jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Patrick J. Willems | Alison Horne | Thomas Van Parys | Sofie Goormachtig | Ive De Smet | Alexander Botzki | Frank Van Breusegem | Kris Gevaert
Source: The Plant Journal (article, 2019-04-20)
Field: Chemistry — Spectroscopy

The Plant <scp>PTM</scp> Viewer, a central resource for exploring plant protein modifications Post-translational modifications (PTMs) of proteins are central in any kind of cellular signaling. Modern mass spectrometry technologies enable comprehensive identification and quantification of various PTMs. Given the increased numbers and types of mapped protein modifications, a database is necessary that simultaneously integrates and compares site-specific information for different PTMs, especially in plants for which the available PTM data are poorly catalogued. Here, we present the Plant PTM Viewer (http://www.psb.ugent.be/PlantPTMViewer), an integrative PTM resource that comprises approximately 370 000 PTM sites for 19 types of protein modifications in plant proteins from five different species. The Plant PTM Viewer provides the user with a protein sequence overview in which the experimentally evidenced PTMs are highlighted together with an estimate of the confidence by which the modified peptides and, if possible, the actual modification sites were identified and with functional protein domains or active site residues. The PTM sequence search tool can query PTM combinations in specific protein sequences, whereas the PTM BLAST tool searches for modified protein sequences to detect conserved PTMs in homologous sequences. Taken together, these tools help to assume the role and potential interplay of PTMs in specific proteins or within a broader systems biology context. The Plant PTM Viewer is an open repository that allows the submission of mass spectrometry-based PTM data to remain at pace with future PTM plant studies. Context (archaeology) ; Computational biology ; Resource (disambiguation) ; Computer science ; Protein superfamily ; Sequence (biology) ; Posttranslational modification ; Biology ; Biochemistry Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Peptidase Inhibition and Analysis
Authors: Vinícius Danilo Nonato Bezzon | Thaís Larissa do Amaral Montanheiro | Beatriz Rossi Canuto de Menezes | Renata Guimarães Ribas | Victor Augusto Nieto Righetti | Karla Faquine Rodrigues | Gilmar Patrocínio Thim
Source: Advances in Materials Science and Engineering (review, 2019-03-03)
Field: Materials Science — Materials Chemistry

Carbon Nanostructure-based Sensors: A Brief Review on Recent Advances A brief review reporting the recent advances on the carbon nanostructured materials-based sensors covering recently published works is presented. Several works dealing with experimental and theoretical data are reviewed and discussed. The main results for carbon nanotubes, nanodiamonds, fullerene, graphene, and hybrid carbon-nanostructured devices that show sensing properties in different fields were considered for the discussions. The goal of this paper was to highlight sensor mechanisms, and the best results reached up to now are creating bases for further applications. Materials science ; Nanotechnology ; Graphene ; Carbon fibers ; Carbon nanotube ; Fullerene ; Nanostructure ; Composite number ; Physics ; Composite material Carbon Nanotubes in Composites ; Graphene research and applications ; Gas Sensing Nanomaterials and Sensors
Authors: Xin Wang | Jinghua Feng | Yawen Cai | Ming Fang | Mingguang Kong | Ahmed Alsaedi | Tasawar Hayat | Xiaoli Tan
Source: The Science of The Total Environment (article, 2019-11-20)
Field: Chemistry — Inorganic Chemistry

Porous biochar modified with polyethyleneimine (PEI) for effective enrichment of U(VI) in aqueous solution Biochar ; Adsorption ; Chemistry ; Aqueous solution ; Alkali metal ; Langmuir adsorption model ; Fourier transform infrared spectroscopy ; Nuclear chemistry ; Ionic strength ; Inorganic chemistry ; Chemical engineering ; Organic chemistry ; Pyrolysis Radioactive element chemistry and processing ; Extraction and Separation Processes ; Analytical chemistry methods development
Authors: Tofik Ahmed Shifa | Alberto Vomiero
Source: Advanced Energy Materials (article, 2019-09-09)
Field: Materials Science — Materials Chemistry

Confined Catalysis: Progress and Prospects in Energy Conversion Abstract Space confined catalysis has emerged as viable strategy for achieving potent and efficient catalysts in various important reactions. It offers a means of creating unique nanoscale chemical environments partitioned from the surrounding bulk space. This gives rise to the phenomena of nanoconfinement, where the energetics and kinetics of catalytic reactions can be modulated upon confining the catalysts in a particular site. Various scaffolds have been reported so far for confinement. Among these, void spaces under the cover of 2D materials, van der Waals (vdW) gaps of layered 2D materials, nanotubes, and porous surfaces have recently won copious attention. In this review, the concept of space confinement with respect to its effect on the electronic and structural properties of a catalyst is discussed. Emphasis is devoted to the catalysis of water splitting and CO 2 reduction reactions. The progress in the design and applications of space confined catalysts is then traced. Finally, a discussion of emerging issues yet to be explored for this strategy to achieve a high efficiency, and future directions with the potential to become a new hotspots are presented. Catalysis ; Nanotechnology ; Materials science ; van der Waals force ; Space (punctuation) ; Nanoscopic scale ; Confined space ; Porosity ; Chemical physics ; Physics ; Computer science ; Chemistry ; Molecule MXene and MAX Phase Materials ; Graphene research and applications ; Advanced Photocatalysis Techniques
Authors: Jipan Yu | Li‐Yong Yuan | Shuai Wang | Jian‐Hui Lan | Lirong Zheng | Chao Xu | Jing Chen | Lin Wang | Zhi‐wei Huang | Wuqing Tao | Zhirong Liu | Zhifang Chai | John K. Gibson | Wei‐Qun Shi
Source: CCS Chemistry (article, 2019-08-01)
Field: Chemistry — Inorganic Chemistry

Phosphonate-Decorated Covalent Organic Frameworks for Actinide Extraction: A Breakthrough Under Highly Acidic Conditions Although solid-phase extraction is a useful approach for metal ion separation from aqueous solutions, existing sorbents suffer from low extraction efficiencies and/or instability when in contact with strong acidic media. We report here the first study on rational design and fabrication of phosphonatedecorated covalent organic frameworks, COF-IHEP1 and COF-IHEP2, for efficient and selective extraction of of uranium (VI) [U(VI)] and plutonium(IV) [Pu(IV)] from highly acidic solutions. We found that the negatively charged frameworks with excellent stability under harsh conditions and strong chelating ability from incorporated phosphonate moieties make the COFs superb U(VI) and Pu(IV) sorbents. In 1 M HNO 3 solution, COF-IHEP1 achieved recorded U(VI) uptake of 112 mgg -1 , with extremely high selectivity toward Pu(IV) even in the presence of large excess of competing metal cations. Our mechanistic study confirms a sandwich-type microstructure of hydrated U(VI) and Pu(IV) cations bound by the oxygen sites of phosphonate groups. These newly fabricated tailored hybrid porous materials could afford new opportunities to achieve highly efficient actinide extraction from acidic nuclear fuel effluents. Phosphonate ; Actinide ; Extraction (chemistry) ; Covalent bond ; Chemistry ; Combinatorial chemistry ; Inorganic chemistry ; Nuclear chemistry ; Radiochemistry ; Organic chemistry Radioactive element chemistry and processing ; Covalent Organic Framework Applications ; Chemical Synthesis and Characterization
Authors: Luigi Bonati | Yueyu Zhang | Michele Parrinello
Source: Proceedings of the National Academy of Sciences (article, 2019-08-15)
Field: Materials Science — Materials Chemistry

Neural networks-based variationally enhanced sampling Sampling complex free-energy surfaces is one of the main challenges of modern atomistic simulation methods. The presence of kinetic bottlenecks in such surfaces often renders a direct approach useless. A popular strategy is to identify a small number of key collective variables and to introduce a bias potential that is able to favor their fluctuations in order to accelerate sampling. Here, we propose to use machine-learning techniques in conjunction with the recent variationally enhanced sampling method [O. Valsson, M. Parrinello, <i>Phys. Rev. Lett.</i> 113, 090601 (2014)] in order to determine such potential. This is achieved by expressing the bias as a neural network. The parameters are determined in a variational learning scheme aimed at minimizing an appropriate functional. This required the development of a more efficient minimization technique. The expressivity of neural networks allows representing rapidly varying free-energy surfaces, removes boundary effects artifacts, and allows several collective variables to be handled. Computer science ; Sampling (signal processing) ; Artificial neural network ; Reinforcement learning ; Minification ; Artificial intelligence ; Machine learning ; Energy (signal processing) ; Mathematics ; Statistics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Block Copolymer Self-Assembly
Authors: Hongjie Dai | Huan Zhang | Liang Ma | Hongyuan Zhou | Yong Yu | Ting Guo | Yuhao Zhang | Huihua Huang
Source: Carbohydrate Polymers (article, 2019-01-06)
Field: Materials Science — Biomaterials

Green pH/magnetic sensitive hydrogels based on pineapple peel cellulose and polyvinyl alcohol: synthesis, characterization and naringin prolonged release Self-healing hydrogels ; Polyvinyl alcohol ; Nanocellulose ; Biocompatibility ; Swelling ; Chemical engineering ; Carboxymethyl cellulose ; Cellulose ; Thermal stability ; Chemistry ; Naringin ; Materials science ; Hydrolysis ; Polymer chemistry ; Nuclear chemistry ; Chromatography ; Organic chemistry ; Composite material Advanced Cellulose Research Studies ; Pineapple and bromelain studies ; Nanocomposite Films for Food Packaging
Authors: Jianlong Wang | Shuting Zhuang
Source: Reviews in Environmental Science and Bio/Technology (article, 2019-07-26)
Field: Chemistry — Inorganic Chemistry

Extraction and adsorption of U(VI) from aqueous solution using affinity ligand-based technologies: an overview In terms of energy resource recovery and environmental protection, the separation of U(VI) from aqueous solutions is vital. Adsorption and solvent extraction are the most common separation technologies, which are also widely used for uranium recovery or removal from aqueous solution. The linear structure of uranyl ion and its multiple coordination feasibilities offer great opportunities for its extractive and adsorptive separation. This review briefly summarized and analyzed the recent advances in the separation of uranyl ions from aqueous solutions, mainly focusing on the selective extraction and adsorption using affinity ligands-based technology, which is promising method due to the high selectivity, capable of recovering uranium at low concentration and complicate aqueous environment. The affinity ligands for uranyl ions, including organophosphorus, calixarenes, amidoxime, imidazole and other derivatives were introduced. These donor ligands for design of extraction solvents or adsorbents for the separation of uranyl ions were summarized. The further research on the coordination chemistry towards uranyl ions and removal mechanisms would provide vital information for the development of more effective ligands with higher affinity, stability and compatibility in various systems, which is still challenging. Uranyl ; Chemistry ; Adsorption ; Uranium ; Aqueous solution ; Imidazole ; Selectivity ; Extraction (chemistry) ; Ligand (biochemistry) ; Coordination complex ; Inorganic chemistry ; Combinatorial chemistry ; Ion ; Chromatography ; Organic chemistry ; Materials science ; Catalysis ; Metal Radioactive element chemistry and processing ; Extraction and Separation Processes ; Chemical Synthesis and Characterization
Authors: Denis Gentili | Massimo Gazzano | Manuela Melucci | Derek Jones | Massimiliano Cavallini
Source: Chemical Society Reviews (review, 2019-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Polymorphism as an additional functionality of materials for technological applications at surfaces and interfaces Polymorphism is a widespread phenomenon occurring in many solid materials having important effects in many scientific disciplines. Since molecular packing can determine the functional properties of materials but is often difficult to control, polymorphism has usually been considered a drawback for technological applications. Thanks to advances in its control over the past few years, polymorphism is now often considered more as an opportunity because it allows a much wider range of functionality in, for example, a solid molecular material, where a corresponding packing type can be selected or even promoted. This tutorial review introduces the reader to the most representative progress in applications of polymorphism as an additional functionality of materials especially in its current promise for technological applications. In addition, it examines the most powerful strategies to control and fully exploit the intrinsic properties of polymorphism and transitions between its various metastable states, through fine-tuning of molecular packing in a reproducible manner. The aim is to create awareness about polymorphism as a novel enabling technology rather than as a problem. Variety (cybernetics) ; Polymorphism (computer science) ; Nanotechnology ; Computer science ; Chemistry ; Materials science ; Artificial intelligence Multiferroics and related materials ; Electronic and Structural Properties of Oxides ; Ferroelectric and Piezoelectric Materials
Authors: Bo Li | Xinghua Li | Boyang Han | Zhijie Chen | Xuekai Zhang | Gang He | Gong Chen
Source: Journal of the American Chemical Society (article, 2019-05-22)
Field: Chemistry — Organic Chemistry

Construction of Natural-Product-Like Cyclophane-Braced Peptide Macrocycles via sp <sup>3</sup> C–H Arylation Cyclic peptides have provided one of the most important platforms for exploration of biorelevant chemical space between small molecules and biologics. However, in comparison with the design and synthesis of small molecules, chemists' ability to fine-tune the three-dimensional structures and properties of cyclic peptides lag far behind. Intrigued by cyclophane peptide natural products, we wondered whether the rigid, planar, and hydrophobic cyclophane motif could provide a new design element for the synthesis of cyclic peptides with well-behaved 3D structures. Herein, we report a generally applicable method for synthesis of natural-product-like cyclophane-braced peptide macrocycles via Pd-catalyzed intramolecular C(sp<sup>3</sup>)-H arylation with aryl iodides at the remote γ position of various N-terminal aliphatic amino acid units using a simple picolinamide directing group. Products of high structural and stereochemical complexity were quickly assembled from easily accessible peptide precursors prepared by standard solid phase peptide synthesis. Many of these peptide macrocycles show highly ordered structures as revealed by X-ray crystallography. Remarkably, the PA-directed C(sp<sup>3</sup>)-H cyclization reaction of unprotected peptide substrates carrying various free polar side chains proceeded with high efficiency and selectivity in aqueous media. This demonstrates not only the synthetic utility of Pd-catalyzed C(sp<sup>3</sup>)-H functionalization reactions, but also offers a valuable new orthogonal reactivity for peptide chemistry. Chemistry ; Cyclophane ; Peptide ; Cyclic peptide ; Combinatorial chemistry ; Natural product ; Peptide synthesis ; Molecule ; Intramolecular force ; Stereochemistry ; Selectivity ; Organic chemistry ; Catalysis Catalytic C–H Functionalization Methods ; Chemical Synthesis and Analysis ; Catalytic Cross-Coupling Reactions
Authors: Brandon Anderson | Truong-Son Hy | Risi Kondor
Source: arXiv (Cornell University) (preprint, 2019-06-06)
Field: Materials Science — Materials Chemistry

Cormorant: Covariant Molecular Neural Networks We propose Cormorant, a rotationally covariant neural network architecture for learning the behavior and properties of complex many-body physical systems. We apply these networks to molecular systems with two goals: learning atomic potential energy surfaces for use in Molecular Dynamics simulations, and learning ground state properties of molecules calculated by Density Functional Theory. Some of the key features of our network are that (a) each neuron explicitly corresponds to a subset of atoms; (b) the activation of each neuron is covariant to rotations, ensuring that overall the network is fully rotationally invariant. Furthermore, the non-linearity in our network is based upon tensor products and the Clebsch-Gordan decomposition, allowing the network to operate entirely in Fourier space. Cormorant significantly outperforms competing algorithms in learning molecular Potential Energy Surfaces from conformational geometries in the MD-17 dataset, and is competitive with other methods at learning geometric, energetic, electronic, and thermodynamic properties of molecules on the GDB-9 dataset. Covariant transformation ; Invariant (physics) ; Artificial neural network ; Statistical physics ; Computer science ; Density functional theory ; Topology (electrical circuits) ; Artificial intelligence ; Physics ; Biological system ; Theoretical computer science ; Computational chemistry ; Chemistry ; Mathematics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Minxiang Zeng | Yanliang Zhang
Source: Journal of Materials Chemistry A (article, 2019-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Colloidal nanoparticle inks for printing functional devices: emerging trends and future prospects Emerging trends and future prospects for nanoparticle synthesis and colloidal ink formulation, additive printing processes, and functional devices are highlighted. Nanotechnology ; Nanoparticle ; Materials science ; Inkwell ; Colloid ; 3D printing ; Colloidal particle ; Engineering ; Chemical engineering ; Metallurgy ; Composite material Gold and Silver Nanoparticles Synthesis and Applications ; Nanomaterials and Printing Technologies ; Quantum Dots Synthesis And Properties
Authors: Chun-Te Wu | Hsiao-Tzu Chang | Chien-Yu Wu | Shiwei Chen | Sih-Ying Huang | Mingxin Huang | Yeong‐Tsuen Pan | Peta Bradbury | Joshua Chou | Hung‐Wei Yen
Source: Materials Today (article, 2019-09-27)
Field: Materials Science — Materials Chemistry

Machine learning recommends affordable new Ti alloy with bone-like modulus Alloy ; Titanium alloy ; Modulus ; Materials science ; Elastic modulus ; Artificial neural network ; Titanium ; Throughput ; Machine learning ; Computer science ; Biomedical engineering ; Composite material ; Metallurgy ; Engineering ; Wireless ; Telecommunications Titanium Alloys Microstructure and Properties ; Orthopaedic implants and arthroplasty ; Machine Learning in Materials Science
Authors: Jesper Byggmästar | A. Hamedani | K. Nordlund | Flyura Djurabekova
Source: Physical review. B./Physical review. B (article, 2019-10-17)
Field: Materials Science — Materials Chemistry

Machine-learning interatomic potential for radiation damage and defects in tungsten Tungsten will be used as a plasma-facing material in fusion power reactors, where the absorption of high-energy neutrons leads to permanent damage in the crystal structure. A comprehensive understanding of the atom-level damage in tungsten has been limited by the slowness of quantum simulations and the insufficient accuracy of classical simulations. This study bridges the gap between the two by developing a machine-learning interatomic potential that allows the simulation of tungsten in extreme environments with quantum accuracy. Tungsten ; Interatomic potential ; Radiation damage ; Radiation ; Materials science ; Computer science ; Physics ; Optics ; Chemistry ; Metallurgy ; Molecular dynamics ; Computational chemistry Nuclear Materials and Properties ; Nuclear Physics and Applications ; Machine Learning in Materials Science
Authors: Daniele Melati | Yuri Grinberg | Mohsen Kamandar Dezfouli | Siegfried Janz | Pavel Cheben | Jens H. Schmid | Alejandro Sánchez-Postigo | Dan-Xia Xu
Source: Nature Communications (article, 2019-10-21)
Field: Materials Science — Materials Chemistry

Mapping the global design space of nanophotonic components using machine learning pattern recognition Nanophotonics finds ever broadening applications requiring complex components with many parameters to be simultaneously designed. Recent methodologies employing optimization algorithms commonly focus on a single performance objective, provide isolated designs, and do not describe how the design parameters influence the device behaviour. Here we propose and demonstrate a machine-learning-based approach to map and characterize the multi-parameter design space of nanophotonic components. Pattern recognition is used to reveal the relationship between an initial sparse set of optimized designs through a significant reduction in the number of characterizing parameters. This defines a design sub-space of lower dimensionality that can be mapped faster by orders of magnitude than the original design space. The behavior for multiple performance criteria is visualized, revealing the interplay of the design parameters, highlighting performance and structural limitations, and inspiring new design ideas. This global perspective on high-dimensional design problems represents a major shift in modern nanophotonic design and provides a powerful tool to explore complexity in next-generation devices. Nanophotonics ; Curse of dimensionality ; Set (abstract data type) ; Dimensionality reduction ; Perspective (graphical) ; Pattern recognition (psychology) ; Feature (linguistics) ; Space (punctuation) Machine Learning in Materials Science ; Neural Networks and Reservoir Computing ; Metamaterials and Metasurfaces Applications
Authors: Kyuhyun Lee | Dongsun Yoo | Wonseok Jeong | Seungwu Han
Source: Computer Physics Communications (article, 2019-05-06)
Field: Materials Science — Materials Chemistry

SIMPLE-NN: An efficient package for training and executing neural-network interatomic potentials Python (programming language) ; Computer science ; Artificial neural network ; Molecular dynamics ; Interatomic potential ; Simple (philosophy) ; Computational science ; Statistical physics ; Algorithm ; Artificial intelligence ; Weighting ; Machine learning ; Computational chemistry ; Physics ; Chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electronic and Structural Properties of Oxides