jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hao Liu | Christopher R. Gough | Qianqian Deng | Zhenggui Gu | Fang Wang | Xiao Hu
Source: International Journal of Molecular Sciences (review, 2020-06-04)
Field: Materials Science — Biomaterials

Recent Advances in Electrospun Sustainable Composites for Biomedical, Environmental, Energy, and Packaging Applications Electrospinning has gained constant enthusiasm and wide interest as a novel sustainable material processing technique due to its ease of operation and wide adaptability for fabricating eco-friendly fibers on a nanoscale. In addition, the device working parameters, spinning solution properties, and the environmental factors can have a significant effect on the fibers' morphology during electrospinning. This review summarizes the newly developed principles and influence factors for electrospinning technology in the past five years, including these factors' interactions with the electrospinning mechanism as well as its most recent applications of electrospun natural or sustainable composite materials in biology, environmental protection, energy, and food packaging materials. Composite material ; Materials science ; Sustainable energy ; Renewable energy ; Engineering Electrospun Nanofibers in Biomedical Applications ; Advanced Sensor and Energy Harvesting Materials ; Conducting polymers and applications
Authors: Manuel Cordova | Matthew D. Wodrich | Benjamin Meyer | Boodsarin Sawatlon | Clémence Corminbœuf
Source: ACS Catalysis (article, 2020-05-26)
Field: Chemistry — Inorganic Chemistry

Data-Driven Advancement of Homogeneous Nickel Catalyst Activity for Aryl Ether Cleavage The increasing urgency to make chemical processes more environmentally friendly while continuing to derive the chemicals required for modern society from renewable resources requires the development of a forthcoming generation of synthetic processes and the catalysts needed to facilitate these reactions. Recently, applications of machine-learning (ML) algorithms involving catalysis have begun to appear with increasing frequency, as they constitute an attractive pathway both for discovering prospective species and identifying trends surrounding catalytic behavior, principally because the number of potential catalysts that can be examined greatly exceeds those found in more traditional experimental or theoretical approaches. Here, we harness a data-driven approach powered by ML in tandem with molecular volcano plots to estimate the activity of over 143,000 homogeneous nickel catalysts bearing phosphine and N-heterocyclic carbene ligands for the reductive C(sp(2))-O cleavage reaction in aryl ether compounds, an important step in the degradation of biomass (lignin) into industrially useful feedstock chemicals. Our computational workflow reveals that a vast majority of Ni-phosphine and Ni-carbene catalysts are not ideally tuned to facilitate this reaction. An analysis of those species identified as being the most promising uncovers a clear catalytic design strategy that can be exploited in an experimental setting to enhance the rate of reductive C(sp(2))-O cleavage of aryl ether compounds. Carbene ; Catalysis ; Aryl ; Chemistry ; Nickel ; Ether ; Phosphine ; Combinatorial chemistry ; Homogeneous catalysis ; Organic chemistry Asymmetric Hydrogenation and Catalysis ; Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion
Authors: Susumu Fujii | Tatsuya Yokoi | Craig A. J. Fisher | Hiroki Moriwake | Masato Yoshiya
Source: Nature Communications (article, 2020-04-15)
Field: Materials Science — Materials Chemistry

Quantitative prediction of grain boundary thermal conductivities from local atomic environments Quantifying the dependence of thermal conductivity on grain boundary (GB) structure is critical for controlling nanoscale thermal transport in many technologically important materials. A major obstacle to determining such a relationship is the lack of a robust and physically intuitive structure descriptor capable of distinguishing between disparate GB structures. We demonstrate that a microscopic structure metric, the local distortion factor, correlates well with atomically decomposed thermal conductivities obtained from perturbed molecular dynamics for a wide variety of MgO GBs. Based on this correlation, a model for accurately predicting thermal conductivity of GBs is constructed using machine learning techniques. The model reveals that small distortions to local atomic environments are sufficient to reduce overall thermal conductivity dramatically. The method developed should enable more precise design of next-generation thermal materials as it allows GB structures exhibiting the desired thermal transport behaviour to be identified with small computational overhead. Thermal conductivity ; Grain boundary ; Thermal ; Nanoscopic scale ; Boundary (topology) ; Materials science ; Molecular dynamics ; Thermal resistance ; Obstacle ; Metric (unit) ; Distortion (music) ; Biological system ; Computer science ; Nanotechnology ; Statistical physics ; Physics ; Chemistry ; Optoelectronics ; Thermodynamics ; Mathematics ; Composite material ; Computational chemistry ; Microstructure Thermal properties of materials ; Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science
Authors: Chen Liu | Wenqian Huang | Guiyan Yang | Qingyan Wang | Jiangbo Li | Liping Chen
Source: Infrared Physics & Technology (article, 2020-08-17)
Field: Chemistry — Analytical Chemistry

Determination of starch content in single kernel using near-infrared hyperspectral images from two sides of corn seeds Hyperspectral imaging ; Partial least squares regression ; Starch ; Kernel (algebra) ; Mean squared error ; Smoothing ; Near-infrared spectroscopy ; Biological system ; Coefficient of determination ; Correlation coefficient ; Mathematics ; Pattern recognition (psychology) ; Artificial intelligence ; Computer science ; Statistics ; Chemistry ; Food science ; Optics ; Biology ; Physics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Meat and Animal Product Quality
Authors: Mohamad Hassan Amin
Source: Catalysts (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Relationship Between the Pore Structure of Mesoporous Silica Supports and the Activity of Nickel Nanocatalysts in the CO2 Reforming of Methane The question remains over the role of the pore structure of the support material on the catalytic behaviour of Ni catalysts during the CO2/dry reforming of methane (DRM). For this reason, a series of mesoporous materials with different pore structures, namely MCM-41, KIT-6, tri-modal porous silica (TMS), SBA-15 and mesostructured cellular foams (MCFs) were synthesised via hydrothermal synthesis methods and further impregnated with 15 wt.% NiO (11.8 wt.% Ni). It was observed that synthesised TMS is a promising catalyst support for DRM as Ni/TMS gave the highest activity and stability among these materials as well as the Ni catalysts supported on classic ordered mesoporous silicates support reported in the literature at the relatively low temperature (700 °C). On the other hand, Ni supported on CMC-41 exhibited the lowest activity among them. To understand the reason for this difference, the physicochemical properties of these materials were characterised in detail. The results show that the thickness of the silica wall and the pore size of the support material play a critical role in the catalytic activity of Ni catalysts in the CO2 reforming of methane. Catalysis ; Nanomaterial-based catalyst ; Materials science ; Carbon dioxide reforming ; Chemical engineering ; Mesoporous material ; Methane ; Mesoporous silica ; Non-blocking I/O ; Porosity ; Hydrothermal circulation ; Nickel ; Nanotechnology ; Nanoparticle ; Chemistry ; Metallurgy ; Syngas ; Organic chemistry ; Composite material Catalytic Processes in Materials Science ; Mesoporous Materials and Catalysis ; Catalysts for Methane Reforming
Authors: Víctor Manuel Pérez Puyana | Mercedes Jiménez‐Rosado | Alberto Romero | Antonio Guerrero
Source: Polymers (review, 2020-07-15)
Field: Materials Science — Biomaterials

Polymer-Based Scaffolds for Soft-Tissue Engineering Biomaterials have been used since ancient times. However, it was not until the late 1960s when their development prospered, increasing the research on them. In recent years, the study of biomaterials has focused mainly on tissue regeneration, requiring a biomaterial that can support cells during their growth and fulfill the function of the replaced tissue until its regeneration. These materials, called scaffolds, have been developed with a wide variety of materials and processes, with the polymer ones being the most advanced. For this reason, the need arises for a review that compiles the techniques most used in the development of polymer-based scaffolds. This review has focused on three of the most used techniques: freeze-drying, electrospinning and 3D printing, focusing on current and future trends. In addition, the advantages and disadvantages of each of them have been compared. Biomaterial ; Tissue engineering ; Regeneration (biology) ; Materials science ; Electrospinning ; Nanotechnology ; Polymer ; Biomedical engineering ; 3D printing ; Polymer science ; Biochemical engineering ; Computer science ; Engineering ; Composite material ; Biology Electrospun Nanofibers in Biomedical Applications ; 3D Printing in Biomedical Research ; Additive Manufacturing and 3D Printing Technologies
Authors: Luigi Calabrese | E. Proverbio
Source: Corrosion and Materials Degradation (review, 2020-12-23)
Field: Materials Science — Metals and Alloys

A Review on the Applications of Acoustic Emission Technique in the Study of Stress Corrosion Cracking The complex nature of the damage evolution in stress corrosion cracking (SCC) leads to explore for new investigation technologies in order to better identify the mechanisms that supervise the initiation and evolution of the damage as well to provide an improvement of knowledge on this critical localized corrosion form during time. Research activities concerning the use of acoustic emission (AE) technique to assess SCC has acquiring considerably relevance in recent decades. The non-invasiveness and the possibility to provide a continuous in situ monitoring of structures and components make this non-destructive technique clearly promising in the field of structural health monitoring. In this concern, this paper aims to be a focused overview on the evaluation of SCC phenomena by AE technique. The main topic of this review is centered on the approaches that can be used in elaborating AE data to better discriminate the mechanisms that contribute to damage propagation in SCC conditions. Based on available literature, investigation approaches assessing AE waveform parameters were classified, evidencing, furthermore, the identified mechanisms that synergistically take place during the material degradation. Eventually, a brief summary and a future trend evaluation was also reported. Acoustic emission ; Stress corrosion cracking ; Corrosion ; Relevance (law) ; Computer science ; Cracking ; Stress (linguistics) ; Stress wave ; Forensic engineering ; Materials science ; Risk analysis (engineering) ; Environmental science ; Engineering ; Structural engineering ; Metallurgy ; Composite material ; Medicine Hydrogen embrittlement and corrosion behaviors in metals ; Non-Destructive Testing Techniques ; Ultrasonics and Acoustic Wave Propagation
Authors: Babak Fazelabdolabadi | Mohammad Hossein Golestan
Source: HighTech and Innovation Journal (article, 2020-10-26)
Field: Chemistry — Inorganic Chemistry

Towards Bayesian Quantification of Permeability in Micro-scale Porous Structures – The Database of Micro Networks This article develops a Bayesian framework to quantify the absolute permeability of water in a porous structure from the geometry and clustering parameters of its underlying pore-throat network. These parameters include the network's diameter, transivity, degree, centrality, assortativity, edge density, K-core decomposition, Kleinberg's hub centrality scores, Kleinberg's authority centrality scores, length, and porosity. In addition, the incorporated clustering aspects of the networks have been determined with respect to several clustering criteria: edge betweenness, greedy optimization of modularity, multi-level optimization of modularity, and short random walks. As such, the article takes the first steps towards creating a database of micro-networks for micro-scale porous structures, to be used as the main input stream for the proposed Bayesian scheme. Doi: 10.28991/HIJ-2020-01-04-02 Full Text: PDF Betweenness centrality ; Assortativity ; Centrality ; Modularity (biology) ; Cluster analysis ; Computer science ; Clustering coefficient ; Bayesian network ; Artificial intelligence ; Complex network ; Mathematics ; Combinatorics Metal-Organic Frameworks: Synthesis and Applications ; Text and Document Classification Technologies ; Surfactants and Colloidal Systems
Authors: M. W. Matsen
Source: The Journal of Chemical Physics (article, 2020-03-16)
Field: Materials Science — Materials Chemistry

Field theoretic approach for block polymer melts: SCFT and FTS This perspective addresses the development of polymer field theory for predicting the equilibrium phase behavior of block polymer melts. The approach is tailored to the high-molecular-weight limit, where universality reduces all systems to the standard Gaussian chain model, an incompressible melt of elastic threads interacting by contact forces. Using mathematical identities, this particle-based version of the model is converted to an equivalent field-based version that depends on fields rather than particle coordinates. The statistical mechanics of the field-based model is typically solved using the saddle-point approximation of self-consistent field theory (SCFT), which equates to mean field theory, but it can also be evaluated using field theoretic simulations (FTS). While SCFT has matured into one of the most successful theories in soft condensed matter, FTS are still in its infancy. The two main obstacles of FTS are the high computational cost and the occurrence of an ultraviolet divergence, but fortunately there has been recent groundbreaking progress on both fronts. As such, FTS are now well poised to become the method of choice for predicting fluctuation corrections to mean field theory. Field theory (psychology) ; Saddle point ; Statistical physics ; Statistical mechanics ; Field (mathematics) ; Mean field theory ; Theoretical physics ; Physics ; Classical mechanics ; Mathematics ; Quantum mechanics ; Geometry ; Mathematical physics Block Copolymer Self-Assembly ; Material Dynamics and Properties ; Rheology and Fluid Dynamics Studies
Authors: Paola Conte | Costantino Fadda | Alessandra Del | Pietro Paolo Urgeghe | Antonio Piga
Source: Foods (review, 2020-04-20)
Field: Chemistry — Organic Chemistry

Table Olives: An Overview on Effects of Processing on Nutritional and Sensory Quality Table olives are a pickled food product obtained by a partial/total debittering and subsequent fermentation of drupes. Their peculiar sensory properties have led to a their widespread use, especially in Europe, as an appetizer or an ingredient for culinary use. The most relevant literature of the last twenty years has been analyzed in this review with the aim of giving an up-to-date overview of the processing and storage effects on the nutritional and sensory properties of table olives. Analysis of the literature has revealed that the nutritional properties of table olives are mainly influenced by the processing method used, even if preharvest-factors such as irrigation and fruit ripening stage may have a certain weight. Data revealed that the nutritional value of table olives depends mostly on the balanced profile of polyunsaturated and monounsaturated fatty acids and the contents of health-promoting phenolic compounds, which are best retained in natural table olives. Studies on the use of low salt brines and of selected starter cultures have shown the possibility of producing table olives with an improved nutritional profile. Sensory characteristics are mostly process-dependent, and a relevant contribute is achieved by starters, not only for reducing the bitterness of fruits, but also for imparting new and typical taste to table olives. Findings reported in this review confirm, in conclusion, that table olives surely constitute an important food source for their balanced nutritional profile and unique sensory characteristics. Table (database) ; Food science ; Ingredient ; Ripening ; Sensory analysis ; Table grape ; Preharvest ; Starter ; Biology ; Horticulture ; Cultivar ; Computer science ; Postharvest Edible Oils Quality and Analysis ; Nutritional Studies and Diet ; Phytochemicals and Antioxidant Activities
Authors: Shuai Chen | Abhijith Surendran | Xihu Wu | Sang Yeon Lee | Meera Stephen | Wei Lin Leong
Source: Advanced Materials Technologies (article, 2020-09-23)
Field: Materials Science — Polymers and Plastics

Recent Technological Advances in Fabrication and Application of Organic Electrochemical Transistors Abstract Organic electrochemical transistors (OECTs), where conjugated polymers undergo doping/dedoping from an electrolyte, are widely studied for applications ranging from switching elements, artificial synapses to transducers for biological sensing. The concurrent transport of electronic and ionic charges within the channel provide OECTs with excellent amplification capability and efficient ion‐to‐electron transduction at low operating voltages (&lt;1 V). Herein, the latest research advances in the fabrication techniques and applications of OECTs are summarized. Particular focus is given on the emerging applications including sensors, neuromorphic devices, and electrophysiological monitoring. Finally, the future challenges and opportunities of conventional OECTs and novel all‐solid‐state OECTs are discussed. Nanotechnology ; Materials science ; Transistor ; Neuromorphic engineering ; Fabrication ; Polymer electrolytes ; Electrolyte ; Bioelectronics ; Biosensor ; Voltage ; Computer science ; Electrical engineering ; Electrode ; Chemistry ; Engineering ; Ionic conductivity Conducting polymers and applications ; Advanced Memory and Neural Computing ; Organic Electronics and Photovoltaics
Authors: Zhijie Kuang | Haohua Chen | Jian Qiu | Zongliang Ou | Yu Lan | Qiuling Song
Source: Chem (article, 2020-07-23)
Field: Chemistry — Organic Chemistry

Cu-Catalyzed Regio- and Stereodivergent Chemoselective sp/sp 1,3- and 1,4-Diborylations of CF3-Containing 1,3-Enynes Nucleophile ; Chemistry ; Conjugated system ; Catalysis ; Molecule ; Combinatorial chemistry ; Group (periodic table) ; Stereochemistry ; Organic chemistry Organoboron and organosilicon chemistry ; Fluorine in Organic Chemistry ; Catalytic C–H Functionalization Methods
Authors: Xin He | Benjamin G. Looker | Kimberly T. Dinh | Amanda W. Stubbs | Tianyang Chen | Randall J. Meyer | Pedro Serna | Yuriy Román‐Leshkov | Kyle M. Lancaster | Mircea Dincă
Source: ACS Catalysis (article, 2020-06-17)
Field: Chemistry — Inorganic Chemistry

Cerium(IV) Enhances the Catalytic Oxidation Activity of Single-Site Cu Active Sites in MOFs The rates of catalytic oxidation of cyclohexane and CO are 4 and 20 times higher, respectively, with Cu supported on a cerium-based metal–organic framework (MOF) than on the structurally analogous zirconium material. Both Ce- and Zr-based copper catalysts feature uncommon three-coordinate CuII sites bearing different nuclearities, as determined by Cu K-edge extended X-ray absorption fine structure analysis. These results offer molecular-level understanding of the metal–support interface in MOF catalysts and establish correlations with the more established literature on zirconia- and ceria-supported heterogeneous catalysis. Cerium ; Catalysis ; Cyclohexane ; Metal-organic framework ; Zirconium ; Cubic zirconia ; Copper ; Metal ; Materials science ; Heterogeneous catalysis ; Inorganic chemistry ; Redox ; Chemistry ; Physical chemistry ; Metallurgy ; Organic chemistry ; Adsorption Metal-Organic Frameworks: Synthesis and Applications ; Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions
Authors: Kiron Kumar Ghosh | Alexander Uttry | Arup Mondal | François Ghiringhelli | Philipp Wedi | Manuel van Gemmeren
Source: Angewandte Chemie International Edition (article, 2020-04-06)
Field: Chemistry — Organic Chemistry

Ligand‐Enabled γ‐C(sp <sup>3</sup> )−H Olefination of Free Carboxylic Acids We report the ligand-enabled C-H activation/olefination of free carboxylic acids in the γ-position. Through an intramolecular Michael addition, δ-lactones are obtained as products. Two distinct ligand classes are identified that enable the challenging palladium-catalyzed activation of free carboxylic acids in the γ-position. The developed protocol features a wide range of acid substrates and olefin reaction partners and is shown to be applicable on a preparatively useful scale. Insights into the underlying reaction mechanism obtained through kinetic studies are reported. Ligand (biochemistry) ; Carboxylic acid ; Chemistry ; Stereochemistry ; Combinatorial chemistry ; Medicinal chemistry ; Organic chemistry ; Biochemistry ; Receptor Catalytic C–H Functionalization Methods ; Synthesis and Catalytic Reactions ; Asymmetric Hydrogenation and Catalysis
Authors: Janis Timoshenko | Hyo Sang Jeon | Ilya Sinev | Felix T. Haase | Antonia Herzog | Beatriz Roldán Cuenya
Source: Chemical Science (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Linking the evolution of catalytic properties and structural changes in copper–zinc nanocatalysts using <i>operando</i> EXAFS and neural-networks Understanding the evolution of unique structural motifs in bimetallic catalysts under reaction conditions, and linking them to the observed catalytic properties is necessary for the rational design of the next generation of catalytic materials. Extended X-ray absorption fine structure (EXAFS) spectroscopy is a premier experimental method to address this issue, providing the possibility to track the changes in the structure of working catalysts. Unfortunately, the intrinsic heterogeneity and enhanced disorder characteristic of catalytic materials experiencing structural transformations under reaction conditions, as well as the low signal-to-noise ratio that is common for <i>in situ</i> EXAFS spectra hinder the application of conventional data analysis approaches. Here we address this problem by employing machine learning methods (artificial neural networks) to establish the relationship between EXAFS features and structural motifs in metals as well as oxide materials. We apply this approach to time-dependent EXAFS spectra acquired from copper-zinc nanoparticles during the electrochemical reduction of CO<sub>2</sub> to reveal the details of the composition-dependent structural evolution and brass alloy formation, and their correlation with the catalytic selectivity of these materials. Nanomaterial-based catalyst ; Copper ; Catalysis ; Extended X-ray absorption fine structure ; Zinc ; Chemistry ; Materials science ; Chemical engineering ; Absorption spectroscopy ; Organic chemistry ; Physics Machine Learning in Materials Science ; Electrochemical Analysis and Applications ; CO2 Reduction Techniques and Catalysts