jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Muhammad Nadeem Hafeez | Christian Celia | Vilma Petrikaitė
Source: Processes (article, 2021-08-27)
Field: Materials Science — Biomaterials

Challenges towards Targeted Drug Delivery in Cancer Nanomedicines Despite cancer nanomedicine celebrates already thirty years since its introduction, together with the achievements and progress in cancer treatment area, it still undergoes serious disadvantages that must be addressed. Since the first observation that macromolecules tend to accumulate in tumor tissue due to fenestrated endothelial of vasculature, considered as the “royal gate” in drug delivery field, more than dozens of nanoformulations have been approved and introduced into the practice for cancer treatment. Lipid, polymeric, and hybrid nanocarriers are biocompatible nano-drug delivery systems (NDDs) having suitable physicochemical properties and modulate payload release in response to specific chemical or physical stimuli. Biopharmaceutical properties of NDDs and their efficacy in animal models and humans can significantly affect their impact and perspective in nanomedicine. One of the future directions could be focusing on personalized cancer treatment, considering the heterogeneity and complexity of each patient tumor tissue and the designing of multifunctional targeted NDDs combining synthetic nanomaterials and biological components, like cellular membranes, circulating proteins, RNAi/DNAi, which enforce the efficacy of NDDs and boost their therapeutic effect. Nanocarriers ; Nanomedicine ; Biopharmaceutical ; Drug delivery ; Nanotechnology ; Biocompatible material ; Cancer ; Cancer therapy ; Drug ; Medicine ; Pharmacology ; Materials science ; Biology ; Biotechnology ; Nanoparticle ; Biomedical engineering ; Internal medicine Nanoparticle-Based Drug Delivery ; Nanoplatforms for cancer theranostics ; RNA Interference and Gene Delivery
Authors: Giovanni Mazzanti
Source: Energies (article, 2021-07-26)
Field: Materials Science — Materials Chemistry

Issues and Challenges for HVDC Extruded Cable Systems The improved features of AC/DC converters, the need to enhance cross-country interconnections, the will to make massive remote renewable energy sources available, and the fear of populations about overhead lines have fostered HVDC cable transmission all over the world, leading in the last two decades to an exponential increase of commissioned HVDC cable projects, particularly of the extruded insulation type. Comprehensive surveys of the issues to be faced by HVDC extruded cable systems appeared in the literature some years ago, but they are not so up-to-date, as HVDC extruded cable technology is developing fast. Therefore, the contribution this paper aims at giving is a systematic, comprehensive and updated summary of the main present and future issues and challenges that HVDC cable systems have to face to further improve their performance and competitiveness, so as to meet the growing quest for clean and available energy worldwide. The topics covered in this review–treated in alphabetical order for the reader’s convenience–are accessories, higher voltage and power, laying environment (submarine and underground cables), modeling, multiterminal HVDC, operation and diagnostics, recyclable insulation, space charge behavior, testing, thermal stability, transient voltages. High-voltage direct current ; Overhead (engineering) ; Electrical engineering ; Electric power transmission ; Engineering ; Power cable ; Power transmission ; Transmission system ; Renewable energy ; Converters ; Transmission (telecommunications) ; High voltage ; Power (physics) ; Computer science ; Voltage ; Direct current High voltage insulation and dielectric phenomena ; Thermal Analysis in Power Transmission ; Lightning and Electromagnetic Phenomena
Authors: Shuo Feng | Huadong Fu | Huiyu Zhou | Yuan Wu | Zhaoping Lü | Hongbiao Dong
Source: npj Computational Materials (article, 2021-01-25)
Field: Materials Science — Materials Chemistry

A general and transferable deep learning framework for predicting phase formation in materials Abstract Machine learning has been widely exploited in developing new materials. However, challenges still exist: small dataset is common for most tasks; new datasets, special descriptors and specific models need to be built from scratch when facing a new task; knowledge cannot be readily transferred between independent models. In this paper we propose a general and transferable deep learning (GTDL) framework for predicting phase formation in materials. The proposed GTDL framework maps raw data to pseudo-images with some special 2-D structure, e.g., periodic table, automatically extracts features and gains knowledge through convolutional neural network, and then transfers knowledge by sharing features extractors between models. Application of the GTDL framework in case studies on glass-forming ability and high-entropy alloys show that the GTDL framework for glass-forming ability outperformed previous models and can correctly predicted the newly reported amorphous alloy systems; for high-entropy alloys the GTDL framework can discriminate five types phases (BCC, FCC, HCP, amorphous, mixture) with accuracy and recall above 94% in fivefold cross-validation. In addition, periodic table knowledge embedded in data representations and knowledge shared between models is beneficial for tasks with small dataset. This method can be easily applied to new materials development with small dataset by reusing well-trained models for related materials. Computer science ; Reuse ; Artificial intelligence ; Deep learning ; Convolutional neural network ; Machine learning ; Deep neural networks ; Pattern recognition (psychology) ; Data mining Machine Learning in Materials Science ; High Entropy Alloys Studies ; nanoparticles nucleation surface interactions
Authors: Pilsun Yoo | Michael Sakano | Saaketh Desai | Md Mahbubul Islam | Peilin Liao | Alejandro Strachan
Source: npj Computational Materials (article, 2021-01-22)
Field: Materials Science — Materials Chemistry

Neural network reactive force field for C, H, N, and O systems Abstract Reactive force fields have enabled an atomic level description of a wide range of phenomena, from chemistry at extreme conditions to the operation of electrochemical devices and catalysis. While significant insight and semi-quantitative understanding have been drawn from such work, the accuracy of reactive force fields limits quantitative predictions. We developed a neural network reactive force field (NNRF) for CHNO systems to describe the decomposition and reaction of the high-energy nitramine 1,3,5-trinitroperhydro-1,3,5-triazine (RDX). NNRF was trained using energies and forces of a total of 3100 molecules (11,941 geometries) and 15 condensed matter systems (32,973 geometries) obtained from density functional theory calculations with semi-empirical corrections to dispersion interactions. The training set is generated via a semi-automated iterative procedure that enables refinement of the NNRF until a desired accuracy is attained. The root mean square (RMS) error of NNRF on a testing set of configurations describing the reaction of RDX is one order of magnitude lower than current state of the art potentials. Force field (fiction) ; Density functional theory ; Decomposition ; Work (physics) ; Current (fluid) ; Field (mathematics) ; Artificial neural network ; Root mean square ; London dispersion force ; Range (aeronautics) ; Molecule ; Chemistry ; Materials science ; Statistical physics ; Computational chemistry ; Physics ; Computer science ; Thermodynamics ; Mathematics ; Quantum mechanics ; van der Waals force ; Organic chemistry Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Fuel Cells and Related Materials
Authors: Yuxiao Zhu | Daniel W. Newbrook | Peng Dai | C.H. de Groot | Ruomeng Huang
Source: Applied Energy (article, 2021-09-27)
Field: Materials Science — Materials Chemistry

Artificial neural network enabled accurate geometrical design and optimisation of thermoelectric generator Thermoelectric generator ; Artificial neural network ; Thermoelectric effect ; Generator (circuit theory) ; Thermoelectric cooling ; Electricity generation ; Power (physics) ; Renewable energy ; Computer science ; Contact resistance ; Thermoelectric materials ; Finite element method ; Mechanical engineering ; Electronic engineering ; Control theory (sociology) ; Engineering ; Materials science ; Artificial intelligence ; Electrical engineering ; Layer (electronics) ; Structural engineering ; Composite material ; Electrical resistivity and conductivity Advanced Thermoelectric Materials and Devices ; Innovative Energy Harvesting Technologies ; Thermal properties of materials
Authors: Hillary Pan | Alex M. Ganose | Matthew K. Horton | Muratahan Aykol | Kristin A. Persson | Nils Zimmermann | Anubhav Jain
Source: Inorganic Chemistry (article, 2021-01-08)
Field: Materials Science — Materials Chemistry

Benchmarking Coordination Number Prediction Algorithms on Inorganic Crystal Structures Coordination numbers and geometries form a theoretical framework for understanding and predicting materials properties. Algorithms to determine coordination numbers automatically are increasingly used for machine learning (ML) and automatic structural analysis. In this work, we introduce MaterialsCoord, a benchmark suite containing 56 experimentally derived crystal structures (spanning elements, binaries, and ternary compounds) and their corresponding coordination environments as described in the research literature. We also describe CrystalNN, a novel algorithm for determining near neighbors. We compare CrystalNN against seven existing near-neighbor algorithms on the MaterialsCoord benchmark, finding CrystalNN to perform similarly to several well-established algorithms. For each algorithm, we also assess computational demand and sensitivity toward small perturbations that mimic thermal motion. Finally, we investigate the similarity between bonding algorithms when applied to the Materials Project database. We expect that this work will aid the development of coordination prediction algorithms as well as improve structural descriptors for ML and other applications. Benchmarking ; Benchmark (surveying) ; Algorithm ; Suite ; Similarity (geometry) ; Ternary operation ; Computer science ; Chemistry ; Artificial intelligence Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Justin S. Smith | Benjamin Nebgen | Nithin Mathew | Jie Chen | Nicholas Lubbers | Leonid Burakovsky | Sergei Tretiak | Hai Ah Nam | Timothy Germann | Saryu Fensin | Kipton Barros
Source: Nature Communications (article, 2021-02-23)
Field: Materials Science — Materials Chemistry

Automated discovery of a robust interatomic potential for aluminum Machine learning, trained on quantum mechanics (QM) calculations, is a powerful tool for modeling potential energy surfaces. A critical factor is the quality and diversity of the training dataset. Here we present a highly automated approach to dataset construction and demonstrate the method by building a potential for elemental aluminum (ANI-Al). In our active learning scheme, the ML potential under development is used to drive non-equilibrium molecular dynamics simulations with time-varying applied temperatures. Whenever a configuration is reached for which the ML uncertainty is large, new QM data is collected. The ML model is periodically retrained on all available QM data. The final ANI-Al potential makes very accurate predictions of radial distribution function in melt, liquid-solid coexistence curve, and crystal properties such as defect energies and barriers. We perform a 1.3M atom shock simulation and show that ANI-Al force predictions shine in their agreement with new reference DFT calculations. Molecular dynamics ; Interatomic potential ; Embedded atom model ; Atom (system on chip) ; Function (biology) ; Potential energy ; Quantum ; Radial distribution function ; Quality (philosophy) Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Advanced Electron Microscopy Techniques and Applications
Authors: Tobias Vornholt | Fadri Christoffel | Michela M. Pellizzoni | Sven Panke | Thomas R. Ward | Markus Jeschek
Source: Science Advances (article, 2021-01-22)
Field: Chemistry — Organic Chemistry

Systematic engineering of artificial metalloenzymes for new-to-nature reactions Artificial metalloenzymes (ArMs) catalyzing new-to-nature reactions could play an important role in transitioning toward a sustainable economy. While ArMs have been created for various transformations, attempts at their genetic optimization have been case specific and resulted mostly in modest improvements. To realize their full potential, methods to rapidly discover active ArM variants for ideally any reaction of interest are required. Here, we introduce a reaction-independent, automation-compatible platform, which relies on periplasmic compartmentalization in <i>Escherichia coli</i> to rapidly and reliably engineer ArMs based on the biotin-streptavidin technology. We systematically assess 400 ArM mutants for five bioorthogonal transformations involving different metals, reaction mechanisms, and reactants, which include novel ArMs for gold-catalyzed hydroamination and hydroarylation. Activity enhancements up to 15-fold highlight the potential of the systematic approach. Furthermore, we suggest smart screening strategies and build machine learning models that accurately predict ArM activity from sequence, which has crucial implications for future ArM development. Computer science ; Biochemical engineering ; Artificial intelligence ; Chemistry ; Combinatorial chemistry ; Engineering Cyclopropane Reaction Mechanisms ; Surface Chemistry and Catalysis ; Chemical Synthesis and Analysis
Authors: Marwa Elkady | Hassan Shokry
Source: Polymers (article, 2021-06-22)
Field: Materials Science — Biomaterials

Photocatalytic Degradation of Malachite Green Dye from Aqueous Solution Using Environmentally Compatible Ag/ZnO Polymeric Nanofibers An efficient, environmentally compatible and highly porous, silver surface-modified photocatalytic zinc oxide/cellulose acetate/ polypyrrole ZnO/CA/Ppy hybrid nanofibers matrix was fabricated using an electrospinning technique. Electrospinning parameters such as solution flow rate, applied voltage and the distance between needles to collector were optimized. The optimum homogenous and uniform ZnO/CA/Ppy polymeric composite nanofiber was fabricated through the dispersion of 0.05% wt ZnO into the dissolved hybrid polymeric solution with an average nanofiber diameter ranged between 125 and 170 nm. The fabricated ZnO-polymeric nanofiber was further surface-immobilized with silver nanoparticles to enhance its photocatalytic activity through the reduction of the nanofiber bandgap. A comparative study between ZnO polymeric nanofiber before and after silver immobilization was investigated using X-ray diffraction (XRD), scanning electron microscope (SEM), transmission electron microscopy (TEM), Fourier transform infrared (FTIR) and thermal gravimetric analysis (TGA). The photocatalytic degradation efficiency of the two different prepared nanofibers before and after nanosilver immobilization for malachite green (MG) dye was compared against various experimental parameters. The optimum degradation efficiency of nanosilver surface-modified ZnO-polymeric nanofibers was recorded as 93.5% for malachite green dye after 1 h compared with 63% for ZnO-polymeric nanofibers. Nanofiber ; Materials science ; Malachite green ; Electrospinning ; Photocatalysis ; Scanning electron microscope ; Chemical engineering ; Fourier transform infrared spectroscopy ; Thermogravimetric analysis ; Aqueous solution ; Composite material ; Polymer ; Adsorption ; Organic chemistry ; Chemistry Electrospun Nanofibers in Biomedical Applications ; Advanced Photocatalysis Techniques ; TiO2 Photocatalysis and Solar Cells
Authors: Glaydson S. dos Reis | Helinando Pequeno de Oliveira | Sylvia H. Larsson | Mikael Thyrel | Éder C. Lima
Source: Nanomaterials (review, 2021-02-07)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A Short Review on the Electrochemical Performance of Hierarchical and Nitrogen-Doped Activated Biocarbon-Based Electrodes for Supercapacitors Cheap and efficient carbon electrodes (CEs) for energy storage systems (ESS) such as supercapacitors (SCs) and batteries are an increasing priority issue, among other things, due to a globally increasing share of intermittent electricity production (solar and wind) and electrification of transport. The increasing consumption of portable and non-portable electronic devices justifies research that enables environmentally and economically sustainable production (materials, processing techniques, and product design) of products with a high electrochemical performance at an acceptable cost. Among all the currently explored CEs materials, biomass-based activated carbons (AC) present enormous potential due to their availability and low-cost, easy processing methods, physicochemical stability, and methods for self-doping. Nitrogen doping methods in CEs for SCs have been demonstrated to enhance its conductivities, surface wettability, and induced pseudocapacitance effect, thereby delivering improved energy/power densities with versatile properties. Herein, a short review is presented, focusing on the different types of natural carbon sources for preparing CEs towards the fabrication of SCs with high electrochemical performance. The influences of ACs' pore characteristics (micro and mesoporosity) and nitrogen doping on the overall electrochemical performance (EP) are addressed. Supercapacitor ; Pseudocapacitance ; Materials science ; Energy storage ; Nanotechnology ; Electrochemistry ; Carbon fibers ; Process engineering ; Electrode ; Composite material ; Chemistry ; Power (physics) ; Engineering Supercapacitor Materials and Fabrication ; Conducting polymers and applications ; Electrochemical sensors and biosensors
Authors: Yiyang Ma | Dongbin Xiong | Xiaofan Lv | Xuesong Zhao | Chenchen Meng | Haijiao Xie | Zhenghua Zhang
Source: Journal of Materials Chemistry A (article, 2021-01-01)
Field: Chemistry — Organic Chemistry

Rapid and long-lasting acceleration of zero-valent iron nanoparticles@Ti<sub>3</sub>C<sub>2</sub>-based MXene/peroxymonosulfate oxidation with bi-active centers toward ranitidine removal Advanced oxidation processes (AOPs) can effectively degrade ranitidine, a pharmaceutical that is a typical precursor of nitrosamine dimethylamine (NDMA), an extremely potent human carcinogen. Dimethylamine ; Ranitidine ; Chemistry ; Nanoparticle ; Nitrosamine ; Carcinogen ; Inorganic chemistry ; Radiochemistry ; Photochemistry ; Nuclear chemistry ; Materials science ; Nanotechnology ; Organic chemistry ; Pharmacology Nanomaterials for catalytic reactions ; Advanced Photocatalysis Techniques ; Environmental remediation with nanomaterials
Authors: P.A. Luque | Alejandra Lopez-Peraza | Osvaldo Jesus Nava-Olivas | Guillermo Amaya Parra | Yolanda Báez-López | Víctor M. Orozco-Carmona | H.E. Garrafa-Gálvez | Manuel J. Chinchillas-Chinchillas
Source: Materials (article, 2021-12-08)
Field: Materials Science — Materials Chemistry

ZnO Semiconductor Nanoparticles and Their Application in Photocatalytic Degradation of Various Organic Dyes The biosynthesis of oxide semiconductor nanoparticles (NPs) using materials found in nature opens a wide field of study focused on sustainability and environmental protection. Biosynthesized NPs have the capacity to eliminate organic dyes, which pollute water and cause severe damage to the environment. In the present work, the green synthesis of zinc oxide (ZnO) NPs was carried out using <i>Capsicum annuum var. Anaheim</i> extract. The photocatalytic elimination of methylene blue (MB), methyl orange (MO), and Rhodamine B (RhB) in UV radiation was evaluated. The materials were characterized by scanning and transmission electron microscopy (SEM and TEM) and SEM-coupled energy dispersive spectroscopy (EDS), attenuated total reflectance-infrared (ATR-IR), X-ray photoelectron spectroscopy (XPS), X-ray diffraction (XRD), Photoluminescence (PL), and ultraviolet-visible spectroscopy (UV-Vis). The TEM analysis showed the NPs have an average size of 40 nm and quasi-spherical shape. ATR-IR showed the ZnO NPs contained functional groups from the extract. The analysis through XRD indicated that the NPs have a hexagonal zincite crystal structure with an average crystallite size of approximately 17 nm. The photoluminescence spectrum (PL) presented an emission band at 402 nm. From the UV-Vis spectra and TAUC model, the band-gap value was found to be 2.93 eV. Finally, the photocatalytic assessment proved the ZnO NPs achieved 100% elimination of MB at 60 min exposure, and 85 and 92% degradation of MO and RhB, respectively, at 180 min. This indicates that ZnO NPs, in addition to using a friendly method for their synthesis, manage to have excellent photocatalytic activity in the degradation of various organic pollutants. Rhodamine B ; Photocatalysis ; Materials science ; Methyl orange ; Photoluminescence ; X-ray photoelectron spectroscopy ; Crystallite ; Scanning electron microscope ; Nanoparticle ; Band gap ; Spectroscopy ; Diffuse reflectance infrared fourier transform ; Nuclear chemistry ; Analytical Chemistry (journal) ; Photochemistry ; Chemical engineering ; Nanotechnology ; Chemistry ; Optoelectronics ; Organic chemistry ; Metallurgy ; Composite material Nanoparticles: synthesis and applications ; Copper-based nanomaterials and applications ; Advanced Nanomaterials in Catalysis
Authors: Lei Zhang | Tie Jun Cui
Source: Research (review, 2021-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Space-Time-Coding Digital Metasurfaces: Principles and Applications Space-time-modulated metastructures characterized by spatiotemporally varying properties have recently attracted great interest and become one of the most fascinating and promising research fields. In the meantime, space-time-coding digital metasurfaces with inherently programmable natures emerge as powerful and versatile platforms for implementing the spatiotemporal modulations, which have been successfully realized and used to manipulate the electromagnetic waves in both the spectral and spatial domains. In this article, we systematically introduce the general concepts and working principles of space-time-coding digital metasurfaces and provide a comprehensive survey of recent advances and representative applications in this field. Specifically, we illustrate the examples of complicated wave manipulations, including harmonic beam control and programmable nonreciprocal effect. The fascinating strategy of space-time-coding opens the door to exciting scenarios for information systems, with abundant applications ranging from wireless communications to imaging and radars. We summarize this review by presenting the perspectives on the existing challenges and future directions in this fast-growing research field. Computer science ; Coding (social sciences) ; Wireless ; Electronic engineering ; Telecommunications ; Engineering Metamaterials and Metasurfaces Applications ; Antenna Design and Analysis ; Advanced Antenna and Metasurface Technologies