jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yohei Onodera | Yasuyuki Takimoto | Hiroyuki Hijiya | Taketoshi Taniguchi | Shingo Urata | Seiji Inaba | Sanae Fujita | Ippei Obayashi | Yasuaki Hiraoka | Shinji Kohara
Source: NPG Asia Materials (article, 2019-12-01)
Field: Materials Science — Ceramics and Composites

Origin of the mixed alkali effect in silicate glass Abstract Silicate glasses have evolved from basic structural materials to enabling materials for advanced applications. In this article, we unravel the origin of the mixed alkali effect for alkali silicate 22.7R 2 O–77.3SiO 2 glasses (R = Na and/or K) by identifying the variation in the alkali ion location around the non-bridging oxygen atoms. To do so, we constructed a state-of-the art structural model, which reproduces both diffraction and NMR data with a particular focus on the behavior of the alkali ions. A novel topological analysis using persistent homology found that sodium-potassium silicate glass shows a significant reduction in large cavities as a result of the mixed alkali effect. Furthermore, a highly correlated pair arrangement between sodium and potassium ions around non-bridging oxygen atoms was identified. The potassium ions can be trapped in K–O polyhedra due to the increased bridging oxygen coordination; therefore, the correlated pair arrangement is likely the intrinsic origin of the mixed alkali effect. Alkali metal ; Silicate ; Sodium silicate ; Materials science ; Potassium silicate ; Potassium ; Ion ; Oxygen ; Inorganic chemistry ; Crystallography ; Chemical engineering ; Chemistry ; Organic chemistry ; Metallurgy Glass properties and applications ; Building materials and conservation ; Theoretical and Computational Physics
Authors: Flavia Libonati | Andre E. Vellwock | Francesco Ielmini | Dilmurat Abliz | Gerhard Ziegmann | L. Vergani
Source: Scientific Reports (article, 2019-02-28)
Field: Materials Science — Biomaterials

Bone-inspired enhanced fracture toughness of de novo fiber reinforced composites Abstract Amplification in toughness and balance with stiffness and strength are fundamental characteristics of biological structural composites, and a long sought-after objective for engineering design. Nature achieves these properties through a combination of multiscale key features. Yet, emulating all these features into synthetic de novo materials is rather challenging. Here, we fine-tune manual lamination, to implement a newly designed bone-inspired structure into fiber-reinforced composites. An integrated approach, combining numerical simulations, ad hoc manufacturing techniques, and testing, yields a novel composite with enhanced fracture toughness and balance with stiffness and strength, offering an optimal lightweight material solution with better performance than conventional materials such as metals and alloys. The results also show how the new design significantly boosts the fracture toughness compared to a classic laminated composite, made of the same building blocks, also offering an optimal tradeoff with stiffness and strength. The predominant mechanism, responsible for the enhancement of fracture toughness in the new material, is the continuous deviation of the crack from a straight path, promoting large energy dissipation and preventing a catastrophic failure. The new insights resulting from this study can guide the design of de novo fiber-reinforced composites toward better mechanical performance to reach the level of synergy of their natural counterparts. Materials science ; Fracture toughness ; Composite material ; Toughness ; Stiffness ; Lamination ; Dissipation ; Fracture (geology) ; Fiber ; Fiber-reinforced composite ; Specific modulus ; Composite number Calcium Carbonate Crystallization and Inhibition ; Bone Tissue Engineering Materials ; Silk-based biomaterials and applications
Authors: Stephen J. I. Shearan | Norbert Stock | Franziska Emmerling | Jan Demel | Paul A. Wright | Konstantinos D. Demadis | Maria Vassaki | Ferdinando Costantino | Riccardo Vivani | Sébastien Sallard | Inés R. Salcedo | Aurelio Cabeza | Marco Taddei
Source: Crystals (article, 2019-05-24)
Field: Chemistry — Inorganic Chemistry

New Directions in Metal Phosphonate and Phosphinate Chemistry In September 2018, the First European Workshop on Metal Phosphonates Chemistry brought together some prominent researchers in the field of metal phosphonates and phosphinates with the aim of discussing past and current research efforts and identifying future directions. The scope of this perspective article is to provide a critical overview of the topics discussed during the workshop, which are divided into two main areas: synthesis and characterisation, and applications. In terms of synthetic methods, there has been a push towards cleaner and more efficient approaches. This has led to the introduction of high-throughput synthesis and mechanochemical synthesis. The recent success of metal–organic frameworks has also promoted renewed interest in the synthesis of porous metal phosphonates and phosphinates. Regarding characterisation, the main advances are the development of electron diffraction as a tool for crystal structure determination and the deployment of in situ characterisation techniques, which have allowed for a better understanding of reaction pathways. In terms of applications, metal phosphonates have been found to be suitable materials for several purposes: they have been employed as heterogeneous catalysts for the synthesis of fine chemicals, as solid sorbents for gas separation, notably CO2 capture, as materials for electrochemical devices, such as fuel cells and rechargeable batteries, and as matrices for drug delivery. Nanotechnology ; Phosphinate ; Phosphonate ; Scope (computer science) ; Chemistry ; Materials science ; Computer science ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Chemical Synthesis and Characterization ; Inorganic Chemistry and Materials
Authors: Paolo Romele | Matteo Ghittorelli | Zsolt M. Kovács‐Vajna | Fabrizio Torricelli
Source: Nature Communications (article, 2019-07-10)
Field: Materials Science — Polymers and Plastics

Ion buffering and interface charge enable high performance electronics with organic electrochemical transistors Organic electrochemical transistors rely on ionic-electronic volumetric interaction to provide a seamless interface between biology and electronics with outstanding signal amplification. Despite their huge potential, further progress is limited owing to the lack of understanding of the device fundamentals. Here, we investigate organic electrochemical transistors in a wide range of experimental conditions by combining electrical analyses and device modeling. We show that the measurements can be quantitatively explained by nanoscale ionic-electronic charge interaction, giving rise to ion buffering and interface charge compensation. The investigation systematically explains and unifies a wide range of experiments, providing the rationale for the development of high-performance electronics. Unipolar inverters - universal building blocks for electronics - with gain larger than 100 are demonstrated. This is the highest gain ever reported, enabling the design of devices and circuits with enhanced performance and opening opportunities for the next-generation integrated bioelectronics and neuromorphic computing. Bioelectronics ; Electronics ; Transistor ; Interface (matter) ; Organic electronics ; Neuromorphic engineering ; Nanotechnology ; Materials science ; Computer science ; Optoelectronics ; Electrical engineering ; Voltage ; Biosensor ; Engineering Conducting polymers and applications ; Advanced Memory and Neural Computing ; Organic Electronics and Photovoltaics
Authors: Sophia Doll | Florian Gnad | Matthias Mann
Source: PROTEOMICS - CLINICAL APPLICATIONS (review, 2019-02-21)
Field: Chemistry — Spectroscopy

The Case for Proteomics and Phospho‐Proteomics in Personalized Cancer Medicine The concept of personalized medicine is predominantly been pursued through genomic and transcriptomic technologies, leading to the identification of multiple mutations in a large variety of cancers. However, it has proven challenging to distinguish driver and passenger mutations and to deal with tumor heterogeneity and resistant clonal populations. More generally, these heterogeneous mutation patterns do not in themselves predict the tumor phenotype. Analysis of the expressed proteins in a tumor and their modification states reveals if and how these mutations are translated to the functional level. It is already known that proteomic changes including posttranslational modifications are crucial drivers of oncogenesis, but proteomics technology has only recently become comparable in depth and accuracy to RNAseq. These advances also allow the rapid and highly sensitive analysis of formalin-fixed and paraffin-embedded biobank tissues, on both the proteome and phosphoproteome levels. In this perspective, pioneering mass spectrometry-based proteomic studies are highlighted that pave the way toward clinical implementation. It is argued that proteomics and phosphoproteomics could provide the missing link to make omics analysis actionable in the clinic. Proteomics ; Proteome ; Computational biology ; Phosphoproteomics ; Biology ; Personalized medicine ; Genomics ; Carcinogenesis ; Precision medicine ; Transcriptome ; Identification (biology) ; Systems biology ; Bioinformatics ; Cancer ; Genome ; Genetics ; Gene ; Phosphorylation ; Protein phosphorylation ; Gene expression Advanced Proteomics Techniques and Applications ; Cancer Genomics and Diagnostics ; Molecular Biology Techniques and Applications
Authors: Kimberley Cousins | Renwu Zhang
Source: Polymers (review, 2019-04-16)
Field: Materials Science — Materials Chemistry

Highly Porous Organic Polymers for Hydrogen Fuel Storage Hydrogen (H<sub>2</sub>) is one of the best candidates to replace current petroleum energy resources due to its rich abundance and clean combustion. However, the storage of H<sub>2</sub> presents a major challenge. There are two methods for storing H<sub>2</sub> fuel, chemical and physical, both of which have some advantages and disadvantages. In physical storage, highly porous organic polymers are of particular interest, since they are low cost, easy to scale up, metal-free, and environmentally friendly. In this review, highly porous polymers for H<sub>2</sub> fuel storage are examined from five perspectives: (a) brief comparison of H<sub>2</sub> storage in highly porous polymers and other storage media; (b) theoretical considerations of the physical storage of H<sub>2</sub> molecules in porous polymers; (c) H<sub>2</sub> storage in different classes of highly porous organic polymers; (d) characterization of microporosity in these polymers; and (e) future developments for highly porous organic polymers for H<sub>2</sub> fuel storage. These topics will provide an introductory overview of highly porous organic polymers in H<sub>2</sub> fuel storage. Polymer ; Hydrogen storage ; Materials science ; Porosity ; Porous medium ; Energy storage ; Chemical engineering ; Nanotechnology ; Waste management ; Composite material ; Engineering Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Membrane Separation and Gas Transport
Authors: Laura Orellana
Source: Frontiers in Molecular Biosciences (review, 2019-11-05)
Field: Materials Science — Materials Chemistry

Large-Scale Conformational Changes and Protein Function: Breaking the in silico Barrier Large-scale conformational changes are essential to link protein structures with their function at the cell and organism scale, but have been elusive both experimentally and computationally. Over the past few years developments in cryo-electron microscopy and crystallography techniques have started to reveal multiple snapshots of increasingly large and flexible systems, deemed impossible only short time ago. As structural information accumulates, theoretical methods become central to understand how different conformers interconvert to mediate biological function. Here we briefly survey current <i>in silico</i> methods to tackle large conformational changes, reviewing recent examples of cross-validation of experiments and computational predictions, which show how the integration of different scale simulations with biological information is already starting to break the barriers between the <i>in silico, in vitro</i>, and <i>in vivo</i> worlds, shedding new light onto complex biological problems inaccessible so far. In silico ; Function (biology) ; Computational biology ; Scale (ratio) ; Cell function ; Computer science ; Chemistry ; Biophysics ; Biological system ; Nanotechnology ; Biology ; Physics ; Materials science ; Cell ; Biochemistry ; Cell biology Enzyme Structure and Function ; Advanced Electron Microscopy Techniques and Applications ; Protein Structure and Dynamics
Authors: Ren‐Yi Zhu | Long Chen | Xiao‐Si Hu | Feng Zhou | Jian Zhou
Source: Chemical Science (article, 2019-11-06)
Field: Chemistry — Organic Chemistry

Enantioselective synthesis of <i>P</i>-chiral tertiary phosphine oxides with an ethynyl group <i>via</i> Cu(<scp>i</scp>)-catalyzed azide–alkyne cycloaddition We report the highly enantioselective synthesis of <i>P</i>-chiral tertiary phosphine oxides featuring an ethynyl group <i>via</i> Cu(i)-catalyzed azide-alkyne cycloaddition. Newly developed chiral pyridinebisoxazolines (PYBOX) bearing a bulky C4 shielding group play an important role in achieving excellent enantioselectivity while suppressing side bis-triazoles formation in desymmetrizing prochiral diethynylphosphine oxides. Notably, by tuning the size of the C4 shielding group, it is possible to achieve excellent remote enantiofacial control in desymmetrizing phosphole oxide-diynes with the prochiral <i>P</i>-center farther from the ethynyl group <i>by four covalent bonds</i>. Time-dependent enantioselectivity is observed for these desymmetric CuAAC reactions, suggesting a synergic combination of a desymmetrization and a kinetic resolution, and our ligands prove to be better than unmodified PYBOX in both steps. This finding contributes to a highly enantioselective kinetic resolution of racemic ethynylphosphine oxides. The resulting chiral ethynylphosphine oxides are versatile <i>P</i>-chiral synthons, which can undergo a number of diversifying reactions to enrich structural diversity. Synthon ; Enantioselective synthesis ; Cycloaddition ; Alkyne ; Phosphine ; Azide ; Chemistry ; Catalysis ; Group (periodic table) ; Combinatorial chemistry ; Stereochemistry ; Medicinal chemistry ; Organic chemistry Synthesis and Catalytic Reactions ; Catalytic C–H Functionalization Methods ; Click Chemistry and Applications
Authors: Timothy L. Burnett | Philip J. Withers
Source: Nature Materials (review, 2019-06-17)
Field: Materials Science — Materials Chemistry

Completing the picture through correlative characterization Bespoke ; Computer science ; Correlative ; Characterization (materials science) ; Exploit ; Digitization ; Suite ; Nanotechnology ; Data science ; Materials science Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Catalysis and Oxidation Reactions
Authors: Ondrej Dyck | Maxim Ziatdinov | David B. Lingerfelt | Raymond R. Unocic | Bethany M. Hudak | Andrew R. Lupini | Stephen Jesse | Sergei V. Kalinin
Source: Nature Reviews Materials (review, 2019-06-07)
Field: Materials Science — Materials Chemistry

Atom-by-atom fabrication with electron beams Atom (system on chip) ; Scanning transmission electron microscopy ; Nanotechnology ; Electron ; Atomic physics ; Cathode ray ; Materials science ; Transmission electron microscopy ; Physics ; Computer science ; Quantum mechanics Quantum Dots Synthesis And Properties ; Advanced Electron Microscopy Techniques and Applications ; Electronic and Structural Properties of Oxides
Authors: Yingshi Chen | Jinfeng Zhu | Yinong Xie | Naixing Feng | Qing Liu
Source: Nanoscale (article, 2019-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Smart inverse design of graphene-based photonic metamaterials by an adaptive artificial neural network The burgeoning research of graphene and other 2D materials enables many unprecedented metamaterials and metadevices for applications on nanophotonics. The design of on-demand graphene-based metamaterials often calls for the solution of a complex inverse problem within a small sampling space, which highly depends on the rich experiences from researchers of nanophotonics. Conventional optimization algorithms could be used for this inverse design, but they converge to local optimal solutions and take significant computational costs with increased nanostructure parameters. Here, we establish a deep learning method based on an adaptive batch-normalized neural network, aiming to implement smart and rapid inverse design for graphene-based metamaterials with on-demand optical responses. This method allows a quick converging speed with high precision and low computational consumption. As typical complex proof-of-concept examples, the optical metamaterials consisting of graphene/dielectric alternating multilayers are chosen to demonstrate the validity of our design paradigm. Our method demonstrates a high prediction accuracy of over 95% after very few training epochs. A universal programming package is developed to achieve the design goals of graphene-based metamaterials with low absorption and near unity absorption, respectively. Our work may find important design applications in the field of nanoscale photonics based on graphene and other 2D materials. Graphene ; Metamaterial ; Inverse ; Artificial neural network ; Photonics ; Materials science ; Nanotechnology ; Computer science ; Photonic crystal ; Artificial intelligence ; Optoelectronics ; Mathematics Metamaterials and Metasurfaces Applications ; Photonic Crystals and Applications ; Plasmonic and Surface Plasmon Research
Authors: Marcos Juanes | Rizalina Tama Saragi | Walther Camináti | Alberto Lesarri
Source: Chemistry - A European Journal (review, 2019-04-19)
Field: Chemistry — Spectroscopy

The Hydrogen Bond and Beyond: Perspectives for Rotational Investigations of Non‐Covalent Interactions In the last decade, experiment and theory have expanded our vision of non-covalent interactions (NCIs), shifting the focus from the conventional hydrogen bond to new bridging interactions involving a variety of weak donor/acceptor partners. Whereas most experimental data originate from condensed phases, the introduction of broadband (chirped-pulse) microwave fast-passage techniques has revolutionized the field of rotational spectroscopy, offering unexplored avenues for high-resolution studies in the gas phase. We present an outlook of hot topics for rotational investigations on isolated intermolecular clusters generated in supersonic jet expansions. Rotational spectra offer very detailed structural data, easily discriminating the isomeric or isotopic composition and effectively cancelling any solvent, crystal, or matrix bias. The direct comparison with quantum mechanical predictions provides insight into the origin of the inter- and intramolecular interactions with much greater precision than any other spectroscopic technique, simultaneously serving as test-bed for fine-tuning of theoretical methods. We present recent examples of rotational investigations around three topics: oligomer formation, chiral recognition, and identification of halogen, chalcogen, pnicogen, or tetrel bonds. The selected examples illustrate the benefits of rotational spectroscopy for the structural and energetic assessment of inter-/intramolecular interactions, which may help to move from fundamental research to applications in supramolecular chemistry and crystal engineering. Covalent bond ; Hydrogen bond ; Bond ; Computational chemistry ; Materials science ; Chemistry ; Chemical physics ; Molecule ; Organic chemistry ; Business Molecular Spectroscopy and Structure ; Quantum, superfluid, helium dynamics ; Advanced Chemical Physics Studies
Authors: Yen‐Ju Wu | Lei Fang | Yibin Xu
Source: npj Computational Materials (article, 2019-05-03)
Field: Materials Science — Materials Chemistry

Predicting interfacial thermal resistance by machine learning Abstract Various factors affect the interfacial thermal resistance (ITR) between two materials, making ITR prediction a high-dimensional mathematical problem. Machine learning is a cost-effective method to address this. Here, we report ITR predictive models based on experimental data. The physical, chemical, and material properties of ITR are categorized into three sets of descriptors, and three algorithms are used for the models. Those descriptors assist the models in reducing the mismatch between predicted and experimental values and reaching high predictive performance of 96%. Over 80,000 material systems composed of 293 materials were inputs for predictions. Among the top-100 high-ITR predictions by the three different algorithms, 25 material systems are repeatedly predicted by at least two algorithms. One of the 25 material systems, Bi/Si achieved the ultra-low thermal conductivity in our previous work. We believe that the predicted high-ITR material systems are potential candidates for thermoelectric applications. This study proposed a strategy for material exploration for thermal management by means of machine learning. Thermal conductivity ; Machine learning ; Thermal ; Computer science ; Artificial intelligence ; Work (physics) ; Thermal resistance ; Materials science ; Mechanical engineering ; Thermodynamics ; Engineering ; Physics ; Composite material Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; Machine Learning in Materials Science
Authors: W. Gerrard | Lars Andersen Bratholm | Martin J. Packer | Adrian J. Mulholland | David R. Glowacki | Craig P. Butts
Source: Bristol Research (University of Bristol) (article, 2019-11-20)
Field: Materials Science — Materials Chemistry

The IMPRESSION machine learning system can predict NMR parameters for 3D structures with similar results to DFT but in seconds rather than hours. Impression ; Quantum chemical ; Computer science ; Quantum ; Artificial intelligence ; Scalar (mathematics) ; Chemical shift ; Algorithm ; Machine learning ; Molecule ; Mathematics ; Physics ; Quantum mechanics ; Geometry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Molecular spectroscopy and chirality
Authors: Deqiao Li | Xianfei Chen | Pan Xiang | Haiying Du | Beibei Xiao
Source: Applied Surface Science (article, 2019-10-08)
Field: Materials Science — Materials Chemistry

Chalcogenated-Ti3C2X2 MXene (X = O, S, Se and Te) as a high-performance anode material for Li-ion batteries MXenes ; Anode ; Materials science ; Electrochemistry ; Ion ; Diffusion barrier ; Open-circuit voltage ; Electrode ; Nanotechnology ; Chemical engineering ; Voltage ; Chemistry ; Physical chemistry ; Electrical engineering ; Layer (electronics) MXene and MAX Phase Materials ; Advancements in Battery Materials ; Ferroelectric and Negative Capacitance Devices