jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zhenguo Gao
Source: Scholarship@Western (Western University) (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

Non-Classical Nucleation Phenomena Study And Following Process Monitoring and Optimization in Solution Crystallization Process Nucleation is a crucial step in the solution crystallization process. Despite their good development, classical nucleation theory and two-step nucleation theory cannot explain all the nucleation phenomena, especially for the non-classical nucleation phenomena which include oiling out, gelation and non-monotonic nucleation. Accordingly, for the non-classical nucleation systems, the crystallization processes are seldom designed based on the nucleation monitoring and supervision. In this thesis, crystallization process optimization was conducted to study the mechanism of non-classical nucleation phenomena and in-line process monitoring technology development.\nTwo kinds of non-classical nucleation phenomena with non-monotonic nucleation rate and gel formation were investigated, and accordingly, two nucleation pathways that self-induced nucleation and jellylike phase mediated nucleation were proposed based on the analysis of in-line spectral monitoring and off-line sample characterizations. Results indicated the agitation level would affect the pre-nucleation clusters’ existence in the non-monotonic nucleation system, and the properties of solvent determined the formation of jellylike phase and the transformation to crystals. Motion-based objects tracking model and the state-of-the-art neural network Mask R-CNN were introduced to monitor the onset of nucleation and following the crystallization process. Combined with a cost-effective camera probe, the developed real-time tracking system can detect the nucleation onset accurately even with ultrasonic irradiation and can extract much more information during the whole crystallization process. Subsequently, ultrasonic irradiation and seeding were used to optimize a non-classical nucleation system that accompanied oiling out phenomenon. Different frequencies and intensities of ultrasonic irradiation and seeds addition time were screened to optimize the nucleation step, which proved their effectiveness of promoting nucleation and narrowing the metastable zone widths of oiling out and nucleation. A fine-tuning of nucleation step was carried out in a mixed suspension mixed product removal (MSMPR)-tubular crystallizer series. The nucleation step was optimized in the MSMPR stage with the aid of principal component analysis, which enabled the growth of crystals in the tubular crystallizer with preferred polymorphism, shape, and size. The study in this thesis provides insights into non-classical nucleation mechanism and nucleation based crystallization process design and optimization. Process (computing) ; Nucleation ; Crystallization ; Process engineering ; Materials science ; Computer science ; Thermodynamics ; Physics ; Engineering Crystallization and Solubility Studies ; Freezing and Crystallization Processes
Authors: Hong Wang | Yunchao Xie | Dawei Li | Heng Deng | Yunxin Zhao | Ming Xin | Jian Lin
Source: arXiv (Cornell University) (preprint, 2019-12-16)
Field: Materials Science — Materials Chemistry

Rapid Identification of X-ray Diffraction Spectra Based on Very Limited Data by Interpretable Convolutional Neural Networks Large volumes of data from material characterizations call for rapid and automatic data analysis to accelerate materials discovery. Herein, we report a convolutional neural network (CNN) that was trained based on theoretic data and very limited experimental data for fast identification of experimental X-ray diffraction (XRD) spectra of metal-organic frameworks (MOFs). To augment the data for training the model, noise was extracted from experimental spectra and shuffled, then merged with the main peaks that were extracted from theoretical spectra to synthesize new spectra. For the first time, one-to-one material identification was achieved. The optimized model showed the highest identification accuracy of 96.7% for the Top 5 ranking among a dataset of 1012 MOFs. Neighborhood components analysis (NCA) on the experimental XRD spectra shows that the spectra from the same material are clustered in groups in the NCA map. Analysis on the class activation maps of the last CNN layer further discloses the mechanism by which the CNN model successfully identifies individual MOFs from the XRD spectra. This CNN model trained by the data-augmentation technique would not only open numerous potential applications for identifying XRD spectra for different materials, but also pave avenues to autonomously analyze data by other characterization tools such as FTIR, Raman, and NMR. Convolutional neural network ; Spectral line ; Identification (biology) ; Characterization (materials science) ; Computer science ; Artificial intelligence ; Raman spectroscopy ; Pattern recognition (psychology) ; Artificial neural network ; Diffraction ; Materials science ; Biological system ; Physics ; Nanotechnology ; Optics Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; X-ray Diffraction in Crystallography
Authors: Alberto Hernández | Adarsh Balasubramanian | Fenglin Yuan | Simon Mason | Tim Mueller
Source: arXiv (Cornell University) (preprint, 2019-04-01)
Field: Materials Science — Materials Chemistry

Fast, accurate, and transferable many-body interatomic potentials by genetic programming. The length and time scales of atomistic simulations are limited by the computational cost of the methods used to predict material properties. In recent years there has been great progress in the use of machine learning algorithms to develop fast and accurate interatomic potential models, but it remains a challenge to develop models that generalize well and are fast enough to be used at extreme time and length scales. To address this challenge, we have developed a machine learning algorithm based on genetic programming that is capable of discovering accurate, computationally efficient many-body potential models. The key to our approach is to explore a hypothesis space of models based on fundamental physical principles and select models within this hypothesis space based on their accuracy, speed, and simplicity. The focus on simplicity reduces the risk of overfitting the training data and increases the chances of discovering a model that generalizes well. Our algorithm was validated by rediscovering an exact Lennard Jones potential and a Sutton Chen embedded atom method potential from training data generated using these models. By using training data generated from density functional theory calculations, we found potential models for elemental copper that are simple, as fast as embedded atom models, and capable of accurately predicting properties outside of their training set. Our approach requires relatively small sets of training data, making it possible to generate training data using highly accurate methods at a reasonable computational cost. We present our approach, the forms of the discovered models, and assessments of their transferability, accuracy and speed. Overfitting ; Simplicity ; Computer science ; Genetic programming ; Set (abstract data type) ; Machine learning ; Simple (philosophy) ; Interatomic potential ; Artificial intelligence ; Algorithm ; Molecular dynamics ; Artificial neural network ; Computational chemistry ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning in Bioinformatics
Authors: Sebastian Dick | Mariví Fernández-Serra
Source: ChemRxiv (preprint, 2019-10-11)
Field: Materials Science — Materials Chemistry

Machine Learning a Highly Accurate Exchange and Correlation Functional of the Electronic Density Density Functional Theory (DFT) is the standard formalism to study the electronic structure of matterat the atomic scale. The balance between accuracy and computational cost thatDFT-based simulations provide allows researchers to understand the structural and dynamical properties of increasingly large and complex systems at the quantum mechanical level.In Kohn-Sham DFT, this balance depends on the choice of exchange and correlation functional, which only existsin approximate form. Increasing the non-locality of this functional and climbing the figurative Jacob's ladder of DFT, one can systematically reduce the amount of approximation involved and thus approach the exact functional. Doing this, however, comes at the price of increased computational cost, and so, for extensive systems, the predominant methods of choice can still be found within the lower-rung approximations. Here we propose a framework to create highly accurate density functionals by using supervised machine learning, termed NeuralXC. These machine-learned functionals are designed to lift the accuracy of local and semilocal functionals to that provided by more accurate methods while maintaining their efficiency. We show that the functionals learn a meaningful representation of the physical information contained in the training data, making them transferable across systems. We further demonstrate how a functional optimized on water can reproduce experimental results when used in molecular dynamics simulations. Finally, we discuss the effects that our method has on self-consistent electron densities by comparing these densities to benchmark coupled-cluster results. Density functional theory ; Computer science ; Orbital-free density functional theory ; Statistical physics ; Locality ; Benchmark (surveying) ; Hybrid functional ; Artificial intelligence ; Theoretical computer science ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Advanced Chemical Physics Studies
Authors: Zhiyong Zou | Xiangwei Wu | Yunbo Bie | Qi Wang | Li Wang | B. S. Zou
Source: Energy Sources Part A Recovery Utilization and Environmental Effects (article, 2019-10-31)
Field: Chemistry — Analytical Chemistry

Combination of hyperspectral sensing images and chemometrics for measuring tensile strength indices of organic plastic sheeting in the field In this paper, hyperspectral sensing image and chemometrics were combined to measure the tensile strength of the organic plastic sheeting of ethylene-vinyl acetate copolymer (EVAC). Three different brands of EVAC sheeting materials were used in the investigation. Spectral data of 978–1676 nm agricultural greenhouse EVAC sheeting were collected by near infrared hyperspectral imager in closed black box. The tensile strength analysis device is used to evaluate the tensile strength indices. The appropriate chemometrics is adopted to build the associated model between the feature spectral data, which expresses the cross-linking attributes of the inner organic molecules, and the tensile strength indices. The cross-validation methods are performed to measure the accuracy and robustness of the training model. The established associated model is then able to evaluate the tensile strength indices of the unknown EVAC sheeting instances. With the leading machine learning algorithm, the corresponding support vector machine regression shows the best detection results, with the percentage of square correlation coefficient (X) to be 86.63% and the lower root mean square error (X) to be 1.049. It seems that the strategy, which integrates the hyperspectral sensing technology and chemometrics, can be implemented in real-time measuring of the tensile strength indices of EVAC organic plastic sheeting according to the experimental results. Ultimate tensile strength ; Hyperspectral imaging ; Chemometrics ; Coefficient of determination ; Support vector machine ; Compressive strength ; Correlation coefficient ; Mean squared error ; Materials science ; Environmental science ; Pattern recognition (psychology) ; Computer science ; Mathematics ; Remote sensing ; Composite material ; Artificial intelligence ; Statistics ; Machine learning ; Geology Spectroscopy and Chemometric Analyses ; Microplastics and Plastic Pollution ; Industrial Vision Systems and Defect Detection
Authors: S.K. Jalali | M.J. Beigrezaee | Sajad Hayati
Source: Superlattices and Microstructures (article, 2019-01-25)
Field: Materials Science — Materials Chemistry

How does porosity affect the free vibration of single-layered graphene sheets? Porosity ; Materials science ; Vibration ; Length scale ; Calibration ; Graphene ; Natural frequency ; Elasticity (physics) ; Porous medium ; Ranging ; Frame (networking) ; Finite element method ; Composite material ; Acoustics ; Mechanics ; Computer science ; Structural engineering ; Physics ; Nanotechnology Nonlocal and gradient elasticity in micro/nano structures ; Force Microscopy Techniques and Applications ; Mechanical and Optical Resonators
Authors: Melvi Viyona | Andasuryani | Rahmi Eka Putri | Diana Pebriani Daulay
Source: IOP Conference Series Earth and Environmental Science (article, 2019-09-01)
Field: Chemistry — Analytical Chemistry

Utilization of Artificial Neural Network (ANN) To Predict Fat Passion Fruit Seed Content (Passiflora ligularis) Based On NIR-S Value Abstract The research was conducted in July-October 2018 at the Laboratory of Food Processing Techniques for Agriculture, Center Instrumentation and AMGIS Laboratory of Agricultural Engineering, Andalas University, Padang. Using the experimental method between wavelength data and fat chemical data of passion fruit seeds. PC utilization as a grouping of data and input data on Artificial Neural Networks (ANN). ANN uses the matlab R2015a application with the TRAINLM training method, Dividerand as a grouping of data and performance based on Mean Squared Error (MSE). Random use of data for set train, set test and set validation. The data set is 70% of the total data, 15% of the other data is used for the set test model, the rest is used for testing the model through a set of validations of 15% of the total sample. The number of samples used was 180 samples. The best value from PC treatment is PC 6 in both original data and SNV. The best value of neuron treatment is on 5 PC 6 neurons in both original data and SNV. The correlation value resulting from the estimation model of passion fruit seed content is 0.85958 with a very strong level of relationship between predictive fat content and laboratory fat content. The best value for validation of MSE on PC 6 neurons 5 with SNV data is 0.95881. Passion fruit ; Artificial neural network ; Data set ; Set (abstract data type) ; Test data ; Test set ; Passiflora ; Computer science ; Mathematics ; MATLAB ; Artificial intelligence ; Mean squared error ; Pattern recognition (psychology) ; Statistics ; Machine learning ; Food science ; Horticulture ; Biology Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Advanced Chemical Sensor Technologies
Authors: Amirreza Hashemi | Ruiqiang Guo | Keivan Esfarjani | Sangyeop Lee
Source: D-Scholarship@Pitt (University of Pittsburgh) (preprint, 2019-08-16)
Field: Materials Science — Materials Chemistry

Ab initio phonon transport across grain boundaries in graphene using machine learning based on small dataset Establishing the structure-property relationship for grain boundaries (GBs) is critical for developing next generation functional materials, but has been severely hampered due to its extremely large configurational space. Atomistic simulations with low computational cost and high predictive power are strongly desirable, but the conventional simulations using empirical interatomic potentials and density functional theory suffer from the lack of predictive power and high computational cost, respectively. A machine learning interatomic potential (MLIP) recently emerged but often requires an extensive size of the training dataset, making it a less feasible approach. Here we demonstrate that an MLIP trained with a rationally designed small training dataset can predict thermal transport across GBs in graphene with ab initio accuracy at an affordable computational cost. In particular, we employed a rational approach based on the structural unit model to find a small set of GBs that can represent the entire configurational space and thus can serve as a cost-effective training dataset for the MLIP. Only 5 GBs were found to be enough to represent the entire configurational space of graphene GBs. Using the atomistic Green's function approach and the MLIP, we revealed that the structure-thermal resistance relation in graphene does not follow the common understanding that large dislocation density causes larger thermal resistance. In fact, thermal resistance is nearly independent of dislocation density at room temperature and is higher when the dislocation density is small at sub-room temperature. We explain this intriguing behavior with the buckling near a GB causing a strong scattering of flexural phonon modes. Graphene ; Phonon ; Grain boundary ; Ab initio ; Density functional theory ; Dislocation ; Materials science ; Interatomic potential ; Thermal conductivity ; Machine learning ; Computer science ; Condensed matter physics ; Artificial intelligence ; Statistical physics ; Molecular dynamics ; Nanotechnology ; Computational chemistry ; Physics ; Quantum mechanics ; Microstructure ; Chemistry Thermal properties of materials ; Machine Learning in Materials Science ; Graphene research and applications
Authors: Vasile‐Dan Hodoroaba
Source:  (article, 2019-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

Challenges in Traceable Size Measurement of Non-Spherical, Non-Monodisperse Nanoparticles - nPSize Size measurement of nanoparticles (NP) becomes a challenging analytical problem when non-spherical shapes must be traceably measured. However, most industrial NPs have irregular shapes and broad size distribution making it often more complex to follow European regulatory to identify a material as a nanomaterial according to which accurate measurement of the smallest dimension and its size Distribution is necessary. The European research project nPSize - Improved traceability chain of nanoparticle size measurements aims to fill this gap by developing potential non-spherical reference nanoparticles, measurement procedures and physical modelling to improve the traceability chain, comparability and compatibility for NP size measurements between different methods. Therefore, new model NP with well-controlled shape has been synthesized and are supposed to be systematically characterized using the traceable methods scanning/transmission electron microscopy, atomic force microscopy and small angle X-ray scattering. Following NP candidates are under investigation with respect to their homogeneity and stability: (i) titania nanoplatelets (10-15 nm thickness x 50-100 nm lateral), (ii) titania bipyramides (~60 nm length x 40 nm width), (iii) titania acicular particles (100 nm length x 15-20 nm width; aspect ratio 5.5/6), (iv) gold nanorods (~10 nm width x 30 nm length), and (v) gold nanocubes (~55 nm x 55 nm x 55 nm). In addition, sample preparation procedures as well as measurement analysis procedures with evaluation of appropriate measurands and descriptors for each material class and method are being developed to support standardization. To underpin the traceability of the size measurement of nonspherical NP, physical modelling of the signals in e.g. electron microscopy techniques will be used and in combination, the implementation of machine learning is aimed to facilitate measurement Analysis procedures, especially regarding the accurate thresholding/segmentation of the NPs.zeige mehr Materials science ; Nanomaterials ; Nanoparticle ; Nanorod ; Nanotechnology ; Nanometrology ; Dispersity ; Traceability ; Acicular ; Transmission electron microscopy ; Analytical Chemistry (journal) ; Atomic force microscopy ; Mathematics ; Chemistry ; Composite material ; Microstructure ; Statistics ; Chromatography Electrostatics and Colloid Interactions ; nanoparticles nucleation surface interactions
Authors: Hang Liu
Source: SPIRE - Sciences Po Institutional REpository (preprint, 2019-06-29)
Field: Materials Science — Materials Chemistry

Etudes in situ des réactions chimiques sur des nanocatalyseurs de modèle ZnO-Pt pour des sources de production d'énergie respectueuses de l'environnement Il a été démontré que le catalyseur ZnO/Pt(111) présente des performances catalytiques remarquables dans l'oxydation du CO à basse température. L'identification des sites actifs dans l'oxydation du CO est importante pour une compréhension mécaniste de la relation structure-réactivité. Nous avons d'abord établi une recette pour fabriquer des films minces de ZnO sur Pt(111) par évaporation par faisceau électronique, caractérisée par STM et LEED. Le film se développe en mode couche par couche, à parti rd'une monocouche de type grapheme jusqu'à la surface ZnO(0001)-Zn terminée. Le rôle des limites ZnO/Pt a été révélé par la STM ex situ après exposition à l'O2:CO. Pour mettre en lumière le rôle des limites, une étude comparative systématique du catalyseur ZnO/Pt(111) avec la surface Pt(111) a été entreprise. La spectroscopie de masse et l'analyse NAP-XPS en phase gazeuse étaient pertinentes pour déterminer les régimes dans lesquels la limitation du transfert de masse commence à se produire, ce qui a permis de discuter de la relation entre les fractions molaires à l'état stable des réactifs/produit et la réactivité de surface, et pour étalonner la densité de surface des adsorbats. Les spectres XPS en phase solide nous ont donné accès à la dynamique du film monocouche ZnO ne couvrant que partiellement la surface Pt(111). Le rôle des hydroxyls liés au ZnO a été mis en évidence par l'observation de la signature chimique des produits de réaction associative CO+OH. Le carboxyle formé à basse temperature peut être l'espèce intermédiaire qui conduit à l'évolution du CO2, les OHs à la limite Pt/ZnO étant le co-catalyseur, ce qui explique l'effet synergique du ZnO et du Pt. Physical chemistry ; Chemistry ; Physics ; Materials science Catalytic Processes in Materials Science
Authors: Khalilalrahman Dehvari | Mohammad Pazouki | Azarmidokht Hosseinnia
Source: International Journal of Engineering (article, 2019-11-01)
Field: Materials Science — Materials Chemistry

Stimuli-responsive Biosynthesis of Gold Nanoparticles: Optimization, Kinetics, and Thermodynamics of Biosorption Green nanotechnology with the goal of producing sustainable nanomaterials in an eco-friendly approach is becoming an increasing necessity for nanomanufacturing industries. In this regards, biosynthesis is well adopted as a viable method for producing benign nanoparticles for biomedical application. The present study aimed at optimization and study of the effects of external stimuli pH and gold ion concentration on the morphology of biosynthesized gold nanoparticles (GNPs) using Fusarium oxysporum. Based on the central composite design, the experimental method was developed at three levels of the operating parameters; the initial gold ion concentration, cell mass, and pH. The X-ray diffraction and transmission electron microscopy showed that the obtained GNPs were impurity-free while the size and shape of particles were a function of the pH and Au3+ concentration. Also, analysis of variance revealed that the cell mass and initial gold ion concentration have a significant effect on biosynthesis of GNPs. The optimal condition was found at the initial gold ion concentration of 550 µM, pH 3.5, and cell mass of 0.047 mg/mL with the obtained gold uptake of 98.29%. Pseudo-second order kinetics model best fitted the experimental results with the activation energy of 73.8 kJ indicating that complex chemisorption is the mechanism of gold biorecovery. Adsorption equilibrium followed Freundlich adsorption model and negative ΔG value at room temperature suggested that the GNPs can be synthesized at ambient temperature and atmosphere via an eco-friendly and economically viable process. Colloidal gold ; Nanomaterials ; Adsorption ; Nanoparticle ; Freundlich equation ; Chemical engineering ; Chemistry ; Transmission electron microscopy ; Biosorption ; Central composite design ; Materials science ; Nanotechnology ; Response surface methodology ; Chromatography ; Organic chemistry Nanoparticles: synthesis and applications