jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jin Wang | Zhijie Zhang | Jiani Zhu | Mengtao Tian | Shuchang Zheng | Fudi Wang | Xudong Wang | Lei Wang
Source: Nature Communications (article, 2020-07-15)
Field: Materials Science — Materials Chemistry

Ion sieving by a two-dimensional Ti3C2Tx alginate lamellar membrane with stable interlayer spacing Two-dimensional membranes attract extensive interest due to the anomalous transport phenomena; however, the ion separation performance is below the theoretical prediction. The stabilization of d-spacing is a key step for enhancing ion selectivity. Here, we demonstrate a strategy for stabilizing the Ti<sub>3</sub>C<sub>2</sub>T<sub>x</sub> laminar architecture by alginate hydrogel pillars. After pillared by Ca-alginate, the nanochannel diameters are effectively fixed at 7.4 ± 0.2 Å, and the membrane presents a permeation cutoff and an outstanding sieving property towards valent cations. When applied for acid recovery, the outstanding H<sup>+</sup>/Fe<sup>2+</sup> selectivity makes the membrane a promising substitution for traditional ion-exchange membranes. Moreover, the ultrathin Mn-alginate pillared membrane with identical d-spacing exhibits 100% Na<sub>2</sub>SO<sub>4</sub> rejection with high water permeance, which is superior to the state-of-the-art nanofiltration membranes. Building on these findings, we demonstrate an efficient method to tune the ion selectivity and introduce a new perspective for energy- and environment-related applications. Membrane ; Permeance ; Selectivity ; Permeation ; Lamellar structure ; Materials science ; Ion ; Chemical engineering ; Nanofiltration ; Diffusion ; Ion exchange ; Ion transporter ; Nanotechnology ; Chemistry ; Composite material ; Organic chemistry ; Thermodynamics MXene and MAX Phase Materials ; Graphene research and applications ; Membrane Separation Technologies
Authors: Bart J. Kooi | Matthias Wuttig
Source: Advanced Materials (review, 2020-04-03)
Field: Materials Science — Materials Chemistry

Chalcogenides by Design: Functionality through Metavalent Bonding and Confinement A unified picture of different application areas for incipient metals is presented. This unconventional material class includes several main-group chalcogenides, such as GeTe, PbTe, Sb<sub>2</sub> Te<sub>3</sub> , Bi<sub>2</sub> Se<sub>3</sub> , AgSbTe<sub>2</sub> and Ge<sub>2</sub> Sb<sub>2</sub> Te<sub>5</sub> . These compounds and related materials show a unique portfolio of physical properties. A novel map is discussed, which helps to explain these properties and separates the different fundamental bonding mechanisms (e.g., ionic, metallic, and covalent). The map also provides evidence for an unconventional, new bonding mechanism, coined metavalent bonding (MVB). Incipient metals, employing this bonding mechanism, also show a special bond breaking mechanism. MVB differs considerably from resonant bonding encountered in benzene or graphite. The concept of MVB is employed to explain the unique properties of materials utilizing it. Then, the link is made from fundamental insights to application-relevant properties, crucial for the use of these materials as thermoelectrics, phase change materials, topological insulators or as active photonic components. The close relationship of the materials' properties and their application potential provides optimization schemes for different applications. Finally, evidence will be presented that for metavalently bonded materials interesting effects arise in reduced dimensions. In particular, the consequences for the crystallization kinetics of thin films and nanoparticles will be discussed in detail. Materials science ; Ionic bonding ; Thermoelectric materials ; Nanotechnology ; Covalent bond ; Mechanism (biology) ; Chemical physics ; Composite material ; Ion Advanced Thermoelectric Materials and Devices ; Phase-change materials and chalcogenides ; Chalcogenide Semiconductor Thin Films
Authors: Yihang Wang | João Marcelo Lamim Ribeiro | Pratyush Tiwary
Source: Current Opinion in Structural Biology (review, 2020-01-20)
Field: Materials Science — Materials Chemistry

Machine learning approaches for analyzing and enhancing molecular dynamics simulations Computer science ; Molecular dynamics ; Focus (optics) ; Perspective (graphical) ; Data science ; Key (lock) ; Software ; Artificial intelligence ; Machine learning ; Chemistry ; Physics ; Computational chemistry Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Sebastian Gisbertz | Bartholomäus Pieber
Source: ChemPhotoChem (article, 2020-02-21)
Field: Materials Science — Materials Chemistry

Heterogeneous Photocatalysis in Organic Synthesis Abstract Visible light photocatalysis relies mainly on expensive noble metal complexes and organic dyes that are not recyclable. Heterogeneous semiconductors, which are mainly applied for artificial photosynthesis and wastewater treatment, are a promising sustainable alternative and gain increasing attention. Inorganic and organic semiconductors with suitable bandgaps are among the most widely studied heterogeneous photocatalysts due to their high stability and recyclability. More recently, microporous materials, such as conjugated organic polymers, covalent organic frameworks, and metal organic frameworks that can be tuned and designed on a molecular level showed promising results. This review provides an overview of the most common heterogeneous photocatalysts with a focus on their applicability in organic synthesis. Photocatalysis ; Metal-organic framework ; Microporous material ; Materials science ; Conjugated microporous polymer ; Nanotechnology ; Polymer ; Catalysis ; Chemistry ; Organic chemistry ; Adsorption Covalent Organic Framework Applications ; Advanced Photocatalysis Techniques ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Zhenzhen Xu | Xiao‐Hong Xiong | Jian‐Bo Xiong | Rajamani Krishna | Libo Li | Yaling Fan | Feng Luo | Banglin Chen
Source: Nature Communications (article, 2020-06-22)
Field: Chemistry — Inorganic Chemistry

A robust Th-azole framework for highly efficient purification of C2H4 from a C2H4/C2H2/C2H6 mixture Separation of C<sub>2</sub>H<sub>4</sub> from C<sub>2</sub>H<sub>4</sub>/C<sub>2</sub>H<sub>2</sub>/C<sub>2</sub>H<sub>6</sub> mixture with high working capacity is still a challenging task. Herein, we deliberately design a Th-metal-organic framework (MOF) for highly efficient separation of C<sub>2</sub>H<sub>4</sub> from a binary C<sub>2</sub>H<sub>6</sub>/C<sub>2</sub>H<sub>4</sub> and ternary C<sub>2</sub>H<sub>4</sub>/C<sub>2</sub>H<sub>2</sub>/C<sub>2</sub>H<sub>6</sub> mixture. The synthesized MOF Azole-Th-1 shows a UiO-66-type structure with fcu topology built on a Th<sub>6</sub> secondary building unit and a tetrazole-based linker. Such noticeable structure, is connected by a N,O-donor ligand with high chemical stability. At 100 kPa and 298 K Azole-Th-1 performs excellent separation of C<sub>2</sub>H<sub>4</sub> (purity > 99.9%) from not only a binary C<sub>2</sub>H<sub>6</sub>/C<sub>2</sub>H<sub>4</sub> (1:9, v/v) mixture but also a ternary mixture of C<sub>2</sub>H<sub>6</sub>/C<sub>2</sub>H<sub>2</sub>/C<sub>2</sub>H<sub>4</sub> (9:1:90, v/v/v), and the corresponding working capacity can reach up to 1.13 and 1.34 mmol g<sup>-1</sup>, respectively. The separation mechanism, as unveiled by the density functional theory calculation, is due to a stronger van der Waals interaction between ethane and the MOF skeleton. Ternary operation ; van der Waals force ; Density functional theory ; Azole ; Thermal stability ; Ligand (biochemistry) ; Chemistry ; Materials science ; Chemical stability ; Crystallography ; Molecule ; Organic chemistry ; Computational chemistry Metal-Organic Frameworks: Synthesis and Applications ; X-ray Diffraction in Crystallography ; Carbon dioxide utilization in catalysis
Authors: Kevin Linka | Markus Hillgärtner | Kian P. Abdolazizi | Roland C. Aydin | Mikhail Itskov | Christian J. Cyron
Source: Journal of Computational Physics (article, 2020-11-24)
Field: Materials Science — Materials Chemistry

Constitutive artificial neural networks: A fast and general approach to predictive data-driven constitutive modeling by deep learning In this paper we introduce constitutive artificial neural networks (CANNs), a novel machine learning architecture for data-driven modeling of the mechanical constitutive behavior of materials. CANNs are able to incorporate by their very design information from three different sources, namely stress-strain data, theoretical knowledge from materials theory, and diverse additional information (e.g., about microstructure or materials processing). CANNs can easily and efficiently be implemented in standard computational software. They require only a low-to-moderate amount of training data and training time to learn without human guidance the constitutive behavior also of complex nonlinear and anisotropic materials. Moreover, in a simple academic example we demonstrate how the input of microstructural data can endow CANNs with the ability to describe not only the behavior of known materials but to predict also the properties of new materials where no stress-strain data are available yet. This ability may be particularly useful for the future in-silico design of new materials. The developed source code of the CANN architecture and accompanying example data sets are available at https://github.com/ConstitutiveANN/CANN. Artificial neural network ; Computer science ; Constitutive equation ; Artificial intelligence ; Nonlinear system ; Experimental data ; Code (set theory) ; Machine learning ; Software ; Test data ; Finite element method ; Engineering ; Set (abstract data type) ; Mathematics ; Software engineering ; Programming language ; Structural engineering Machine Learning in Materials Science ; Elasticity and Material Modeling ; Composite Material Mechanics
Authors: S. Loridant
Source: Catalysis Today (article, 2020-04-01)
Field: Materials Science — Materials Chemistry

Raman spectroscopy as a powerful tool to characterize ceria-based catalysts Catalysis ; Nanoclusters ; Raman spectroscopy ; Characterization (materials science) ; Redox ; In situ ; Oxide ; Spectroscopy ; Materials science ; Chemistry ; Photochemistry ; Nanotechnology ; Heterogeneous catalysis ; Inorganic chemistry ; Combinatorial chemistry ; Chemical engineering ; Organic chemistry Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Advanced Nanomaterials in Catalysis
Authors: J. Massoudi | M. Smari | K. Nouri | E. Dhahri | K. Khirouni | Sylvain Bertaina | Lotfi Bessais | E.K. Hlil
Source: RSC Advances (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Magnetic and spectroscopic properties of Ni–Zn–Al ferrite spinel: from the nanoscale to microscale This article presents the annealing effect on the structural, elastic, thermodynamic, optical, magnetic, and electric properties of Ni<sub>0.6</sub>Zn<sub>0.4</sub>Fe<sub>1.5</sub>Al<sub>0.5</sub>O<sub>4</sub> (NZFAO) nanoparticles (NPs). The samples were successfully synthesized by the sol-gel method followed by annealing of the as-synthesized at 600, 800, 900, 1050, and 1200 °C. This approach yielded the formation of a highly crystalline structure with crystallite size ranging from 17 nm to 40 nm. X-ray diffraction (XRD), scanning electron microscopy (SEM) techniques, as well as energy disperse spectroscopy (EDS), Fourier transform infrared (FTIR) and Raman spectroscopy, were used in order to determine the structural and morphological properties of the prepared samples. Rietveld XRD refinement reveals that Ni-Zn-Al ferrite nanoparticles crystallize in inverse cubic (<i>Fd</i>3̄<i>m</i>) spinel structure. Using FTIR spectra, the elastic and thermodynamic properties were estimated. It was observed that the particle size had a pronounced effect on elastic and thermodynamic properties. Magnetic measurements were performed up to 700 K. The prepared ferrite samples present the highest Curie temperature, which decreases with increasing particle size and which is consistent with finite-size scaling. The thickness of the surface shell of about 1 nm was estimated from size-dependent magnetization measurements using the core-shell model. Besides, spin resonance, magnetostriction, temperature coefficient of resistance (TCR), and electrical resistivity properties have been scientifically studied and appear to be different according to their size. The optical properties of synthesized NZFAO nanoparticles were investigated, and the differences caused by the particle sizes are discussed on the basis of the phonon confinement effect. This effect was also inspected by the Raman analysis. Tuning of the physical properties suggests that the Ni-Zn-Al ferrite samples may be promising for multifunctional diverse applications. Materials science ; Crystallite ; Raman spectroscopy ; Spinel ; Analytical Chemistry (journal) ; Zinc ferrite ; Fourier transform infrared spectroscopy ; Magnetization ; Annealing (glass) ; Particle size ; Nanoparticle ; Curie temperature ; Ferrite (magnet) ; Chemical engineering ; Nanotechnology ; Ferromagnetism ; Condensed matter physics ; Composite material ; Metallurgy ; Chemistry ; Physical chemistry ; Magnetic field ; Optics Magnetic Properties and Synthesis of Ferrites ; Multiferroics and related materials ; Magnetic properties of thin films
Authors: Elsa Olivetti | Jacqueline M. Cole | Edward Kim | Olga Kononova | Gerbrand Ceder | T. Yong-Jin Han | Anna M. Hiszpanski
Source: Applied Physics Reviews (article, 2020-12-01)
Field: Materials Science — Materials Chemistry

Data-driven materials research enabled by natural language processing and information extraction Given the emergence of data science and machine learning throughout all aspects of society, but particularly in the scientific domain, there is increased importance placed on obtaining data. Data in materials science are particularly heterogeneous, based on the significant range in materials classes that are explored and the variety of materials properties that are of interest. This leads to data that range many orders of magnitude, and these data may manifest as numerical text or image-based information, which requires quantitative interpretation. The ability to automatically consume and codify the scientific literature across domains—enabled by techniques adapted from the field of natural language processing—therefore has immense potential to unlock and generate the rich datasets necessary for data science and machine learning. This review focuses on the progress and practices of natural language processing and text mining of materials science literature and highlights opportunities for extracting additional information beyond text contained in figures and tables in articles. We discuss and provide examples for several reasons for the pursuit of natural language processing for materials, including data compilation, hypothesis development, and understanding the trends within and across fields. Current and emerging natural language processing methods along with their applications to materials science are detailed. We, then, discuss natural language processing and data challenges within the materials science domain where future directions may prove valuable. Computer science ; Data science ; Information extraction ; Variety (cybernetics) ; Field (mathematics) ; Domain (mathematical analysis) ; Data processing ; Natural language ; Natural (archaeology) ; Artificial intelligence ; Natural language processing Machine Learning in Materials Science ; Topic Modeling
Authors: Harsh Kumar | Kanchan Bhardwaj | Ruchi Sharma | Eugenie Nepovimová | Kamil Kuča | Daljeet Singh Dhanjal | Rachna Verma | Prerna Bhardwaj | Somesh Sharma | Dinesh Kumar
Source: Molecules (review, 2020-06-18)
Field: Materials Science — Biomaterials

Fruit and Vegetable Peels: Utilization of High Value Horticultural Waste in Novel Industrial Applications Fruits and vegetables are the highly used food products amongst the horticultural crops. These items are consumed uncooked, nominally cooked or fully cooked, according to their nature and cooking process. With the change in diet habits and rising population, the production, as well as the processing of horticultural crops, has exponentially improved to meet its increasing demand. A large amount of peel waste is generated from fruit and vegetable-based industries and household kitchen and has led to a big nutritional and economic loss and environmental problems. Processing of fruits and vegetables alone generates a significant waste, which amounts to 25-30% of the total product. Most common wastes include pomace, peels, rind and seeds, which are highly rich in valuable bioactive compounds such as carotenoids, enzymes, polyphenols, oils, vitamins and many other compounds. These bioactive compounds show their application in various industries such as food to develop edible films, food industries for probiotics and other industries for valuable products. The utilization of these low-cost waste horticultural wastes for producing the value-added product is a novel step in its sustainable utilization. The present review intends to summarize the different types of waste originating from fruits as well as vegetables peels and highlight their potential in developing edible films, probiotics, nanoparticles, carbon dots, microbial media, biochar and biosorbents. Pomace ; Biochar ; Population ; Food processing ; Nutraceutical ; Value added ; Biotechnology ; Food waste ; Food science ; Raw material ; Food industry ; Business ; Waste management ; Environmental science ; Chemistry ; Biology ; Engineering Nanocomposite Films for Food Packaging ; Food Waste Reduction and Sustainability ; Date Palm Research Studies
Authors: Sung Jean Park
Source: International Journal of Nanomedicine (review, 2020-08-01)
Field: Materials Science — Biomaterials

&lt;p&gt;Protein–Nanoparticle Interaction: Corona Formation and Conformational Changes in Proteins on Nanoparticles&lt;/p&gt; Nanoparticles (NPs) are highly potent tools for the diagnosis of diseases and specific delivery of therapeutic agents. Their development and application are scientifically and industrially important. The engineering of NPs and the modulation of their in vivo behavior have been extensively studied, and significant achievements have been made in the past decades. However, in vivo applications of NPs are often limited by several difficulties, including inflammatory responses and cellular toxicity, unexpected distribution and clearance from the body, and insufficient delivery to a specific target. These unfavorable phenomena may largely be related to the in vivo protein-NP interaction, termed "protein corona." The layer of adsorbed proteins on the surface of NPs affects the biological behavior of NPs and changes their functionality, occasionally resulting in loss-of-function or gain-of-function. The formation of a protein corona is an intricate process involving complex kinetics and dynamics between the two interacting entities. Structural changes in corona proteins have been reported in many cases after their adsorption on the surfaces of NPs that strongly influence the functions of NPs. Thus, understanding of the conformational changes and unfolding process of proteins is very important to accelerate the biomedical applications of NPs. Here, we describe several protein corona characteristics and specifically focus on the conformational fluctuations in corona proteins induced by NPs. Biophysics ; Nanoparticle ; In vivo ; Corona (planetary geology) ; Nanotechnology ; Chemistry ; Protein adsorption ; Peptide ; Materials science ; Adsorption ; Biochemistry ; Biology Nanoparticle-Based Drug Delivery ; Polymer Surface Interaction Studies ; Protein Interaction Studies and Fluorescence Analysis
Authors: Jinzhe Zeng | Liqun Cao | Mingyuan Xu | Tong Zhu | John Z. H. Zhang
Source: Nature Communications (article, 2020-11-11)
Field: Materials Science — Materials Chemistry

Complex reaction processes in combustion unraveled by neural network-based molecular dynamics simulation Combustion is a complex chemical system which involves thousands of chemical reactions and generates hundreds of molecular species and radicals during the process. In this work, a neural network-based molecular dynamics (MD) simulation is carried out to simulate the benchmark combustion of methane. During MD simulation, detailed reaction processes leading to the creation of specific molecular species including various intermediate radicals and the products are intimately revealed and characterized. Overall, a total of 798 different chemical reactions were recorded and some new chemical reaction pathways were discovered. We believe that the present work heralds the dawn of a new era in which neural network-based reactive MD simulation can be practically applied to simulating important complex reaction systems at ab initio level, which provides atomic-level understanding of chemical reaction processes as well as discovery of new reaction pathways at an unprecedented level of detail beyond what laboratory experiments could accomplish. Molecular dynamics ; Combustion ; Chemical reaction ; Work (physics) ; Benchmark (surveying) ; Computer science ; Artificial neural network ; Biological system ; Chemistry ; Computational chemistry ; Physical chemistry ; Artificial intelligence ; Physics ; Thermodynamics Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Computational Drug Discovery Methods
Authors: Min Zhang | Han Chen | Wen‐Qiang Cao | Mao‐Sheng Cao | Huijing Yang | Jie Yuan
Source: Nano-Micro Letters (article, 2020-11-20)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A Nano-Micro Engineering Nanofiber for Electromagnetic Absorber, Green Shielding and Sensor The role of electron transport characteristics in electromagnetic (EM) attenuation can be generalized to other EM functional materials. The integrated functions of efficient EM absorption and green shielding open the view of EM multifunctional materials. A novel sensing mechanism based on intrinsic EM attenuation performance and EM resonance coupling effect is revealed. It is extremely unattainable for a material to simultaneously obtain efficient electromagnetic (EM) absorption and green shielding performance, which has not been reported due to the competition between conduction loss and reflection. Herein, by tailoring the internal structure through nano-micro engineering, a NiCo<sub>2</sub>O<sub>4</sub> nanofiber with integrated EM absorbing and green shielding as well as strain sensing functions is obtained. With the improvement of charge transport capability of the nanofiber, the performance can be converted from EM absorption to shielding, or even coexist. Particularly, as the conductivity rising, the reflection loss declines from - 52.72 to - 10.5 dB, while the EM interference shielding effectiveness increases to 13.4 dB, suggesting the coexistence of the two EM functions. Furthermore, based on the high EM absorption, a strain sensor is designed through the resonance coupling of the patterned NiCo<sub>2</sub>O<sub>4</sub> structure. These strategies for tuning EM performance and constructing devices can be extended to other EM functional materials to promote the development of electromagnetic driven devices. Electromagnetic shielding ; Nanofiber ; Nano- ; Materials science ; Nanotechnology ; Composite material Electromagnetic wave absorption materials ; Advanced Antenna and Metasurface Technologies ; Metamaterials and Metasurfaces Applications