jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kazuki Morita | Daniel W. Davies | Keith T. Butler | Aron Walsh
Source: The Journal of Chemical Physics (article, 2020-07-10)
Field: Materials Science — Materials Chemistry

Modeling the dielectric constants of crystals using machine learning The relative permittivity of a crystal is a fundamental property that links microscopic chemical bonding to macroscopic electromagnetic response. Multiple models, including analytical, numerical, and statistical descriptions, have been made to understand and predict dielectric behavior. Analytical models are often limited to a particular type of compound, whereas machine learning (ML) models often lack interpretability. Here, we combine supervised ML, density functional perturbation theory, and analysis based on game theory to predict and explain the physical trends in optical dielectric constants of crystals. Two ML models, support vector regression and deep neural networks, were trained on a dataset of 1364 dielectric constants. Analysis of Shapley additive explanations of the ML models reveals that they recover correlations described by textbook Clausius-Mossotti and Penn models, which gives confidence in their ability to describe physical behavior, while providing superior predictive power. Dielectric ; Artificial neural network ; Support vector machine ; Permittivity ; Property (philosophy) ; Regression ; Regression analysis ; Perturbation (astronomy) ; Supervised learning Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Crystallography and molecular interactions
Authors: Nathan C. Frey | Matthew K. Horton | Jason M. Munro | Sinéad M. Griffin | Kristin A. Persson | Vivek B. Shenoy
Source: Science Advances (article, 2020-12-09)
Field: Materials Science — Materials Chemistry

High-throughput search for magnetic and topological order in transition metal oxides The discovery of intrinsic magnetic topological order in MnBi<sub>2</sub>Te<sub>4</sub> has invigorated the search for materials with coexisting magnetic and topological phases. These multiorder quantum materials are expected to exhibit new topological phases that can be tuned with magnetic fields, but the search for such materials is stymied by difficulties in predicting magnetic structure and stability. Here, we compute more than 27,000 unique magnetic orderings for more than 3000 transition metal oxides in the Materials Project database to determine their magnetic ground states and estimate their effective exchange parameters and critical temperatures. We perform a high-throughput band topology analysis of centrosymmetric magnetic materials, calculate topological invariants, and identify 18 new candidate ferromagnetic topological semimetals, axion insulators, and antiferromagnetic topological insulators. To accelerate future efforts, machine learning classifiers are trained to predict both magnetic ground states and magnetic topological order without requiring first-principles calculations. Throughput ; Topology (electrical circuits) ; Order (exchange) ; High-throughput screening ; Computer science ; Nanotechnology ; Materials science ; Biology ; Bioinformatics ; Mathematics ; Telecommunications ; Combinatorics Electronic and Structural Properties of Oxides ; Advanced Condensed Matter Physics ; Machine Learning in Materials Science
Authors: Jiapeng Liu | Francesco Ciucci
Source: Journal of The Electrochemical Society (article, 2020-01-02)
Field: Chemistry — Electrochemistry

The Deep-Prior Distribution of Relaxation Times Electrochemical impedance spectroscopy (EIS) is the established tool for the study of many electrochemical experiments. While the analysis of EIS data is challenging, this can be assisted by the distribution of relaxation time (DRT) method. However, obtaining the DRT is difficult as the underlying problem is ill-posed. Inspired by recent advances in image analysis, we develop a completely new approach, named the deep prior distribution of relaxation time (DP-DRT), for the deconvolution of the EIS to obtain the DRT. The DP-DRT uses a deep neural network fed with a single random input to deconvolve the DRT and fit the EIS data. The DP-DRT has the peculiarity of having a number of parameters much larger than the number of observations. Further, unlike most supervised deep learning models, large datasets are not needed as the DP-DRT is trained against a single available EIS spectrum. The DP-DRT was successfully tested against both synthetic and real experiments displaying considerable promise and opportunities for extensions. Deconvolution ; Relaxation (psychology) ; Computer science ; Deep learning ; Dielectric spectroscopy ; Artificial intelligence ; Artificial neural network ; Electrical impedance ; Distribution (mathematics) ; Algorithm ; Pattern recognition (psychology) ; Electrochemistry ; Mathematics ; Chemistry ; Electrical engineering ; Electrode ; Engineering Electrochemical Analysis and Applications ; Conducting polymers and applications ; Advanced Chemical Sensor Technologies
Authors: Hao Wu | Xin Gao | Lei Zhang | Guo Dong Bai | Qiang Cheng | Lianlin Li | Tie Jun Cui
Source: Light Science & Applications (article, 2020-12-14)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Harmonic information transitions of spatiotemporal metasurfaces Facilitated by ultrafast dynamic modulations, spatiotemporal metasurfaces have been identified as a pivotal platform for manipulating electromagnetic waves and creating exotic physical phenomena, such as dispersion cancellation, Lorentz reciprocity breakage, and Doppler illusions. Motivated by emerging information-oriented technologies, we hereby probe the information transition mechanisms induced by spatiotemporal variations and present a general model to characterize the information processing capabilities of the spatiotemporal metasurface. Group theory and abstract number theory are adopted through this investigation, by which the group extension and independent controls of multiple harmonics are proposed and demonstrated as two major tools for information transitions from the spatiotemporal domain to the spectra-wavevector domain. By incorporating Shannon's entropy theory into the proposed model, we further discover the corresponding information transition efficiencies and the upper bound of the channel capacity of the spatiotemporal metasurface. The results of harmonic information transitions show great potential in achieving high-capacity versatile information processing systems with spatiotemporal metasurfaces. Mutual information ; Information theory ; Computer science ; Physics ; Harmonics ; Entropy (arrow of time) ; Mathematics ; Artificial intelligence ; Quantum mechanics Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Advanced Antenna and Metasurface Technologies
Authors: Tiago Bresolin | J.R.R. Dórea
Source: Frontiers in Genetics (review, 2020-08-20)
Field: Chemistry — Analytical Chemistry

Infrared Spectrometry as a High-Throughput Phenotyping Technology to Predict Complex Traits in Livestock Systems High-throughput phenotyping technologies are growing in importance in livestock systems due to their ability to generate real-time, non-invasive, and accurate animal-level information. Collecting such individual-level information can generate novel traits and potentially improve animal selection and management decisions in livestock operations. One of the most relevant tools used in the dairy and beef industry to predict complex traits is infrared spectrometry, which is based on the analysis of the interaction between electromagnetic radiation and matter. The infrared electromagnetic radiation spans an enormous range of wavelengths and frequencies known as the electromagnetic spectrum. The spectrum is divided into different regions, with near- and mid-infrared regions being the main spectral regions used in livestock applications. The advantage of using infrared spectrometry includes speed, non-destructive measurement, and great potential for on-line analysis. This paper aims to review the use of mid- and near-infrared spectrometry techniques as tools to predict complex dairy and beef phenotypes, such as milk composition, feed efficiency, methane emission, fertility, energy balance, health status, and meat quality traits. Although several research studies have used these technologies to predict a wide range of phenotypes, most of them are based on Partial Least Squares (PLS) and did not considered other machine learning (ML) techniques to improve prediction quality. Therefore, we will discuss the role of analytical methods employed on spectral data to improve the predictive ability for complex traits in livestock operations. Furthermore, we will discuss different approaches to reduce data dimensionality and the impact of validation strategies on predictive quality. Livestock ; Throughput ; Computer science ; Quality (philosophy) ; Electromagnetic spectrum ; Environmental science ; Biochemical engineering ; Telecommunications ; Biology ; Engineering ; Physics ; Ecology Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Effects of Environmental Stressors on Livestock
Authors: Antonios Keirouz | Norbert Radacsi | Qun Ren | Alex Dommann | Guido Beldi | Katharina Maniura‐Weber | René M. Rossi | Giuseppino Fortunato
Source: Journal of Nanobiotechnology (article, 2020-03-18)
Field: Materials Science — Biomaterials

Nylon-6/chitosan core/shell antimicrobial nanofibers for the prevention of mesh-associated surgical site infection The state-of-the-art hernia meshes, used in hospitals for hernia repair, are predominantly polymeric textile-based constructs that present high mechanical strength, but lack antimicrobial properties. Consequently, preventing bacterial colonization of implanted prosthetic meshes is of major clinical relevance for patients undergoing hernia repair. In this study, the co-axial electrospinning technique was investigated for the development of a novel mechanically stable structure incorporating dual drug release antimicrobial action. Core/shell structured nanofibers were developed, consisting of Nylon-6 in the core, to provide the appropriate mechanical stability, and Chitosan/Polyethylene oxide in the shell to provide bacteriostatic action. The core/shell structure consisted of a binary antimicrobial system incorporating 5-chloro-8-quinolinol in the chitosan shell, with the sustained release of Poly(hexanide) from the Nylon-6 core of the fibers. Homogeneous nanofibers with a "beads-in-fiber" architecture were observed by TEM, and validated by FTIR and XPS. The composite nanofibrous meshes significantly advance the stress-strain responses in comparison to the counterpart single-polymer electrospun meshes. The antimicrobial effectiveness was evaluated in vitro against two of the most commonly occurring pathogenic bacteria; S. aureus and P. aeruginosa, in surgical site infections. This study illustrates how the tailoring of core/shell nanofibers can be of interest for the development of active antimicrobial surfaces. Electrospinning ; Nanofiber ; Chitosan ; Materials science ; Antimicrobial ; Nylon 6 ; Composite material ; Chemical engineering ; Polymer ; Nanotechnology ; Biomedical engineering ; Chemistry ; Medicine ; Organic chemistry Electrospun Nanofibers in Biomedical Applications ; Wound Healing and Treatments ; Advanced Sensor and Energy Harvesting Materials
Authors: Suresh Thangudu | Fong‐Yu Cheng | Chia‐Hao Su
Source: Polymers (review, 2020-12-20)
Field: Materials Science — Biomaterials

Advancements in the Blood–Brain Barrier Penetrating Nanoplatforms for Brain Related Disease Diagnostics and Therapeutic Applications Noninvasive treatments to treat the brain-related disorders have been paying more significant attention and it is an emerging topic. However, overcoming the blood brain barrier (BBB) is a key obstacle to most of the therapeutic drugs to enter into the brain tissue, which significantly results in lower accumulation of therapeutic drugs in the brain. Thus, administering the large quantity/doses of drugs raises more concerns of adverse side effects. Nanoparticle (NP)-mediated drug delivery systems are seen as potential means of enhancing drug transport across the BBB and to targeted brain tissue. These systems offer more accumulation of therapeutic drugs at the tumor site and prolong circulation time in the blood. In this review, we summarize the current knowledge and advancements on various nanoplatforms (NF) and discusses the use of nanoparticles for successful cross of BBB to treat the brain-related disorders such as brain tumors, Alzheimer’s disease, Parkinson’s disease, and stroke. Blood–brain barrier ; Medicine ; Drug delivery ; Disease ; Drug ; Brain disease ; Drug delivery to the brain ; Adverse effect ; Pharmacology ; Intensive care medicine ; Neuroscience ; Central nervous system ; Pathology ; Internal medicine ; Nanotechnology ; Psychology Nanoparticle-Based Drug Delivery ; Nanoplatforms for cancer theranostics ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Michael Green | Anh ThiVan Tran | Xiaobo Chen
Source: Advanced Materials Interfaces (article, 2020-08-02)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Obtaining Strong, Broadband Microwave Absorption of Polyaniline Through Data‐Driven Materials Discovery Abstract Microwave absorbing materials (MAMs) are highly utilized in the defense and telecommunications industries, as means for reducing radar cross sections for stealth technology, and providing electromagnetic interference shielding for the information processing and transport capabilities in electronic devices. Polyaniline materials have attracted enormous attention in the field of MAM development due to their strong dielectric properties. In this manuscript, the strong dielectric action of polyaniline is utilized to demonstrate for the first time how to mathematically model the EM response of materials as a means for accurately simulating, predicting, and optimizing the microwave absorption performance. Using this process, the microwave absorbing properties of polyaniline are successfully optimized and record‐breaking performances are demonstrated for both the reflection loss and effective bandwidth, yielding experimentally‐derived responses of RL = −88.5 dB at 7.0 GHz, 3.8 mm and Δ f 10 = 5.5 GHz at 2.1 mm. The methods presented herein are generalizable and have the potential to be broadly applicable to MAM research and development. Polyaniline ; Microwave ; Materials science ; Broadband ; Reflection loss ; Electromagnetic shielding ; Absorption (acoustics) ; Dielectric ; Optoelectronics ; Bandwidth (computing) ; Engineering physics ; Nanotechnology ; Computer science ; Telecommunications ; Polymer ; Composite material ; Physics Electromagnetic wave absorption materials ; Advanced Antenna and Metasurface Technologies ; Metamaterials and Metasurfaces Applications
Authors: Shuang Xu | Wen‐Cui Li | Cheng‐Tong Wang | Lei Tang | Guang‐Ping Hao | An‐Hui Lu
Source: Angewandte Chemie International Edition (article, 2020-12-18)
Field: Chemistry — Inorganic Chemistry

Self‐Pillared Ultramicroporous Carbon Nanoplates for Selective Separation of CH<sub>4</sub>/N<sub>2</sub> There is growing evidence that pillaring up a densely packed ultramicroporous two-dimensional (2D) structure is an effective strategy to reduce their internal diffusion. Reliable pillaring paradigms, however, is rather challenging. Here we report a one-pot multi-component sequential assembly method for the preparation of a new self-pillared 2D polymer and ultramicroporous carbon with integrated surface protrusions. The molecular level pillaring process is surprisingly fast, that is, in 10 min. The thickness of nanoplate edge and the density (roughness), angle as well as height of protrusions can be precisely tuned. Exemplified in coal bed methane purification/separation, this unique pillared 2D carbons exhibit a CH<sub>4</sub> /N<sub>2</sub> selectivity up to 24 at a low CH<sub>4</sub> partial pressure and two orders of magnitude faster CH<sub>4</sub> diffusion kinetics than the commercial carbon molecular sieves. This solution synthesis methodology is generalizable for creation and fine tuning of pillared 2D heterostructures. Molecular sieve ; Materials science ; Chemical engineering ; Carbon fibers ; Selectivity ; Diffusion ; Methane ; Nanotechnology ; Polymer ; Adsorption ; Catalysis ; Chemistry ; Organic chemistry ; Composite material ; Composite number Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Zeolite Catalysis and Synthesis
Authors: Alexandra-Elisabeta Stamate | Octavian Dumitru Pavel | Rodica Zãvoianu | Ioan‐Cezar Marcu
Source: Catalysts (review, 2020-01-01)
Field: Materials Science — Materials Chemistry

Highlights on the Catalytic Properties of Polyoxometalate-Intercalated Layered Double Hydroxides: A Review Layered double hydroxides (LDH) are an extended class of two-dimensional anionic materials that are known for their unique lamellar structure, versatile composition, and tunable properties. The layered architecture allows the intercalation between the positively charged sheets of a vast variety of anionic species, including oxometalates and polyoxometalates (POM). The hybrid composites that were developed using POM and LDH show great advantages when compared to both parent materials causing the appearance of new functionalities, which may lead to remarkable contributions in many areas of application, especially in catalysis. The current review paper emphases all of the crucial works already existing in literature that are related to the large group of POM-LDH solids and their use as catalysts for fine organic synthesis. The new trends in the development of the POM-LDH catalysts are highlighted based on the overview of 121 scientific articles that were published between 1984 and 2019. The main topics are focused primarily on the synthesis, characterization, and the catalytic applications of different LDH systems hosting polyoxometalates with low, medium, and high nuclearity. The intense exploration of the POM-LDH field has led to the obtaining of countless effective catalysts used in various types of reactions, from condensation, esterification, halodecarboxylation, to oxidation and epoxidation. Layered double hydroxides ; Polyoxometalate ; Catalysis ; Lamellar structure ; Intercalation (chemistry) ; Nanotechnology ; Materials science ; Chemistry ; Chemical engineering ; Combinatorial chemistry ; Inorganic chemistry ; Organic chemistry ; Metallurgy Layered Double Hydroxides Synthesis and Applications ; Polyoxometalates: Synthesis and Applications ; Inorganic Chemistry and Materials
Authors: Ali Mirtaghavi | Jikui Luo | Rajendran Muthuraj
Source: Journal of Composites Science (review, 2020-10-19)
Field: Materials Science — Biomaterials

Recent Advances in Porous 3D Cellulose Aerogels for Tissue Engineering Applications: A Review Current approaches in developing porous 3D scaffolds face various challenges, such as failure of mimicking extracellular matrix (ECM) native building blocks, non-sustainable scaffold fabrication techniques, and lack of functionality. Polysaccharides and proteins are sustainable, inexpensive, biodegradable, and biocompatible, with structural similarities to the ECM. As a result, 3D-structured cellulose (e.g., cellulose nanofibrils, nanocrystals and bacterial nanocellulose)-based aerogels with high porosity and interconnected pores are ideal materials for biomedical applications. Such 3D scaffolds can be prepared using a green, scalable, and cost-effective freeze-drying technique. The physicochemical, mechanical, and biological characteristics of the cellulose can be improved by incorporation of proteins and other polysaccharides. This review will focus on recent developments related to the cellulose-based 3D aerogels prepared by sustainable freeze-drying methods for tissue engineering applications. We will also provide an overview of the scaffold development criteria; parameters that influenced the aerogel production by freeze-drying; and in vitro and in vivo studies of the cellulose-based porous 3D aerogel scaffolds. These efforts could potentially help to expand the role of cellulose-based 3D scaffolds as next-generation biomaterials. Cellulose ; Nanocellulose ; Scaffold ; Materials science ; Aerogel ; Bacterial cellulose ; Tissue engineering ; Nanotechnology ; Porosity ; Chemical engineering ; Biomedical engineering ; Composite material ; Engineering Advanced Cellulose Research Studies ; Aerogels and thermal insulation ; Electrospun Nanofibers in Biomedical Applications
Authors: Fatih Yanar | Ali Mosayyebi | Claudio Nastruzzi | Dario Carugo | Xunli Zhang
Source: Pharmaceutics (article, 2020-10-22)
Field: Materials Science — Biomaterials

Continuous-Flow Production of Liposomes with a Millireactor under Varying Fluidic Conditions Continuous-flow production of liposomes using microfluidic reactors has demonstrated advantages compared to batch methods, including greater control over liposome size and size distribution and reduced reliance on post-production processing steps. However, the use of microfluidic technology for the production of nanoscale vesicular systems (such as liposomes) has not been fully translated to industrial scale yet. This may be due to limitations of microfluidic-based reactors, such as low production rates, limited lifetimes, and high manufacturing costs. In this study, we investigated the potential of millimeter-scale flow reactors (or millireactors) with a serpentine-like architecture, as a scalable and cost-effective route to the production of nanoscale liposomes. The effects on liposome size of varying inlet flow rates, lipid type and concentration, storage conditions, and temperature were investigated. Liposome size (i.e., mean diameter) and size dispersity were characterised by dynamic light scattering (DLS); z-potential measurements and TEM imaging were also carried out on selected liposome batches. It was found that the lipid type and concentration, together with the inlet flow settings, had significant effects on the properties of the resultant liposome dispersion. Notably, the millifluidic reactor was able to generate liposomes with size and dispersity ranging from 54 to 272 nm, and from 0.04 to 0.52 respectively, at operating flow rates between 1 and 10 mL/min. Moreover, when compared to a batch ethanol-injection method, the millireactor generated liposomes with a more therapeutically relevant size and size dispersity. Dispersity ; Liposome ; Microfluidics ; Particle size ; Volumetric flow rate ; Materials science ; Flow focusing ; Continuous production ; Dynamic light scattering ; Nanotechnology ; Nanoparticle ; Chemistry ; Composite material ; Mechanics ; Polymer chemistry Nanoparticle-Based Drug Delivery ; Innovative Microfluidic and Catalytic Techniques Innovation ; Microfluidic and Capillary Electrophoresis Applications