jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Landon T. Hansen | David T. Fullwood | Eric R. Homer | R. H. Wagoner | Hojun Lim | Jay Carroll | Guowei Zhou | Hyuk Jong Bong
Source: Materials Science and Engineering A (article, 2019-11-18)
Field: Materials Science — Materials Chemistry

An investigation of geometrically necessary dislocations and back stress in large grained tantalum via EBSD and CPFEM Materials science ; Electron backscatter diffraction ; Stress (linguistics) ; Misorientation ; Crystal twinning ; Dislocation ; Deformation (meteorology) ; Composite material ; Grain boundary ; Microstructure Microstructure and mechanical properties ; Fusion materials and technologies ; Microstructure and Mechanical Properties of Steels
Authors: Mariem Ben Ameur | Abderrahim El Mahi | Jean-Luc Rebière | Isabelle Gimenez | Moez Beyaoui | Moez Abdennadher | Mohamed Haddar
Source: Engineering Fracture Mechanics (article, 2019-06-11)
Field: Materials Science — Polymers and Plastics

Investigation and identification of damage mechanisms of unidirectional carbon/flax hybrid composites using acoustic emission Acoustic emission ; Materials science ; Composite material ; Ultimate tensile strength ; Scanning electron microscope ; Carbon fibers ; Tensile testing ; Structural engineering ; Composite number Natural Fiber Reinforced Composites ; Mechanical Behavior of Composites ; Cellular and Composite Structures
Authors: Badr M. Thamer | Ali Aldalbahi | Meera Moydeen Abdulhameed | Abdullah M. Al‐Enizi | Hany El‐Hamshary | Mohamed H. El‐Newehy
Source: Scientific Reports (article, 2019-12-19)
Field: Materials Science — Biomaterials

Fabrication of functionalized electrospun carbon nanofibers for enhancing lead-ion adsorption from aqueous solutions Electrospinning technique is a simple and cheap method for fabrication of electrospun nanofibers (ENFs), which in turn can converted into electrospun carbon nanofibers (ECNFs) by carbonization process. The controlling of the ECNFs properties (e.g. surface area, porosity, diameters) during fabrication, make it superior over the other carbon nanomaterials. The aim of our study is to modify the surface of ECNFs to increase its hydrophilicity and in turn its efficiency in removing lead ions (Pb<sup>2+</sup>) from aqueous systems. The surface modification was carried out in two steps starting from oxidation of pristine ECNFs to produce oxidized ECNFs (o-ECNFs), followed by covalently bonded of melamine, and poly(m-phenylene diamine) for forming melamine-functionalized ECNFs (melam-ECNFs) and poly(m-phenylene diamine)-functionalized ECNFs (PmPDA-ECNFs), respectively. The as-prepared materials were characterized in routine way. The ability of the as-prepared materials towards adsorption of Pb<sup>2+</sup> ions as heavy metal was investigated with the study of some factors such as pH solution, contact time, initial concentration and temperature. The adsorption process was analyzed isothermally, and kinetically. According to the values of the thermodynamic parameters, the adsorption of Pb<sup>2+</sup> ions onto the functionalized ECNFs was endothermic and spontaneous, except with melam-ECNFs was exothermic. Adsorption ; Electrospinning ; Materials science ; Aqueous solution ; Chemical engineering ; Melamine ; Carbonization ; Nanofiber ; Surface modification ; Fabrication ; Carbon nanofiber ; Nanomaterials ; Metal ions in aqueous solution ; Carbon fibers ; Specific surface area ; Endothermic process ; Polymer chemistry ; Nanotechnology ; Chemistry ; Carbon nanotube ; Organic chemistry ; Metal ; Composite number ; Composite material ; Polymer ; Catalysis Electrospun Nanofibers in Biomedical Applications ; Supercapacitor Materials and Fabrication ; Graphene and Nanomaterials Applications
Authors: Xiaoteng Zhang | Dongmei Jiang | Yiqun Xiao | Junchang Chen | Shuai Hao | Liangshu Xia
Source: Journal of Chemistry (article, 2019-04-23)
Field: Chemistry — Inorganic Chemistry

Adsorption of Uranium(VI) from Aqueous Solution by Modified Rice Stem The biosorption is an effective and economical method to deal with the wastewater with low concentrations of uranium. In this study, we present a systematic investigation of the adsorption properties, such as the kinetics, thermodynamics, and mechanisms, of modified rice stems. The rice stems treated with 0.5 mol/L NaOH solutions show higher removal percentage of uranium than those unmodified under the conditions of initial pH (pH = 4.0), absorbent dosage (5–8 g/L), temperature ( T = 298 K), and adsorption equilibrium time ( t = 180 min). The removal percentage of uranium(VI) decreases with increasing initial concentration of uranium(VI). The Langmuir isotherm model, which suggests predominant monolayered sorption, is better than Freundlich and Temkin models to elucidate the adsorption isotherm of adsorbed uranium. Kinetic analyses indicate that the uranium(VI) adsorption of the modified rice stem is mainly controlled by surface adsorption. The pseudo-second-order kinetic model, with the correlation coefficient of R 2 = 0.9992, fits the adsorption process much better than other kinetic models (e.g., pseudo-second-order kinetic model, Elovich kinetic model, and intraparticle diffusion model). The thermodynamic parameters Δ G 0 , Δ H 0 , and Δ S 0 demonstrate that the adsorption of uranium(VI) is an endothermic and spontaneous process, which can be promoted by temperature. The adsorption of uranium can change the morphology and the structure characteristics of the modified rice stem through interaction with the adsorption sites, such as O-H, C=O, Si=O, and P-O on the surface. Chemistry ; Adsorption ; Uranium ; Biosorption ; Endothermic process ; Sorption ; Freundlich equation ; Aqueous solution ; Langmuir ; Langmuir adsorption model ; Diffusion ; Nuclear chemistry ; Inorganic chemistry ; Thermodynamics ; Physical chemistry ; Metallurgy ; Materials science Radioactive element chemistry and processing ; Extraction and Separation Processes ; Hydrocarbon exploration and reservoir analysis
Authors: Yun Cheng | Jiwen Xing | Changsheng Bu | Jubing Zhang | Guilin Piao | Yaji Huang | Hao Xie | Xinye Wang
Source: Minerals (article, 2019-09-27)
Field: Materials Science — Biomaterials

Dehydroxylation and Structural Distortion of Kaolinite as a High-Temperature Sorbent in the Furnace As a high-temperature sorbent, kaolinite undergoes the flash calcination process in the furnace resulting in the dehydroxylation and structural distortion, which are closely related to its heavy metal/alkali metal adsorption characteristics. We investigated the flash calcination of kaolinite by the experiments using a drop tube furnace and by the characterization of flash-calcined products using thermogravimetric-differential scanning calorimeter (TG-DSC), X-ray diffraction (XRD), Fourier Transform Infrared Spectrometer (FTIR)and nuclear magnetic resonance (NMR). There were three kinds of hydroxyl groups in kaolinite during flash calcination at 800–1300 °C, E-type (~50%, easy), D-type (~40%, difficult) and U-type (~10%, unable) according to the removal difficulty. The hydroxyl groups activation was believed to be the first step of the removal of E-type and D-type hydroxyl groups. The kinetics model of dehydroxylation groups at 900–1200 °C was established following Arrhenius equation with the activation energy of 140 kJ/mol and the pre-exponential factor of 1.32 × 106 s−1. At 800 °C, the removal of E-type hydroxyl groups resulted in the conversion of a part of VI-coordinated Al in kaolinite to V-coordinated Al and the production of meta-kaolinite. When the temperature rose up to 1200 °C, mullite was produced and a part of V-coordinated Al converted to IV-coordinated Al and VI-coordinated Al. Finally, the adsorption characteristics of kaolinite was discussed according to the results of dehydroxylation and structural distortion. Kaolinite ; Calcination ; Fourier transform infrared spectroscopy ; Thermogravimetric analysis ; Adsorption ; Arrhenius equation ; Differential scanning calorimetry ; Chemistry ; Mullite ; Activation energy ; Inorganic chemistry ; Materials science ; Chemical engineering ; Mineralogy ; Physical chemistry ; Organic chemistry ; Catalysis ; Thermodynamics Clay minerals and soil interactions ; Coal and Its By-products ; Recycling and utilization of industrial and municipal waste in materials production
Authors: Janja Mirtič | Helena Balažic | Špela Zupančič | Julijana Kristl
Source: Polymers (article, 2019-04-16)
Field: Materials Science — Biomaterials

Effect of Solution Composition Variables on Electrospun Alginate Nanofibers: Response Surface Analysis Alginate is a promising biocompatible and biodegradable polymer for production of nanofibers for drug delivery and tissue engineering. However, alginate is difficult to electrospin due to its polyelectrolyte nature. The aim was to improve the 'electrospinability' of alginate with addition of exceptionally high molecular weight poly(ethylene oxide) (PEO) as a co-polymer. The compositions of the polymer-blend solutions for electrospinning were varied for PEO molecular weight, total (alginate plus PEO) polymer concentration, and PEO proportion in the dry alginate-PEO polymer mix used. These were tested for rheology (viscosity, complex viscosity, storage and loss moduli) and conductivity, and the electrospun nanofibers were characterized by scanning electron microscopy. One-parameter-at-a-time approach and response surface methodology (RSM) were used to optimize the polymer-blend solution composition to obtain defined nanofibers. Both approaches revealed that the major influence on nanofiber formation and diameter were total polymer concentration and PEO proportion. These polymer-blend solutions of appropriate conductivity and viscosity enabled fine-tuning of nanofiber diameter. PEO molecular weight of 2-4 million Da greatly improved the electrospinnability of alginate, producing nanofibers with >85% alginate. This study shows that RSM can be used to design nanofibers with optimal alginate and co-polymer contents to provide efficient scaffold material for regenerative medicine. Electrospinning ; Nanofiber ; Materials science ; Composition (language) ; Response surface methodology ; Chemical engineering ; Nanotechnology ; Composite material ; Chemistry ; Polymer ; Chromatography ; Engineering Electrospun Nanofibers in Biomedical Applications ; Advanced Sensor and Energy Harvesting Materials ; Conducting polymers and applications
Authors: Andrea Ninarello | Jérôme J. Crassous | Divya Paloli | Fabrizio Camerin | Nicoletta Gnan | Lorenzo Rovigatti | Peter Schurtenberger | Emanuela Zaccarelli
Source: Macromolecules (article, 2019-10-01)
Field: Materials Science — Materials Chemistry

Modeling Microgels with a Controlled Structure across the Volume Phase Transition Thermoresponsive microgels are soft colloids that find widespread use as model systems for soft matter physics. Their complex internal architecture, made of a disordered and heterogeneous polymer network, has been so far a major challenge for computer simulations. In this work, we put forward a coarse-grained model of microgels whose structural properties are in quantitative agreement with results obtained with small-angle X-ray scattering experiments across a wide range of temperatures, encompassing the volume phase transition. These results bridge the gap between experiments and simulations of individual microgel particles, paving the way to theoretically address open questions about their bulk properties with unprecedented nano- and microscale resolution. Soft matter ; Microscale chemistry ; Chemical physics ; Phase transition ; Polymer ; Scattering ; Materials science ; Colloid ; Work (physics) ; Volume (thermodynamics) ; Phase (matter) ; Range (aeronautics) ; Excluded volume ; Statistical physics ; Nanotechnology ; Physics ; Chemistry ; Thermodynamics ; Optics ; Physical chemistry ; Composite material Material Dynamics and Properties ; Block Copolymer Self-Assembly ; Pickering emulsions and particle stabilization
Authors: Evan C. Wegener | Brandon C. Bukowski | Dali Yang | Zhenwei Wu | A. Jeremy Kropf | W. Nicholas Delgass | Jeffrey Greeley | Guanghui Zhang | Jeffrey T. Miller
Source: ChemCatChem (article, 2019-12-09)
Field: Materials Science — Materials Chemistry

Intermetallic Compounds as an Alternative to Single‐atom Alloy Catalysts: Geometric and Electronic Structures from Advanced X‐ray Spectroscopies and Computational Studies Abstract Pt−Fe intermetallic compound (IMC) catalysts, including Pt 3 Fe, PtFe and PtFe 3 , are shown to have advantageous catalytic properties similar to those reported for single‐atom alloy catalysts. Suppresion of the Pt ensembles responsible for hydrogenolysis results in high olefin selectivity during propane dehydrogenation. In situ resonant inelastic X‐ray scattering (RIXS) results and density functional theory calculations show that these changes are associated with a decrease in the average energy of the filled 5d states of Pt in the Pt−Fe intermetallic compound structures compared to monometallic Pt, accompanied by an increase in the average energy of the unfilled valence bands. The decrease in energy of the filled Pt 5d orbitals increases with increasing Fe content in the IMC, i.e. PtFe 3 &gt;PtFe&gt;Pt 3 Fe. These results demonstrate that by altering the stoichiometry of IMC catalysts it is possible to control both the ensemble size and electronic properties of active sites, which affords another mechanism for tuning catalytic properties, in addition to changing promoter metals. The present study demonstrates the potential of ordered intermetallic compounds as an alternative to traditional solid‐solution single‐atom alloys to serve as catalysts with well‐defined and uniform active sites. Intermetallic ; Catalysis ; Dehydrogenation ; Materials science ; Valence (chemistry) ; Alloy ; Hydrogenolysis ; Density functional theory ; Stoichiometry ; Atom (system on chip) ; Crystallography ; Physical chemistry ; Chemistry ; Computational chemistry ; Metallurgy ; Organic chemistry Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Advanced Chemical Physics Studies
Authors: Hongshuai Wang | Yujin Ji | Youyong Li
Source: Wiley Interdisciplinary Reviews Computational Molecular Science (article, 2019-06-13)
Field: Materials Science — Materials Chemistry

Simulation and design of energy materials accelerated by machine learning Abstract In the light of mature mathematical algorithms and material database construction, a basic research framework of machine learning (ML) method integrated with computational chemistry toolkits exhibits great potentials and advantages in the field of material researches. In this review, we introduce a work flow of ML in energy materials and demonstrate its recent applications in accelerating the material exploration, especially significant progresses in designing novel catalysts, organic and inorganic battery materials and metal–organic framework materials. As a rising research direction, we also identify the prospects and challenges of ML. More automated and intelligent workflows will be widely used in energy material design with the development of ML. Our review provides a guideline to study and design energy materials in the framework of ML. This article is categorized under: Structure and Mechanism &gt; Computational Materials Science Workflow ; Computer science ; Field (mathematics) ; Material Design ; Battery (electricity) ; Systems engineering ; Work (physics) ; Process engineering ; Nanotechnology ; Mechanical engineering ; Materials science ; Engineering ; Database Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; Inorganic Chemistry and Materials
Authors: Nongnuch Artrith
Source: Journal of Physics Energy (article, 2019-05-09)
Field: Materials Science — Materials Chemistry

Machine learning for the modeling of interfaces in energy storage and conversion materials Abstract The properties and atomic-scale dynamics of interfaces play an important role for the performance of energy storage and conversion devices such as batteries and fuel cells. In this topical review, we consider recent progress in machine-learning (ML) approaches for the computational modeling of materials interfaces. ML models are computationally much more efficient than first principles methods and thus allow to model larger systems and extended timescales, a necessary prerequisites for the accurate description of many interface properties. Here we review the recent major developments of ML-based interatomic potentials for atomistic modeling and ML approaches for the direct prediction of materials properties. This is followed by a discussion of ML applications to solid–gas, solid–liquid, and solid–solid interfaces as well as to nanostructured and amorphous phases that commonly form in interface regions. We then highlight how ML has been used to obtain important insights into the structure and stability of interfaces, interfacial reactions, and mass transport at interfaces. Finally, we offer a perspective on the current state of ML potential development and identify future directions and opportunities for this exciting research field. Interface (matter) ; Computer science ; Stability (learning theory) ; Field (mathematics) ; Amorphous solid ; Energy storage ; Nanotechnology ; Materials science ; Chemistry ; Machine learning ; Physics Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Electrocatalysts for Energy Conversion
Authors: Yihao Liu | Ziheng Hu | Zhiguang Suo | Lianzhe Hu | Lingyan Feng | Xiuqing Gong | Yi Liu | Jincang Zhang
Source: Science China Technological Sciences (review, 2019-02-22)
Field: Materials Science — Materials Chemistry

High-throughput experiments facilitate materials innovation: A review Throughput ; Scope (computer science) ; Computer science ; Materials informatics ; Software deployment ; Characterization (materials science) ; Nanotechnology ; Wireless ; Materials science ; Health informatics ; Software engineering Machine Learning in Materials Science ; Ferroelectric and Negative Capacitance Devices ; Electronic and Structural Properties of Oxides
Authors: Li Ju | Aihua Lyu | Hongxia Hao | Wen Shen | Hua Cui
Source: Analytical Chemistry (article, 2019-06-11)
Field: Materials Science — Materials Chemistry

Deep Learning-Assisted Three-Dimensional Fluorescence Difference Spectroscopy for Identification and Semiquantification of Illicit Drugs in Biofluids The fast identification and quantification of illicit drugs in biofluids are of great significance in clinical detection. However, existing drug detection strategies cannot fully meet clinical needs, and the on-site identification and quantification of various illicit drugs in biofluids remain a great challenge. Here, we report the development of a deep learning-assisted three-dimensional (3D) fluorescence difference spectroscopy for rapid identification and semiquantification of illicit drugs in biofluids. This strategy introduces highly fluorescent silver nanoclusters into the biofluids with illicit drugs as signal sources. The interaction between silver nanoclusters and drug molecules changed the fluorescence performance of the mixture. Deep learning methods were applied to grasp the subtle fingerprint information from the 3D fluorescence difference spectra to identify and semiquantify various illicit drugs in biofluids, including codeine, 4,5-methylene-dioxy amphetamine, 3,4-methylene dioxy methamphetamine, meperidine, and methcathinone. This approach can achieve a high prediction accuracy rate of 88.07% and a broad detection range from 2 μg/mL to 100 mg/mL. It opens up a new way for the detection of small molecules with or without fluorescence in complicated matrixes. Chemistry ; Nanoclusters ; Fluorescence ; Fluorescence spectroscopy ; Organic chemistry Machine Learning in Materials Science ; Nanocluster Synthesis and Applications ; Computational Drug Discovery Methods
Authors: Ágnes Szécsényi | Elena V. Khramenkova | Ivan Yu. Chernyshov | Guanna Li | Jorge Gascón | Evgeny A. Pidko
Source: ACS Catalysis (article, 2019-09-04)
Field: Materials Science — Materials Chemistry

Breaking Linear Scaling Relationships with Secondary Interactions in Confined Space: A Case Study of Methane Oxidation by Fe/ZSM-5 Zeolite &lt;p&gt;Linear energy scaling laws connect the kinetic and thermodynamic parameters of key elementary steps for heterogeneously catalyzed reactions over defined active sites on open surfaces. Such scaling laws provide a framework for a rapid computational activity screening of families of catalysts, but they also effectively impose a fundamental limit on the theoretically attainable activity. Understanding the limits of applicability of the linear scaling laws is therefore crucial for the development of predictive models in catalysis. In this work, we computationally investigate the role of secondary effects of the active site environment on the reactivity of defined Fe complexes in ZSM-5 zeolite toward methane oxofunctionalization. The computed C-H activation barriers over Fe-sites at different locations inside the zeolite pores generally follow the associated reaction enthalpies and the hydrogen affinities of the active site, reflecting the O-H bond strength. Nevertheless, despite the close similarity of the geometries and intrinsic reactivities of the considered active complexes, substantial deviations from these linear scaling relations are apparent from the DFT calculations. We identify three major factors behind these deviations, namely, (1) confinement effects due to the zeolite micropores, (2) coordinative flexibility, and (3) multifunctionality of the active site. The latter two phenomena impact the mechanism of the catalytic reaction by providing a cooperative reaction channel for the substrate activation or by enabling the stabilization of the intrazeolite complex along the reaction path. These computational findings point to the need for the formulation of multidimensional property-Activity relationships accounting for both the intrinsic chemistry of the reactive ensembles and secondary effects due to their environmental and dynamic characteristics.&lt;/p&gt; Scaling ; Zeolite ; Catalysis ; Chemistry ; Chemical physics ; Active site ; Activation energy ; Transition state ; Computational chemistry ; Thermodynamics ; Physical chemistry ; Physics ; Organic chemistry ; Mathematics ; Geometry Catalytic Processes in Materials Science ; Electrocatalysts for Energy Conversion ; Catalysis and Oxidation Reactions
Authors: Daniel W. Davies | Keith T. Butler | Adam Jackson | Jonathan M. Skelton | Kazuki Morita | Aron Walsh
Source: The Journal of Open Source Software (article, 2019-06-10)
Field: Materials Science — Materials Chemistry

SMACT: Semiconducting Materials by Analogy and Chemical Theory Analogy ; Materials science ; Theoretical physics ; Engineering physics ; Nanotechnology ; Epistemology ; Physics ; Philosophy Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Surface and Thin Film Phenomena