jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ali. A. Salem | Rahisham Abd Rahman | M. S. Kamarudin | Nordiana Azlin Othman | Nor Akmal Mohd Jamail | Haziah Abdul Hamid | Mohd Taufiq Ishak
Source: Bulletin of Electrical Engineering and Informatics (article, 2020-03-23)
Field: Materials Science — Materials Chemistry

An alternative approaches to predict flashover voltage on polluted outdoor insulators using artificial intelligence techniques This paper presents an alternative approach for predicting critical voltage of pollution flashover by using Artificial Intelligence (AI) technique. Data from experimental works combined with the theoretical results from well-known theoretical modelling are used to derive algorithm for Artificial Neural Network (ANN) and Adaptive Neuro-fuzzy Inference System (ANFIS) for determining critical voltage of flashover. Series of laboratory testing and measurement are carried for 1:1, 1:5 and 1:10 ratios of top to bottom surface salt deposit density on cup and pin insulators. Insulators variables such as height H, diameter D, form factor F, creepage distance L, equivalent salt deposit density (ESDD) and flashover voltage correction are identified and used to train the AI network. Comparative studies have evidently shown that the proposed (AI) technique gives the satisfactory results compared to the analytical model and test data with the Coefficient of determination R-Square value of more than 97%. Artificial neural network ; Arc flash ; Voltage ; Adaptive neuro fuzzy inference system ; Engineering ; Fuzzy logic ; Artificial intelligence ; Computer science ; Fuzzy control system ; Electrical engineering High voltage insulation and dielectric phenomena ; Impact of Light on Environment and Health
Authors: Silvio Franz | Antonio Sclocchi | Pierfrancesco Urbani
Source: SciPost Physics (article, 2020-07-23)
Field: Materials Science — Materials Chemistry

Critical energy landscape of linear soft spheres We show that soft spheres interacting with a linear ramp potential when overcompressed beyond the jamming point fall in an amorphous solid phase which is critical, mechanically marginally stable and share many features with the jamming point itself. In the whole phase, the relevant local minima of the potential energy landscape display an isostatic contact network of perfectly touching spheres whose statistics is controlled by an infinite lengthscale. Excitations around such energy minima are non-linear, system spanning, and characterized by a set of non-trivial critical exponents. We perform numerical simulations to measure their values and show that, while they coincide, within numerical precision, with the critical exponents appearing at jamming, the nature of the corresponding excitations is richer. Therefore, linear soft spheres appear as a novel class of finite dimensional systems that self-organize into new, critical, marginally stable, states. Maxima and minima ; SPHERES ; Energy landscape ; Jamming ; Critical point (mathematics) ; Measure (data warehouse) ; Energy (signal processing) ; Phase transition ; Point (geometry) Material Dynamics and Properties ; Theoretical and Computational Physics ; Advanced Physical and Chemical Molecular Interactions
Authors: Wenjie Liu | Junxiu Chen | Yuxiang Wang | Peipei Gao | Zhibin Lei | Xu Ma
Source: Complexity (article, 2020-03-31)
Field: Materials Science — Materials Chemistry

Quantum-Based Feature Selection for Multiclassification Problem in Complex Systems with Edge Computing The complex systems with edge computing require a huge amount of multifeature data to extract appropriate insights for their decision making, so it is important to find a feasible feature selection method to improve the computational efficiency and save the resource consumption. In this paper, a quantum-based feature selection algorithm for the multiclassification problem, namely, QReliefF, is proposed, which can effectively reduce the complexity of algorithm and improve its computational efficiency. First, all features of each sample are encoded into a quantum state by performing operations CMP and R y , and then the amplitude estimation is applied to calculate the similarity between any two quantum states (i.e., two samples). According to the similarities, the Grover–Long method is utilized to find the nearest k neighbor samples, and then the weight vector is updated. After a certain number of iterations through the above process, the desired features can be selected with regards to the final weight vector and the threshold τ . Compared with the classical ReliefF algorithm, our algorithm reduces the complexity of similarity calculation from O (MN) to O ( M ), the complexity of finding the nearest neighbor from O ( M ) to <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mrow><mml:mi>O</mml:mi><mml:mfenced open="(" close=")" separators="|"><mml:mrow><mml:msqrt><mml:mi>M</mml:mi></mml:msqrt></mml:mrow></mml:mfenced></mml:mrow></mml:math>, and resource consumption from O (MN) to O (MlogN). Meanwhile, compared with the quantum Relief algorithm, our algorithm is superior in finding the nearest neighbor, reducing the complexity from O ( M ) to <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mml:mrow><mml:mi>O</mml:mi><mml:mfenced open="(" close=")" separators="|"><mml:mrow><mml:msqrt><mml:mi>M</mml:mi></mml:msqrt></mml:mrow></mml:mfenced></mml:mrow></mml:math>. Finally, in order to verify the feasibility of our algorithm, a simulation experiment based on Rigetti with a simple example is performed. Computer science ; Algorithm ; Similarity (geometry) ; k-nearest neighbors algorithm ; Computational complexity theory ; Feature (linguistics) ; Feature selection ; Quantum computer ; Enhanced Data Rates for GSM Evolution ; Quantum ; Artificial intelligence ; Image (mathematics) ; Physics Machine Learning in Materials Science ; Quantum Computing Algorithms and Architecture ; Advanced Memory and Neural Computing
Authors: Chuyang Xie | Xiguang Gao | Huajun Zhang | Yingdong Song
Source: Science and Engineering of Composite Materials (article, 2020-01-01)
Field: Materials Science — Ceramics and Composites

Multiscale acoustic emission of C/SiC mini-composites and damage identification using pattern recognition Abstract In this paper, multiscale acoustic emission (AE) signal analysis was applied to acoustic emission data processing to classify the AE signals produced during the tensile process of C/SiC mini-composites. An established unsupervised clustering algorithm was provided to classify an unknown set of AE data into reasonable classes. In order to correctly match the obtained classes of the AE signals with the damage mode of the sample, three scales of materials were involved. Single fiber tensile test and fiber bundle tensile test were firstly performed to achieve the characteristics of AE signal of fiber fracture. Parameter analysis and waveform analysis were added to extract the different features of each class of signals in the In-situ tensile test of C/SiC mini-composite. The change of strain field on the sample surface analyzed by DIC (Digital Image Correlation) revealed the corresponding relationship between matrix cracking and AE signals. Microscopic examinationwas used to correlate the clusters to the damage mode. By analyzing the evolution process of signal activation for each class against the load, it also provided a reliable basis for the correlation between the obtained classes of the AE signals and the damage mechanism of the material. Acoustic emission ; Materials science ; Composite material ; Ultimate tensile strength ; Digital image correlation ; Tensile testing ; Waveform ; SIGNAL (programming language) ; Cluster analysis ; Fracture (geology) ; Fiber ; Pattern recognition (psychology) ; Computer science ; Artificial intelligence Advanced ceramic materials synthesis ; Rock Mechanics and Modeling ; Ultrasonics and Acoustic Wave Propagation
Authors: Alessandro Paolo Daga | Alessandro Fasana | Luigi Garibaldi | Stefano Marchesiello
Source: PHM Society European Conference (article, 2020-07-22)
Field: Chemistry — Analytical Chemistry

On the use of PCA for Diagnostics via Novelty Detection: interpretation, practical application notes and recommendation for use The Principal Component Analysis (PCA) is the simplest eigenvector-based multivariate data analysis tool and dates back to 1901 when Karl Pearson proposed it as a way for finding the best fitting d-1 hyperplane of a system of points in a d-dimensional (Euclidean) space. Over the time, the PCA evolved in different fields with several different names and with different scopes, but, in its essence, it is always an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components. Generalizing Pearson’s purpose, the knowledge derived by such an analysis is mostly used to find a subspace which effectively and efficiently summarizes the original system of points by losing a minimum amount of information. In the field of Diagnostics, the fundamental task of detecting damage is basically a binary classification problem which is in many cases tackled via Novelty Detection: an observation is classified as novel if it differs significantly from other observations. Novelty can, in principle, be assessed directly in the original space, but the effectiveness of the estimated novelty can be improved by taking advantage of the PCA. In this work, the traditional PCA will be compared to a robust modification that is commonly used in the field of diagnostics to face the issue of confounding influences which could affect the novelty-damage correspondence. Comparisons will be made to shed light on the main misleading aspects of PCA, and finally, define a unique, theoretically justified procedure for Diagnostics via Novelty Detection. Principal component analysis ; Novelty detection ; Novelty ; Subspace topology ; Pattern recognition (psychology) ; Artificial intelligence ; Computer science ; Set (abstract data type) ; Curse of dimensionality ; Interpretation (philosophy) ; Transformation (genetics) ; Field (mathematics) ; Mathematics ; Data mining Spectroscopy and Chemometric Analyses ; Fault Detection and Control Systems ; Advanced Chemical Sensor Technologies
Authors: Yurii Shepelytskyi | Camryn J. Newman | Vira Grynko | Lauren E. Seveney | Brenton DeBoef | Francis Hane | Mitchell S. Albert
Source: Molecules (review, 2020-11-27)
Field: Materials Science — Biomaterials

Cyclodextrin-Based Contrast Agents for Medical Imaging Cyclodextrins (CDs) are naturally occurring cyclic oligosaccharides consisting of multiple glucose subunits. CDs are widely used in host-guest chemistry and biochemistry due to their structural advantages, biocompatibility, and ability to form inclusion complexes. Recently, CDs have become of high interest in the field of medical imaging as a potential scaffold for the development of a large variety of the contrast agents suitable for magnetic resonance imaging, ultrasound imaging, photoacoustic imaging, positron emission tomography, single photon emission computed tomography, and computed tomography. The aim of this review is to summarize and highlight the achievements in the field of cyclodextrin-based contrast agents for medical imaging. Contrast (vision) ; Computer science ; Medicine ; Medical physics ; Artificial intelligence Nanoparticle-Based Drug Delivery ; Atomic and Subatomic Physics Research ; Nanoplatforms for cancer theranostics
Authors: Álvaro de Jesús Ruíz-Baltazar | Néstor Méndez‐Lozano | Daniel Larrañaga Ordaz | Simón Yobanny Reyes‐López | Marco Antonio Zamora-Antuñano | R. Pérez
Source: Processes (article, 2020-08-06)
Field: Materials Science — Materials Chemistry

Magnetic Nanoparticles of Fe3O4 Biosynthesized by Cnicus benedictus Extract: Photocatalytic Study of Organic Dye Degradation and Antibacterial Behavior Currently, the use of sustainable chemistry as an ecological alternative for the generation of products or processes that are free of a polluting substance has assumed a preponderant role. The aim of this work is to propose a bioinspired, facile, low cost, non-toxic, and environmentally friendly alternative to obtaining magnetic nanoparticles with a majority phase of magnetite (Fe3O4). It is important to emphasize that the synthesis was based on the chemical reduction through the Cnicus benedictus extract, whose use as reducing agent has not been reported in the synthesis of iron oxides nanoparticles. In addition, the Cnicus benedictus is an abundant endemic plant in Mexico with several medicinal properties and a large number of natural antioxidants. The obtained nanoparticles exhibited significant magnetic and antibacterial properties and an enhanced photocatalytic activity. The crystallite size of the Fe3O4 nanoparticles (Fe3O4 NP’s) was calculated by the Williamson-Hall method. The photocatalytic properties of the Fe3O4 NP’s were studied by kinetics absorptions models in the Congo red (CR) degradation. Finally, the antibacterial effects of the Fe3O4 NPs were evaluated mediated the Kirby–Bauer method against Escherichia coli and Staphylococcus aureus bacteria. This route offers a green alternative to obtain Fe3O4 NPs with remarkable magnetic, photocatalytic, and antibacterial properties. Photocatalysis ; Nanoparticle ; Antibacterial activity ; Environmentally friendly ; Magnetic nanoparticles ; Magnetite ; Degradation (telecommunications) ; Congo red ; Chemistry ; Chemical engineering ; Materials science ; Nanotechnology ; Catalysis ; Bacteria ; Organic chemistry ; Metallurgy ; Biology ; Ecology ; Adsorption Nanoparticles: synthesis and applications ; Laser-Ablation Synthesis of Nanoparticles
Authors: Hamid Ziyat | Mohammed Naciri Bennani | Hassan Hajjaj | Omar Qabaqous | Said Arhzaf | Soumiya Mekdad | Safae Allaoui
Source: Journal of Chemistry (article, 2020-02-10)
Field: Materials Science — Biomaterials

Adsorption of Thymol onto Natural Clays of Morocco: Kinetic and Isotherm Studies The present work aims to study the affinity of a component of the thyme essential oil “thymol” to natural Moroccan clay “Rhassoul” using the adsorption technique. The physicochemical characterizations of the purified and modified clay were carried out by X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), DTA/TGA, and SEM-EDX. Thymol adsorption tests on the purified Rhassoul (Rh-P) and the modified one by CTAB (Rh-CTAB) were followed by UV-visible spectroscopy. They show that the adsorption isotherms can be described by the Freundlich model and that the kinetics of adsorption is in accordance with the pseudo-second-order model for the two clays. Adsorption capacities obtained were of the order of 6 mg/g for the purified Rhassoul and 16 mg/g for the modified Rhassoul by cetyltrimethylammonium bromide (CTAB). These values show that the modified Rhassoul has a better adsorption capacity compared to the purified Rhassoul. Chemistry ; Adsorption ; Freundlich equation ; Thymol ; Fourier transform infrared spectroscopy ; Bromide ; Nuclear chemistry ; Kinetics ; Infrared spectroscopy ; Chromatography ; Inorganic chemistry ; Essential oil ; Chemical engineering ; Organic chemistry Clay minerals and soil interactions ; Adsorption, diffusion, and thermodynamic properties of materials ; Mesoporous Materials and Catalysis
Authors: Ethan A. Scott | Elbara Ziade | Christopher B. Saltonstall | Anthony E. McDonald | Mark A. Rodriguez | Patrick E. Hopkins | Thomas E. Beechem | David P. Adams
Source: Journal of Applied Physics (article, 2020-10-19)
Field: Materials Science — Materials Chemistry

Thermal conductivity of (Ge2Sb2Te5)1−<i>x</i>C<i>x</i> phase change films Germanium–antimony–telluride has emerged as a nonvolatile phase change memory material due to the large resistivity contrast between amorphous and crystalline states, rapid crystallization, and cyclic endurance. Improving thermal phase stability, however, has necessitated further alloying with optional addition of a quaternary species (e.g., C). Here, the thermal transport implications of this additional species are investigated using frequency-domain thermoreflectance in combination with structural characterization derived from x-ray diffraction and Raman spectroscopy. Specifically, the room temperature thermal conductivity and heat capacity of (Ge2Sb2Te5)1−xCx are reported as a function of carbon concentration (x≤0.12) and anneal temperature (T≤350°C) with results assessed in reference to the measured phase, structure, and electronic resistivity. Phase stability imparted by the carbon comes with comparatively low thermal penalty as materials exhibiting similar levels of crystallinity have comparable thermal conductivity despite the addition of carbon. The additional thermal stability provided by the carbon does, however, necessitate higher anneal temperatures to achieve similar levels of structural order. Materials science ; Thermal conductivity ; Thermal stability ; Raman spectroscopy ; Electrical resistivity and conductivity ; Crystallinity ; Amorphous solid ; Analytical Chemistry (journal) ; Germanium ; Crystallization ; Phase (matter) ; X-ray crystallography ; Telluride ; Carbon fibers ; Crystallography ; Diffraction ; Silicon ; Chemistry ; Metallurgy ; Composite material ; Optics Phase-change materials and chalcogenides ; Advanced Thermoelectric Materials and Devices ; Chalcogenide Semiconductor Thin Films
Authors: Xiang He | Jianping Chen | Shane Albin | Zan Zhu | Wei‐Ning Wang
Source: Advanced Powder Technology (article, 2020-12-03)
Field: Chemistry — Inorganic Chemistry

Data-driven parameter optimization for the synthesis of high-quality zeolitic imidazolate frameworks via a microdroplet route Zeolitic imidazolate framework ; Imidazolate ; Metal-organic framework ; Materials science ; Quality (philosophy) ; Nanotechnology ; Process engineering ; Chemical engineering ; Chemistry ; Organic chemistry ; Engineering ; Adsorption Metal-Organic Frameworks: Synthesis and Applications ; Membrane Separation and Gas Transport ; Zeolite Catalysis and Synthesis
Authors: Wei Sun Leong | Giuseppe Arrabito | G. Prestopino
Source: Crystals (article, 2020-04-16)
Field: Materials Science — Materials Chemistry

RETRACTED: Artificial Intelligence Algorithm Enabled Industrial-Scale Graphene Characterization No characterization method is available to quickly perform quality inspection of 2D materials produced on an industrial scale. This hinders the adoption of 2D materials for product manufacturing in many industries. Here, we report an artificial-intelligence-assisted Raman analysis to quickly probe the quality of centimeter-large graphene samples in a non-destructive manner. Chemical vapor deposition of graphene is devised in this work such that two types of samples were obtained: layer-plus-islands and layer-by-layer graphene films, at centimeter scales. Using these samples, we implemented and integrated an unsupervised learning algorithm with an automated Raman spectroscopy to precisely cluster 20,250 and 18,000 Raman spectra collected from layer-plus-islands and layer-by-layer graphene films, respectively, into five and two clusters. Each cluster represents graphene patches with different layer numbers and stacking orders. For instance, the two clusters detected in layer-by-layer graphene films represent monolayer and bilayer graphene based on their Raman fingerprints. Our intelligent Raman analysis is fully automated, with no human operation involved, is highly reliable (99.95% accuracy), and can be generalized to other 2D materials, paving the way towards industrialization of 2D materials for various applications in the future. Graphene ; Raman spectroscopy ; Materials science ; Characterization (materials science) ; Layer (electronics) ; Chemical vapor deposition ; Nanotechnology ; Stacking ; Monolayer ; Artificial intelligence ; Optoelectronics ; Computer science ; Chemistry ; Optics ; Physics Graphene research and applications ; 2D Materials and Applications ; Thermal properties of materials
Authors: Thomas Szkopek | Eli Martel
Source: Progress in Quantum Electronics (article, 2020-12-30)
Field: Materials Science — Materials Chemistry

Suspended graphene electromechanical switches for energy efficient electronics Graphene ; Electronics ; Materials science ; Voltage ; Nanotechnology ; Optoelectronics ; Electrical engineering ; Engineering physics ; Physics ; Engineering Graphene research and applications ; Nanowire Synthesis and Applications ; Advanced Memory and Neural Computing
Authors: Munasir Munasir | R. Kusumawati | Diah Hari Kusumawati | Zainul Arifin Imam Supardi | Ahmad Taufiq | Darminto Darminto
Source: International Journal of Engineering (article, 2020-01-01)
Field: Chemistry — Organic Chemistry

Characterization of Fe3O4/rGO Composites from Natural Sources: Application for Dyes Color Degradation in Aqueous Solution The magnetite (Fe3O4) nanoparticle and graphene oxide (GO) have become interesting materials due to their advanced applications. In this work, we investigated the fabrication of Fe3O4 nanoparticles (NPs) from iron sands and reduced graphene oxide (rGO) NPs from natural graphite. The core-shell fabrication of the Fe3O4/rGO was conducted by means of ex-situ method using ethanol as the medium. The crystal structure of Fe3O4/rGO was observed using X-ray diffraction (XRD) and functional groups were examined using Fourier transform infra-red (FTIR) spectroscopy. The characteristic of the disturbance originated by carbon atoms was investigated by Raman spectroscopy. The morphological, particle sizes and formation studied with transmission electron microscopy (TEM). The magnetic properties were analyzed using vibrating sample magnetometer (VSM). Furthermore, analysis of the adsorption performance, namely: dye-removal efficiency (DRE) and degradation rate (DR), as candidate materials absorbent were performed by means of UV-Vis spectroscopy. The data analysis of structure and phase of Fe3O4/rGO presented cubic spinel structure with crystallite size of 26-38 nm. The functional group analysis presented the existence of C-OH, C=O, C-O, and Fe-O. The micrograph analysis from the TEM image showed the particle size of the sample was in the range of 10 - 30 nm. Along with the thickening shell, the saturation magnetization of Fe3O4/rGO decreased from 22.60 to 18.48 emu/g and decreased from 29.21 to 10.45 emu/g for Fe3O4. Finally, the rGO composition affects the shell wall, which encloses Fe3O4 as the core. Interestingly, an increase in absorption characteristic of natural dyes Fe3O4/rGO enhanced by the decrease of the shell thickness. Graphene ; Materials science ; Raman spectroscopy ; Oxide ; Fourier transform infrared spectroscopy ; Magnetite ; Scanning electron microscope ; Chemical engineering ; Transmission electron microscopy ; Nanoparticle ; Crystallite ; Graphite ; Magnetic nanoparticles ; Analytical Chemistry (journal) ; Nanotechnology ; Composite material ; Chemistry ; Metallurgy ; Optics ; Organic chemistry Nanomaterials for catalytic reactions