jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kaushik Sit | Arup Kumar Das | Debojyoti Mukherjee | Nasirul Haque | Suhas Deb | Arpan Kumar Pradhan | Sovan Dalai | Biswendu Chatterjee
Source: IEEE Sensors Journal (article, 2021-02-24)
Field: Materials Science — Materials Chemistry

Condition Monitoring of Overhead Polymeric Insulators Employing Hyperbolic Window Stockwell Transform of Surface Leakage Current Signals In this article, an efficient technique has been proposed to estimate the contamination level of overhead polymeric insulators. Deposition of contamination on polymeric insulator surface, is a serious issue as it often results in the flashover and even insulator failure. For estimating the severity of contamination level, surface leakage current (SLC) signals of a 11kV polymeric insulator with contaminated surface has been analyzed in time-frequency domain through hyperbolic window stockwell transform (HST). HST is more flexible than classical stockwell transform. Also, HST can able to handle both the low and high frequencies adequately. Considering the advantage, HST has been used here to estimate contamination degree from SLC signature. HST analysis of SLC signal returned a 2d complex time-frequency HS matrix. The complex time-frequency HS matrix has been separated into magnitude and phase spectrum. Based on the phase and magnitude spectrum, 15 statistical features, namely HST features has been extracted. Thereafter, 5 relevant HST features have been selected through least absolute shrinkage and selection operator (LASSO) feature selection technique. Finally, these relevant HST features are fed to four machine learning classifiers for estimation of contamination degree. It has also been observed that, the proposed framework method offered better classification accuracy compared to other standard time-frequency analysis and existing methods available in literature. Leakage (economics) ; Insulator (electricity) ; S transform ; Frequency domain ; Overhead (engineering) ; Computer science ; Pattern recognition (psychology) ; Electronic engineering ; Biological system ; Mathematics ; Artificial intelligence ; Engineering ; Wavelet transform ; Discrete wavelet transform ; Wavelet ; Mathematical analysis ; Electrical engineering High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Power Quality and Harmonics
Authors: Qing‐Jie Li | David Sprouster | Guiqiu Zheng | Jörg Neuefeind | Alexander Braatz | Joanna McFarlane | Daniel Olds | Stephen Lam | Ju Li | Boris Khaykovich
Source: ACS Applied Energy Materials (article, 2021-03-16)
Field: Chemistry — Inorganic Chemistry

Complex Structure of Molten NaCl–CrCl <sub>3</sub> Salt: Cr–Cl Octahedral Network and Intermediate-Range Order The resurgence of the molten-salt nuclear reactors (MSRs) creates interesting problems in molten-salt chemistry. As MSRs operate, the composition and physical properties of salts change because of fission and corrosion. Since Cr is the principal corrosion product and NaCl is a common constituent, we studied the atomic structure of molten NaCl–CrCl3. We found networks of CrCl63- octahedra and an intermediate-range order with a nonmonotonic temperature behavior with a remarkable agreement between measurements and ab initio simulations. Even though the corrosion results in minute quantities of dissolved Cr, the speciation of Cr could lead to changes in molten-salt properties in nuclear and solar salts. In particular, we found a much lower melting temperature than expected and a broad metastable liquid–solid coexistence phase. The availability of Cr isotopes with very different neutron scattering properties makes Cr an ideal model multivalent ion for experimental validation of new atomistic models such as neural network interatomic potentials. Molten salt ; Octahedron ; Salt (chemistry) ; Metastability ; Interatomic potential ; Chemistry ; Fission products ; Neutron scattering ; Neutron diffraction ; Chemical physics ; Materials science ; Neutron ; Inorganic chemistry ; Crystallography ; Molecular dynamics ; Physical chemistry ; Crystal structure ; Computational chemistry ; Radiochemistry ; Nuclear physics ; Physics Zeolite Catalysis and Synthesis ; Solid-state spectroscopy and crystallography ; Molten salt chemistry and electrochemical processes
Authors: Carlos Eduardo Crestani | André Bernardo | Caliane Bastos Borba Costa | Marco Giulietti
Source: Powder Technology (article, 2021-02-15)
Field: Chemistry — Analytical Chemistry

An artificial neural network model applied to convert sucrose chord length distributions into particle size distributions Biological system ; Standard deviation ; Artificial neural network ; Crystallization ; Particle size ; Particle-size distribution ; Materials science ; Chord (peer-to-peer) ; Mathematics ; Computer science ; Statistics ; Thermodynamics ; Engineering ; Artificial intelligence ; Physics ; Chemical engineering Spectroscopy and Chemometric Analyses ; Crystallization and Solubility Studies ; Fault Detection and Control Systems
Authors: Alberto Torres | Luana S. Pedroza | Mariví Fernández-Serra | Alexandre Reily Rocha
Source: The Journal of Physical Chemistry B (article, 2021-09-20)
Field: Materials Science — Materials Chemistry

Using Neural Network Force Fields to Ascertain the Quality of <i>Ab Initio</i> Simulations of Liquid Water Accurately simulating the properties of bulk water, despite the apparent simplicity of the molecule, is still a challenge. In order to fully understand and reproduce its complex phase diagram, it is necessary to perform simulations at the <i>ab initio</i> level, including quantum mechanical effects both for electrons and nuclei. This comes at a high computational cost, given that the structural and dynamical properties tend to require long timescales and large simulation cells. In this work, we evaluate the errors that density functional theory (DFT)-based simulations routinely incur into due time- and size-scale limitations. These errors are evaluated using neural-network-trained force fields that are accurate at the level of DFT methods. We compare different exchange and correlation potentials for properties of bulk water that require large timescales. We show that structural properties are less dependent on the system size and that dynamical properties such as the diffusion coefficient have a strong dependence on the simulation size and timescale. Our results facilitate comparisons of DFT-based simulation results with experiments and offer a path to discriminate between model and convergence errors in these simulations. Statistical physics ; Ab initio ; Work (physics) ; Molecular dynamics ; Density functional theory ; Force field (fiction) ; Diffusion ; Convergence (economics) ; Representation (politics) ; Quantum ; Artificial neural network ; Computer science ; Physics ; Thermodynamics ; Quantum mechanics ; Machine learning Machine Learning in Materials Science ; Quantum, superfluid, helium dynamics ; Spectroscopy and Quantum Chemical Studies
Authors: Cheng‐Wei Ju | Ethan French | Nadav Geva | Alexander W. Kohn | Zhou Lin
Source: The Journal of Physical Chemistry Letters (article, 2021-09-24)
Field: Materials Science — Materials Chemistry

Stacked Ensemble Machine Learning for Range-Separation Parameters Density functional theory-based high-throughput materials and drug discovery has achieved tremendous success in recent decades, but its power on organic semiconducting molecules suffered catastrophically from the self-interaction error until the nonempirical but expensive optimally tuned range-separated hybrid (OT-RSH) functionals were developed. An OT-RSH transitions from a short-range (semi)local functional to a long-range Hartree-Fock exchange at a distance characterized by a molecule-specific range-separation parameter (ω). Herein, we propose a stacked ensemble machine learning model that provides an accelerated alternative of OT-RSH based on system-dependent structural and electronic configurations. We trained ML-ωPBE, the first functional in our series, using a database of 1970 molecules with sufficient structural and functional diversity, and assessed its accuracy and efficiency using another 1956 molecules. Compared with nonempirical OT-ωPBE, ML-ωPBE reaches a mean absolute error of 0.00504<i>a</i><sub>0</sub><sup>-1</sup> for optimal ω's, reduces the computational cost by 2.66 orders of magnitude, and achieves comparable predictive power in optical properties. Range (aeronautics) ; Density functional theory ; Molecule ; Hybrid functional ; Ensemble learning ; Statistical physics ; Materials science ; Computer science ; Biological system ; Computational physics ; Physics ; Molecular physics ; Chemistry ; Computational chemistry ; Machine learning ; Quantum mechanics ; Biology Machine Learning in Materials Science ; Perovskite Materials and Applications ; Spectroscopy and Quantum Chemical Studies
Authors: Mehdi Hadjianfar | Dariush Semnani | Jaleh Varshosaz | Sajad Mohammadi | Sayed Pedram Rezazadeh Tehrani
Source: Advanced Pharmaceutical Bulletin (article, 2021-09-29)
Field: Materials Science — Biomaterials

5FU-loaded PCL/Chitosan/Fe<sub>3</sub>O<sub>4</sub> Core-Shell Nanofibers Structure: An Approach to Multi-Mode Anticancer System <b><i>Purpose:</i></b> 5-Fluorouracil (5FU) and Fe<sub>3</sub>O<sub>4</sub> nanoparticles were encapsulated in core-shell polycaprolactone (PCL)/chitosan (CS) nanofibers as a multi-mode anticancer system to study drug release sustainability. The structure of the core-shell drug delivery system was also optimized according to drug release behavior by artificial intelligence. <b><i>Methods:</i></b> The core-shell nanofibers were electrospun by a coaxial syringe. Artificial neural network (ANN) was used for function approximation to estimate release parameters. A genetic algorithm was then used for optimizing the structure. Chemical assay of the optimized sample was performed by Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), and energy-dispersive X-ray spectroscopy (EDX). vibration sample magnetometer (VSM) test was conducted to measure the real amount of loaded magnetic nanoparticles. HepG2 cell cytotoxicity was studied and the results for the optimized samples with and without Fe<sub>3</sub>O<sub>4</sub> after 72 hours were reported. <b><i>Results:</i></b> Feeding ratio of sheath to core and the amount of CS, Fe<sub>3</sub>O<sub>4</sub>, and 5FU had a statistical effect on nanofibers diameters, which were 300-450 nm. The drug loading efficiency of these nanofibers was 65-86%. ANN estimated the release parameters with an error of 10%. The temperature increased about 5.6°C in the alternative magnetic field (AMF) of 216 kA.m<sup>-1</sup>~300 kHz and 4.8°C in the AMF of 154 kA.m<sup>-1</sup>~400 kHz after 20 minutes. HepG2 cell cytotoxicity for the optimized samples with and without Fe<sub>3</sub>O<sub>4</sub> after 72 hours were 39.7% and 38.8%, respectively. <b><i>Conclusion:</i></b> Since this core-shell drug release system was more sustainable compared to the blend structure despite the low half-life of 5FU, it is suggested to utilize it as post-surgical implants for various cancer treatments such as liver or colorectal cancer in the future. This system is capable of providing chemotherapy and hyperthermia simultaneously. Nanofiber ; Chitosan ; Materials science ; Fourier transform infrared spectroscopy ; Nanoparticle ; Drug delivery ; Analytical Chemistry (journal) ; Biomedical engineering ; Nuclear chemistry ; Nanotechnology ; Chemical engineering ; Composite material ; Chromatography ; Chemistry ; Medicine Electrospun Nanofibers in Biomedical Applications ; Conducting polymers and applications ; Polymer Nanocomposite Synthesis and Irradiation
Authors: Brahmanandam Javvaji | Bohayra Mortazavi | Xiaoying Zhuang | Timon Rabczuk
Source: Carbon (article, 2021-09-15)
Field: Materials Science — Materials Chemistry

Exploring tensile piezoelectricity and bending flexoelectricity of diamane monolayers by machine learning Flexoelectricity ; Piezoelectricity ; Janus ; Dipole ; Materials science ; Monolayer ; Condensed matter physics ; Polarization (electrochemistry) ; Piezoelectric coefficient ; Polarization density ; Nanotechnology ; Composite material ; Chemistry ; Physics ; Physical chemistry ; Magnetic field Nonlocal and gradient elasticity in micro/nano structures ; Innovative Energy Harvesting Technologies ; Mechanical and Optical Resonators
Authors: Yi Zhong | Chenlei Ru | Shufang Wang | Zhenhao Li | Yiyu Cheng
Source: Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy (article, 2021-08-06)
Field: Chemistry — Analytical Chemistry

An online, non-destructive method for simultaneously detecting chemical, biological, and physical properties of herbal injections using hyperspectral imaging with artificial intelligence Hyperspectral imaging ; Chemistry ; Pattern recognition (psychology) ; Convolutional neural network ; Artificial intelligence ; Computer science Spectroscopy and Chemometric Analyses ; Remote-Sensing Image Classification ; Phytochemistry and Biological Activities
Authors: Leonardo Zepeda-Núñez | Yixiao Chen | Jiefu Zhang | Weile Jia | Linfeng Zhang | Lin Lin
Source: Journal of Computational Physics (article, 2021-06-29)
Field: Materials Science — Materials Chemistry

Deep Density: Circumventing the Kohn-Sham equations via symmetry preserving neural networks Kohn–Sham equations ; Artificial neural network ; Density functional theory ; Translational symmetry ; Locality ; Statistical physics ; Electron density ; Topology (electrical circuits) ; Leverage (statistics) ; Computer science ; Physics ; Quantum mechanics ; Mathematics ; Artificial intelligence ; Electron ; Condensed matter physics ; Combinatorics Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Advanced Chemical Physics Studies
Authors: Junya Ohyama | Airi Hirayama | Nahoko Kondou | Hiroshi Yoshida | Masato Machida | Shun Nishimura | Kenji Hirai | Itsuki Miyazato | Keisuke Takahashi
Source: Scientific Reports (article, 2021-01-22)
Field: Materials Science — Materials Chemistry

Data science assisted investigation of catalytically active copper hydrate in zeolites for direct oxidation of methane to methanol using H2O2 Dozens of Cu zeolites with MOR, FAU, BEA, FER, CHA and MFI frameworks are tested for direct oxidation of CH<sub>4</sub> to CH<sub>3</sub>OH using H<sub>2</sub>O<sub>2</sub> as oxidant. To investigate the active structures of the Cu zeolites, 15 structural variables, which describe the features of the zeolite framework and reflect the composition, the surface area and the local structure of the Cu zeolite active site, are collected from the Database of Zeolite Structures of the International Zeolite Association (IZA). Also analytical studies based on inductively coupled plasma-optical emission spectrometry (ICP-OES), X-ray fluorescence (XRF), N<sub>2</sub> adsorption specific surface area measurement and X-ray absorption fine structure (XAFS) spectral measurement are performed. The relationships between catalytic activity and the structural variables are subsequently revealed by data science techniques, specifically, classification using unsupervised and supervised machine learning and data visualization using pairwise correlation. Based on the unveiled relationships and a detailed analysis of the XAFS spectra, the local structures of the Cu zeolites with high activity are proposed. Zeolite ; X-ray absorption fine structure ; Copper ; Chemistry ; Adsorption ; Methanol ; Catalysis ; Active site ; Physical chemistry ; Spectroscopy ; Physics ; Organic chemistry Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Dili Shen | Wuyi Ming | Xinggui Ren | Zhuobin Xie | Xuewen Liu
Source: Micromachines (review, 2021-08-24)
Field: Materials Science — Surfaces, Coatings and Films

Progress in Non-Traditional Processing for Fabricating Superhydrophobic Surfaces When the water droplets are on some superhydrophobic surfaces, the surface only needs to be inclined at a very small angle to make the water droplets roll off. Hence, building a superhydrophobic surface on the material substrate, especially the metal substrate, can effectively alleviate the problems of its inability to resist corrosion and easy icing during use, and it can also give it special functions such as self-cleaning, lubrication, and drag reduction. Therefore, this study reviews and summarizes the development trends in the fabrication of superhydrophobic surface materials by non-traditional processing techniques. First, the principle of the superhydrophobic surfaces fabricated by laser beam machining (LBM) is introduced, and the machining performances of the LBM process, such as femtosecond laser, picosecond laser, and nanosecond laser, for fabricating the surfaces are compared and summarized. Second, the principle and the machining performances of the electrical discharge machining (EDM), for fabricating the superhydrophobic surfaces, are reviewed and compared, respectively. Third, the machining performances to fabricate the superhydrophobic surfaces by the electrochemical machining (ECM), including electrochemical oxidation process and electrochemical reduction process, are reviewed and grouped by materials fabricated. Lastly, other non-traditional machining processes for fabricating superhydrophobic surfaces, such as ultrasonic machining (USM), water jet machining (WJM), and plasma spraying machining (PSM), are compared and summarized. Moreover, the advantage and disadvantage of the above mentioned non-traditional machining processes are discussed. Thereafter, the prospect of non-traditional machining for fabricating the desired superhydrophobic surfaces is proposed. Machining ; Materials science ; Electrochemical machining ; Fabrication ; Nanotechnology ; Substrate (aquarium) ; Mechanical engineering ; Composite material ; Metallurgy ; Electrode ; Engineering ; Electrolyte Surface Modification and Superhydrophobicity ; Advanced Machining and Optimization Techniques ; Advanced Surface Polishing Techniques
Authors: Taoreed O. Owolabi | Mohd Amiruddin Abd Rahman
Source: Polymers (article, 2021-08-12)
Field: Materials Science — Polymers and Plastics

Modeling the Optical Properties of a Polyvinyl Alcohol-Based Composite Using a Particle Swarm Optimized Support Vector Regression Algorithm We developed particle swarm optimization-based support vector regression (PSVR) and ordinary linear regression (OLR) models for estimating the refractive index (n) and energy gap (E) of a polyvinyl alcohol composite. The n-PSVR model, which can estimate the refractive index of a polyvinyl alcohol composite using the energy gap as a descriptor, performed better than the n-OLR model in terms of root mean square error (RMSE) and mean absolute error (MAE) metrics. The E-PSVR model, which can predict the energy gap of a polyvinyl alcohol composite using its refractive index descriptor, outperformed the E-OLR model, which uses similar descriptor based on several performance measuring metrics. The n-PSVR and E-PSVR models were used to investigate the influences of sodium-based dysprosium oxide and benzoxazinone derivatives on the energy gaps of a polyvinyl alcohol polymer composite. The results agreed well with the measured values. The models had low mean absolute percentage errors after validation with external data. The precision demonstrated by these predictive models will enhance the tailoring of the optical properties of polyvinyl alcohol composites for the desired applications. Costs and experimental difficulties will be reduced. Polyvinyl alcohol ; Mean squared error ; Particle swarm optimization ; Refractive index ; Materials science ; Composite number ; Linear regression ; Mean absolute percentage error ; Algorithm ; Mathematics ; Composite material ; Statistics Polymer Nanocomposite Synthesis and Irradiation ; Conducting polymers and applications ; Analytical Chemistry and Sensors
Authors: Giovanni Marco Saladino | Nuzhet I. Kilic | Bertha Brodin | Bejan Hamawandi | İdris Yazgan | Hans M. Hertz | Muhammet S. Toprak
Source: Nanomaterials (article, 2021-08-24)
Field: Materials Science — Materials Chemistry

Carbon Quantum Dots Conjugated Rhodium Nanoparticles as Hybrid Multimodal Contrast Agents Nanoparticle (NP)-based contrast agents enabling different imaging modalities are sought for non-invasive bio-diagnostics. A hybrid material, combining optical and X-ray fluorescence is presented as a bioimaging contrast agent. Core NPs based on metallic rhodium (Rh) have been demonstrated to be potential X-ray Fluorescence Computed Tomography (XFCT) contrast agents. Microwave-assisted hydrothermal method is used for NP synthesis, yielding large-scale NPs within a significantly short reaction time. Rh NP synthesis is performed by using a custom designed sugar ligand (LODAN), constituting a strong reducing agent in aqueous solution, which yields NPs with primary amines as surface functional groups. The amino groups on Rh NPs are used to directly conjugate excitation-independent nitrogen-doped carbon quantum dots (CQDs), which are synthesized through citrate pyrolysis in ammonia solution. CQDs provided the Rh NPs with optical fluorescence properties and improved their biocompatibility, as demonstrated in vitro by Real-Time Cell Analysis (RTCA) on a macrophage cell line (RAW 264.7). The multimodal characteristics of the hybrid NPs are confirmed with confocal microscopy, and X-ray Fluorescence (XRF) phantom experiments. Biocompatibility ; Nanoparticle ; Fluorescence ; Materials science ; Conjugated system ; Rhodium ; Nanotechnology ; Aqueous solution ; Nuclear chemistry ; Chemistry ; Catalysis ; Organic chemistry ; Polymer ; Optics Carbon and Quantum Dots Applications ; Nanocluster Synthesis and Applications ; Advanced biosensing and bioanalysis techniques