C(OH)═NH, is feasible in spite of an energy barrier of 130 kJ mol<sup>-1</sup>. It is speculated that imidic acid can itself be formed from abundant precursors, H<sub>2</sub>O and CH<sub>3</sub>C≡N, in an acid-induced, water addition, autocatalytic reaction on water-ice grains. H<sub>3</sub>CC≡N<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mover><mml:mo>→</mml:mo><mml:mrow><mml:msub><mml:mrow><mml:mi>H</mml:mi></mml:mrow><mml:mrow><mml:mn>3</mml:mn></mml:mrow></mml:msub><mml:msup><mml:mrow><mml:mi>O</mml:mi></mml:mrow><mml:mrow><mml:mo>+</mml:mo></mml:mrow></mml:msup></mml:mrow></mml:mover></mml:math>H<sub>3</sub>CC≡NH<sup>+</sup> + H<sub>2</sub>O<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mover><mml:mo>→</mml:mo><mml:mrow><mml:msub><mml:mrow><mml:mi>H</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msub><mml:mrow><mml:mi>O</mml:mi></mml:mrow></mml:mrow></mml:mover></mml:math>H<sub>3</sub>CC(O<sup>+</sup>H<sub>2</sub>)═NH<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mover><mml:mo>→</mml:mo><mml:mrow><mml:msub><mml:mrow><mml:mi>H</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msub><mml:mrow><mml:mi>O</mml:mi></mml:mrow></mml:mrow></mml:mover></mml:math>H<sub>3</sub>CC(OH)═NH + H<sub>3</sub>O<sup></sup>. Acetamide ; Chemistry ; Autocatalysis ; Computational chemistry ; Physical chemistry ; Organic chemistry ; Catalysis Molecular Spectroscopy and Structure ; Advanced Chemical Physics Studies ; Chemical Reactions and Mechanisms
Authors: Runfang Mao | Jared O’Leary | Ali Mesbah | Jeetain Mittal
Source: JACS Au (article, 2022-07-19)
Field: Materials Science — Materials Chemistry

A Deep Learning Framework Discovers Compositional Order and Self-Assembly Pathways in Binary Colloidal Mixtures Binary colloidal superlattices (BSLs) have demonstrated enormous potential for the design of advanced multifunctional materials that can be synthesized via colloidal self-assembly. However, mechanistic understanding of the three-dimensional self-assembly of BSLs is largely limited due to a lack of tractable strategies for characterizing the many two-component structures that can appear during the self-assembly process. To address this gap, we present a framework for colloidal crystal structure characterization that uses branched graphlet decomposition with deep learning to systematically and quantitatively describe the self-assembly of BSLs at the single-particle level. Branched graphlet decomposition is used to evaluate local structure via high-dimensional neighborhood graphs that quantify both structural order (e.g., body-centered-cubic vs face-centered-cubic) and compositional order (e.g., substitutional defects) of each individual particle. Deep autoencoders are then used to efficiently translate these neighborhood graphs into low-dimensional manifolds from which relationships among neighborhood graphs can be more easily inferred. We demonstrate the framework on in silico systems of DNA-functionalized particles, in which two well-recognized design parameters, particle size ratio and interparticle potential well depth can be adjusted independently. The framework reveals that binary colloidal mixtures with small interparticle size disparities (i.e., A- and B-type particle radius ratios of <i>r</i> <sub>A</sub>/<i>r</i> <sub>B</sub> = 0.8 to <i>r</i> <sub>A</sub>/<i>r</i> <sub>B</sub> = 0.95) can promote the self-assembly of defect-free BSLs much more effectively than systems of identically sized particles, as nearly defect-free BCC-CsCl, FCC-CuAu, and IrV crystals are observed in the former case. The framework additionally reveals that size-disparate colloidal mixtures can undergo nonclassical nucleation pathways where BSLs evolve from dense amorphous precursors, instead of directly nucleating from dilute solution. These findings illustrate that the presented characterization framework can assist in enhancing mechanistic understanding of the self-assembly of binary colloidal mixtures, which in turn can pave the way for engineering the growth of defect-free BSLs. Binary number ; Colloidal crystal ; Self-assembly ; Colloid ; Materials science ; Particle (ecology) ; Chemical physics ; Ternary operation ; Colloidal particle ; Nanotechnology ; Statistical physics ; Physics ; Computer science ; Chemistry ; Mathematics ; Physical chemistry Material Dynamics and Properties ; Photonic Crystals and Applications ; Pickering emulsions and particle stabilization
Authors: Zhen Tian | Shan Gao | Shuangxi Jing | Li Jin
Source: Engineering Failure Analysis (article, 2022-04-29)
Field: Materials Science — Materials Chemistry

Reliability and fatigue life analysis of key parts of shearer Drum ; Reliability (semiconductor) ; Spiral (railway) ; Structural engineering ; Engineering ; Stress (linguistics) ; Coal mining ; Coupling (piping) ; Shell (structure) ; Process (computing) ; Mechanical engineering ; Computer science ; Coal Metal Alloys Wear and Properties ; Metallurgy and Material Forming ; Tunneling and Rock Mechanics
Authors: Connor Allen | Albert P. Bartók
Source: Machine Learning Science and Technology (article, 2022-10-17)
Field: Materials Science — Materials Chemistry

Optimal data generation for machine learned interatomic potentials Abstract Machine learning interatomic potentials (MLIPs) are routinely used atomic simulations, but generating databases of atomic configurations used in fitting these models is a laborious process, requiring significant computational and human effort. A computationally efficient method is presented to generate databases of atomic configurations that contain optimal information on the small-displacement regime of the potential energy surface of bulk crystalline matter. Utilising non-diagonal supercell (Lloyd-Williams and Monserrat 2015 Phys. Rev. B 92 184301), an automatic process is suggested for ab initio data generation. MLIPs were fitted for Al, W, Mg and Si, which very closely reproduce the ab initio phonon and elastic properties. The protocol can be easily adapted to other materials and can be inserted in the workflow of any flavour of MLIP generation. Interatomic potential ; Ab initio ; Workflow ; Ab initio quantum chemistry methods ; Computer science ; Supercell ; Diagonal ; Phonon ; Process (computing) ; Computational science ; Algorithm ; Physics ; Molecular dynamics ; Condensed matter physics ; Quantum mechanics ; Database ; Mathematics ; Geometry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques
Authors: Yuhao Xie | Siwei Yang | Shenghua Zhou | Jiazhen Liu | Shuai Zhao | Shangzhong Jin | Qiang Chen | Pei Liang
Source: Journal of Raman Spectroscopy (article, 2022-10-26)
Field: Chemistry — Analytical Chemistry

SE‐ResNet‐based classifier for highly similar mixtures based on Raman spectrum: Classification for alcohol systems as an example Abstract Raman spectroscopy is widely used in the identification of substances. Raman spectra contain molecular information from various components and interference from noise and instruments. Therefore, using Raman spectroscopy to identify components is still challenging, especially for substances with high similarity. In this study, a class of highly similar products (methanol and ethanol) and a multicomponent mixture of propanol and water were tested using a portable Raman spectrometer. They are divided into 11 categories according to the volume fraction ratio. A total of 5,060 groups of Raman spectrum data were obtained, constituting the data set of this study. The deep neural network structure adopted in this study is based on ResNet architecture, on which the SE module in the attention mechanism is added, which increases the weight of some spectral features and achieves significant performance improvement. Finally, SE‐ResNet achieves a recall of 0.95, a precision rate of 0.95, a Micro‐F1 of 0.95, and an accuracy rate of 99.20%. Raman spectroscopy ; Analytical Chemistry (journal) ; Pattern recognition (psychology) ; Chemistry ; Near-infrared spectroscopy ; Artificial neural network ; Spectrometer ; Biological system ; Classifier (UML) ; Artificial intelligence ; Computer science ; Chromatography ; Optics ; Physics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Mengshu Ge | Xiaozhi Liu | Zhicheng Zhao | Fei Su | Lin Gu | Dong Su
Source: Advanced Theory and Simulations (article, 2022-02-09)
Field: Materials Science — Materials Chemistry

Ensemble Machine‐Learning‐Based Analysis for In Situ Electron Diffraction Abstract In situ transmission electron microscopy is an important characterization approach for exploring the structural dynamics of materials. However, the recorded high resolution in situ videos normally have tremendous amount of data, which is challenging for quantitative analysis. In case of in situ electron diffraction (ED), the classical analysis method only tracks changes of the integral profile and ignores important information of position, intensity, and distribution angle due to the lack of a proper data processing tool. In this work, an ensemble machine‐learning‐based framework which enables the fore‐and‐after tracking of all diffraction spots of an in situ ED video is established. As demonstrated in the case of the lithiation of the Co 3 O 4 nanoparticles, the method precisely quantifies the changes of lattice parameters and then unveils the inhomogeneous structural evolution induced by the insertion of lithium ions. This method is generally applicable to analyze in situ ED data derived from any dynamical processes, enlarging the capability to grasp the key information from massive in situ data. Diffraction ; In situ ; Characterization (materials science) ; Electron diffraction ; Transmission electron microscopy ; GRASP ; Computer science ; Lattice (music) ; Ion ; Materials science ; Artificial intelligence ; Nanotechnology ; Optics ; Physics Machine Learning in Materials Science ; Surface and Thin Film Phenomena ; Advanced Electron Microscopy Techniques and Applications
Authors: Zhuoyan Zhou | Lilong Zhao | Xinyang Zhang | Fenping Cui | Linfeng Guo
Source: Optics Express (article, 2022-10-18)
Field: Chemistry — Analytical Chemistry

Real-time in-situ optical detection of fluid viscosity based on the Beer-Lambert law and machine learning As an important physical quantity to describe the resistance of fluid to flow, viscosity is an essential property of fluids in fluid mechanics, chemistry, medicine, as well as hydraulic engineering. While traditional measurement methods, including the rotating-cylinder method, capillary tube method and falling sphere method, have significant drawbacks especially in terms of accuracy, response time and the sample must be made to move. In this work, a novel Beer-Lambert law-based method was proposed for the viscosity measurement. Specifically, this work demonstrates that viscosity can be quantitatively reflected by spectral line intensity, and castor oil was selected due to its viscous temperature properties (viscosity has been accurately measured under different temperature), and its transmission spectrum at different temperatures ranging from 10 to 50°C was detected firstly. Then, the principal component analysis (PCA) was employed to obtain the intrinsic features of the transmission spectrum. Finally, the processed data was utilized to train and verify the radial basis function (RBF) neural network. As a result, the accuracy of the predictions conducted by means of the RBF reached 98.45%, which indicates the complicated and non-linear relationships between spectra formation and viscosity can be depicted well by RBF. The results show that the real-time in-situ optical detection method adopted in this work represents a great leap forward in the viscosity measurement, which fundamentally reforms the traditional viscosity measurement methods. Viscosity ; Beer–Lambert law ; Mechanics ; Work (physics) ; Optics ; Materials science ; Thermodynamics ; Physics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Parvesh Antil | Anil Saroha | Chander Jakhar | Manpreet Singh | Rajesh Singh
Source: International Journal on Interactive Design and Manufacturing (IJIDeM) (article, 2022-07-07)
Field: Materials Science — Materials Chemistry

Optimization of wear behavior of straw combine blade through RSM and ANN models Response surface methodology ; Straw ; Blade (archaeology) ; Abrasive ; Artificial neural network ; Agricultural engineering ; Design of experiments ; Materials science ; Engineering ; Mechanical engineering ; Process engineering ; Computer science ; Machine learning ; Mathematics ; Agronomy ; Statistics Metal Alloys Wear and Properties ; Metallurgy and Material Forming ; Advanced machining processes and optimization
Authors: Kun Wu | Min Zhang | Gang Wang | Xu Chen | Jun Wu
Source: Agriculture (article, 2022-06-30)
Field: Chemistry — Analytical Chemistry

A Continuous Single-Layer Discrete Tiling System for Online Detection of Corn Impurities and Breakage Rates In order to improve the accuracy and efficiency of the methods that are used for the detection of impurities in and the breakage rate of harvested corn grains, we propose a classification and identification method using a feature threshold and a backpropagation (BP) neural network that is based on a genetic algorithm. We also constructed a continuous single-layer discrete tile detection system for application to harvested corn grains containing impurities and broken kernels. We conducted an evaluation of the proposed approach with a three-factor and three-level orthogonal experimental design. By setting the working parameters, we realized the continuous single-layer discrete tiling of the grains and 50 grain materials were collected on average in a single picture. In the static test, the error between the system monitoring value and the manual detection value was small, the maximum absolute errors of the breakage and impurity rates were 2.16% and 1.03%, and the average time that was required for each image recognition was 1.71 s. In the experimental environment, the maximum absolute error values of the breakage and impurity rates were 3.48% and 1.78%. The system’s identification accuracy and processing time meet the requirements of the online detection of corn characteristics in grain harvesting. Breakage ; Backpropagation ; Impurity ; Artificial neural network ; Biological system ; Feature (linguistics) ; Threshold limit value ; Algorithm ; Materials science ; Artificial intelligence ; Statistics ; Mathematics ; Computer science ; Composite material ; Chemistry Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Meat and Animal Product Quality
Authors: Yinran Xiong | Wuye Yang | Huiyun Liao | Zhenlin Gong | Xu Zhenzhen | Yiping Du | Wei Li
Source: Chemometrics and Intelligent Laboratory Systems (article, 2022-03-04)
Field: Chemistry — Analytical Chemistry

Soft variable selection combining partial least squares and attention mechanism for multivariable calibration Partial least squares regression ; Overfitting ; Feature selection ; Lasso (programming language) ; Softmax function ; Mathematics ; Variable (mathematics) ; Selection (genetic algorithm) ; Ordinary least squares ; Variable elimination ; Function (biology) ; Calibration ; Algorithm ; Artificial neural network ; Computer science ; Mathematical optimization ; Artificial intelligence ; Statistics Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Advanced Chemical Sensor Technologies