jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kan Hatakeyama‐Sato | Yasuhiko Igarashi | Takahiro Kashikawa | Kôichi Kimura | Kenichi Oyaizu
Source: Digital Discovery (article, 2022-12-02)
Field: Materials Science — Materials Chemistry

Quantum circuit learning as a potential algorithm to predict experimental chemical properties Quantum circuit learning is examined to predict standard functions and molecular properties. The algorithm may be used for extrapolating predictions. Quantum chemical ; Algorithm ; Computer science ; Quantum ; Quantum circuit ; Quantum algorithm ; Physics ; Quantum mechanics ; Quantum error correction ; Molecule Machine Learning in Materials Science ; Quantum Computing Algorithms and Architecture ; Computational Drug Discovery Methods
Authors: Ryota Kamiike | Tomohiro Hirano | Koichi Ute
Source: Polymer (article, 2022-08-08)
Field: Chemistry — Analytical Chemistry

Statistical determination of chemical composition and blending fraction of copolymers by multivariate analysis of 1H NMR spectra of binary blends of the copolymers Copolymer ; Partial least squares regression ; Acrylonitrile ; Ternary operation ; Materials science ; Mole fraction ; Binary number ; Polymer blend ; Proton NMR ; Lasso (programming language) ; Chemistry ; Mathematics ; Polymer ; Statistics ; Organic chemistry ; Physical chemistry ; Computer science ; Composite material Spectroscopy and Chemometric Analyses ; NMR spectroscopy and applications ; Metabolomics and Mass Spectrometry Studies
Authors: Sepideh Hashemi | Baskar Ganapathysubramanian | Stephen Casey | Ji Su | Surya R. Kalidindi
Source: Integrating materials and manufacturing innovation (article, 2022-07-18)
Field: Materials Science — Materials Chemistry

Feature Engineering for Microstructure–Property Mapping in Organic Photovoltaics Organic solar cell ; Feature (linguistics) ; Property (philosophy) ; High fidelity ; Computer science ; Microstructure ; Salient ; Photovoltaic system ; Gaussian ; Materials science ; Artificial intelligence ; Engineering ; Physics ; Electrical engineering ; Composite material Machine Learning in Materials Science ; Gaussian Processes and Bayesian Inference ; CCD and CMOS Imaging Sensors
Authors: S. K. Amizhtan | R. Akash | Ramesh L. Gardas | R. Sarathi | B. Aryanandiny
Source: IEEE Access (article, 2022-12-26)
Field: Materials Science — Materials Chemistry

Understanding the Electro-Rheological Aspects of Nano Silica Based Ester Fluid With Surfactants and Deep Learning-Based Prediction of ECT The present study deals with understanding the impact of different surfactants with nano-silica based ester fluid on its electrical and rheological properties. The stability of nanofluid is of prime concerns, which is achieved by the inclusion of ionic and non-ionic surfactants viz. cetyl trimethyl ammonium bromide (CTAB), oleic acid and Span-80. It is observed that CTAB as surfactant has shown discharge resistance property along with high breakdown strength enhancement of up to 39.5% with ester nanofluid. It is observed that fluorescent fiber technique is more sensitive to identify the inception of corona discharge as compared to Ultra high frequency sensor. The permittivity and tan <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\delta $ </tex-math></inline-formula> of the fluid have shown marginal increase with the addition of surfactants, irrespective of temperature of the fluid. Rheological properties of the liquid showed only Newtonian flow behaviour even upon inclusion of the surfactant. The viscosity of the base fluid and the nanofluids exhibits similar decay rate at higher temperatures. Electrostatic charging tendency (ECT) derives the correlation between rotation speed and the static current measured adopting spinning disc technique. In addition, the performance of Long Short-Term Memory (LSTM) model is efficient compared to Artificial Neural Network (ANN) model adopted in predicting the charging tendency incorporating electro-rheological parameters at distinct temperatures. The electrical and rheological characteristics suggests the implication of nanofluids with surfactant to enhance the insulation performance for transformer applications. Rheology ; Materials science ; Nano- ; Nanoparticle ; Chemical engineering ; Computer science ; Artificial intelligence ; Nanotechnology ; Composite material ; Engineering High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Aerosol Filtration and Electrostatic Precipitation
Authors: Seol-Hyun Noh
Source: Informatics (article, 2022-09-27)
Field: Materials Science — Materials Chemistry

Predicting Future Promising Technologies Using LSTM With advances in science and technology and changes in industry, research on promising future technologies has emerged as important. Furthermore, with the advent of a ubiquitous and smart environment, governments and enterprises are required to predict future promising technologies on which new important core technologies will be developed. Therefore, this study aimed to establish science and technology development strategies and support business activities by predicting future promising technologies using big data and deep learning models. The names of the “TOP 10 Emerging Technologies” from 2018 to 2021 selected by the World Economic Forum were used as keywords. Next, patents collected from the United States Patent and Trademark Office and the Science Citation Index (SCI) papers collected from the Web of Science database were analyzed using a time-series forecast. For each technology, the number of patents and SCI papers in 2022, 2023 and 2024 were predicted using the long short-term memory model with the number of patents and SCI papers from 1980 to 2021 as input data. Promising technologies are determined based on the predicted number of patents and SCI papers for the next three years. Keywords characterizing future promising technologies are extracted by analyzing abstracts of patent data collected for each technology and the term frequency-inverse document frequency is measured for each patent abstract. The research results can help business managers make optimal decisions in the present situation and provide researchers with an understanding of the direction of technology development. Trademark ; Big data ; Data science ; Emerging technologies ; Computer science ; Patent analysis ; Technology forecasting ; Citation ; Business model ; Index (typography) ; Business ; Artificial intelligence ; World Wide Web ; Marketing ; Data mining Machine Learning in Materials Science ; Intellectual Property and Patents ; Innovation Diffusion and Forecasting
Authors: Podchara Rattanakawin | Kenji Yoshimoto | Yuta Hikima | Shinsuke Nagamine | Yu-Han Jiang | Masatoshi Tosaka | Shigeru Yamago | Masahiro Ohshima
Source: Macromolecules (article, 2022-06-08)
Field: Materials Science — Polymers and Plastics

Control of the Cell Structure of UV-Induced Chemically Blown Nanocellular Foams by Self-Assembled Block Copolymer Morphology A method to fabricate highly ordered nanocellular foam by incorporating the UV-induced chemical foaming technique with self-assembly of a block copolymer, poly(methyl methacrylate-block-tert-butyl acrylate) (PMMA-b-PtBA), was reported in our previous communication [Rattanakawin, P. ACS Macro Lett. 2020, 9 (10), 1433–1438]. Cells with a size of a few tens of nanometers were successfully generated within the cylindrical PtBA-rich domains by heating the UV-irradiated self-assembled PMMA-b-PtBA. Here, we show how other self-assembled morphologies of PMMA-b-PtBA, such as lamellae and PMMA-rich cylinders, affect the formation of nanocellular foams. It is demonstrated that cells generated from the lamella templates are largely expandable compared to those from the cylindrical templates, mainly due to the increase in gas amount produced within the PtBA-rich domains. Meanwhile, the lamellar framework is no longer maintained and transformed into a microcellular structure when the foaming temperature is increased near the glass transition temperature of PMMA. We show that such a drastic change in cell structure may be mitigated by increasing the molecular weight of PMMA. Copolymer ; Lamellar structure ; Materials science ; Lamella (surface anatomy) ; Methyl methacrylate ; Acrylate ; Morphology (biology) ; Glass transition ; Chemical engineering ; Polymer chemistry ; Methacrylate ; Composite material ; Methyl acrylate ; Nanostructure ; Polymer ; Nanotechnology Polymer Foaming and Composites ; Block Copolymer Self-Assembly ; Pickering emulsions and particle stabilization
Authors: Nova Yuliasari | Amri Amri | Risfidian Mohadi | Elfita Elfita | Aldes Lesbani
Source: Communications in Science and Technology (article, 2022-12-28)
Field: Materials Science — Materials Chemistry

Modification of pristine layered double hydroxide to form metal oxide composites as an anionic dye photodegradation catalysts Pristine layered double hydroxide (LDH) in the form of clay Mg/Al LDH was enhanced its catalytic ability by impregnating metal oxides to form Mg/Al-metal oxide composites in congo red (CR) degradation. The composite was calcined at a not high temperature of 300 oC and characterized using SEM and DRUV. In this report, the photodegradation of CR as anionic dye was optimized based on the variables of pH, catalyst weight and time radiation. The stability of the catalyst was studied from the percent degradation in the recycling test. The characterization of the catalyst that has undergone 5th regeneration cycles was carried out using XRD and FTIR. The results of this study revealed that catalysis by Mg/Al-metal oxide composites resulted in a better percent degradation, rate constant and materials stability than pristine Mg/Al LDH. Mg/Al LDH, Mg/Al-TiO2 and Mg/Al-ZnO catalyzed the photodegradation of CR by 65.97%, 73.06 % and 86.86%, respectively. Photodegradation ; Catalysis ; Hydroxide ; Calcination ; Materials science ; Oxide ; Metal ; Fourier transform infrared spectroscopy ; Layered double hydroxides ; Degradation (telecommunications) ; Photocatalysis ; Nuclear chemistry ; Chemical engineering ; Inorganic chemistry ; Chemistry ; Metallurgy ; Organic chemistry Layered Double Hydroxides Synthesis and Applications ; Magnesium Oxide Properties and Applications
Authors: Eric Sperlich | Alexandra Kelling | George Kwesiga | Bernd Schmidt
Source: CrystEngComm (article, 2022-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

Intermolecular interactions in the solid-state structures of isoflavones: the relationship between supramolecular structure, torsion angle, and macroscopic properties The different macroscopic properties of closely related isoflavones were associated with the occurrence of intermolecular interactions and different torsion angles in the solid-state. Intermolecular force ; Torsion (gastropod) ; Supramolecular chemistry ; Isoflavones ; Solid-state ; Intermolecular interaction ; Materials science ; Chemical physics ; Crystallography ; Chemistry ; Crystal structure ; Molecule ; Physical chemistry ; Organic chemistry Crystallography and molecular interactions ; Crystallization and Solubility Studies ; Inorganic Fluorides and Related Compounds
Authors: Michele Simoncelli | Nicola Marzari | Francesco Mauri
Source: Physical Review X (preprint, 2022-10-31)
Field: Materials Science — Materials Chemistry

Wigner Formulation of Thermal Transport in Solids A formulation for heat conduction discusses how particle- and wavelike thermal transport mechanisms can emerge and coexist and provides a criterion to assess their relative strength and the crossover where one or the other dominates. Physics ; Heat current ; Phonon ; Thermal conduction ; Thermal conductivity ; Boltzmann equation ; Condensed matter physics ; Degenerate energy levels ; Transport phenomena ; Quantum mechanics ; Classical mechanics Thermal properties of materials ; Thermoelastic and Magnetoelastic Phenomena ; Quantum, superfluid, helium dynamics
Authors: Mohammad Mehdi Amin | Nasrin Bagheri | Farzaneh Mohammadi | Bahare Dehdashti
Source: International Journal of Chemical Engineering (article, 2022-01-01)
Field: Chemistry — Analytical Chemistry

Sensitivity Analysis with the Monte Carlo Method and Prediction of Atenolol Removal Using Modified Multiwalled Carbon Nanotubes Based on the Response Surface Method: Isotherm and Kinetics Studies Atenolol (ATN) is a β ‐blocker drug extensively used to treat arrhythmias and high blood pressure. Because the human body cannot metabolize it completely, this drug has been commonly found in many environmental matrices. In the present study, the response surface method (RSM) was used for adsorption prediction of ATN on modified multiwalled carbon nanotubes (M‐MWCNTs) by NaOCl and ultrasonic. The sensitivity analysis was done by the Monte Carlo method. Sensitivity analysis was performed to determine the effective parameter by the Monte Carlo simulator. Statistical analysis of variance (ANOVA) was performed by using the nonlinear second‐order model of RSM. The influential parameters including contact time (min), adsorbent dosage (g/L), pH, and the initial concentration (mg/L) of ATN were investigated, and optimal conditions were determined. Kinetic of ATN adsorption on M‐MWCNTs was evaluated using pseudo‐first, pseudo‐second‐order, and intraparticle diffusion models. Equilibrium isotherms for this system were analyzed by the ISOFIT software. As per our result, optimum conditions in the adsorption experiments were pH 7, 60 min of contact time, 0.5 mg/L ATN initial concentration, and 150 mg/L adsorbent dose. In terms of ATN removal efficiency, coefficients of R 2 and adjusted R 2 were 0.999 and 0.998, respectively. Sensitivity analysis also showed that contact time has the greatest effect on increasing the removal of ATN. Pseudo‐first‐order ( R 2 value of 0.99) was the best kinetic model for the adsorption of ATN, and for isotherm, BET (AIC C value of 3.27) was the best model that fit the experimental data. According to the obtained results from sensitive analysis, time was the most important parameter, and after that, the adsorbent dose and pH affect positively on ATN removal efficiency. It can be concluded that the modified multiwalled carbon nanotubes can be applied as one of the best adsorbents to remove ATN from the aqueous solution. Adsorption ; Monte Carlo method ; Response surface methodology ; Materials science ; Diffusion ; Activated carbon ; Sensitivity (control systems) ; Kinetics ; Chemistry ; Chromatography ; Analytical Chemistry (journal) ; Thermodynamics ; Mathematics ; Physics ; Statistics ; Physical chemistry Analytical chemistry methods development ; Analytical Chemistry and Chromatography ; Analytical Chemistry and Sensors
Authors: J. Buendia-Garcia | Marion Lacoue-Nègre | Julien Gornay | Silvia Mas-Garcia | Ryad Bendoula | Jean‐Michel Roger
Source: Fuel (article, 2022-10-15)
Field: Chemistry — Analytical Chemistry

Variable selection and data fusion for diesel cetane number prediction Block (permutation group theory) ; Feature selection ; Sensor fusion ; Covariance ; Cetane number ; Variable (mathematics) ; Statistics ; Computer science ; Fusion ; Mathematics ; Artificial intelligence ; Chemistry Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Wenmin FAN | Sen Yang
Source: Food Science and Technology (article, 2022-01-01)
Field: Chemistry — Analytical Chemistry

Identification of milled rice varieties using machine vision Machine vision detection has the advantages of non-contact, non-destructive, and is suitable for large-scale and high-speed detection. However, existing studies only adopted one or one type image feature in the varieties identification of milled rice, and contained limited explanation of the influence of the image features on the identification accuracy. Thus, this paper developed an identification model for milled rice varieties based on multiple image features and explored the contributions of each image feature on the identification accuracy. The extracted image features were thirteen morphology features, eighteen color features, and four texture features. The training and testing data sets were one hundred and forty milled rice samples. The partial least squares algorithm was used to identify milled rice varieties. The multivariate data analysis was used to get the influence of the image features on the identification accuracy. Experiment results showed that, by selecting twenty features from the original thirty-five features, the identification accuracies were 100%, 70%, 100%, and 100% for milled rice varieties YG, WC, XS, and JN. Identification (biology) ; Artificial intelligence ; Pattern recognition (psychology) ; Feature (linguistics) ; Machine vision ; Texture (cosmology) ; Image (mathematics) ; Computer science ; Computer vision ; Botany Spectroscopy and Chemometric Analyses ; GABA and Rice Research ; Identification and Quantification in Food
Authors: Danvendra Singh | Apurv Sibal | Deepika Sharma | Sumit Sharma | Dániel Sebők | Bhabani K. Satapathy | Parikshit Goswami | Ákos Kukovecz | Amit Rawal
Source: Mechanics of Materials (article, 2022-10-04)
Field: Materials Science — Biomaterials

A combinatorial approach to the elastic response of electrospun mats: Architectural framework and single fiber properties Materials science ; Composite material ; Elastic modulus ; Micromechanics ; Fiber ; Parametric statistics ; Porosity ; Curvature ; Biological system ; Geometry ; Mathematics Electrospun Nanofibers in Biomedical Applications ; Bone Tissue Engineering Materials ; Silk-based biomaterials and applications
Authors: Jamal Nayief Sultan | Emad Toma Karash | Mohammad Takey Elias Kassim | Adel M. Ali | Hssein A. Ibrhim
Source: International Journal of Heat and Technology (article, 2022-12-31)
Field: Materials Science — Materials Chemistry

The Effect of Carburization and Repeated Heat Treatment with Different Solutions on the Fatigue Resistance of Medium Carbon Steel Many mechanical parts are exposed to failure as a result of mechanical stresses for design or metallurgical reasons, and the phenomenon of fatigue represents the largest area and reaches (90%) of the faults of engineering parts that are subject to periodic stresses. The risk of fatigue failure occurs without warning, so the phenomenon of fatigue resistance has taken up a large part of the research and studies concerned with the dissolution of metals. This article aims to study the effect of fatigue resistance of ASTM 1050 steel. Carbonation, repeated quenching at different temperatures (780 &amp; 770℃) using seven different solutions, and tempering at repeated tempering after each quenching at temperatures (230 &amp; 250℃). The stress resistance of all the studied samples decreases after the second cooling in distilled water, with the exception of the samples that were initially quenched with the same solution and then quenched again. This is one of the most significant findings. Another finding is that following a second chilling in distilled water-based solution, the resistance to fatigue stress rises, increasing by up to (8.5%) in comparison to samples that were first quenched in the same solution then diluted. Tempering ; Quenching (fluorescence) ; Materials science ; Metallurgy ; Carburizing ; Distilled water ; Carbonation ; Carbon steel ; Dissolution ; Stress (linguistics) ; Composite material ; Chemistry ; Corrosion Metal Alloys Wear and Properties ; Surface Treatment and Residual Stress ; Erosion and Abrasive Machining
Authors: Soo-In Sohn | Subramani Pandian | Young‐Ju Oh | John‐Lewis Zinia Zaukuu | Yong‐ho Lee | Eun-Kyoung Shin
Source: International Journal of Molecular Sciences (article, 2022-10-24)
Field: Chemistry — Analytical Chemistry

Discrimination of Brassica juncea Varieties Using Visible Near-Infrared (Vis-NIR) Spectroscopy and Chemometrics Methods Brown mustard (<i>Brassica juncea</i> (L.) is an important oilseed crop that is mostly used to produce edible oils, industrial oils, modified lipids and biofuels in subtropical nations. Due to its higher level of commercial use, the species has a huge array of varieties/cultivars. The purpose of this study is to evaluate the use of visible near-infrared (Vis-NIR) spectroscopy in combination with multiple chemometric approaches for distinguishing four <i>B. juncea</i> varieties in Korea. The spectra from the leaves of four different growth stages of four <i>B. juncea</i> varieties were measured in the Vis-NIR range of 325-1075 nm with a stepping of 1.5 nm in reflectance mode. For effective discrimination, the spectral data were preprocessed using three distinct approaches, and eight different chemometric analyses were utilized. After the detection of outliers, the samples were split into two groups, one serving as a calibration set and the other as a validation set. When numerous preprocessing and chemometric approaches were applied for discriminating, the combination of standard normal variate and deep learning had the highest classification accuracy in all the growth stages achieved up to 100%. Similarly, few other chemometrics also yielded 100% classification accuracy, namely, support vector machine, generalized linear model, and the random forest. Of all the chemometric preprocessing methods, Savitzky-Golay filter smoothing provided the best and most convincing discrimination. The findings imply that chemometric methods combined with handheld Vis-NIR spectroscopy can be utilized as an efficient tool for differentiating <i>B. juncea</i> varieties in the field in all the growth stages. Chemometrics ; Brassica ; Near-infrared spectroscopy ; Spectroscopy ; Near infrared reflectance spectroscopy ; Infrared spectroscopy ; Analytical Chemistry (journal) ; Chemistry ; Botany ; Biology ; Chromatography ; Physics ; Organic chemistry Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Meat and Animal Product Quality
Authors: Zhanhan Hu | Danaysa Macías Hernández | Nemury Silega Martínez
Source: Nonlinear Engineering (article, 2022-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Analysis of radiation effects of semiconductor devices based on numerical simulation Fermi–Dirac Abstract To study the radiation effect of Fermi–Dirac (F–D) semiconductor devices based on numerical simulation, two methods are used. One is based on the combination of F–D statistical method and computer simulation. The method discusses the influence of temperature and light energy on the carrier number by starting from an intrinsic silicon semiconductor and carries out computer simulation on the carrier number in intrinsic silicon semiconductor. TID Sim, a three-dimensional parallel solver for ionizing radiation effects of semiconductor devices, is developed. The ionization radiation damage of typical metal oxide semiconductor (MOS) FET NMOS and bipolar transistor GLPNP is simulated. It was proved that the variation trend was close to a straight line in the temperature range (278–358 K) studied in this article. The results are consistent with those of the statistical distribution of semiconductor carriers. This method is suitable for calculating the number of semiconductor carriers, and it is an effective method to study the problems related to carrier distribution. Semiconductor ; Semiconductor device ; Intrinsic semiconductor ; NMOS logic ; Materials science ; Transistor ; Impact ionization ; Optoelectronics ; Silicon ; Fermi–Dirac statistics ; Computational physics ; Physics ; Ionization ; Voltage ; Nanotechnology ; Quantum mechanics Ga2O3 and related materials ; Applied Advanced Technologies ; Radiation Shielding Materials Analysis