jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yongju Kim | Hyung Keun Park | Jaimyun Jung | Peyman Asghari‐Rad | Seung‐Chul Lee | Jin You Kim | Hwan Gyo Jung | Hyoung Seop Kim
Source: Materials & Design (article, 2021-02-05)
Field: Materials Science — Metals and Alloys

Exploration of optimal microstructure and mechanical properties in continuous microstructure space using a variational autoencoder Data-driven approaches enable a deep understanding of microstructure and mechanical properties of materials and greatly promote one's capability in designing new advanced materials. Deep learning-based image processing outperforms conventional image processing techniques with unsupervised learning. This study employs a variational autoencoder (VAE) to generate a continuous microstructure space based on synthetic microstructural images. The structure-property relationships are explored using a computational approach with microstructure quantification, dimensionality reduction, and finite element method (FEM) simulations. The FEM of representative volume element (RVE) with a microstructure-based constitutive model model is proposed for predicting the overall stress-strain behavior of the investigated dual-phase steels. Then, Gaussian process regression (GPR) is used to make connections between the latent space point and the ferrite grain size as inputs and mechanical properties as outputs. The GPR with VAE successfully predicts the newly generated microstructures with target mechanical properties with high accuracy. This work demonstrates that a variety of microstructures can be candidates for designing the optimal material with target properties in a continuous manner. Microstructure ; Materials science ; Autoencoder ; Finite element method ; Curse of dimensionality ; Artificial intelligence ; Computer science ; Artificial neural network ; Structural engineering ; Composite material Hydrogen embrittlement and corrosion behaviors in metals ; Microstructure and Mechanical Properties of Steels ; Non-Destructive Testing Techniques
Authors: Shuangshuang Zhang | Zihe Li | Kun Luo | Julong He | Yufei Gao | Alexander V Soldatov | Vicente Benavides | Kaiyuan Shi | Anmin Nie | Bin Zhang | Wentao Hu | Mengdong Ma | Yong Liu | Bin Wen | Guoying Gao | Bing Liu | Yang Zhang | Yu Shu | Dongli Yu | Xiang-Feng Zhou | Zhisheng Zhao | Bo Xu | Lei Su | Guoqiang Yang | Olga P Chernogorova | Yongjun Tian
Source: National Science Review (article, 2021-08-03)
Field: Chemistry — Organic Chemistry

Discovery of carbon-based strongest and hardest amorphous material Carbon is one of the most fascinating elements due to its structurally diverse allotropic forms stemming from its bonding varieties (<i>sp</i>, <i>sp</i> <sup>2</sup> and <i>sp</i> <sup>3</sup>). Exploring new forms of carbon has been the eternal theme of scientific research. Herein, we report on amorphous (AM) carbon materials with a high fraction of <i>sp</i> <sup>3</sup> bonding recovered from compression of fullerene C<sub>60</sub> under high pressure and high temperature, previously unexplored. Analysis of photoluminescence and absorption spectra demonstrates that they are semiconducting with a bandgap range of 1.5-2.2 eV, comparable to that of widely used AM silicon. Comprehensive mechanical tests demonstrate that synthesized AM-III carbon is the hardest and strongest AM material known to date, and can scratch diamond crystal and approach its strength. The produced AM carbon materials combine outstanding mechanical and electronic properties, and may potentially be used in photovoltaic applications that require ultrahigh strength and wear resistance. Carbon fibers ; Amorphous solid ; Photoluminescence ; Amorphous carbon ; Fullerene ; Band gap ; Diamond ; Allotropy Fullerene Chemistry and Applications ; Diamond and Carbon-based Materials Research ; Boron and Carbon Nanomaterials Research
Authors: Xue Bai | Jiali Wang | Qingxin Mu | Gaoxing Su
Source: Frontiers in Bioengineering and Biotechnology (review, 2021-03-31)
Field: Materials Science — Biomaterials

In vivo Protein Corona Formation: Characterizations, Effects on Engineered Nanoparticles’ Biobehaviors, and Applications Understanding the basic interactions between engineered nanoparticles (ENPs) and biological systems is essential for evaluating ENPs' safety and developing better nanomedicine. Profound interactions between ENPs and biomolecules such as proteins are inevitable to occur when ENPs are administered or exposed to biological systems, for example, through intravenous injection, oral, or respiration. As a key component of these interactions, protein corona (PC) is immediately formed surrounding the outlayer of ENPs. PC formation is crucial because it gives ENPs a new biological identity by altering not only the physiochemical properties, but also the biobehaviors of ENPs. In the past two decades, most investigations about PC formation were carried out with <i>in vitro</i> systems which could not represent the true events occurring within <i>in vivo</i> systems. Most recently, studies of <i>in vivo</i> PC formation were reported, and it was found that the protein compositions and structures were very different from those formed <i>in vitro</i>. Herein, we provide an in-time review of the recent investigations of this <i>in vivo</i> PC formation of ENPs. In this review, commonly used characterization methods and compositions of <i>in vivo</i> PC are summarized firstly. Next, we highlight the impacts of the <i>in vivo</i> PC formation on absorption, blood circulation, biodistribution, metabolism, and toxicity of administered ENPs. We also introduce the applications of modulating <i>in vivo</i> PC formation in nanomedicine. We further discuss the challenges and future perspectives. In vivo ; Nanomedicine ; Nanotechnology ; Chemistry ; Biomolecule ; Biodistribution ; Biophysics ; Nanoparticle ; Materials science ; Biology ; Biotechnology Nanoparticle-Based Drug Delivery ; Characterization and Applications of Magnetic Nanoparticles ; Minerals Flotation and Separation Techniques
Authors: Reta Birhanu Kitata | Wai-Kok Choong | Chia-Feng Tsai | Pei-Yi Lin | Bo-Shiun Chen | Yun-Chien Chang | Alexey I. Nesvizhskii | Ting-Yi Sung | Yu‐Ju Chen
Source: Nature Communications (article, 2021-05-05)
Field: Chemistry — Spectroscopy

A data-independent acquisition-based global phosphoproteomics system enables deep profiling Phosphoproteomics can provide insights into cellular signaling dynamics. To achieve deep and robust quantitative phosphoproteomics profiling for minute amounts of sample, we here develop a global phosphoproteomics strategy based on data-independent acquisition (DIA) mass spectrometry and hybrid spectral libraries derived from data-dependent acquisition (DDA) and DIA data. Benchmarking the method using 166 synthetic phosphopeptides shows high sensitivity (<0.1 ng), accurate site localization and reproducible quantification (~5% median coefficient of variation). As a proof-of-concept, we use lung cancer cell lines and patient-derived tissue to construct a hybrid phosphoproteome spectral library covering 159,524 phosphopeptides (88,107 phosphosites). Based on this library, our single-shot streamlined DIA workflow quantifies 36,350 phosphosites (19,755 class 1) in cell line samples within two hours. Application to drug-resistant cells and patient-derived lung cancer tissues delineates site-specific phosphorylation events associated with resistance and tumor progression, showing that our workflow enables the characterization of phosphorylation signaling with deep coverage, high sensitivity and low between-run missing values. Phosphoproteomics ; Workflow ; Computer science ; Computational biology ; Profiling (computer programming) ; Systems biology ; Bioinformatics ; Phosphorylation ; Biology ; Protein phosphorylation ; Cell biology ; Database Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Genomics and Phylogenetic Studies
Authors: Ziwei Ye | Chunchun Li | Qinglu Chen | Yikai Xu | Steven E. J. Bell
Source: Nanoscale (article, 2021-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Self-assembly of colloidal nanoparticles into 2D arrays at water–oil interfaces: rational construction of stable SERS substrates with accessible enhancing surfaces and tailored plasmonic response Self-assembly at water-oil interfaces has been shown to be a cheap, convenient and efficient route to obtain densely packed layers of plasmonic nanoparticles which have small interparticle distances. This creates highly plasmonically active materials that can be used to give strong SERS enhancement and whose structure means that they are well suited to creating the highly stable, reproducible and uniform substrates that are needed to allow routine and accurate quantitative SERS measurements. A variety of methods have been developed to induce nanoparticle self-assembly at water-oil interfaces, fine tune the surface chemistry and adjust the position of the nanoparticles at the interface but only some of these are compatible with eventual use in SERS, where it is important that target molecules can access the active surface unimpeded. Similarly, it is useful to transform liquid plasmonic arrays into easy-to-handle free-standing solid films but these can only be used as solid SERS substrates if the process leaves the surface nanoparticles exposed. Here, we review the progress made in these research areas and discuss how these developments may lead towards achieving rational construction of tailored SERS substrates for sensitive and quantitative SERS analysis. Plasmon ; Nanotechnology ; Materials science ; Nanoparticle ; Plasmonic nanoparticles ; Construct (python library) ; Rational design ; Self-assembly ; Colloid ; Computer science ; Chemical engineering ; Optoelectronics ; Engineering Gold and Silver Nanoparticles Synthesis and Applications ; Pickering emulsions and particle stabilization ; Surfactants and Colloidal Systems
Authors: Sk Manirul Haque | Jorge Alfredo Ardila‐Rey | Yunusa Umar | Abdullahi Abubakar Mas’ud | Firdaus Muhammad‐Sukki | Binta Hadi Jume | Habibur Rahman | Nurul Aini Bani
Source: Energies (article, 2021-05-11)
Field: Materials Science — Materials Chemistry

Application and Suitability of Polymeric Materials as Insulators in Electrical Equipment In this paper, the applications of thermoplastic, thermoset polymers, and a brief description of the functions of each subsystem are reviewed. The synthetic route and characteristics of polymeric materials are presented. The mechanical properties of polymers such as impact behavior, tensile test, bending test, and thermal properties like mold stress-relief distortion, generic thermal indices, relative thermal capability, and relative thermal index are mentioned. Furthermore, this paper covers the electrical behavior of polymers, mainly their dielectric strength. Different techniques for evaluating polymers’ suitability applied for electrical insulation are covered, such as partial discharge and high current arc resistance to ignition. The polymeric materials and processes used for manufacturing cables at different voltage ranges are described, and their applications to high voltage DC systems (HVDC) are discussed. The evolution and limitations of polymeric materials for electrical application and their advantages and future trends are mentioned. However, to reduce the high cost of filler networks and improve their technical properties, new techniques need to be developed. To overcome limitations associated with the accuracy of the techniques used for quantifying residual stresses in polymers, new techniques such as indentation are used with higher force at the stressed location. Materials science ; Thermosetting polymer ; Polymer ; Composite material ; Thermoplastic elastomer ; Voltage ; Thermoplastic ; Mechanical engineering ; Electrical engineering High voltage insulation and dielectric phenomena ; Electrical Fault Detection and Protection ; Electrostatic Discharge in Electronics
Authors: Shampa Raghunathan | U. Deva Priyakumar
Source: International Journal of Quantum Chemistry (article, 2021-12-27)
Field: Materials Science — Materials Chemistry

Molecular representations for machine learning applications in chemistry Abstract Machine learning (ML) methods enable computers to address problems by learning from existing data. Such applications are becoming commonplace in molecular sciences. Interest in applying ML techniques across chemical compound space, from predicting properties to designing molecules and materials is in the surge. Especially, ML models have started to accelerate computational chemistry, and are often as accurate as state‐of‐the‐art electronic/atomistic models. Being an integral part of the ML architecture, representation of a molecular entity, uniquely encoded, plays a crucial role to what extent an ML model would be accurately predicting the desired property. This review aims to demonstrate a hierarchy of representations which has been introduced, to capture all degrees of freedom of a molecule or an atom the best, to map the quantum mechanical properties. We discuss their diverse applications how they have been instrumental in harnessing the growing field of ML accelerated computational modeling. Representation (politics) ; Computer science ; Hierarchy ; Field (mathematics) ; Chemical space ; Property (philosophy) ; Molecular machine ; Theoretical computer science ; Artificial intelligence ; Machine learning ; Chemistry ; Nanotechnology ; Materials science ; Mathematics ; Drug discovery Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Xin Qian | Ronggui Yang
Source: Materials Science and Engineering R Reports (article, 2021-09-14)
Field: Materials Science — Materials Chemistry

Machine learning for predicting thermal transport properties of solids Thermal conductivity ; Phonon ; Bottleneck ; Boltzmann equation ; Boltzmann machine ; Thermal ; Amorphous solid Thermal properties of materials ; Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices
Authors: Jacob Kerner | Alan B. Dogan | Horst A. von Recum
Source: Acta Biomaterialia (review, 2021-06-01)
Field: Materials Science — Materials Chemistry

Machine learning and big data provide crucial insight for future biomaterials discovery and research Computer science ; Machine learning ; Artificial intelligence ; Data science ; Variety (cybernetics) ; Field (mathematics) ; Identification (biology) ; Predictive modelling Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Kevin Maik Jablonka | Daniele Ongari | Seyed Mohamad Moosavi | Berend Smit
Source: Nature Chemistry (article, 2021-07-05)
Field: Materials Science — Materials Chemistry

Using collective knowledge to assign oxidation states of metal cations in metal–organic frameworks Protonation ; Chemistry ; Oxidation state ; Metal ; Metal ions in aqueous solution ; Metal-organic framework ; Computational chemistry ; Ion ; Organic chemistry Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; Computational Drug Discovery Methods
Authors: Timothy K. Mulenga | Albert Uchenna Ude | Vivekanandhan Chinnasamy
Source: Fibers (review, 2021-01-14)
Field: Materials Science — Polymers and Plastics

Techniques for Modelling and Optimizing the Mechanical Properties of Natural Fiber Composites: A Review The study of natural fiber-based composites through the use of computational techniques for modelling and optimizing their properties has emerged as a fast-growing approach in recent years. Ecological concerns associated with synthetic fibers have made the utilisation of natural fibers as a reinforcing material in composites a popular approach. Computational techniques have become an important tool in the hands of many researchers to model and analyze the characteristics that influence the mechanical properties of natural fiber composites. This recent trend has led to the development of many advanced computational techniques and software for a profound understanding of the characteristics and performance behavior of composite materials reinforced with natural fibers. The large variations in the characteristics of natural fiber-based composites present a great challenge, which has led to the development of many computational techniques for composite materials analysis. This review seeks to infer, from conventional to contemporary sources, the computational techniques used in modelling, analyzing, and optimizing the mechanical characteristics of natural fiber reinforced composite materials. Natural fiber ; Composite number ; Natural (archaeology) ; Materials science ; Composite material ; Fiber-reinforced composite ; Fiber ; Computational model ; Computer science ; Simulation ; Geology Natural Fiber Reinforced Composites ; Mechanical Engineering and Vibrations Research ; Tribology and Wear Analysis
Authors: Qidong Xu | Lingchao Xia | Qijiao He | Zengjia Guo | Meng Ni
Source: Applied Energy (article, 2021-03-28)
Field: Materials Science — Materials Chemistry

Thermo-electrochemical modelling of high temperature methanol-fuelled solid oxide fuel cells Solid oxide fuel cell ; Methanol ; Materials science ; Operating temperature ; Overpotential ; Chemical engineering ; Hydrogen ; Electrochemistry ; Chemistry ; Thermodynamics ; Anode ; Organic chemistry ; Electrode Advancements in Solid Oxide Fuel Cells ; Electronic and Structural Properties of Oxides ; Catalysis and Oxidation Reactions
Authors: Marina Santiago Franco | Eliza Rocha Gomes | Marjorie Coimbra Roque | Mônica Cristina Oliveira
Source: Frontiers in Oncology (review, 2021-03-16)
Field: Materials Science — Biomaterials

Triggered Drug Release From Liposomes: Exploiting the Outer and Inner Tumor Environment Since more than 40 years liposomes have being extensively studied for their potential as carriers of anticancer drugs. The basic principle behind their use for cancer treatment consists on the idea that they can take advantage of the leaky vasculature and poor lymphatic drainage present at the tumor tissue, passively accumulating in this region. Aiming to further improve their efficacy, different strategies have been employed such as PEGlation, which enables longer circulation times, or the attachment of ligands to liposomal surface for active targeting of cancer cells. A great challenge for drug delivery to cancer treatment now, is the possibility to trigger release from nanosystems at the tumor site, providing efficacious levels of drug in the tumor. Different strategies have been proposed to exploit the outer and inner tumor environment for triggering drug release from liposomes and are the focus of this review. Liposome ; Drug delivery ; Drug ; Lymphatic system ; Cancer ; Cancer research ; Medicine ; Cancer treatment ; Pharmacology ; Nanotechnology ; Immunology ; Internal medicine ; Materials science Nanoparticle-Based Drug Delivery ; Nanoplatforms for cancer theranostics ; RNA Interference and Gene Delivery
Authors: Xiaopeng Han | Ying Huang | Jiaming Wang | Guozheng Zhang | Tiehu Li | Panbo Liu
Source: Composites Part B Engineering (article, 2021-11-03)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Flexible hierarchical ZnO/AgNWs/carbon cloth-based film for efficient microwave absorption, high thermal conductivity and strong electro-thermal effect Materials science ; Microwave ; Optoelectronics ; Electronics ; Absorption (acoustics) ; Electrical conductor ; Thermal conductivity ; Reflection loss ; Thermal ; Nanotechnology ; Composite material ; Electrical engineering ; Composite number ; Computer science ; Telecommunications Electromagnetic wave absorption materials ; Advanced Antenna and Metasurface Technologies ; Metamaterials and Metasurfaces Applications
Authors: Iran Rocha Segundo | Elisabete F. Freitas | Verônica Teixeira Franco Castelo Branco | Salmon Landi | Manuel F. M. Costa | Joaquim Carneiro
Source: Renewable and Sustainable Energy Reviews (article, 2021-08-05)
Field: Materials Science — Polymers and Plastics

Review and analysis of advances in functionalized, smart, and multifunctional asphalt mixtures Road pavements are designed to withstand road traffic and weathering actions while ensuring comfortable and safe riding conditions as well as low costs and damage to the environment. When a road pavement has additional abilities or reacts to an external stimulus, it is considered to be smart and multifunctional. Examples of such abilities that have been investigated in asphalt mixtures are photocatalytic, superhydrophobic, self-cleaning, de-icing/anti-icing, self-healing, thermochromic, and latent heat thermal energy storage abilities. These abilities are developed using different materials such as nano/microparticles (including semiconductor materials and microcapsules), fibers, phase change materials (PCMs), and dyes, often using dissimilar techniques such as spray coating, volume incorporation, spreading, and asphalt binder modification. Owing to their large surface areas, road pavements are true recipients for large amounts of nano/micromaterials, and consequently, act as important “tools” to stimulate an emerging sector related to the scale of production of materials in the form of nanoparticles. Moreover, smart and multifunctional road pavements can be included in the domain of clean technology (e.g., photocatalytic pavements that promote the environmental depollution of NOx-type gases emitted as vehicle exhaust gases). In this context, they can contribute to materializing the transition to a novel socio-economic model known as “Green Recovery” that is environmentally friendly, sustainable, and inclusive. This model is a very important path toward economic and employment recovery, a vision to which many countries are strongly committed. Therefore, this work reviews new capabilities imparted to asphalt mixtures and provides recommendations. Asphalt ; Materials science ; Nanotechnology ; Engineering ; Composite material Polymer composites and self-healing ; Asphalt Pavement Performance Evaluation ; Polymer Nanocomposites and Properties
Authors: Rifan Hardian | Stefano Dissegna | Aladin Ullrich | Philip L. Llewellyn | Marie‐Vanessa Coulet | Roland A. Fischer
Source: Chemistry - A European Journal (article, 2021-02-14)
Field: Chemistry — Inorganic Chemistry

Tuning the Properties of MOF‐808 via Defect Engineering and Metal Nanoparticle Encapsulation Defect engineering and metal encapsulation are considered as valuable approaches to fine-tune the reactivity of metal-organic frameworks. In this work, various MOF-808 (Zr) samples are synthesized and characterized with the final aim to understand how defects and/or platinum nanoparticle encapsulation act on the intrinsic and reactive properties of these MOFs. The reactivity of the pristine, defective and Pt encapsulated MOF-808 is quantified with water adsorption and CO<sub>2</sub> adsorption calorimetry. The results reveal strong competitive effects between crystal morphology and missing linker defects which in turn affect the crystal morphology, porosity, stability, and reactivity. In spite of leading to a loss in porosity, the introduction of defects (missing linkers or Pt nanoparticles) is beneficial to the stability of the MOF-808 towards water and could also be advantageously used to tune adsorption properties of this MOF family. Encapsulation (networking) ; Nanoparticle ; Materials science ; Metal-organic framework ; Nanotechnology ; Chemical engineering ; Chemistry ; Computer science ; Engineering ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; ZnO doping and properties