jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Andressa Trentin | Amirhossein Pakseresht | Alicia Durán | Yolanda Castro | Dušan Galusek
Source: Polymers (review, 2022-06-07)
Field: Materials Science — Materials Chemistry

Electrochemical Characterization of Polymeric Coatings for Corrosion Protection: A Review of Advances and Perspectives The development of anti-corrosion polymeric coatings has grown exponentially in the fields of material science, chemistry, engineering, and nanotechnology during the last century and has prompted the evolution of efficient characterization techniques. Nowadays, polymeric coatings represent a well-established protection system that provides a barrier between a metallic substrate and the environment. However, the increase in complexity and functionality of these coatings requires high-precision techniques capable of predicting failures and providing smart protection. This review summarizes the state of the art for the main electrochemical techniques, emphasizing devices that track the anti-corrosion properties of polymeric coatings from the macroscale to the nanoscale. An overview of the advances in accelerated corrosion testing and the electrochemical characterization of coatings is explored, including insights into their advantages and limitations. In addition, the challenges and potential applications of the theoretical approaches are summarized based on current knowledge. Finally, this work provides the reader with the trends and challenges of designing future technologies and models capable of tracking corrosion and predicting failures. Corrosion ; Characterization (materials science) ; Nanotechnology ; Materials science ; Computer science ; Metallurgy Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Yuting Huang | Nian‐Ke Chen | Zhen‐Ze Li | Xuepeng Wang | Hong‐Bo Sun | Shengbai Zhang | Xianbin Li
Source: InfoMat (article, 2022-07-12)
Field: Materials Science — Materials Chemistry

Two‐dimensional In<sub>2</sub>Se<sub>3</sub>: A rising advanced material for ferroelectric data storage Abstract Ferroelectric memory is a promising candidate for next‐generation nonvolatile memory owing to its outstanding performance such as low power consumption, fast speed, and high endurance. However, the ferroelectricity of conventional ferroelectric materials will be eliminated by the depolarization field when the size drops to the nanometer scale. As a result, the miniaturization of ferroelectric devices was hindered, which makes ferroelectric memory unable to keep up with the development of integrated‐circuit (IC) miniaturization. Recently, a two‐dimensional (2D) In 2 Se 3 was reported to maintain stable ferroelectricity at the ultrathin scale, which is expected to break through the bottleneck of miniaturization. Soon, devices based on 2D In 2 Se 3 , including the ferroelectric field‐effect transistor, ferroelectric channel transistor, synaptic ferroelectric semiconductor junction, and ferroelectric memristor were demonstrated. However, a comprehensive understanding of the structures and the ferroelectric‐switching mechanism of 2D In 2 Se 3 is still lacking. Here, the atomic structures of different phases, the dynamic mechanism of ferroelectric switching, and the performance/functions of the latest devices of 2D In 2 Se 3 are reviewed. Furthermore, the correlations among the structures, the properties, and the device performance are analyzed. Finally, several crucial problems or challenges and possible research directions are put forward. We hope that this review paper can provide timely knowledge and help for the research community to develop 2D In 2 Se 3 based ferroelectric memory and computing technology for practical industrial applications. image Ferroelectricity ; Miniaturization ; Materials science ; Ferroelectric capacitor ; Non-volatile memory ; Transistor ; Nanotechnology ; Optoelectronics ; Computer science ; Electrical engineering ; Dielectric ; Engineering ; Voltage 2D Materials and Applications ; Ferroelectric and Piezoelectric Materials ; Advanced Memory and Neural Computing
Authors: Fei Wang | Zhao Zhang | Imran Shakir | Chengbing Yu | Yuxi Xu
Source: Advanced Science (review, 2022-01-27)
Field: Materials Science — Materials Chemistry

2D Polymer Nanosheets for Membrane Separation Since the discovery of single-layer graphene in 2004, the family of 2D inorganic nanosheets is considered as ideal membrane materials due to their ultrathin atomic thickness and fascinating physicochemical properties. However, the intrinsically nonporous feature of 2D inorganic nanosheets hinders their potential to achieve a higher flux to some extent. Recently, 2D polymer nanosheets, originated from the regular and periodic covalent connection of the building units in 2D plane, have emerged as promising candidates for preparing ultrafast and highly selective membranes owing to their inherently tunable and ordered pore structure, light weight, and high specific surface. In this review, the synthetic methodologies (including top-down and bottom-up methods) of 2D polymer nanosheets are first introduced, followed by the summary of 2D polymer nanosheets-based membrane fabrication as well as membrane applications in the fields of gas separation, water purification, organic solvent separation, and ion exchange/transport in fuel cells and lithium-sulfur batteries. Finally, based on their current achievements, the authors' personal insights are put forward into the existing challenges and future research directions of 2D polymer nanosheets for membrane separation. The authors believe this comprehensive review on 2D polymer nanosheets-based membrane separation will definitely inspire more studies in this field. Membrane ; Polymer ; Nanotechnology ; Materials science ; Gas separation ; Graphene ; Fabrication ; Chemical engineering ; Chemistry Covalent Organic Framework Applications ; Membrane Separation Technologies ; Membrane Separation and Gas Transport
Authors: Zhoulong Fan | Xiangyang Chen | Keita Tanaka | Han Seul Park | Nelson Y. S. Lam | Jonathan J. Wong | K. N. Houk | Jin‐Quan Yu
Source: Nature (article, 2022-08-09)
Field: Chemistry — Organic Chemistry

Molecular editing of aza-arene C–H bonds by distance, geometry and chirality Bicyclic molecule ; Steric effects ; Stereochemistry ; Chemistry ; Chirality (physics) ; Hydrogen bond ; Bespoke ; Pharmacophore ; Combinatorial chemistry ; Molecule ; Physics ; Organic chemistry Catalytic C–H Functionalization Methods ; Synthesis and Catalytic Reactions ; Radical Photochemical Reactions
Authors: Sayan Bhowmik | Ananth Govind Rajan
Source: iScience (review, 2022-01-29)
Field: Materials Science — Materials Chemistry

Chemical vapor deposition of 2D materials: A review of modeling, simulation, and machine learning studies Chemical vapor deposition (CVD) is extensively used to produce large-area two-dimensional (2D) materials. Current research is aimed at understanding mechanisms underlying the nucleation and growth of various 2D materials, such as graphene, hexagonal boron nitride (hBN), and transition metal dichalcogenides (e.g., MoS<sub>2</sub>/WSe<sub>2</sub>). Herein, we survey the vast literature regarding modeling and simulation of the CVD growth of 2D materials and their heterostructures. We also focus on newer materials, such as silicene, phosphorene, and borophene. We discuss how density functional theory, kinetic Monte Carlo, and reactive molecular dynamics simulations can shed light on the thermodynamics and kinetics of vapor-phase synthesis. We explain how machine learning can be used to develop insights into growth mechanisms and outcomes, as well as outline the open knowledge gaps in the literature. Our work provides consolidated theoretical insights into the CVD growth of 2D materials and presents opportunities for further understanding and improving such processes. Chemical vapor deposition ; Computer science ; Nanotechnology ; Materials science Graphene research and applications ; 2D Materials and Applications ; Nanowire Synthesis and Applications
Authors: Bikramjit Basu | N. H. Gowtham | Yang Xiao | Surya R. Kalidindi | Kam W. Leong
Source: Acta Biomaterialia (review, 2022-02-22)
Field: Materials Science — Materials Chemistry

Biomaterialomics: Data science-driven pathways to develop fourth-generation biomaterials Adaptability ; Computer science ; Metadata ; Relevance (law) ; Pipeline (software) ; Data science ; Artificial intelligence ; Nanotechnology ; Systems engineering ; Engineering ; Materials science Machine Learning in Materials Science ; Additive Manufacturing and 3D Printing Technologies ; 3D Printing in Biomedical Research
Authors: Aswathi Soni | Yash Dixit | Marlon M. Reis | Gale Brightwell
Source: Comprehensive Reviews in Food Science and Food Safety (review, 2022-06-10)
Field: Chemistry — Analytical Chemistry

Hyperspectral imaging and machine learning in food microbiology: Developments and challenges in detection of bacterial, fungal, and viral contaminants Hyperspectral imaging (HSI) is a robust and nondestructive method that can detect foreign particles such as microbial, chemical, and physical contamination in food. This review summarizes the work done in the last two decades in this field with a highlight on challenges, risks, and research gaps. Considering the challenges of using HSI on complex matrices like food (e.g., the confounding and masking effects of background signals), application of machine learning and modeling approaches that have been successful in achieving better accuracy as well as increasing the detection limit have also been discussed here. Foodborne microbial contaminants such as bacteria, fungi, viruses, yeast, and protozoa are of interest and concern to food manufacturers due to the potential risk of either food poisoning or food spoilage. Detection of these contaminants using fast and efficient methods would not only prevent outbreaks and recalls but will also increase consumer acceptance and demand for shelf-stable food products. The conventional culture-based methods for microbial detection are time and labor-intensive, whereas hyperspectral imaging (HSI) is robust, nondestructive with minimum sample preparation, and has gained significant attention due to its rapid approach to detection of microbial contaminants. This review is a comprehensive summary of the detection of bacterial, viral, and fungal contaminants in food with detailed emphasis on the specific modeling and datamining approaches used to overcome the specific challenges associated with background and data complexity. Hyperspectral imaging ; Contamination ; Biochemical engineering ; Environmental science ; Food microbiology ; Food safety ; Biotechnology ; Food spoilage ; Computer science ; Artificial intelligence ; Biology ; Food science ; Ecology ; Bacteria ; Engineering Spectroscopy and Chemometric Analyses ; Listeria monocytogenes in Food Safety ; Identification and Quantification in Food
Authors: M. Saleem | M.S. Akbar
Source: Polymers (review, 2022-01-21)
Field: Materials Science — Materials Chemistry

Review of the Performance of High-Voltage Composite Insulators In the present literature survey, we focused on the performance of polymeric materials encompassing silicone rubber (SiR), ethylene propylene diene monomer (EPDM) and epoxy resins loaded with micro, nano, and micro/nano hybrid fillers. These insulators are termed as composite insulators. The scope of the added fillers/additives was limited to the synthetic inorganic family. Special attention was directed to understanding the effect of fillers on the improvement of the thermal conductivity, dielectric strength, mechanical strength, corona discharge resistance, and tracking and erosion resistance performance of polymeric materials for use as high-voltage transmission line insulators. The survey showed that synthetic inorganic fillers, which include silica (SiO<sub>2</sub>) and hexagonal boron nitride (h-BN), are potential fillers to improve insulation performance of high-voltage insulators. Furthermore, nano and micro/nano filled composites performed better due to the better interaction between the filler and polymer matrix as compared to their only micro- or nano filled counterparts. Finally, some aspects requiring future work to further exploit fillers are identified and discussed. Materials science ; Composite material ; Silicone rubber ; Filler (materials) ; Epoxy ; Nanocomposite ; Dielectric strength ; Nano- ; Polymer ; Dielectric High voltage insulation and dielectric phenomena ; Dielectric materials and actuators ; Power Transformer Diagnostics and Insulation
Authors: Emiel Hoogeboom | Victor García Satorras | Clément Vignac | Max Welling
Source: arXiv (Cornell University) (preprint, 2022-03-31)
Field: Materials Science — Materials Chemistry

Equivariant Diffusion for Molecule Generation in 3D This work introduces a diffusion model for molecule generation in 3D that is equivariant to Euclidean transformations. Our E(3) Equivariant Diffusion Model (EDM) learns to denoise a diffusion process with an equivariant network that jointly operates on both continuous (atom coordinates) and categorical features (atom types). In addition, we provide a probabilistic analysis which admits likelihood computation of molecules using our model. Experimentally, the proposed method significantly outperforms previous 3D molecular generative methods regarding the quality of generated samples and efficiency at training time. Equivariant map ; Diffusion ; Categorical variable ; Probabilistic logic ; Computation ; Computer science ; Euclidean geometry ; Generative grammar ; Mathematics ; Algorithm ; Pure mathematics ; Artificial intelligence ; Physics ; Geometry ; Machine learning ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Cell Image Analysis Techniques
Authors: Ajaypal Kaur | Komal Pandey | Ramandeep Kaur | Nisha Vashishat | Manpreet Kaur
Source: Chemosensors (article, 2022-09-15)
Field: Materials Science — Materials Chemistry

Nanocomposites of Carbon Quantum Dots and Graphene Quantum Dots: Environmental Applications as Sensors Carbon-based quantum dots and their nanocomposites have sparked immense interest for researchers as sensors due to their attractive physico-chemical properties caused by edge effects and quantum confinement. In this review article, we have discussed the synthesis and application of nanocomposites of graphene quantum dots (GQDs) and carbon quantum dots (CQDs). Different synthetic strategies for CQDs, GQDs, and their nanocomposites, are categorized as top-down and bottom-up approaches which include laser ablation, arc-discharge, chemical oxidation, ultrasonication, oxidative cleavage, microwave synthesis, thermal decomposition, solvothermal or hydrothermal method, stepwise organic synthesis, carbonization from small molecules or polymers, and impregnation. A comparison of methodologies is presented. The environmental application of nanocomposites of CQDs/GQDs and pristine quantum dots as sensors are presented in detail. Their applications envisage important domains dealing with the sensing of pollutant molecules. Recent advances and future perspective in the use of CQDs, GQDs, and their nanocomposites as sensors are also explored. Quantum dot ; Nanocomposite ; Graphene ; Materials science ; Carbon quantum dots ; Nanotechnology ; Nanoparticle ; Carbon fibers ; Carbonization ; Molecule ; Chemistry ; Composite material ; Organic chemistry ; Scanning electron microscope Carbon and Quantum Dots Applications ; Graphene and Nanomaterials Applications ; Nanocluster Synthesis and Applications
Authors: Richard Parker | Tianheng Zhao | Bruno Frka‐Petesic | Silvia Vignolini
Source: Nature Communications (article, 2022-06-13)
Field: Materials Science — Biomaterials

Cellulose photonic pigments When pursuing sustainable approaches to fabricate photonic structures, nature can be used as a source of inspiration for both the nanoarchitecture and the constituent materials. Although several biomaterials have been promised as suitable candidates for photonic materials and pigments, their fabrication processes have been limited to the small to medium-scale production of films. Here, by employing a substrate-free process, structurally coloured microparticles are produced via the confined self-assembly of a cholesteric cellulose nanocrystal (CNC) suspension within emulsified microdroplets. Upon drying, the droplets undergo multiple buckling events, which allow for greater contraction of the nanostructure than predicted for a spherical geometry. This buckling, combined with a solvent or thermal post-treatment, enables the production of dispersions of vibrant red, green, and blue cellulose photonic pigments. The hierarchical structure of these pigments enables the deposition of coatings with angular independent colour, offering a consistent visual appearance across a wide range of viewing angles. Materials science ; Fabrication ; Cellulose ; Photonics ; Nanotechnology ; Nanostructure ; Structural coloration ; Nanocellulose ; Iridescence ; Roll-to-roll processing ; Self-assembly ; Substrate (aquarium) ; Photonic crystal ; Chemical engineering ; Optoelectronics ; Optics Advanced Cellulose Research Studies ; Photonic Crystals and Applications
Authors: Hong Zhou | Liangge Xu | Zhihao Ren | Jiaqi Zhu | Chengkuo Lee
Source: Nanoscale Advances (review, 2022-11-07)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine learning-augmented surface-enhanced spectroscopy toward next-generation molecular diagnostics The world today is witnessing the significant role and huge demand for molecular detection and screening in healthcare and medical diagnosis, especially during the outbreak of COVID-19. Surface-enhanced spectroscopy techniques, including Surface-Enhanced Raman Scattering (SERS) and Infrared Absorption (SEIRA), provide lattice and molecular vibrational fingerprint information which is directly linked to the molecular constituents, chemical bonds, and configuration. These properties make them an unambiguous, nondestructive, and label-free toolkit for molecular diagnostics and screening. However, new issues in molecular diagnostics, such as increasing molecular species, faster spread of viruses, and higher requirements for detection accuracy and sensitivity, have brought great challenges to detection technology. Advancements in artificial intelligence and machine learning (ML) techniques show promising potential in empowering SERS and SEIRA with rapid analysis and automatic data processing to jointly tackle the challenge. This review introduces the combination of ML and SERS/SEIRA by investigating how ML algorithms can be beneficial to SERS/SEIRA, discussing the general process of combining ML and SEIRA/SERS, highlighting the molecular diagnostics and screening applications based on ML-combined SEIRA/SERS, and providing perspectives on the future development of ML-integrated SEIRA/SERS. In general, this review offers comprehensive knowledge about the recent advances and the future outlook regarding ML-integrated SEIRA/SERS for molecular diagnostics and screening. Raman scattering ; Surface (topology) ; Nanotechnology ; Materials science ; Absorption (acoustics) ; Computer science ; Spectroscopy ; Raman spectroscopy ; Systems engineering ; Physics ; Engineering ; Optics Gold and Silver Nanoparticles Synthesis and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research ; Machine Learning and ELM
Authors: Valentine Chikaodili Anadebe | Vitalis Ikenna Chukwuike | Sethupathy Ramanathan | Rakesh Chandra Barik
Source: Process Safety and Environmental Protection (article, 2022-10-13)
Field: Materials Science — Materials Chemistry

Cerium-based metal organic framework (Ce-MOF) as corrosion inhibitor for API 5L X65 steel in CO2- saturated brine solution: XPS, DFT/MD-simulation, and machine learning model prediction Cerium ; Brine ; Corrosion ; X-ray photoelectron spectroscopy ; Corrosion inhibitor ; Metal ; Materials science ; Metal-organic framework ; Inorganic chemistry ; Metallurgy ; Chemistry ; Chemical engineering ; Engineering ; Organic chemistry Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Xiujuan Zou | Youming Zhang | Ruoyu Lin | Guangxing Gong | Shuming Wang | Shining Zhu | Zhenlin Wang
Source: Nature Communications (article, 2022-06-07)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Pixel-level Bayer-type colour router based on metasurfaces The three primary colour model, i.e., red, green, and blue model, based on the colour perception of the human eye, has been widely used in colour imaging. The most common approach for obtaining colour information is to use a Bayer colour filter, which filters colour light with four pixels of an imaging sensor to form an effective colour pixel. However, its energy utilization efficiency and colour collection efficiency are limited to a low level due to the three-channel filtering nature. Here, by employing an inverse-design method, we demonstrate a pixel-level metasurface-based Bayer-type colour router that presents peak colour collection efficiencies of 58%, 59%, and 49% for red, green and blue light, and an average energy utilization efficiency as high as 84% over the visible region (400 nm-700 nm), which is twice as high as that of a commercial Bayer colour filter. Furthermore, by using a 200 µm × 200 µm metasurface-based colour router sample working with a monochromatic imaging sensor, colour imaging is further realized, obtaining an image intensity twice that achieved by a commercial Bayer colour filter. Our work innovates the mechanism of high-efficiency spectrum information acquisition, which is expected to have promising applications in the development of next-generation imaging systems. Pixel ; Computer science ; Type (biology) ; Router ; Artificial intelligence ; Computer vision ; Computer graphics (images) ; Biology ; Computer network ; Paleontology Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Advanced Wireless Communication Technologies
Authors: Yoshihiro Hayashi | Junichiro Shiomi | Junko Morikawa | Ryo Yoshida
Source: npj Computational Materials (article, 2022-11-08)
Field: Materials Science — Materials Chemistry

RadonPy: automated physical property calculation using all-atom classical molecular dynamics simulations for polymer informatics Abstract The spread of data-driven materials research has increased the need for systematically designed materials property databases. However, the development of polymer databases has lagged far behind other material systems. We present RadonPy, an open-source library that can automate the complete process of all-atom classical molecular dynamics (MD) simulations applicable to a wide variety of polymeric materials. Herein, 15 different properties were calculated for more than 1000 amorphous polymers. The MD-calculated properties were systematically compared with experimental data to validate the calculation conditions; the bias and variance in the MD-calculated properties were successfully calibrated by a machine learning technique. During the high-throughput data production, we identified eight amorphous polymers with extremely high thermal conductivity (&gt;0.4 W ∙ m –1 ∙ K –1 ) and their underlying mechanisms. Similar to the advancement of materials informatics since the advent of computational property databases for inorganic crystals, database construction using RadonPy will promote the development of polymer informatics. Polymer ; Materials science ; Amorphous solid ; Thermal conductivity ; Thermal conduction ; Molecular dynamics ; Chemical physics ; Thermodynamics ; Computational chemistry ; Chemistry ; Composite material ; Crystallography ; Physics Machine Learning in Materials Science ; Thermal properties of materials ; Phase Equilibria and Thermodynamics