jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mahmoud Elsawy | Mickaël Binois | Régis Duvigneau | Stéphane Lanteri | Patrice Genevet
Source: Optics Express (article, 2021-08-19)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Optimization of metasurfaces under geometrical uncertainty using statistical learning The performance of metasurfaces measured experimentally often discords with expected values from numerical optimization. These discrepancies are attributed to the poor tolerance of metasurface building blocks with respect to fabrication uncertainties and nanoscale imperfections. Quantifying their efficiency drop according to geometry variation are crucial to improve the range of application of this technology. Here, we present a novel optimization methodology to account for the manufacturing errors related to metasurface designs. In this approach, accurate results using probabilistic surrogate models are used to reduce the number of costly numerical simulations. We employ our procedure to optimize the classical beam steering metasurface made of cylindrical nanopillars. Our numerical results yield a design that is twice more robust compared to the deterministic case. Nanopillar ; Probabilistic logic ; Computer science ; Optics ; Range (aeronautics) ; Uncertainty quantification ; Surrogate model ; Materials science ; Physics ; Nanotechnology ; Nanostructure ; Artificial intelligence Metamaterials and Metasurfaces Applications ; Acoustic Wave Phenomena Research ; Photonic Crystals and Applications
Authors: Fan Liu | Fang Wang | Guiping Liao | Xin Lu | Jiayi Yang
Source: Applied Sciences (article, 2021-06-21)
Field: Chemistry — Analytical Chemistry

Prediction of Oleic Acid Content of Rapeseed Using Hyperspectral Technique In order to detect the oleic acid content of rapeseed quickly and accurately, we propose, in this paper, an artificial BP neural networks based model for predicting oleic acid content by using rapeseed’s hyperspectral information. Four types of spectral features are selected for our investigation, namely multifractal index, sensitive band, trilateral parameters, and spectral index. Both univariate variable and multiple variables are considered as our model input. The result shows that the combined feature has higher precision and better stability than when using a single parameter. An interesting finding shows that the combined feature involving multifractal parameters can significantly improve the model performance. Taking the combined feature {MF-h(0), SB-DR574, SPI-NDSI(R575, R576)} as the model input, the constructed BP (back propagation) neural networks model has the highest precision, with the coefficient of determination (R2) 0.8753, root mean square error (RMSE) 1.0301, and relative error (RE) 1.047%. This result provides some experience for the rapid detection of rapeseed’s oleic acid content. Rapeseed ; Hyperspectral imaging ; Univariate ; Oleic acid ; Mean squared error ; Artificial neural network ; Pattern recognition (psychology) ; Feature (linguistics) ; Content (measure theory) ; Mathematics ; Computer science ; Artificial intelligence ; Statistics ; Multivariate statistics ; Chemistry ; Food science Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Metabolomics and Mass Spectrometry Studies
Authors: Ziyu Zhao | Xiaoxia Yang | Zhedong Ge | Hui Guo | Yucheng Zhou
Source: BioResources (article, 2021-05-24)
Field: Chemistry — Organic Chemistry

Wood microscopic image identification method based on convolution neural network To prevent the illegal trade of precious wood in circulation, a wood species identification method based on convolutional neural network (CNN), namely PWoodIDNet (Precise Wood Specifications Identification) model, is proposed. In this paper, the PWoodIDNet model for the identification of rare tree species is constructed to reduce network parameters by decomposing convolutional kernel, prevent overfitting, enrich the diversity of features, and improve the performance of the model. The results showed that the PWoodIDNet model can effectively improve the generalization ability, the characterization ability of detail features, and the recognition accuracy, and effectively improve the classification of wood identification. PWoodIDNet was used to analyze the identification accuracy of microscopic images of 16 kinds of wood, and the identification accuracy reached 99%, which was higher than the identification accuracy of several existing classical convolutional neural network models. In addition, the PWoodIDNet model was analyzed to verify the feasibility and effectiveness of the PWoodIDNet model as a wood identification method, which can provide a new direction and technical solution for the field of wood identification. Identification (biology) ; Convolutional neural network ; Overfitting ; Pattern recognition (psychology) ; Computer science ; Artificial intelligence ; Kernel (algebra) ; Convolution (computer science) ; Generalization ; Artificial neural network ; Field (mathematics) ; Mathematics ; Botany Wood and Agarwood Research ; Industrial Vision Systems and Defect Detection ; Microbial Metabolism and Applications
Authors: Supun S. Mohottalalage | Dipak Aryal | Bryce Thurston | Gary S. Grest | Dvora Perahia
Source: Macromolecules (article, 2021-12-23)
Field: Materials Science — Polymers and Plastics

Effects of Ionic Group Distribution on the Structure and Dynamics of Amorphous Polymer Melts Ionizable groups tethered to a polymer backbone often associate to form ionic clusters, whose features are determined by the balance of the polymer backbone and electrostatics. These assemblies impact the dynamics of the macromolecules and their ability to transport ions. Here, using fully atomistic molecular dynamics (MD) simulations, we investigate the effects of the distribution of ionizable groups along the polymer backbone on cluster characteristics and the resulting impacts on the structure and dynamics of amorphous polymers. Particularly, we probe polystyrene sulfonates (PSS) with random, precise, and block configurations of the SO3– sulfonate groups along the backbone with Na+ as the counterion. We find that the distribution of the ionic groups affects the shape and distribution of the clusters as well as the internal packing of the ionizable groups in the cluster and the number of unique chains that participate in each cluster, affecting the structure and the dynamics of the polymers. The signature of ionic clusters, observed in the static structure factor S(q) for all three distributions, is significantly more pronounced for the precise and blocky polymers compared to the random one. Remarkably, we find that the local mobility of the polymer segments is not only affected by the number and size of the clusters but also by the number of polymer chains associated with clusters. Polymer ; Ionic bonding ; Amorphous solid ; Molecular dynamics ; Counterion ; Chemical physics ; Chemistry ; Cluster (spacecraft) ; Polystyrene sulfonate ; Macromolecule ; Polymer chemistry ; Polystyrene ; Crystallography ; Ion ; Computational chemistry ; Organic chemistry Polymer crystallization and properties ; Membrane Separation and Gas Transport ; Material Dynamics and Properties
Authors: Yoshifumi Nishimura | Hiromi Nakai
Source: Chemistry Letters (article, 2021-05-27)
Field: Chemistry — Physical and Theoretical Chemistry

Quantum Chemical Calculations for up to One Hundred Million Atoms Using D<scp>cdftbmd</scp> Code on Supercomputer Fugaku DCDFTBMD developed in our group was an efficient quantum chemical calculation code based on the divide-and-conquer density functional tight-binding (DC-DFTB) method. The bottleneck for its applications to over tens of million atoms was the required memory size. Herein, we optimized array setting and a newly implemented direct self-consistent charge (SCC) algorithm. Using 6144 nodes of the supercomputer Fugaku, the modified code enabled energy calculations at the SCC and non-SCC levels for approximately 5.4 10 7 and 1.1 10 8 atoms, respectively. Chemistry ; Supercomputer ; Quantum chemical ; Code (set theory) ; Parallel computing ; Computational science ; Computational chemistry ; Molecule ; Programming language ; Organic chemistry ; Computer science Advanced Physical and Chemical Molecular Interactions ; Advanced Chemical Physics Studies ; Atomic and Molecular Physics
Authors: Verona Biancardi Oliveira | César Augusto Gonçalves Beatrice | Ricardo Mendes Leal Neto | Wágner Batista Silva | Luiz Antônio Pessan | Walter José Botta Filho | Daniel Rodrigo Leiva
Source: Materials Research (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Hydrogen Absorption/Desorption Behavior of a Cold-Rolled TiFe Intermetallic Compound The (de)hydrogenation properties of a TiFe intermetallic compound (IMC) alloy activated by cold rolling inside a glovebox at ambient temperature were investigated by kinetic measurements, x-ray powder diffraction (XRD), and transmission electron microscopy (TEM). Rate-limiting steps were identified by testing kinetic models on hydrogen absorption and desorption curves. To prevent surface poisoning during air exposure, the TiFe IMC was also cold rolled with polytetrafluoroethylene (PTFE) and ultra-high molecular weight polyethylene (UHMWPE). The addition of either PTFE or UHMWPE to nanostructured TiFe by cold rolling did not produce a polymer-metal composite with O2 poisoning resistance, regardless of the polymer-mixing way adopted. This occurred because large surfaces of the particles were not adequately coated with polymer. The results identify challenges to the TiFe IMC polymer-covered material that must be overcome before a methodology can significantly contribute to the formation of nanostructured TiFe-polymer composites with enhanced hydrogen storage properties. The diffusion-controlled reactions in the cold-rolled TiFe IMC without polymer were prevalent in all cases, as predicted by the Jander three-dimensional diffusion model. The main contribution of this work regards estimation of the amount of hydrogen released, which was 0.60% after 6 min and reproducible for the three subsequent cycles. Materials science ; Intermetallic ; Polymer ; Hydrogen storage ; Alloy ; Hydrogen ; Desorption ; Composite material ; Diffusion ; Chemical engineering ; Glovebox ; Polyethylene ; Metallurgy ; Chemistry ; Organic chemistry ; Thermodynamics Hydrogen Storage and Materials ; Catalysis and Hydrodesulfurization Studies ; Catalytic Processes in Materials Science
Authors: Iqra Mubeen | Muhammad Akhyar Farrukh
Source: Inorganic and Nano-Metal Chemistry (article, 2021-09-14)
Field: Materials Science — Materials Chemistry

Mechanisms of green synthesis of iron nanoparticles using <i>Trifolium alexandrinum</i> extract and degradation of methylene blue Iron nanoparticles (FeNPs) were synthesized via sustainable route of green synthesis to investigate their activity as photocatalyst against environmental pollutants. Nanoparticles were synthesized from leaf extract of Trifolium alexandrinum as a source of stabilizing, capping and reducing agent. Structural and morphological features of these synthesized nanoparticles were characterized by using various techniques including UV-Vis spectroscopy, FTIR, XRD, SEM and DLS analysis. FeNPs were successfully applied as photocatalyst to remove the methylene blue (MB). Mechanism of removal was investigated by fitting the data in pseudo-first-order and pseudo-second-order adsorption and reduction models along with intraparticle diffusion model. FeNPs were found highly active with more than 97% degradation of MB within 35 minutes. Stability of catalyst to be reused was checked out and significant results found up to 3 cycles. Nontoxic, low cost, green synthesized FeNPs were proven efficient as catalyst for the degradation of MB. Methylene blue ; Nanoparticle ; Catalysis ; Photocatalysis ; Degradation (telecommunications) ; Adsorption ; Nuclear chemistry ; Fourier transform infrared spectroscopy ; Reducing agent ; Industrial and production engineering ; Chemistry ; Chemical engineering ; Materials science ; Nanotechnology ; Organic chemistry ; Computer science Nanoparticles: synthesis and applications ; Environmental remediation with nanomaterials ; Nanomaterials for catalytic reactions
Authors: He Sun | Rui Li | Huijie Li | Zhengyan Weng | Guangfu Wu | Peter Kerns | Steven L. Suib | Xueju Wang | Yi Zhang
Source: ACS Applied Materials & Interfaces (article, 2021-07-09)
Field: Materials Science — Surfaces, Coatings and Films

Bioinspired Oil-Infused Slippery Surfaces with Water and Ion Barrier Properties Encapsulation materials play an important role in many applications including wearable electronics, medical devices, underwater robotics, marine skin tagging system, food packaging, and energy conversation and storage devices. To date, all the encapsulation materials, including polymer layers and inorganic materials, are <i>solid</i> materials. These solid materials suffer from limited barrier lifetimes due to pinholes, cracks, and nanopores or from complicated fabrication processes and limited stretchability for interfacing with complex 3D surfaces. This paper reports a solution to this material challenge by demonstrating bioinspired oil-infused slippery surfaces with excellent waterproof property for the first time. A water vapor transmission test shows that locking a thin layer of oil on the silicone elastomer improves the water vapor barrier performance by three orders of magnitude. Accelerated lifetime tests suggest robust water barrier characteristics that approach 226 days at 37 °C even under severe mechanical damage. A combination of temperature- and thickness-dependent experimental measurements and reaction-diffusion modeling reveals the key waterproof property. In addition to serving as a barrier to water, the oil-infused surface demonstrates an attractive ion barrier property. All these exceptional properties suggest the potential applications of slippery surfaces as encapsulation materials for medical devices, underwater electronics, and many others. Materials science ; Nanotechnology ; Fabrication ; Elastomer ; Electronics ; Diffusion barrier ; Flexible electronics ; Encapsulation (networking) ; Composite material ; Layer (electronics) ; Computer science Surface Modification and Superhydrophobicity ; Advanced Sensor and Energy Harvesting Materials ; Silicone and Siloxane Chemistry
Authors: Elena Vismara | Giulia Bertolini | Chiara Bongio | Nicolò Massironi | Marco Zarattini | Daniele Nanni | Cesare Cosentino | Giangiacomo Torri
Source: Nanomaterials (article, 2021-02-13)
Field: Materials Science — Biomaterials

Nanocellulose from Cotton Waste and Its Glycidyl Methacrylate Grafting and Allylation: Synthesis, Characterization and Adsorption Properties Nanocellulose (NC) is getting ahead as a renewable, biodegradable and biocompatible biomaterial. The NCs for this study were recovered from industrial cotton waste (CFT) by acid hydrolysis (HNC) and by 2,2,6,6-tetramethylpiperidine-1-oxyl (TEMPO) mediated oxidation (ONC). They were functionalized by radical based glycidyl methacrylate (GMA) grafting providing crystalline HNC-GMA and ONC-GMA, and by allylation (ALL) providing amorphous HNC-ALL and ONC-ALL. HNC, ONC and their derivatives were chemically and morphologically characterized. Crystalline NCs were found capable to adsorb, from diluted water solution (2 × 10<sup>-3</sup> M), the antibiotics vancomycin (VC), ciprofloxacin (CP), amoxicillin (AM) and the disinfectant chlorhexidine (CHX), while amorphous NCs did not show any significant adsorption properties. Adsorption capability was quantified by measuring the concentration change in function of the contact time. The adsorption kinetics follow the pseudo-second order model and show complex adsorption mechanisms investigated by an intraparticle diffusion model and interpreted by structure-property relationships. ONC and ONC-GMA loaded with VC, and HNC and HNC-GMA loaded with CP were not colonized by <i>Staphylococcus aureus</i> and by <i>Klebsiella pneumonia</i> and suggested long lasting release capability. Our results can envisage developing CFT derived NCs for environmental applications (water remediation) and for biomedical applications (antibacterial NC). Among the future developments, it could also be of interest to take advantage of acidic, glycidyl and allyl groups' reactivity to provide other NCs from the NC object of this study. Nanocellulose ; Glycidyl methacrylate ; Adsorption ; Chemistry ; Grafting ; Methacrylate ; Chemical engineering ; Oxidized cellulose ; Cellulose ; Nuclear chemistry ; Materials science ; Polymer chemistry ; Organic chemistry ; Polymer ; Polymerization Advanced Cellulose Research Studies ; Nanocomposite Films for Food Packaging ; Dyeing and Modifying Textile Fibers
Authors: Yoshihisa Fujii | Taiki Tominaga | Daiki Murakami | Masaru Tanaka | Hideki Seto
Source: Frontiers in Chemistry (article, 2021-10-29)
Field: Materials Science — Polymers and Plastics

Local Dynamics of the Hydration Water and Poly(Methyl Methacrylate) Chains in PMMA Networks The dynamic behavior of water molecules and polymer chains in a hydrated poly(methyl methacrylate) (PMMA) matrix containing a small amount of water molecules was investigated. Water molecules have been widely recognized as plasticizers for activating the segmental motion of polymer chains owing to their ability to reduce the glass transition temperature. In this study, combined with judicious hydrogen/deuterium labeling, we conducted quasi-elastic neutron scattering (QENS) experiments on PMMA for its dry and hydrated states. Our results clearly indicate that the dynamics of hydrated polymer chains are accelerated, and that individual water molecules are slower than bulk water. It is therefore suggested that the hydration water affects the local motion of PMMA and activates the local relaxation process known as restricted rotation, which is widely accepted to be generally insensitive to changes in the microenvironment. Polymer ; Glass transition ; Methyl methacrylate ; Molecule ; Methacrylate ; Neutron scattering ; Chemical engineering ; Deuterium ; Relaxation (psychology) ; Materials science ; Poly(methyl methacrylate) ; Chemical physics ; Polymer chemistry ; Plasticizer ; Hydrogen bond ; Molecular dynamics ; Chemistry ; Scattering ; Monomer ; Composite material ; Organic chemistry ; Computational chemistry Polymer crystallization and properties ; Advanced Sensor and Energy Harvesting Materials ; Polymer Nanocomposites and Properties
Authors: Austin Ladshaw | Josh A. Pihl
Source: Applied Catalysis B: Environmental (article, 2021-11-06)
Field: Materials Science — Materials Chemistry

Measurement and Modeling of the effects of exhaust composition and hydrothermal aging on the ammonia storage capacity of a commercial Cu-SSZ-13 catalyst Ammonia ; Catalysis ; Hydrothermal circulation ; Adsorption ; Chemistry ; Brønsted–Lowry acid–base theory ; Inorganic chemistry ; NOx ; Chemical engineering ; Materials science ; Environmental chemistry ; Physical chemistry ; Organic chemistry ; Engineering Catalytic Processes in Materials Science ; Nanomaterials for catalytic reactions ; Ammonia Synthesis and Nitrogen Reduction
Authors: B. Andriyevsky | I. E. Barchiy | I.P. Studenyak | А. І. Кашуба | M. Piasecki
Source: Scientific Reports (article, 2021-09-24)
Field: Materials Science — Materials Chemistry

Electron, phonon and thermoelectric properties of Cu7PS6 crystal calculated at DFT level The promising class of the environment-friendly thermoelectrics is the copper-based argyrodite-type ion-conducting crystals exhibiting just extraordinary low thermal conductivity below the glass limit associated with the molten copper sublattice leading to a softening of phonon modes. To explain why the argyrodite structure containing copper ions favors the low thermal conductivity, we have utilized the ab initio calculations of the electron, phonon, and thermoelectric properties of Cu<sub>7</sub>PS<sub>6</sub> crystal in the framework of the density functional and Boltzmann transport theories. To obtain the reliable thermoelectric properties of Cu<sub>7</sub>PS<sub>6</sub>, we take into account the dependence of the electron effective mass m<sup>*</sup> on the redundant carrier concentration n. We propose to use the Burstein-Moss effect for the calculation of the electron effective mass m<sup>*</sup> of a semiconductor. We have found the strong nonlinear character of copper atom vibrations in Cu<sub>7</sub>PS<sub>6</sub> which exceeds substantially the similar values for phosphorous and sulfur atoms. The large vibration nonlinearity of the copper atoms found in Cu<sub>7</sub>PS<sub>6</sub> explains the diffusion-like heat transfer and the relatively low coefficient of the lattice thermal conductivity (κ = 0.7 W/(m K)), which is favorable to achieve the large thermoelectric figure of merit. Thermoelectric effect ; Thermoelectric materials ; Materials science ; Thermal conductivity ; Copper ; Phonon ; Seebeck coefficient ; Condensed matter physics ; Effective mass (spring–mass system) ; Thermodynamics ; Physics ; Composite material Advanced Thermoelectric Materials and Devices ; Chalcogenide Semiconductor Thin Films ; Semiconductor materials and interfaces
Authors: А. С. Семенов | Johannes Trapp | Michael Nöthe | Oliver Eberhardt | Bernd Kieback | Thomas Wallmersperger
Source: Computational Mechanics (article, 2021-03-22)
Field: Materials Science — Ceramics and Composites

Thermo-electro-mechanical modeling of spark plasma sintering processes accounting for grain boundary diffusion and surface diffusion Abstract In the present research, a numerical modeling approach of the initial stage of consolidation during spark plasma sintering on the microscopic scale is presented. The solution of a fully coupled thermo-electro-mechanical problem also accounting for grain boundary and surface diffusion is found by using a staggered way. The finite-element method is applied for solving the thermo-electro-mechanical problem while the finite-difference method is applied for the diffusion problem. A Lagrange-based non-linear formulation is used to deal with the detailed description of plastic and creep strain accumulation. The numerical model is developed for simulating the structural evolution of the involved particles during sintering of powder compacts taking into account both the free surface diffusion of the particles and the grain boundary diffusion at interparticle contact areas. The numerical results obtained by using the two-particle model—as a representative volume element of the powder—are compared with experimental results for the densification of a copper powder compact. The numerical and experimental results are in excellent agreement. Spark plasma sintering ; Materials science ; Consolidation (business) ; Grain boundary diffusion coefficient ; Mechanics ; Diffusion ; Finite element method ; Grain boundary ; Creep ; Sintering ; Boundary value problem ; Metallurgy ; Thermodynamics ; Mathematics ; Mathematical analysis ; Physics ; Microstructure ; Accounting Advanced ceramic materials synthesis ; Aluminum Alloys Composites Properties ; Powder Metallurgy Techniques and Materials