jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: N. Huber
Source: Materials (article, 2021-04-07)
Field: Materials Science — Materials Chemistry

A Strategy for Dimensionality Reduction and Data Analysis Applied to Microstructure–Property Relationships of Nanoporous Metals Nanoporous metals, with their complex microstructure, represent an ideal candidate for the development of methods that combine physics, data, and machine learning. The preparation of nanporous metals via dealloying allows for tuning of the microstructure and macroscopic mechanical properties within a large design space, dependent on the chosen dealloying conditions. Specifically, it is possible to define the solid fraction, ligament size, and connectivity density within a large range. These microstructural parameters have a large impact on the macroscopic mechanical behavior. This makes this class of materials an ideal science case for the development of strategies for dimensionality reduction, supporting the analysis and visualization of the underlying structure-property relationships. Efficient finite element beam modeling techniques were used to generate ~200 data sets for macroscopic compression and nanoindentation of open pore nanofoams. A strategy consisting of dimensional analysis, principal component analysis, and machine learning allowed for data mining of the microstructure-property relationships. It turned out that the scaling law of the work hardening rate has the same exponent as the Young's modulus. Simple linear relationships are derived for the normalized work hardening rate and hardness. The hardness to yield stress ratio is not limited to 1, as commonly assumed for foams, but spreads over a large range of values from 0.5 to 3. Materials science ; Nanoporous ; Microstructure ; Nanoindentation ; Elastic modulus ; Dimensionality reduction ; Scaling ; Hardening (computing) ; Work hardening ; Composite material ; Nanotechnology ; Computer science ; Geometry ; Machine learning ; Mathematics Nanoporous metals and alloys ; Electrocatalysts for Energy Conversion ; Anodic Oxide Films and Nanostructures
Authors: Esme Işık | İbrahim Işık | Hüseyin Toktamış
Source: Radiation effects and defects in solids (article, 2021-07-27)
Field: Chemistry — Spectroscopy

Analysis and estimation of fading time from thermoluminescence glow curve by using artificial neural network The artificial neural network (ANN) is an information processing technology inspired by the information processing technique of the human brain. The way the simple biological nervous system works is imitated with ANN. In this study, an ANN model is proposed to analyze and simulate TL intensity of experimental data of quartz crystals with respect to the fading. In this model, network type and transfer function are chosen as the feed-forward backpropagation algorithm and Tansig respectively for the training of the proposed ANN model. The optimization process is also chosen as Levenberg–Marquardt in this study. The performance criteria of the proposed method were evaluated according to the coefficient of determination (R2) and mean-squared error (MSE) techniques. After simulation results are obtained, the TL glow curve of the prediction results of quartz crystal is obtained as a function of fading time irradiated with β-source at 70 Gy for stored in 64 h at room temperature. Fading ; Artificial neural network ; Thermoluminescence ; Backpropagation ; Mean squared error ; Algorithm ; Biological system ; Computer science ; Materials science ; Artificial intelligence ; Mathematics ; Statistics ; Luminescence ; Optoelectronics Spectroscopy and Laser Applications ; Infrared Target Detection Methodologies ; Advanced Chemical Sensor Technologies
Authors: Meimei Liu | Zexin Yu | Hongjian Wu | Hanlin Liao | Qixin Zhu | Sihao Deng
Source: Journal of Thermal Spray Technology (article, 2021-05-12)
Field: Chemistry — Analytical Chemistry

Implementation of Artificial Neural Networks for Forecasting the HVOF Spray Process and HVOF Sprayed Coatings Thermal spraying ; Materials science ; Coating ; Process (computing) ; Porosity ; Artificial neural network ; Volumetric flow rate ; Gas dynamic cold spray ; Indentation hardness ; Process engineering ; Mechanical engineering ; Metallurgy ; Composite material ; Computer science ; Engineering ; Mechanics ; Microstructure ; Machine learning Petroleum Processing and Analysis ; Fluid Dynamics and Mixing ; High-Temperature Coating Behaviors
Authors: Zihao Ding | Chaoyi Zhu | Marc De Graef
Source: Materials Characterization (article, 2021-06-01)
Field: Materials Science — Materials Chemistry

Determining crystallographic orientation via hybrid convolutional neural network A recent paradigm shift in the electron diffraction community has benefited from accessibility of large data sets and ever more complex designs of convolutional neural networks (CNNs). However, this shift from conventional feature engineering to analyzing high-level features extracted from CNN is often accompanied by a reduction in accuracy and sensitivity. Particularly, CNN based crystal orientation indexing using electron backscatter diffraction is sensitive to noise, reducing the overall accuracy. In this study, a new hybrid indexing approach has been developed to integrate dictionary indexing (DI) with a trained CNN to achieve extraordinary speed and robustness against noise simultaneously. Convolutional neural network ; Robustness (evolution) ; Search engine indexing ; Pattern recognition (psychology) ; Electron backscatter diffraction ; Artificial intelligence ; Materials science ; Computer science ; Diffraction ; Artificial neural network ; Orientation (vector space) ; Optics ; Mathematics ; Physics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Nuclear Physics and Applications
Authors: Qi An
Source: Physical Review Materials (article, 2021-10-12)
Field: Materials Science — Materials Chemistry

Mitigating amorphization in superhard boron carbide by microalloying-induced stacking fault formation The abnormal brittle failure of superhard boron carbide $({\mathrm{B}}_{4}\mathrm{C})$ and other icosahedral solids arises from the shear-induced amorphization. Mitigating the amorphization in these materials remains challenging due to the lack of other deformation mechanisms such as mobile dislocations. This paper illustrates the shear-induced amorphization process of ${\mathrm{B}}_{4}\mathrm{C}$ from molecular dynamics (MD) simulations using quantum-mechanics-derived machine-learning force field. The amorphization in ${\mathrm{B}}_{4}\mathrm{C}$ initiates from the disintegration of icosahedral clusters, and then this icosahedral deconstruction propagates and merges to form an amorphous region with 2--3 nm in width, leading to the following cavitation and brittle failure. More interesting, the deformation mechanism transforms from amorphization to stacking fault (SF) formation by microalloying aluminum (Al) into ${\mathrm{B}}_{4}\mathrm{C}$. This SF formation originates from the enhanced icosahedral slip as the Al is incorporated into the C-B-C chain to form a C-Al-C chain. This paper illustrates a deformation mechanism of superhard icosahedral solids and provides a strategy for suppressing the amorphization and brittle failure of ${\mathrm{B}}_{4}\mathrm{C}$. Icosahedral symmetry ; Materials science ; Boron carbide ; Brittleness ; Crystallography ; Stacking fault ; Amorphous solid ; Deformation mechanism ; Boron ; Carbide ; Condensed matter physics ; Metallurgy ; Dislocation ; Composite material ; Physics ; Microstructure Diamond and Carbon-based Materials Research ; Boron and Carbon Nanomaterials Research ; High-pressure geophysics and materials
Authors: Balázs Chiovini | Dénes Pálfi | Myrtill Majoros | Gábor Juhász | Gergely Szalay | Gergely Katona | Milán Szöri | Orsolya Frigyesi | Csilla Lukácsné Haveland | Gábor Szabó | Ferenc Erdélyi | Zoltán Máté | Zoltán Szadai | Miklós Madarász | Miklós Dékány | Imre G. Csizmadia | Ervin Kovács | Balázs Rózsa | Zoltán Mucsi
Source: ACS Omega (article, 2021-06-03)
Field: Materials Science — Materials Chemistry

Theoretical Design, Synthesis, and In Vitro Neurobiological Applications of a Highly Efficient Two-Photon Caged GABA Validated on an Epileptic Case In this paper, we present an additional, new cage-GABA compound, called 4-amino-1-(4'-dimethylaminoisopropoxy-5',7'-dinitro-2',3'-dihydro-indol-1-yl)-1-oxobutane-γ-aminobutyric acid (iDMPO-DNI-GABA), and currently, this compound is the only photoreagent, which can be applied for GABA uncaging without experimental compromises. By a systematic theoretical design and successful synthesis of several compounds, the best reagent exhibits a high two-photon efficiency within the 700-760 nm range with excellent pharmacological behavior, which proved to be suitable for a complex epileptic study. Quantum chemical design showed that the optimal length of the cationic side chain enhances the two-photon absorption by 1 order of magnitude due to the cooperating internal hydrogen bonding to the extra nitro group on the core. This feature increased solubility while suppressing membrane permeability. The efficiency was demonstrated in a systematic, wide range of in vitro single-cell neurophysiological experiments by electrophysiological as well as calcium imaging techniques. Scalable inhibitory ion currents were elicited by iDMPO-DNI-GABA with appropriate spatial-temporal precision, blocking both spontaneous and evoked cell activity with excellent efficiency. Additionally, to demonstrate its applicability in a real neurobiological study, we could smoothly and selectively modulate neuronal activities during artificial epileptic rhythms first time in a neural network of GCaMP6f transgenic mouse brain slices. Inhibitory postsynaptic potential ; gamma-Aminobutyric acid ; Biophysics ; Chemistry ; Calcium imaging ; Combinatorial chemistry ; Computer science ; Neuroscience ; Biological system ; Materials science ; Calcium ; Biochemistry ; Biology ; Receptor Photochromic and Fluorescence Chemistry ; Neuroscience and Neuropharmacology Research ; Photoreceptor and optogenetics research
Authors: Anushikha Singh | Hardik Gupta | Arti Srivastava | Ashutosh Srivastava | Rakesh Chandra Joshi | Malay Kishore Dutta
Source: Journal of Food Processing and Preservation (article, 2021-04-24)
Field: Chemistry — Analytical Chemistry

A novel pilot study on imaging‐based identification of fish exposed to heavy metal (Hg) contamination Heavy metal pollution in water negatively affects the health status of fishes and makes it toxic to human health. Conventional chemical-based methods for fish quality assessment are destructive, highly time-consuming, and require expensive machines and expert manpower. In this novel pilot study, the image of the gill tissue of fish is considered as the main region of interest to analyze the discriminatory behavior between normal and heavy-metal exposed fish to develop an automatic and nondestructive approach using computer vision and machine learning algorithms. The discriminatory behavior of extracted features was analyzed with the help of p-value criteria, and the proposed framework is found efficient to identify heavy metal exposed fish where the value of the area is in the range of 82% to 92% under the curve of the receiver operating characteristic. This paper establishes the scientific proof of concept of using gill tissue of the fish for the identification of heavy metal exposed fish. Novelty impact statement An automatic and non-destructive method is proposed for the identification of heavy metal exposed fish using computer vision and machine learning algorithms. The region of interest is automatically segmented from an image using thresholding and morphological operations. This paper establishes the scientific proof of concept of using gill tissue of the fish for the identification of heavy metal exposed fish. Identification (biology) ; Fish <Actinopterygii> ; Computer science ; Thresholding ; Artificial intelligence ; Fish products ; Contamination ; Environmental science ; Computer vision ; Pattern recognition (psychology) ; Image (mathematics) ; Biology ; Ecology ; Fishery Spectroscopy and Chemometric Analyses ; Water Quality Monitoring Technologies ; Identification and Quantification in Food
Authors: Giuseppe Bonifazi | Riccardo Gasbarrone | Silvia Serranti
Source: Detritus (article, 2021-06-06)
Field: Chemistry — Analytical Chemistry

DETECTING CONTAMINANTS IN POST-CONSUMER PLASTIC PACKAGING WASTE BY A NIR HYPERSPECTRAL IMAGING-BASED CASCADE DETECTION APPROACH Recycling of post-consumer packaging wastes involves a complex chain of activities, usually based on three main stages, that is: i) collection from households or recovery from Municipal solid waste (MSW), ii) sorting and, finally, iii) mechanical recycling. The systematic identification of impurities inside plastic packaging waste streams, and the assessment of the different occurring materials, can be considered as one of the key issues to certify and to classify waste materials fed to recycling plants and to perform a full control of the resulting processed fractions and byproducts, that have to comply with market demands. The utilization of a Near InfraRed (NIR) – HyperSpectral Imaging (HSI) based methods, along with chemometrics and machine learning techniques, can fulfill these goals. In this paper, the HSI-based sorting logics, to apply, to implement and to set up to perform an automatic separation of paper, cardboard, plastics and multilayer packaging are investigated. Hyperspectral imaging ; cardboard ; Sorting ; Plastic waste ; Chemometrics ; Waste management ; Process engineering ; Plastic packaging ; Municipal solid waste ; Environmental science ; Identification (biology) ; Raw material ; Computer science ; Contamination ; Biochemical engineering ; Materials science ; Artificial intelligence ; Engineering ; Machine learning ; Chemistry Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Identification and Quantification in Food
Authors: Dilip Sing | Subhadip Banerjee | Shibu Narayan Jana | Ranajoy Mallik | Sudarshana Ghosh Dastidar | Kalyan Majumdar | Amitabha Bandyopadhyay | Rajib Bandyopadhyay | Pulok K. Mukherjee
Source: Frontiers in Pharmacology (article, 2021-05-06)
Field: Chemistry — Analytical Chemistry

Estimation of Andrographolides and Gradation of Andrographis paniculata Leaves Using Near Infrared Spectroscopy Together With Support Vector Machine <i>Andrographis paniculata</i> (Burm. F) Nees, has been widely used for upper respiratory tract and several other diseases and general immunity for a historically long time in countries like India, China, Thailand, Japan, and Malaysia. The vegetative productivity and quality with respect to pharmaceutical properties of <i>Andrographis paniculata</i> varies considerably across production, ecologies, and genotypes. Thus, a field deployable instrument, which can quickly assess the quality of the plant material with minimal processing, would be of great use to the medicinal plant industry by reducing waste, and quality grading and assurance. In this paper, the potential of near infrared reflectance spectroscopy (NIR) was to estimate the major group active molecules, the andrographolides in <i>Andrographis paniculata</i>, from dried leaf samples and leaf methanol extracts and grade the plant samples from different sources. The calibration model was developed first on the NIR spectra obtained from the methanol extracts of the samples as a proof of concept and then the raw ground samples were estimated for gradation. To grade the samples into three classes: good, medium and poor, a model based on a machine learning algorithm - support vector machine (SVM) on NIR spectra was built. The tenfold classification results of the model had an accuracy of 83% using standard normal variate (SNV) preprocessing. Andrographis paniculata ; Support vector machine ; Near infrared reflectance spectroscopy ; Gradation ; Mathematics ; Near-infrared spectroscopy ; Environmental science ; Biotechnology ; Traditional medicine ; Biology ; Computer science ; Artificial intelligence ; Medicine Spectroscopy and Chemometric Analyses ; Andrographolide Research and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Runkai Chen | Antonio Cammi | Marcus Seidl | Rafael Macián‐Juan | Xiang Wang
Source: Expert Systems with Applications (article, 2021-04-07)
Field: Materials Science — Materials Chemistry

Calculation of gamma-ray exposure buildup factor based on backpropagation neural network Backpropagation ; Monte Carlo method ; Artificial neural network ; Isotropy ; Energy (signal processing) ; Algorithm ; Computer science ; Homogeneous ; Mathematics ; Artificial intelligence ; Physics ; Statistics ; Statistical physics ; Optics Radiation Shielding Materials Analysis ; Advanced X-ray and CT Imaging ; Non-Destructive Testing Techniques
Authors: Melody Su | Ryan Grimes | Sunidhi Garg | Dezhen Xue | Prasanna V. Balachandran
Source: ACS Applied Materials & Interfaces (article, 2021-10-27)
Field: Materials Science — Materials Chemistry

Machine-Learning-Enabled Prediction of Adiabatic Temperature Change in Lead-Free BaTiO<sub>3</sub>-Based Electrocaloric Ceramics In this paper, we develop a data-driven machine learning (ML) approach to predict the adiabatic temperature change (Δ<i>T</i>) in BaTiO<sub>3</sub>-based ceramics as a function of chemical composition, temperature, and applied electric field. The data set was curated from a survey of published electrocaloric measurements. Each chemical composition was represented by elemental descriptors of A-site and B-site elements. Pair-wise statistical correlation analysis was used to remove linearly correlated descriptors. We trained two separate regression-based ML models for indirect and direct measurements and found that both are capable of capturing the general trend of the temperature vs Δ<i>T</i> curve for various applied electric fields. We then complemented the regression models with a classification learning model that predicts the expected phase as a function of chemical composition and temperature. The combined regression and classification learning ML models predict a global maxima in Δ<i>T</i> near rhombohedral to cubic or tetragonal to cubic phase transition regions. An interactive, open source web application is developed to enable interested users to query our trained models and accelerate the design of novel BaTiO<sub>3</sub>-based ceramics with targeted phase and Δ<i>T</i> properties for electrocaloric applications. Materials science ; Adiabatic process ; Phase transition ; Ceramic ; Machine learning ; Electric field ; Phase (matter) ; Tetragonal crystal system ; Field (mathematics) ; Artificial intelligence ; Computer science ; Thermodynamics ; Physics ; Mathematics Ferroelectric and Piezoelectric Materials ; Ferroelectric and Negative Capacitance Devices ; Electronic and Structural Properties of Oxides
Authors: Qiwu Luo | Jian Zhou | Weichuang Li | Chunhua Yang | Weihua Gui
Source: IEEE Sensors Journal (article, 2021-12-08)
Field: Chemistry — Analytical Chemistry

Interference Fringe Suppression for Oxygen Concentration Measurement Using Adaptive Harmonic Feeding Generative Adversarial Network This paper proposes an efficient interference fringe suppression method for the oxygen concentration measurement system by adopting emerging machine learning techniques. First, the interfered and interference-free signal datasets are generated on HITRAN molecular spectroscopic database after a transmission factor is considered in the wavelength-modulation-based TDLAS (TDLAS/WMS) theory. Then, an adaptive harmonic feeding generative adversarial network (AHFGAN) is developed to deal with the task of interference fringe suppression, where a novel adaptive weighted scheme is proposed to guide the weight learning process based on the data prior knowledge of dispersion degree refined from a large number of harmonic signals. Based on the AHFGAN, nearly perfect interference-free harmonic signals are directly learnt from the real-world TDLAS system, with an average absolute oxygen concentration inversion error of 0.57% when applied in an actual pharmaceutical production line, which performs better than other five recent state-of-the-arts. Interference (communication) ; Computer science ; Harmonic ; HITRAN ; Electronic engineering ; Algorithm ; Artificial intelligence ; Optics ; Engineering ; Physics ; Telecommunications ; Acoustics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Fault Detection and Control Systems
Authors: Hieu Chi Phan | Thanh-Luan Le | H. Nguyen‐Xuan
Source: International Journal of Hydrogen Energy (article, 2021-12-16)
Field: Materials Science — Metals and Alloys

A semi-empirical approach and uncertainty analysis to pipes under hydrogen embrittlement degradation Dimensionless quantity ; Hydrogen embrittlement ; Computer science ; Residual ; Fracture (geology) ; Hydrogen ; Embrittlement ; Materials science ; Algorithm ; Thermodynamics ; Chemistry Hydrogen embrittlement and corrosion behaviors in metals ; Fatigue and fracture mechanics ; Material Properties and Failure Mechanisms
Authors: Yixuan Wu | Yong‐Huan Yun | Jian Chen | Dongli Liu
Source: Food Analytical Methods (article, 2021-03-17)
Field: Chemistry — Analytical Chemistry

Discrimination of Waxy Wheats Using Near-Infrared Hyperspectral Spectroscopy Hyperspectral imaging ; Support vector machine ; Linear discriminant analysis ; Partial least squares regression ; Biological system ; Amylose ; Pattern recognition (psychology) ; Principal component analysis ; Mathematics ; Artificial intelligence ; Chemistry ; Starch ; Food science ; Computer science ; Biology ; Statistics Spectroscopy and Chemometric Analyses ; Food composition and properties ; Meat and Animal Product Quality
Authors: Evan Askanazi | Suhas Yadav | Ilya Grinberg
Source: Computational Materials Science (article, 2021-07-21)
Field: Materials Science — Materials Chemistry

Prediction of the Curie temperatures of ferroelectric solid solutions using machine learning methods Hyperparameter ; Curie temperature ; Support vector machine ; Ferroelectricity ; Random forest ; Valence (chemistry) ; Artificial intelligence ; Feature selection ; Computer science ; Ionic radius ; Curie ; Machine learning ; Algorithm ; Materials science ; Mathematics ; Chemistry ; Physics ; Condensed matter physics ; Dielectric Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Neural Networks and Applications
Authors: Yanru Zhao | Shiyan Fang | Yongkai Ye | Keqiang Yu
Source: Vibrational Spectroscopy (article, 2021-02-19)
Field: Chemistry — Analytical Chemistry

Chemometric development using portable molecular vibrational spectrometers for rapid evaluation of AVC (Valsa mali Miyabe et Yamada) infection of apple trees Raman spectroscopy ; Principal component analysis ; Spectrometer ; Receiver operating characteristic ; Analytical Chemistry (journal) ; Partial least squares regression ; Wavelength ; Materials science ; Chemistry ; Computer science ; Mathematics ; Chromatography ; Optics ; Artificial intelligence ; Physics ; Statistics ; Optoelectronics Spectroscopy and Chemometric Analyses ; Plant Pathogens and Fungal Diseases ; Essential Oils and Antimicrobial Activity