jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Urška Jančič | Mojca Božič | Silvo Hribernik | Tamilselvan Mohan | Rupert Kargl | Karin Stana Kleinschek | Selestina Gorgieva
Source: Cellulose (article, 2021-09-21)
Field: Materials Science — Biomaterials

High oxygen barrier chitosan films neutralized by alkaline nanoparticles Abstract The most frequent neutralisation procedure, applied on chitosan (CS) films includes treatment with NaOH base. Such treatment endows CS films with stability in water, yet, same can significantly decrease the film performance. In the present paper, we investigate Mg(OH) 2 nanoparticles as a neutralisation agent for CS solutions followed by casting into films. This is combined and compared with classical casting and film drying from non-neutralized solutions followed by NaOH treatment after film formation. The influence on the properties of resulting films is investigated in detail and large differences are found for structure and barrier properties. The stable, opaque-to-transparent CS films (depending on Mg(OH) 2 content and post-treatment) were obtained by facile casting method of neat CS or CS–Mg(OH) 2 dispersions, in the complete absence of cross-linkers and plasticizers. FTIR data demonstrate the Mg(OH) 2 and NaOH deprotonation effect, and strongly suggest intensive H-bonding interaction between CS and Mg(OH) 2 . X-ray photoelectron spectroscopy showed differences in the hydroxide content and protonation of CS nitrogen. The reduction of surface roughness and increase of homogeneity, the tensile strength and elongation, as well as thermal stability and excellent oxygen barrier properties were measured for CS enclosing the Mg(OH) 2 nanoparticles. Further treatment with 1 M NaOH causes re-packing of CS polymer chains, improving the crystallinity and water vapour barrier properties, degrading the mechanical properties by increasing the films brittleness and increasing the char formation due to reduced thermal stability. Graphic abstract Crystallinity ; Materials science ; Chemical engineering ; Thermal stability ; Ultimate tensile strength ; Fourier transform infrared spectroscopy ; Casting ; Nanoparticle ; Polymer ; Chitosan ; Siloxane ; Hydrogen bond ; Composite material ; Nanotechnology ; Organic chemistry ; Molecule ; Chemistry Nanocomposite Films for Food Packaging ; biodegradable polymer synthesis and properties ; Polymer Surface Interaction Studies
Authors: Olgun Güven
Source: Applied Sciences (article, 2021-08-27)
Field: Materials Science — Materials Chemistry

Radiation-Assisted Synthesis of Polymer-Based Nanomaterials Radiation technology has long been proven as a simple, rapid, green and sustainable technology with macroscale applications in healthcare, industry and environment. Its merits, however, have not been fully utilized in today’s ever growing nanotechnology. Ionizing radiation has beneficial effects for the synthesis and modification of structure and properties of nanomaterials. This paper intends to update the application of ionizing radiation in the development of various nanomaterials under the categories: (i) carbon-based nanomaterials, (ii) metal-based nanomaterials, (iii) polymer-based nanomaterials, (iv) polymer nanocomposites and (v) nano-scale grafting for advanced membrane applications. Nanomaterials ; Nanotechnology ; Materials science Carbon Nanotubes in Composites ; Polymer Nanocomposite Synthesis and Irradiation ; Graphene and Nanomaterials Applications
Authors: David P. Mills | Peter Evans
Source: Chemistry - A European Journal (review, 2021-01-16)
Field: Chemistry — Organic Chemistry

f‐Block Phospholyl and Arsolyl Chemistry The f-block chemistry of phospholyl and arsolyl ligands, heavier p-block analogues of substituted cyclopentadienyls (Cp<sup>R</sup> , C<sub>5</sub> R<sub>5</sub> ) where one or more CR groups are replaced by P or As atoms, is less developed than for lighter isoelectronic C<sub>5</sub> R<sub>5</sub> rings. Heterocyclopentadienyl complexes can exhibit properties that complement and contrast with Cp<sup>R</sup> chemistry. Given that there has been renewed interest in phospholyl and arsolyl f-block chemistry in the last two decades, coinciding with a renaissance in f-block solution chemistry, a review of this field is timely. Here, the syntheses of all structurally characterised examples of lanthanide and actinide phospholyl and arsolyl complexes to date are covered, including benzannulated derivatives, and together with group 3 complexes for completeness. The physicochemical properties of these complexes are reviewed, with the intention of motivating further research in this field. Block (permutation group theory) ; Chemistry ; Stereochemistry ; Mathematics ; Combinatorics Organometallic Complex Synthesis and Catalysis ; Synthesis and characterization of novel inorganic/organometallic compounds ; Synthesis and Reactivity of Heterocycles
Authors: Pengfei She | Yanyan Qin | Yun Ma | Feiyang Li | Jinyu Lü | Peiling Dai | Hao Hu | Xiangjie Liu | Shujuan Liu | Wei Huang | Qiang Zhao
Source: Science China Materials (article, 2021-01-15)
Field: Materials Science — Materials Chemistry

Lifetime-tunable organic persistent room-temperature phosphorescent salts for large-area security printing Phosphorescence ; Materials science ; Phosphonium ; Alkyl ; Counterion ; Crystallinity ; Commercialization ; Nanotechnology ; Ion ; Organic chemistry ; Polymer chemistry ; Chemistry ; Business Luminescence and Fluorescent Materials ; Organic Light-Emitting Diodes Research ; Perovskite Materials and Applications
Authors: Min Ji Kim | Mina Ahn | Kyung‐Ryang Wee
Source: Materials Advances (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Facile intra- and intermolecular charge transfer control for efficient mechanofluorochromic material The mechanofluorochromism is related to the optical properties according to the electron push–pull substituent effect. As the intramolecular charge transfer decreases, strong intermolecular interactions occur, leading to mechanofluorochromism. Intermolecular force ; Intramolecular force ; Substituent ; Charge (physics) ; Transfer (computing) ; Electron transfer ; Chemical physics ; Materials science ; Chemistry ; Computational chemistry ; Photochemistry ; Molecule ; Stereochemistry ; Organic chemistry ; Computer science ; Physics ; Quantum mechanics Luminescence and Fluorescent Materials ; Porphyrin and Phthalocyanine Chemistry ; Photodynamic Therapy Research Studies
Authors: Sumaira Anjum | Amna Komal Khan | Anza Qamar | Noor Fatima | Samantha Drouet | Sullivan Renouard | Jean Philippe Blondeau | Bilal Haider Abbasi | Christophe Hano
Source: International Journal of Molecular Sciences (article, 2021-10-19)
Field: Materials Science — Materials Chemistry

Light Tailoring: Impact of UV-C Irradiation on Biosynthesis, Physiognomies, and Clinical Activities of Morus macroura-Mediated Monometallic (Ag and ZnO) and Bimetallic (Ag–ZnO) Nanoparticles A nano-revolution based on the green synthesis of nanomaterials could affect all areas of human life, and nanotechnology represents a propitious platform for various biomedical applications. During the synthesis of nanoparticles, various factors can control their physiognomies and clinical activities. Light is one of the major physical factors that can play an important role in tuning/refining the properties of nanoparticles. In this study, biocompatible monometallic (AgNPs and ZnONPs) and bimetallic Ag-ZnONPs (0.1/0.1 and 0.1/0.5) were synthesized under UV-C light irradiation from the leaf extract of <i>Morus macroura</i>, which possesses enriched TPC (4.238 ± 0.26 mg GAE/g DW) and TFC (1.073 ± 0.18 mg QE/g DW), as well as strong FRSA (82.39%). These green synthesized NPs were evaluated for their anti-diabetic, anti-glycation, and biocompatibility activities. Furthermore, their anti-cancerous activity against HepG2 cell lines was assessed in terms of cell viability, production of reactive oxygen/nitrogen species, mitochondrial membrane potential, and apoptotic caspase-3/7 expression and activity. Synthesized NPs were characterized by techniques including ultraviolet-visible spectroscopy, SEM, EDX, FTIR, and XRD. UV-C mediated monometallic and bimetallic NPs showed well-defined characteristic shapes with a more disperse particle distribution, definite crystalline structures, and reduced sizes as compared to their respective controls. In the case of clinical activities, the highest anti-diabetic activity (67.77 ± 3.29% against α-amylase and 35.83 ± 2.40% against α-glucosidase) and anti-glycation activity (37.68 ± 3.34% against pentosidine-like AGEs and 67.87 ± 2.99% against vesperlysine-like AGEs) was shown by UV-C mediated AgNPs. The highest biocompatibility (IC<sub>50</sub> = 14.23 ± 1.68 µg/mL against brine shrimp and 2.48 ± 0.32% hemolysis of human red blood cells) was shown by UV-C mediated ZnONPs. In the case of anti-cancerous activities, the lowest viability (23.45 ± 1.40%) with enhanced ROS/NOS production led to a significant disruption of mitochondrial membrane potential and greater caspase-3/7 gene expression and activity by UV-C mediated bimetallic Ag-ZnONPs (0.1/0.5). The present work highlights the positive effects of UV-C light on physico-chemical physiognomies as well as the clinical activities of NPs. Bimetallic strip ; Biosynthesis ; Irradiation ; Nanoparticle ; Materials science ; Zinc ; Chemistry ; Nuclear chemistry ; Nanotechnology ; Metallurgy ; Biochemistry ; Metal ; Enzyme Nanoparticles: synthesis and applications ; Medical and Biological Ozone Research ; Anesthesia and Neurotoxicity Research
Authors: Eddy Plasquy | José M. Garcı́a | María del Carmen Florido Fernández | Rafael R. Sola‐Guirado | Juan Francisco García Martín
Source: Processes (article, 2021-08-30)
Field: Chemistry — Organic Chemistry

Cold Storage and Temperature Management of Olive Fruit: The Impact on Fruit Physiology and Olive Oil Quality—A Review Cold storage of olive fruit has been the subject of study for over more than 50 years. From the 1990s on, an increasing amount of knowledge is build-up about the impact of the conservation on the physiological response of the fruit as well as on the quality of the extracted oil therefrom. This review offers a comprehensive synopsis of this research, discusses the most important influential factors and summarizes the results on the influence of the studied parameters on both the fruit and the oil. Currently, changing climatic conditions, new harvesting techniques and a more demanding consumer market are triggering the need to broaden this strict focus on conservation. A more dynamic view on the effects of temperature from the moment the fruit is harvested up to the oil extraction process, reveals the necessity to manage this crucial influential factor more diversely. An overview of how this management can take form is structured through a focus on the different phases of the postharvest processing and the widely different harvesting scales. Future prospects of research are presented based on the actual state of the art of cold storage research as well as on the necessities that come forward from a broader fruit temperature management perspective. Postharvest ; Olive oil ; Quality (philosophy) ; Cold storage ; Perspective (graphical) ; Process (computing) ; Business ; Biotechnology ; Agricultural engineering ; Environmental science ; Computer science ; Food science ; Biology ; Horticulture ; Engineering Edible Oils Quality and Analysis ; Postharvest Quality and Shelf Life Management ; Essential Oils and Antimicrobial Activity
Authors: K.G.U.R. Kumarasinghe | W. C. H. Silva | M. D. A. Fernando | Lalinda Palliyaguru | Savidya Jayawardena | M. Shimomura | Neluka Fernando | Chinthika Gunasekara | P.M. Jayaweera
Source: BioMed Research International (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

One‐Pot Reducing Agent‐Free Synthesis of Silver Nanoparticles/Nitrocellulose Composite Surface Coating with Antimicrobial and Antibiofilm Activities Nitrocellulose with silver nanoparticle (AgNP/NC) composite was prepared <i>in situ</i> using Ag(CH<sub>3</sub>CO<sub>2</sub>) and nitrocellulose without any reducing agent. The composite materials synthesized were spray coated onto glass substrates to obtain thin films. The AgNPs/NC composites were characterized by ultraviolet-visible, Fourier transform infrared, X-ray photoelectron spectroscopy, scanning electron microscopy, and transmission electron microscopy. The antimicrobial activity of AgNPs/NC composite was investigated by tube method and time-kill kinetic studies against three microbial species, including <i>Pseudomonas aeruginosa</i> (ATCC 27853), <i>Staphylococcus aureus</i> (ATCC 25923), and <i>Candida albicans</i> (ATCC 10231). The antibiofilm activities were qualitatively determined against all three organisms. Prepared AgNPs/NC films exhibited good antimicrobial activity and significant inhibition of biofilm development against all three microbial species. The effective dispersion of AgNPs/NC in biofilm was responsible for the significant antibiofilm activity of the prepared material. The reported AgNPs/NC composite can be used as coating additive in bacteriocidal paint which can be applied onto surfaces such as in healthcare environments. Silver nanoparticle ; Biofilm ; Antimicrobial ; Composite number ; Nuclear chemistry ; Candida albicans ; Fourier transform infrared spectroscopy ; Materials science ; Transmission electron microscopy ; Coating ; Nanoparticle ; Chemistry ; Scanning electron microscope ; Chemical engineering ; Microbiology ; Nanotechnology ; Bacteria ; Composite material ; Organic chemistry ; Biology Nanoparticles: synthesis and applications ; Antimicrobial agents and applications
Authors: Blake MacQueen | Rasika Jayarathna | Jochen Lauterbach
Source: Current Opinion in Chemical Engineering (article, 2021-12-29)
Field: Materials Science — Materials Chemistry

Knowledge extraction in catalysis utilizing design of experiments and machine learning Computer science ; Machine learning ; Artificial intelligence ; Catalysis ; Biochemical engineering ; Engineering ; Chemistry Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Catalytic Processes in Materials Science
Authors: Hongcan Liu | Shun Ma | Junjun Wu | Yingkai Wang | Xinghui Wang
Source: Frontiers in Energy Research (article, 2021-02-10)
Field: Materials Science — Materials Chemistry

Recent Advances in Screening Lithium Solid-State Electrolytes Through Machine Learning Compared to liquid electrolytes, lithium solid-state electrolytes have received increased attention in the field of all-solid-state lithium ion batteries due to safety requirements and higher energy density. However, solid-state electrolytes face many challenges, including lower ionic conductivity, complex interfaces, and unstable physical or electrochemical properties. One of the most effective strategies is to find a new type of lithium solid-state electrolyte with improved properties. Traditional trial and error methods require resources and time to verify the new solid-state electrolytes. Recently, new lithium solid-state electrolytes were predicted through machine learning (ML), which has proved to be an efficient and reliable method for screening new functional materials. This paper reviews the lithium solid-state electrolytes that have been discovered based on ML algorithms. The selection and preprocessing of datasets in ML technology are initially discussed before describing the latest developments in screening lithium solid-state electrolytes through different ML algorithms in detail. Lastly, the stability of candidate solid-state electrolytes and the challenges of discovering new lithium solid-state electrolytes through ML are highlighted. Electrolyte ; Lithium (medication) ; Fast ion conductor ; Ionic conductivity ; Materials science ; Solid-state ; Electrochemistry ; Computer science ; Nanotechnology ; Chemistry ; Electrode ; Physical chemistry Machine Learning in Materials Science ; Advancements in Battery Materials ; Advanced Battery Materials and Technologies
Authors: Jinxin Yu | Shengkun Xi | Shaobin Pan | Yongjie Wang | Qinghua Peng | Rongpei Shi | Cuiping Wang | Xingjun Liu
Source: Journal of Materials Informatics (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Machine learning-guided design and development of metallic structural materials In recent years, the advent of machine learning (ML) in materials science has provided a new tool for accelerating the design and discovery of new materials with a superior combination of mechanical properties for structural applications. In this review, we provide a brief overview of the current status of the ML-aided design and development of metallic alloys for structural applications, including high-performance copper alloys, nickel- and cobalt-based superalloys, titanium alloys for biomedical applications and high strength steel. We also present our perspectives regarding the further acceleration of data-driven discovery, development, design and deployment of metallic structural materials and the adoption of ML-based techniques in this endeavor. Superalloy ; Materials science ; Structural material ; Systems engineering ; Titanium alloy ; Computer science ; Metallurgy ; Engineering ; Alloy Machine Learning in Materials Science ; Titanium Alloys Microstructure and Properties ; Aluminum Alloy Microstructure Properties
Authors: Giorgos Nikoulis | Jesper Byggmästar | Joseph Kioseoglou | K. Nordlund | Flyura Djurabekova
Source: Journal of Physics Condensed Matter (article, 2021-05-21)
Field: Materials Science — Materials Chemistry

Machine-learning interatomic potential for W–Mo alloys In this work, we develop a machine-learning interatomic potential for W<sub><i>x</i></sub>Mo<sub>1-<i>x</i></sub>random alloys. The potential is trained using the Gaussian approximation potential framework and density functional theory data produced by the Vienna<i>ab initio</i>simulation package. The potential focuses on properties such as elastic properties, melting, and point defects for the whole range of W<sub><i>x</i></sub>Mo<sub>1-<i>x</i></sub>compositions. Moreover, we use all-electron density functional theory data to fit an adjusted Ziegler-Biersack-Littmarck potential for the short-range repulsive interaction. We use the potential to investigate the effect of alloying on the threshold displacement energies and find a significant dependence on the local chemical environment and element of the primary recoiling atom. Interatomic potential ; Density functional theory ; Atom (system on chip) ; Ab initio ; Gaussian ; Range (aeronautics) ; Coherent potential approximation ; Statistical physics ; Materials science ; Molecular dynamics ; Electronic structure ; Thermodynamics ; Condensed matter physics ; Physics ; Chemistry ; Computational chemistry ; Quantum mechanics ; Computer science Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; Nuclear Materials and Properties
Authors: Xu Long | Minghui Mao | Changheng Lu | Ruiwen Li | Fengrui Jia
Source: Journal of Micromechanics and Molecular Physics (article, 2021-03-01)
Field: Materials Science — Materials Chemistry

Modeling of heterogeneous materials at high strain rates with machine learning algorithms trained by finite element simulations Great progress has been made in the dynamic mechanical properties of concrete which is usually assumed to be homogenous. In fact, concrete is a typical heterogeneous material, and the meso-scale structure with aggregates has a significant effect on its macroscopic mechanical properties of concrete. In this paper, concrete is regarded as a two-phase composite material, that is, a combination of aggregate inclusion and mortar matrix. To create the finite element (FE) models, the Monte Carlo method is used to place the aggregates as random inclusions into the mortar matrix of the cylindrical specimens. To validate the numerical simulations of such an inclusion-matrix model at high strain rates, the comparisons with experimental results using the split Hopkinson pressure bar are made and good agreement is achieved in terms of dynamic increasing factor. By performing more extensive FE predictions, the influences of aggregate size and content on the macroscopic dynamic properties (i.e., peak dynamic strength) of concrete materials subjected to high strain rates are further investigated based on the back-propagation (BP) artificial neural network method. It is found that the particle size of aggregate has little effect on the dynamic mechanical properties of concrete but the peak dynamic strength of concrete increases obviously with the content increase of aggregate. After detailed comparisons with FE simulations, machine learning predictions based on the BP algorithm show good applicability for predicting dynamic mechanical strength of concrete with different aggregate sizes and contents. Instead of FE analysis with complicated meso-scale aggregate pre-processing, time-consuming simulation and laborious post-processing, machine learning predictions reproduce the stress–strain curves of concrete materials under high strain rates and thus the constitutive behavior can be efficiently predicted. Aggregate (composite) ; Materials science ; Split-Hopkinson pressure bar ; Monte Carlo method ; Mortar ; Finite element method ; Matrix (chemical analysis) ; Artificial neural network ; Properties of concrete ; Dynamic loading ; Composite material ; Structural engineering ; Strain rate ; Compressive strength ; Computer science ; Engineering ; Mathematics ; Machine learning High-Velocity Impact and Material Behavior ; Structural Response to Dynamic Loads ; Rock Mechanics and Modeling
Authors: Tao Wang | Guomin Li | Xinjiang Cui | Frank Abild‐Pedersen
Source: Proceedings of the National Academy of Sciences (article, 2021-03-12)
Field: Materials Science — Materials Chemistry

Identification of earth-abundant materials for selective dehydrogenation of light alkanes to olefins Selective ethane dehydrogenation (EDH) is an attractive on-purpose strategy for industrial ethylene production. Design of an effective, stable, and earth-abundant catalyst to replace noble metal Pt is the main obstacle for its large-scale application. Herein, we report an experimentally validated theoretical framework to discover promising catalysts for EDH, which combines descriptor-based microkinetic modeling, high-throughput computations, machine-learning concepts, and experiments. Our approach efficiently evaluates 1,998 bimetallic alloys by using accurately calculated C and CH<sub>3</sub> adsorption energies and identifies a small number of new promising noble-metal-free catalysts for selective EDH. A Ni<sub>3</sub>Mo alloy predicted to be promising is successfully synthesized, and experimentally proven to outperform Pt in selective ethylene production from EDH, representing an important contribution to the improvement of light alkane dehydrogenation to olefins. These results will provide essential additions in the discovery and application of earth-abundant materials in catalysis. Dehydrogenation ; Bimetallic strip ; Catalysis ; Alkane ; Chemistry ; Heterogeneous catalysis ; Combinatorial chemistry ; Noble metal ; Materials science ; Organic chemistry ; Chemical engineering ; Nanotechnology Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Catalytic Processes in Materials Science