jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Basheer A. Alshammari | Fahad S. Al‐Mubaddel | Mohammad Rezaul Karim | Mokarram Hossain | Abdullah S. Al-Mutairi | Arthur Wilkinson
Source: Polymers (article, 2019-08-28)
Field: Materials Science — Polymers and Plastics

Addition of Graphite Filler to Enhance Electrical, Morphological, Thermal, and Mechanical Properties in Poly (Ethylene Terephthalate): Experimental Characterization and Material Modeling Poly(ethylene terephthalate)/graphite (PET/G) micro-composites were fabricated by the melt compounding method using a minilab extruder. The carbon fillers were found to act as nucleating agents for the PET matrix and hence accelerated crystallization and increased the degree of crystallinity. TGA showed that carbon fillers improved the resistance to thermal and thermo-oxidative degradation under both air and nitrogen atmospheres. However, a poor agreement was observed at higher loadings of the filler where the composites displayed reduced reinforcement efficiency. The results demonstrate that the addition of graphite at loading >14.5 wt.% made electrically conductive composites. It was calculated that the electric conductivities of PET/graphite micro-composites were enhanced, above the percolation threshold values by two orders of magnitudes compared to the PET matrix. The minimum value of conductivity required to avoid electrostatic charge application of an insulating polymer was achieved, just above the threshold values. The addition of graphite also improved thermal stability of PET, accelerated its crystallization process and increased the degree of crystallinity. Microscopic results exhibit no indication of aggregations at 2 wt.% graphite, whereas more agglomeration and rolling up could be seen as the graphite content was increased in the PET matrix (in particular, above the percolation threshold value). Furthermore, based on the mechanical experimental characterization of the PET/graphite micro-composites, a large deformation-based mathematical model is proposed for material behavior predictions. The model fits well the experimental data and predicts other mechanical data that are not included in the parameter identification. Materials science ; Composite material ; Graphite ; Percolation threshold ; Crystallinity ; Crystallization ; Percolation (cognitive psychology) ; Electrical resistivity and conductivity ; Chemical engineering Polymer crystallization and properties ; Fiber-reinforced polymer composites ; Natural Fiber Reinforced Composites
Authors: Kui Yang | Shuang Chao | Feiyu Zhang | Yuxin Pei | Zhichao Pei
Source: Chemical Communications (article, 2019-01-01)
Field: Chemistry — Organic Chemistry

Recent advances in the development of rotaxanes and pseudorotaxanes based on pillar[<i>n</i>]arenes: from construction to application Mechanically inter-locked molecules (MIMs) mainly including rotaxanes and pseudorotaxanes have attracted enormous attention for the fabrication of molecular machines, which can be constructed from macrocyclic molecules as the main building blocks through host-guest complexation. Pillar[n]arenes, as a new class of macrocyclic molecules, have a rigid pillar-like shaped architecture with an electron-rich cavity and two fine-tuneable rims. This unique symmetrical and rigid structure, easy modification and excellent properties in host-guest complexation provides pillar[n]arenes with the ability to form various interesting supramolecular systems. Importantly, the π-rich cavity of pillar[n]arenes also endows them with the ability to construct MIMs. This article will provide a comprehensive review of recent advances in pillar[n]arene-based MIMs, primarily focusing on rotaxanes and pseudorotaxanes, and their synthetic methods will be highlighted. In addition, the applications of pillar[n]arene-based MIMs will be summarized while current limitations and perspectives on the construction of pillar[n]arene-based MIMs will also be outlined. Pillar ; Nanotechnology ; Catenane ; Chemistry ; Materials science ; Engineering ; Molecule ; Organic chemistry ; Mechanical engineering Supramolecular Chemistry and Complexes ; Molecular Sensors and Ion Detection ; Porphyrin and Phthalocyanine Chemistry
Authors: Moh. Adhib Ulil Absor | Fumiyuki Ishii
Source: Physical review. B./Physical review. B (article, 2019-09-03)
Field: Materials Science — Materials Chemistry

Intrinsic persistent spin helix state in two-dimensional group-IV monochalcogenide <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>M</mml:mi><mml:mi>X</mml:mi></mml:mrow></mml:math> monolayers (<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>M</mml:mi><mml:mo>=</mml:mo><mml:mi>Sn</mml:mi></mml:mrow></mml:math> or Ge and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>X</mml:mi><mml:mo>=</mml:mo><mml:mi mathvariant="normal">S</mml:mi></mml:mrow></mml:math>, Se, or Te) Energy-saving spintronics are believed to be implementable on systems hosting the persistent spin helix (PSH) since they support an extraordinarily long spin lifetime of carriers. However, achieving the PSH requires a unidirectional spin configuration in the momentum space, which is practically nontrivial due to the stringent conditions for fine-tuning the Rashba and Dresselhaus spin-orbit couplings. Here, we predict that the PSH can be intrinsically achieved on a two-dimensional (2D) group-IV monochalcogenide $MX$ monolayer, a new class of the noncentrosymmetric 2D materials having in-plane ferroelectricity. Due to the ${C}_{2v}$ point-group symmetry in the $MX$ monolayer, a unidirectional spin configuration is preserved in the out-of-plane direction and thus maintains the PSH that is similar to the [110] Dresselhaus model in the [110]-oriented quantum well. Our first-principle calculations on various $MX$ ($M=$ Sn, Ge; $X=$ S, Se, Te) monolayers confirmed that such typical spin configuration is observed, in particular, at near the valence-band maximum where a sizable spin splitting and a substantially small wavelength of the spin polarization are achieved. Importantly, we observe reversible out-of-plane spin orientation under opposite in-plane ferroelectric polarization, indicating that an electrically controllable PSH for spintronic applications is plausible. Spintronics ; Physics ; Condensed matter physics ; Spin (aerodynamics) ; Spin polarization ; Quantum mechanics ; Ferromagnetism ; Electron 2D Materials and Applications ; Physics of Superconductivity and Magnetism ; Quantum and electron transport phenomena
Authors: Muzaffar Iqbal | Akbar Ali | Khuram Shahzad Ahmad | Farhat Mehmood Rana | Jamshid Khan | Karim Khan | Khalid Hussain Thebo
Source: SN Applied Sciences (article, 2019-05-28)
Field: Materials Science — Materials Chemistry

Synthesis and characterization of transition metals doped CuO nanostructure and their application in hybrid bulk heterojunction solar cells Materials science ; Doping ; Dopant ; Heterojunction ; Scanning electron microscope ; Nanostructure ; Transmission electron microscopy ; Energy conversion efficiency ; Spectroscopy ; Analytical Chemistry (journal) ; Solar cell ; Transition metal ; Nanotechnology ; Chemical engineering ; Optoelectronics ; Chemistry ; Catalysis Copper-based nanomaterials and applications ; ZnO doping and properties ; Electronic and Structural Properties of Oxides
Authors: Wen Fu | Li Wang | Jianning Huang | Cuiwen Liu | Wenlong Peng | Haotuo Xiao | Shenglin Li
Source: Advances in Polymer Technology (article, 2019-08-01)
Field: Materials Science — Polymers and Plastics

Mechanical Properties and Mullins Effect in Natural Rubber Reinforced by Grafted Carbon Black The obvious polarity difference between the carbon black (CB) and the natural rubber (NR) causes the CB hard to be dispersed in the NR matrix when the addition amount is large. In this paper, polyethylene glycol (PEG) was grafted onto the surface of CB by the liquid phase. The grafted carbon black (GCB) was prepared and applied to reinforce NR. The main physical and mechanical properties of NR were improved because of the better compatibility between GCB and NR. The Mullins effect of the vulcanizate was calculated by the cyclic stress-strain experiment. The results showed that the Mullins effect both existed in the virgin NR system and filled NR system. The degree of Mullins effect was increased with the increase of the filler addition, but that was different for CB and GCB. When the filler addition was below 20 phr, the Mullins effect of NR/GCB was stronger than that of NR/CB. However, when the filler addition was over 30 phr, the Mullins effect of NR/CB was stronger than that of NR/GCB. The Mullins effect was affected by the heat treatment temperature and time. The mechanisms of the Mullins effect were analyzed. Materials science ; Carbon black ; Natural rubber ; Composite material ; Polyethylene glycol ; Compatibility (geochemistry) ; Filler (materials) ; Polyethylene ; Chemical engineering Polymer Nanocomposites and Properties ; Tribology and Wear Analysis ; Natural Fiber Reinforced Composites
Authors: Manoj Sharma | Savas Delikanli | Hilmi Volkan Demir
Source: Proceedings of the IEEE (article, 2019-10-18)
Field: Materials Science — Materials Chemistry

Two-Dimensional CdSe-Based Nanoplatelets: Their Heterostructures, Doping, Photophysical Properties, and Applications In the past decade, colloidal quantum wells, also known as 2-D semiconductor nanoplatelets (NPLs), have been added to the colloidal nanocrystal (NC) family. Through the unique control of the thickness with monolayer precision, these novel materials exhibit strong 1-D quantum confinement that offers unique optical properties along with the possibility of fabricating advanced heterostructures, which are not possible with other quantum-confined nanostructures. The 2-D CdX (X = Se, S)-based NPLs provide high color purities, fast fluorescence lifetimes, and large exciton binding energies. This review covers the latest developments in the successful utilization of these flat NCs in different nanophotonic device applications. The synthesis of the advanced heterostructures of flat 2-D NCs (e.g., core-shell, core-crown, and core-crown-shell) has matured very rapidly, and new exciting optical and electronic applications are emerging. Doping of these atomically thin NCs also offers new possibilities for their utilization in different solar light harvesting, magnetic, electronic, and lasing applications. This review also includes the recent advancements in the understanding of their unique optical properties that are of utmost importance for their practical implementation in light-emitting devices and lasers. Finally, we present a future perspective on their successful utilization in different nanophotonic applications. Heterojunction ; Nanophotonics ; Materials science ; Lasing threshold ; Nanotechnology ; Quantum dot ; Nanocrystal ; Optoelectronics ; Doping ; Semiconductor ; Monolayer ; Nanomaterials ; Exciton ; Physics Quantum Dots Synthesis And Properties ; Chalcogenide Semiconductor Thin Films ; Nanocluster Synthesis and Applications
Authors: Ieng Chim Wan | Martin D. Witte | Adriaan J. Minnaard
Source: Organic Letters (article, 2019-09-12)
Field: Chemistry — Organic Chemistry

From <scp>d</scp>- to <scp>l</scp>-Monosaccharide Derivatives via Photodecarboxylation–Alkylation Photodecarboxylation–alkylation of conformationally locked monosaccharides leads to inversion of stereochemistry at C5. This allows the synthesis of l-sugars from their readily available d-counterparts. Via this strategy, methyl l-guloside was synthesized from methyl d-mannoside in 21% yield over six steps. Chemistry ; Monosaccharide ; Alkylation ; Yield (engineering) ; Stereochemistry ; Organic chemistry ; Catalysis Carbohydrate Chemistry and Synthesis ; Polyamine Metabolism and Applications ; GABA and Rice Research
Authors: Edgar A. Engel | Andrea Anelli | Albert Hofstetter | Federico Paruzzo | Lyndon Emsley | Michele Ceriotti
Source: Physical Chemistry Chemical Physics (article, 2019-01-01)
Field: Chemistry — Spectroscopy

A Bayesian approach to NMR crystal structure determination Nuclear Magnetic Resonance (NMR) spectroscopy is particularly well suited to determine the structure of molecules and materials in powdered form. Structure determination usually proceeds by finding the best match between experimentally observed NMR chemical shifts and those of candidate structures. Chemical shifts for the candidate configurations have traditionally been computed by electronic-structure methods, and more recently predicted by machine learning. However, the reliability of the determination depends on the errors in the predicted shifts. Here we propose a Bayesian framework for determining the confidence in the identification of the experimental crystal structure, based on knowledge of the typical errors in the electronic structure methods. We demonstrate the approach on the determination of the structures of six organic molecular crystals. We critically assess the reliability of the structure determinations, facilitated by the introduction of a visualization of the similarity between candidate configurations in terms of their chemical shifts and their structures. We also show that the commonly used values for the errors in calculated <sup>13</sup>C shifts are underestimated, and that more accurate, self-consistently determined uncertainties make it possible to use <sup>13</sup>C shifts to improve the accuracy of structure determinations. Finally, we extend the recently-developed ShiftML model to render it more efficient, accurate, and, most importantly, to evaluate the uncertainties in its predictions. By quantifying the confidence in structure determinations based on ShiftML predictions we further substantiate that it provides a valid replacement for first-principles calculations in NMR crystallography. Crystal structure prediction ; Chemical shift ; Bayesian probability ; Similarity (geometry) ; Reliability (semiconductor) ; Nuclear magnetic resonance spectroscopy ; Crystal structure ; Visualization Advanced NMR Techniques and Applications ; NMR spectroscopy and applications ; X-ray Diffraction in Crystallography
Authors: Jared C. Stanley | Felix Mayr | Alessio Gagliardi
Source: Advanced Theory and Simulations (article, 2019-11-18)
Field: Materials Science — Materials Chemistry

Machine Learning Stability and Bandgaps of Lead‐Free Perovskites for Photovoltaics Abstract Compositional engineering of perovskites has enabled the precise control of material properties required for their envisioned applications in photovoltaics. However, challenges remain to address efficiency, stability, and toxicity simultaneously. Mixed lead‐free and inorganic perovskites have recently demonstrated potential for resolving such issues but their composition space is gigantic, making it difficult to discover promising candidates even using high‐throughput methods. A machine learning approach employing a generalized element‐agnostic fingerprint is shown to rapidly and accurately predict key properties using a new database of 344 perovskites generated with density functional theory. Bandgap, formation energy, and convex hull distance are predicted using validation subsets to within 146 meV, 15 meV per atom, and 11 meV per atom, respectively. The resulting model is used to predict trends in entirely different chemical spaces, and perform rapid composition and configuration space sampling without the need for expensive ab initio simulations. Photovoltaics ; Stability (learning theory) ; Band gap ; Ab initio ; Atom (system on chip) ; Materials science ; Throughput ; Computer science ; Density functional theory ; Convex hull ; Chemical stability ; Computational science ; Nanotechnology ; Chemical physics ; Optoelectronics ; Physics ; Chemistry ; Computational chemistry ; Regular polygon ; Machine learning ; Mathematics ; Parallel computing ; Thermodynamics ; Electrical engineering ; Engineering ; Quantum mechanics ; Photovoltaic system Machine Learning in Materials Science ; Perovskite Materials and Applications ; Chalcogenide Semiconductor Thin Films
Authors: Natalia Sizochenko | Michael Syzochenko | Natalja Fjodorova | Bakhtiyor Rasulev | Jerzy Leszczyński
Source: Ecotoxicology and Environmental Safety (article, 2019-09-30)
Field: Materials Science — Materials Chemistry

Evaluating genotoxicity of metal oxide nanoparticles: Application of advanced supervised and unsupervised machine learning techniques Context (archaeology) ; Artificial intelligence ; Machine learning ; Genotoxicity ; Computer science ; Unsupervised learning ; Missing data ; Chemistry ; Biology Nanoparticles: synthesis and applications
Authors: Rico Friedrich | Demet Usanmaz | Corey Oses | Andrew Supka | Marco Fornari | Marco Buongiorno Nardelli | Cormac Toher | Stefano Curtarolo
Source: npj Computational Materials (article, 2019-05-15)
Field: Materials Science — Materials Chemistry

Coordination corrected ab initio formation enthalpies Abstract The correct calculation of formation enthalpy is one of the enablers of ab-initio computational materials design. For several classes of systems (e.g. oxides) standard density functional theory produces incorrect values. Here we propose the “coordination corrected enthalpies” method (CCE), based on the number of nearest neighbor cation–anion bonds, and also capable of correcting relative stability of polymorphs. CCE uses calculations employing the Perdew, Burke and Ernzerhof (PBE), local density approximation (LDA) and strongly constrained and appropriately normed (SCAN) exchange correlation functionals, in conjunction with a quasiharmonic Debye model to treat zero-point vibrational and thermal effects. The benchmark, performed on binary and ternary oxides (halides), shows very accurate room temperature results for all functionals, with the smallest mean absolute error of 27(24) meV/atom obtained with SCAN. The zero-point vibrational and thermal contributions to the formation enthalpies are small and with different signs—largely canceling each other. Ternary operation ; Ab initio ; Density functional theory ; Chemistry ; Thermodynamics ; Atom (system on chip) ; Enthalpy ; Ab initio quantum chemistry methods ; Standard enthalpy of formation ; Debye model ; Computational chemistry ; Physical chemistry ; Materials science ; Physics ; Molecule ; Computer science Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; X-ray Diffraction in Crystallography
Authors: Takahiro Ishikawa | Takashi Miyake | Katsuya Shimizu
Source: Physical review. B./Physical review. B (article, 2019-11-06)
Field: Materials Science — Materials Chemistry

Materials informatics based on evolutionary algorithms: Application to search for superconducting hydrogen compounds We present a materials informatics approach to search for superconducting hydrogen compounds, which is based on a genetic algorithm and a genetic programing. This method consists of five stages: (i) collection of physical and chemical property data, (ii) development of superconductivity predictor based on the collected data by a genetic programing, (iii) prediction of potential candidates for high temperature superconductivity by regression analysis, (iv) crystal structure search of the candidates by a genetic algorithm, and (v) validation of the superconductivity by first-principles calculations. By repeatedly performing the process as (i) $\ensuremath{\rightarrow}$ (ii) $\ensuremath{\rightarrow}$ (iii) $\ensuremath{\rightarrow}$ (iv) $\ensuremath{\rightarrow}$ (v) $\ensuremath{\rightarrow}$ (i) $\ensuremath{\rightarrow}\phantom{\rule{4pt}{0ex}}\ensuremath{\cdots}$, the database and predictor are further improved, which leads to an efficient search for superconducting materials. Using the first-principles data of binary hydrogen compounds, many of which have not been experimentally realized yet, we applied this method to hypothetical ternary ones and predicted ${\mathrm{KScH}}_{12}$ with a modulated hydrogen cage showing the superconducting critical temperature of 122 K at 300 GPa and ${\mathrm{GaAsH}}_{6}$ showing 98 K at 180 GPa. Superconductivity ; Ternary operation ; Hydrogen ; Algorithm ; Genetic algorithm ; Physics ; Crystallography ; Condensed matter physics ; Computer science ; Machine learning ; Chemistry ; Quantum mechanics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Superconductivity in MgB2 and Alloys
Authors: Sagar Vyas | Elyas Goli | Xiang Zhang | Philippe H. Geubelle
Source: Composites Science and Technology (article, 2019-09-25)
Field: Chemistry — Organic Chemistry

Manufacturing of unidirectional glass-fiber-reinforced composites via frontal polymerization: A numerical study Materials science ; Dicyclopentadiene ; Composite material ; Volume fraction ; Glass fiber ; Front velocity ; Fiber ; Finite element method ; Polymerization ; Phase (matter) ; Polymer ; Front (military) ; Thermodynamics Photopolymerization techniques and applications ; Additive Manufacturing and 3D Printing Technologies ; Nanofabrication and Lithography Techniques
Authors: Johannes Breitling | A Deshmane | Steffen Goerke | Andreas Korzowski | Kai Herz | Mark E. Ladd | Klaus Scheffler | Peter Bachert | Moritz Zaiß
Source: NMR in Biomedicine (article, 2019-07-30)
Field: Materials Science — Materials Chemistry

Adaptive denoising for chemical exchange saturation transfer MR imaging High image signal-to-noise ratio (SNR) is required to reliably detect the inherently small chemical exchange saturation transfer (CEST) effects in vivo. In this study, it was demonstrated that identifying spectral redundancies of CEST data by principal component analysis (PCA) in combination with an appropriate data-driven extraction of relevant information can be used for an effective and robust denoising of CEST spectra. The relationship between the number of relevant principal components and SNR was studied on fitted in vivo Z-spectra with artificially introduced noise. Three different data-driven criteria to automatically determine the optimal number of necessary components were investigated. In addition, these criteria facilitate straightforward assessment of data quality that could provide guidance for CEST MR protocols in terms of SNR. Insights were applied to achieve a robust denoising of highly sampled low power Z-spectra of the human brain at 3 and 7 T. The median criterion provided the best estimation for the optimal number of components consistently for all three investigated artificial noise levels. Application of the denoising technique to in vivo data revealed a considerable increase in image quality for the amide and rNOE contrast with a considerable SNR gain. At 7 T the denoising capability was quantified to be comparable or even superior to an averaging of six measurements. The proposed denoising algorithm enables an efficient and robust denoising of CEST data by combining PCA with appropriate data-driven truncation criteria. With this generally applicable technique at hand, small CEST effects can be reliably detected without the need for repeated measurements. Noise reduction ; Principal component analysis ; Pattern recognition (psychology) ; Computer science ; Artificial intelligence ; Noise (video) ; Signal-to-noise ratio (imaging) ; Image (mathematics) Lanthanide and Transition Metal Complexes ; Advanced MRI Techniques and Applications ; Electron Spin Resonance Studies