jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Elena Rodríguez | Gorka Elordi | José Valecillos | Sepideh Izaddoust | Javier Bilbao | José M. Arandes | Pedro Castaño
Source: Fuel Processing Technology (article, 2019-04-25)
Field: Materials Science — Polymers and Plastics

Coke deposition and product distribution in the co-cracking of waste polyolefin derived streams and vacuum gas oil under FCC unit conditions Fluid catalytic cracking ; Coke ; Product distribution ; Chemical engineering ; Vacuum distillation ; Cracking ; Catalysis ; Gasoline ; Hydrodesulfurization ; Materials science ; Wax ; Fuel oil ; Organic chemistry ; Pyrolysis ; Coker unit ; Waste management ; Chemistry ; Distillation Polymer crystallization and properties ; Fiber-reinforced polymer composites ; Thermochemical Biomass Conversion Processes
Authors: Quan Manh Phung | Carlos Martín−Fernández | Jeremy N. Harvey | Milica Feldt
Source: Journal of Chemical Theory and Computation (article, 2019-07-02)
Field: Chemistry — Inorganic Chemistry

Ab Initio Calculations for Spin-Gaps of Non-Heme Iron Complexes We employed our recently proposed multireference approach CASPT2/CC to calculate the quintet-triplet gaps Δ<i>E</i><sub>TQ</sub> of a series of non-heme Fe<sup>IV</sup>═O species and subsequently used these results to benchmark density functional theory (DFT) as well as two variants of local coupled-cluster approaches (DLPNO-CCSD(T) and LUCCSD(T0)). We showed that current implementations of the local coupled-cluster method are not sufficiently accurate. DLPNO-CCSD(T) systematically overstabilizes the quintet state, whereas LUCCSD(T0) overestimates the triplet one. This sort of systematic bias may be helpful in improving local correlation methods and can also be used as the basis for a simple correction scheme. Coupled cluster ; Benchmark (surveying) ; Ab initio ; Density functional theory ; Cluster (spacecraft) ; Spin (aerodynamics) ; Series (stratigraphy) ; Computational chemistry ; sort ; Spin states ; Chemistry ; Statistical physics ; Physics ; Computer science ; Molecule ; Atomic physics ; Quantum mechanics ; Thermodynamics Metal-Catalyzed Oxygenation Mechanisms ; Magnetism in coordination complexes ; CO2 Reduction Techniques and Catalysts
Authors: Zhenxiong Huang | Lang Liu | Guojian Li | Hong Li | Dapeng Ye | Xiaoli Li
Source: Molecules (article, 2019-03-29)
Field: Chemistry — Analytical Chemistry

Nondestructive Determination of Diastase Activity of Honey Based on Visible and Near-Infrared Spectroscopy The activities of enzymes are the basis of evaluating the quality of honey. Beekeepers usually use concentrators to process natural honey into concentrated honey by concentrating it under high temperatures. Active enzymes are very sensitive to high temperatures and will lose their activity when they exceed a certain temperature. The objective of this work is to study the kinetic mechanism of the temperature effect on diastase activity and to develop a nondestructive approach for quick determination of the diastase activity of honey through a heating process based on visible and near-infrared (Vis/NIR) spectroscopy. A total of 110 samples, including three species of botanical origin, were used for this study. To explore the kinetic mechanism of diastase activity under high temperatures, the honey of three kinds of botanical origins were processed with thermal treatment to obtain a variety of diastase activity. Diastase activity represented with diastase number (DN) was measured according to the national standard method. The results showed that the diastase activity decreased with the increase of temperature and heating time, and the sensitivity of acacia and longan to temperature was higher than linen. The optimum temperature for production and processing is 60 °C. Unsupervised clustering analysis was adopted to detect spectral characteristics of these honeys, indicating that different botanical origins of honeys can be distinguished in principal component spaces. Partial least squares (PLS) and least squares-support vector machine (LS-SVM) algorithms were applied to develop quantitative relationships between Vis/NIR spectroscopy and diastase activity. The best result was obtained through Gaussian filter smoothing-standard normal variate (GF-SNV) pretreatment and the LS-SVM model, known as GF-SNV-LS-SVM, with a determination coefficient (<i>R</i>²) of prediction of 0.8872, and root mean square error (RMSE) of prediction of 0.2129. The overall results of this paper showed that the diastase activity of honey can be determined quickly and non-destructively with Vis/NIR spectral methods, which can be used to detect DN in the process of honey production and processing, and to maximize the nutrient content of honey. Diastase ; Chemistry ; Partial least squares regression ; Fourier transform infrared spectroscopy ; Spectroscopy ; Infrared spectroscopy ; Analytical Chemistry (journal) ; Food science ; Chromatography ; Chemical engineering ; Organic chemistry ; Machine learning ; Computer science Spectroscopy and Chemometric Analyses ; Bee Products Chemical Analysis ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Haijun Zhao | Fang Dong | Weiliang Han | Zhicheng Tang
Source: Industrial & Engineering Chemistry Research (article, 2019-09-09)
Field: Materials Science — Materials Chemistry

Study of Morphology-Dependent and Crystal-Plane Effects of CeMnO<sub><i>x</i></sub> Catalysts for 1,2-Dichlorobenzene Catalytic Elimination Catalytic reactions usually proceed on a catalyst surface, and surface properties could be fine-tuned by the catalyst shape and exposed crystal-plane. In the present work, morphology-dependent CeMnOx nanostructures (rods, octahedrons, and cubes) were synthesized via the hydrothermal method, and they exposed special crystal-planes of {111}/{100}/{110}, {111}, and {100} for CeMnOx rods, octahedrons, and cubes, respectively. The best catalytic activity for the catalytic combustion reaction of 1,2-dichlorobenzene (o-DCB) whether in dry or wet conditions was observed over CeMnOx rods followed by CeMnOx octahedrons and cubes. The optimum catalytic performance of CeMnOx rods could be associated to its high reducibility and large amounts of oxygen vacancies. An interesting phenomenon was observed on kinetic results that the variation of active energy, reaction rate, and TOF were inconsistent with T90, and they could be explained by weak and medium strong acidity, indicating the shape effects of CeMnOx nanostructures. Catalysis ; Rod ; Crystal (programming language) ; Morphology (biology) ; Activation energy ; Materials science ; Chemical engineering ; Crystallography ; Kinetic energy ; Nanostructure ; Catalytic combustion ; Chemistry ; Nanotechnology ; Physical chemistry ; Organic chemistry ; Physics Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Nanomaterials for catalytic reactions
Authors: Oleksii Zhelavskyi | Alexander Kyrychenko
Source: Journal of Molecular Graphics and Modelling (article, 2019-04-12)
Field: Materials Science — Biomaterials

Atomistic molecular dynamics simulations of the LCST conformational transition in poly(N-vinylcaprolactam) in water Lower critical solution temperature ; Molecular dynamics ; Aqueous solution ; Polymer ; Chemical physics ; Materials science ; Thermodynamics ; Phase transition ; Hydrogen bond ; Chemistry ; Polymer chemistry ; Molecule ; Physical chemistry ; Computational chemistry ; Copolymer ; Organic chemistry ; Physics biodegradable polymer synthesis and properties ; Advanced Polymer Synthesis and Characterization ; Polymer crystallization and properties
Authors: Raquel Mato | Rubén Manzano | Efraím Reyes | Luisa Carrillo | Uxue Uria | José L. Vicário
Source: Journal of the American Chemical Society (article, 2019-05-29)
Field: Chemistry — Organic Chemistry

Catalytic Enantioselective Transannular Morita–Baylis–Hillman Reaction Catalytic and enantioselective approaches to transannular reactions are very limited and mostly are based on chiral Lewis acid catalyzed pericyclic reactions. In this report, we present an efficient and straightforward methodology to access bicyclic carbo- and heterocyclic scaffolds combining different ring sizes through transannular Morita-Baylis-Hillman reaction catalyzed by a chiral enantiopure bifunctional phosphine. The reaction is remarkably wide in scope and enables the use of a variety of medium and large size ketoenone substrates leading to the final products in high yields and providing excellent stereocontrol in the formation of a quaternary stereogenic center at the ring fusion. Moreover, its potential as a general tool in organic synthesis has been highlighted through the accomplishment of the first enantioselective total synthesis of (-)-γ-gurjunene, a sesquiterpene natural product. Enantioselective synthesis ; Chemistry ; Stereocenter ; Pericyclic reaction ; Enantiopure drug ; Bifunctional ; Bicyclic molecule ; Baylis–Hillman reaction ; Catalysis ; Ring (chemistry) ; Organocatalysis ; Lewis acids and bases ; Stereochemistry ; Combinatorial chemistry ; Organic chemistry Asymmetric Synthesis and Catalysis ; Marine Sponges and Natural Products ; Synthetic Organic Chemistry Methods
Authors: Kuldeep K. Bansal | Deepakkumar Mishra | Ari Rosling | Jessica M. Rosenholm
Source: Applied Sciences (article, 2019-12-31)
Field: Materials Science — Biomaterials

Therapeutic Potential of Polymer-Coated Mesoporous Silica Nanoparticles Mesoporous silica nanoparticles (MSNs) find tremendous applications in drug delivery due to several advantages such as their easy fabrication process, high drug loading, biodegradability, biocompatibility, and so forth. Nevertheless, despite several advantages, the use of this striking drug delivery carrier is restricted due to premature drug release owing to the porous structure. Coating of the pores using polymers has emerged as a great solution to this problem. Polymer coatings, which act as gatekeepers, avoid the premature release of loaded content from MSNs and offers the opportunity for controlled and targeted drug delivery. Therefore, in this review, we have compiled the polymer-based coating approaches used in recent years for improving the drug delivery capability of MSNs. This manuscript provides an insight into the research about the potential of polymer-coated MSNs, allowing the selection of right polymer for coating purposes according to the desired application. Mesoporous silica ; Nanotechnology ; Drug delivery ; Coating ; Materials science ; Polymer ; Biocompatibility ; Nanoparticle ; Mesoporous material ; Chemistry ; Composite material ; Organic chemistry Nanoparticle-Based Drug Delivery ; Advanced Drug Delivery Systems ; Dendrimers and Hyperbranched Polymers
Authors: Tiago Serodre | Norma P Oliveira | Douglas R. Miquita | Max Ferreira | Adelina P. Santos | Valdirene Gonzaga de Resende | Clascídia Aparecida Furtado
Source: Journal of the Brazilian Chemical Society (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

Surface Silanization of Graphene Oxide Under Mild Reaction Conditions The functionalization of graphene oxide with organosilanes is an important strategy to yield graphene-silica composites as well as to create nanomaterials for use as reinforcement in polymer nanocomposites and corrosion-inhibiting coatings for metals, among other uses. However, depending on the reaction conditions used, the organosilanes tend to self-condense, encapsulating the graphene oxide in a silica-like layer which can impair its properties by hiding its real surface and two-dimensionality. In this paper we describe a facile route for the functionalization of commercial graphene oxide with (3-aminopropyl) triethoxysilane using low concentrations and mild reaction conditions, and yielding amine and silanol surface-modified graphene nanohybrids while preserving its two-dimensional characteristics. The material obtained was characterized by X-ray photoelectron spectroscopy (XPS), Fourier transform infrared spectroscopy (FTIR), potentiometric titration, thermogravimetric analysis and electronic microscopy, evidencing the covalent and superficial nature of the functionalization. Silanization ; Graphene ; Oxide ; Materials science ; Chemical engineering ; Chemistry ; Composite material ; Nanotechnology ; Metallurgy ; Engineering Graphene research and applications ; Molecular Junctions and Nanostructures ; Graphene and Nanomaterials Applications
Authors: Shaofei Wen | Yunpeng Wang | Bijiao Lan | Weida Zhang | Zhuo Shi | Shichao Lv | Yu‐Jun Zhao | Jianrong Qiu | Shifeng Zhou
Source: Advanced Science (article, 2019-06-28)
Field: Materials Science — Ceramics and Composites

Pressureless Crystallization of Glass for Transparent Nanoceramics Transparent nanoceramics embedded with highly dense crystalline domains are promising for applications in missile guidance, infrared night vision, and laser and nuclear radiation detection. Unfortunately, current nanoceramics are strictly constrained by the stringent construction procedures such as super-high pressure and containerless processing. Here, a pressureless crystallization engineering strategy in glass for elaboration of transparent nanoceramics and fibers is proposed and experimentally demonstrated. By intentional creation of a sharp contrast between nucleation and growth rates, the crystal growth rate during glass crystallization can be significantly suppressed. Importantly, this unique phase-transition habit enables the achievement of transparent nanoceramics and even smooth fibers with extremely tiny crystalline size (≈20 nm) and high crystallinity (≈97%) under atmospheric pressure. This allows the generation of an attractive nonlinear optical response such as dynamic optical filtering and luminescence in the mid-infrared waveband of 4300-4950 nm. These findings highlight that the strategy to switch the phase-transition habit of glass into the unconventional crystallization regime may provide new opportunities for the creation of next-generation nanoceramics and fibers. Materials science ; Crystallization ; Crystallinity ; Nucleation ; Laser ; Phase transition ; Crystal growth ; Nanotechnology ; Optoelectronics ; Composite material ; Chemical engineering ; Optics ; Crystallography ; Chemistry Glass properties and applications ; Nonlinear Optical Materials Studies ; Laser-Ablation Synthesis of Nanoparticles
Authors: Yongzhe Tang | Peng Zhang | Ming‐Xiao Zhu | Jiacai Li | Yuxia Li | Ziguo Wang | Liangsong Huang
Source: Materials (article, 2019-12-09)
Field: Materials Science — Materials Chemistry

Temperature Effects on the Dielectric Properties and Breakdown Performance of h-BN/Epoxy Composites Epoxy-boron nitride composites are promising insulating materials, and it is highly important to understand their insulating performances at different temperatures with different nano-doping amounts. In this study, we investigated the effects of different mass fractions of epoxy-micron hexagonal boron nitride composites on their thermal conductivity, as well as the effects of temperature and mass fraction on their insulating performances. The results demonstrated that the thermal conductivity of epoxy-micron hexagonal boron nitride composites was superior to that of neat epoxy. The thermal conductivity of epoxy-micron hexagonal boron nitride composites increased with the mass fraction of hexagonal boron nitride, and their dielectric constant and dielectric loss increased with temperature. The dielectric constant of epoxy-micron hexagonal boron nitride composites decreased as the mass fraction of hexagonal boron nitride increased, while their dielectric losses decreased and then increased as the mass fraction of hexagonal boron nitride increased. Due to internal heat accumulation, the alternating current breakdown strength of epoxy-micron hexagonal boron nitride composites increased and then decreased as the mass fraction of hexagonal boron nitride increased. Additionally, as the temperature increased, the composites transitioned from the glassy state to the rubbery or viscous state, and the breakdown strength significantly degraded. Materials science ; Epoxy ; Composite material ; Dielectric ; Boron nitride ; Mass fraction ; Nitride ; Thermal conductivity ; Boron ; Chemistry ; Optoelectronics ; Layer (electronics) High voltage insulation and dielectric phenomena ; Dielectric materials and actuators ; Thermal properties of materials
Authors: Vasilios Duros | Jonathan Grizou | Abhishek Sharma | S. Hessam M. Mehr | Andrius Bubliauskas | Przemysław Frei | Haralampos N. Miras | Leroy Cronin
Source: Journal of Chemical Information and Modeling (article, 2019-04-26)
Field: Materials Science — Materials Chemistry

Intuition-Enabled Machine Learning Beats the Competition When Joint Human-Robot Teams Perform Inorganic Chemical Experiments Traditionally, chemists have relied on years of training and accumulated experience in order to discover new molecules. But the space of possible molecules is so vast that only a limited exploration with the traditional methods can be ever possible. This means that many opportunities for the discovery of interesting phenomena have been missed, and in addition, the inherent variability of these phenomena can make them difficult to control and understand. The current state-of-the-art is moving toward the development of automated and eventually fully autonomous systems coupled with in-line analytics and decision-making algorithms. Yet even these, despite the substantial progress achieved recently, still cannot easily tackle large combinatorial spaces, as they are limited by the lack of high-quality data. Herein, we explore the utility of active learning methods for exploring the chemical space by comparing the collaboration between human experimenters with an algorithm-based search against their performance individually to probe the self-assembly and crystallization of the polyoxometalate cluster Na<sub>6</sub>[Mo<sub>120</sub>Ce<sub>6</sub>O<sub>366</sub>H<sub>12</sub>(H<sub>2</sub>O)<sub>78</sub>]·200H<sub>2</sub>O (1). We show that the robot-human teams are able to increase the prediction accuracy to 75.6 ± 1.8%, from 71.8 ± 0.3% with the algorithm alone and 66.3 ± 1.8% from only the human experimenters demonstrating that human-robot teams can beat robots or humans working alone. Robot ; Intuition ; Chemical space ; Computer science ; Artificial intelligence ; Human–computer interaction ; Machine learning ; Data science ; Cognitive science ; Psychology ; Chemistry ; Drug discovery Machine Learning in Materials Science ; Nanocluster Synthesis and Applications ; Polyoxometalates: Synthesis and Applications
Authors: Edward Kim | Kevin Huang | Olga Kononova | Gerbrand Ceder | Elsa Olivetti
Source: Matter (article, 2019-05-29)
Field: Materials Science — Materials Chemistry

Distilling a Materials Synthesis Ontology Readability ; CLARITY ; Computer science ; Narrative ; Reading (process) ; Artificial intelligence ; Data science ; Linguistics ; Literature ; Chemistry ; Art Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Software Engineering Research
Authors: Wenyuan Yang | Hong Liang | Feng Peng | Zili Liu | Jie Liu | Zhiwei Qiao
Source: Nanomaterials (article, 2019-03-20)
Field: Chemistry — Inorganic Chemistry

Computational Screening of Metal–Organic Framework Membranes for the Separation of 15 Gas Mixtures The Monte Carlo and molecular dynamics simulations are employed to screen the separation performance of 6013 computation-ready, experimental metal⁻organic framework membranes (CoRE-MOFMs) for 15 binary gas mixtures. After the univariate analysis, principal component analysis is used to reduce 44 performance metrics of 15 mixtures to a 10-dimension set. Then, four machine learning algorithms (decision tree, random forest, support vector machine, and back propagation neural network) are combined with <i>k</i> times repeated <i>k</i>-fold cross-validation to predict and analyze the relationships between six structural feature descriptors and 10 principal components. Based on the linear correlation value <i>R</i> and the root mean square error predicted by the machine learning algorithm, the random forest algorithm is the most suitable for the prediction of the separation performance of CoRE-MOFMs. One descriptor, pore limiting diameter, possesses the highest weight importance for each principal component index. Finally, the 30 best CoRE-MOFMs for each binary gas mixture are screened out. The high-throughput computational screening and the microanalysis of high-dimensional performance metrics can provide guidance for experimental research through the relationships between the multi-structure variables and multi-performance variables. Principal component analysis ; Univariate ; Monte Carlo method ; Random forest ; Artificial neural network ; Binary number ; Computation ; Decision tree ; Mean squared error ; Computer science ; Dimensionality reduction ; Support vector machine ; Artificial intelligence ; Algorithm ; Machine learning ; Mathematics ; Multivariate statistics ; Statistics Metal-Organic Frameworks: Synthesis and Applications ; Membrane Separation and Gas Transport ; Covalent Organic Framework Applications
Authors: Ziyad Chaker | Mathieu Salanne | Jean‐Marc Delaye | Thibault Charpentier
Source: Physical Chemistry Chemical Physics (article, 2019-01-01)
Field: Materials Science — Ceramics and Composites

NMR shifts in aluminosilicate glasses <i>via</i> machine learning Machine learning (ML) approaches are investigated for the prediction of nuclear magnetic resonance (NMR) parameters in aluminosilicate glasses, for which NMR has proven to be a cutting-edge method over the last decade. DFT computations have emerged as a new dimension for complementing these NMR methods although suffering from severe limitations in terms of size, time and computational resources consumption. While previous approaches tend to use DFT-GIPAW calculations for the prediction of NMR parameters in glassy systems, we propose to employ ML methods, characterized by a speed similar to that of classical molecular dynamics while the accuracy of ab initio methods can be reached. We design ML procedures to predict the isotropic magnetic shielding (σiso) for different multicomponent relevant glass compositions. The ML predictions of σiso deviate from DFT-GIPAW calculations, when including relaxed and room-temperature structures, by 0.7 ppm for 29Si (1.0% of the total span of the calculated ) and 1.5 ppm for 17O (1.9%) in SiO2 glasses, 1.4 ppm for 23Na (1.5%) in Na2O-SiO2 and 1.5 ppm for 27Al (2.1%) in Al2O3-Na2O-SiO2 systems. We compare the performances obtained for a set of three descriptors suitable for encoding atomic local environments information (atom-centered representations) together with seven popular ML algorithms with a focus on the simple (but robust) linear ridge regression (LRR) and the popular smooth overlap of atomic positions (SOAP) descriptor. Aluminosilicate ; Atom (system on chip) ; occam ; Nuclear magnetic resonance spectroscopy ; Chemistry ; Isotropy ; Chemical shift ; Nuclear magnetic resonance ; Computational physics ; Computational chemistry ; Artificial intelligence ; Computer science ; Physical chemistry ; Physics ; Optics Glass properties and applications ; Phase-change materials and chalcogenides ; Mineralogy and Gemology Studies