jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Manoj Kumar Yadav | Ashok Kumar Gupta | Partha Ghosal | Abhijit Mukherjee
Source: Journal of Environmental Science and Health Part A (article, 2019-08-09)
Field: Materials Science — Materials Chemistry

Modeling and analysis of adsorptive removal of arsenite by Mg–Fe–(CO<sub>3</sub>) layer double hydroxide with its application in real-life groundwater The kinetic, isotherm and thermodynamic modeling of the adsorption of arsenite by layered double hydroxide have been performed to analyze the feasibility, efficacy and mechanism of the system. The fast uptake was observed during the initial phase of the process, which reached equilibrium at 240 min following Elovich model. The diffusion kinetic model exhibited that the rate-limiting step of adsorption was controlled by film diffusion as well as intraparticle diffusion. The isotherm modeling revealed the applicability of the Freundlich equation with the <i>K</i><sub>f</sub> values as 8.19-13.99 (mg g<sup>-1</sup>)(L mg<sup>-1</sup>)<sup>1/</sup><i><sup>n</sup></i> at 283-323 K showing the increasing trend of adsorption capacity, which was further confirmed by the positive value of Δ<i>H</i><sup>0</sup> (9.49 kJ mol<sup>-1</sup>) demonstrating the endothermic nature of the adsorption process. The spontaneous nature of the adsorption reaction was established by the negative values of Δ<i>G</i><sup>0</sup>. Application of the calcined Mg-Fe-LDH adsorbent for the removal of arsenic from real arsenic contaminated groundwater was also successfully performed. The effect of process parameters of the adsorption system was modeled by an artificial neural network (ANN) for adsorption capacity and removal efficiency. The optimized model exhibited high <i>R</i><sup>2</sup>, <i>F</i>-value and low values of error functions, establishing the significant applicability of the ANN model. Adsorption ; Endothermic process ; Freundlich equation ; Hydroxide ; Arsenite ; Arsenic ; Chemistry ; Diffusion ; Calcination ; Inorganic chemistry ; Thermodynamics ; Physical chemistry ; Organic chemistry Layered Double Hydroxides Synthesis and Applications ; Arsenic contamination and mitigation ; Adsorption and biosorption for pollutant removal
Authors: Leonardo Zepeda-Núñez | Yixiao Chen | Jiefu Zhang | Weile Jia | Linfeng Zhang | Lin Lin
Source: arXiv (Cornell University) (preprint, 2019-11-27)
Field: Materials Science — Materials Chemistry

Deep Density: circumventing the Kohn-Sham equations via symmetry preserving neural networks The recently developed Deep Potential [Phys. Rev. Lett. 120, 143001, 2018] is a powerful method to represent general inter-atomic potentials using deep neural networks. The success of Deep Potential rests on the proper treatment of locality and symmetry properties of each component of the network. In this paper, we leverage its network structure to effectively represent the mapping from the atomic configuration to the electron density in Kohn-Sham density function theory (KS-DFT). By directly targeting at the self-consistent electron density, we demonstrate that the adapted network architecture, called the Deep Density, can effectively represent the electron density as the linear combination of contributions from many local clusters. The network is constructed to satisfy the translation, rotation, and permutation symmetries, and is designed to be transferable to different system sizes. We demonstrate that using a relatively small number of training snapshots, Deep Density achieves excellent performance for one-dimensional insulating and metallic systems, as well as systems with mixed insulating and metallic characters. We also demonstrate its performance for real three-dimensional systems, including small organic molecules, as well as extended systems such as water (up to $512$ molecules) and aluminum (up to $256$ atoms). Kohn–Sham equations ; Density functional theory ; Artificial neural network ; Leverage (statistics) ; Translational symmetry ; Topology (electrical circuits) ; Locality ; Homogeneous space ; Symmetry (geometry) ; Electron density ; Computer science ; Statistical physics ; Materials science ; Physics ; Quantum mechanics ; Artificial intelligence ; Mathematics ; Electron ; Condensed matter physics ; Geometry Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Advanced Chemical Physics Studies
Authors: Aleksandar Kondinski | Jens Moons | Yujie Zhang | Jakob Bussé | Wim M. De Borggraeve | Erík Nies | Tatjana N. Parac‐Vogt
Source: Journal of Chemical Education (article, 2019-10-30)
Field: Materials Science — Materials Chemistry

Modeling of Nanomolecular and Reticular Architectures with 6-fold Grooved, Programmable Interlocking Disks Single-type, 6-fold symmetrically grooved, and commercially accessible interlocking disks (ILDs) have been used for modeling of sp2 hybridized carbon-based nanoarchitectures and complex polyhedral and reticular material models. In the case of the carbon-based nanoarchitectures, we showcase that the primary ILDs can be directly used for representing individual atoms and bonds. Further on, the spatial connectivity of the primary ILDs can be extended by assembly of symmetrical secondary building units (SBUs). The constructed (deci)meter scale models are robust, light, scalable, and suitable for classroom demonstrations. The ILD technique is also suitable for use in workshops for facile discovery-based learning of nanomolecular structure, showing promise for wider use in the chemistry curriculum. Interlocking ; Fold (higher-order function) ; Scalability ; Reticular connective tissue ; Nanotechnology ; Deci- ; Computer science ; Materials science ; Engineering ; Mechanical engineering ; Programming language ; Biology Carbon Nanotubes in Composites ; Graphene research and applications ; Surface Chemistry and Catalysis
Authors: Olli-Pekka Koistinen | Vilhjálmur Ásgeirsson | Aki Vehtari | Hannes Jónsson
Source: ChemRxiv (preprint, 2019-10-22)
Field: Materials Science — Materials Chemistry

Nudged Elastic Band Calculations Accelerated with Gaussian Process Regression Based on Inverse Inter-Atomic Distances Calculations of minimum energy paths for atomic rearrangements using the nudged elastic band method can be accelerated with Gaussian process regression to reduce the number of energy and atomic force evaluations needed for convergence. Problems can arise, however, when configurations with large forces due to short distance between atoms are included in the data set. Here, a significant improvement to the Gaussian process regression approach is obtained by basing the difference measure between two atomic configurations in the covariance function on the inverted inter-atomic distances and by adding a new early stopping criterion for the path relaxation phase. This greatly improves the performance of the method in two applications where the original formulation does not work well: a dissociative adsorption of an H2 molecule on a Cu(110) Surface and a diffusion hop of an H2O molecule on an ice Ih(0001) surface. Also, the revised method works better in the previously analyzed benchmark application to rearrangement transitions of a heptamer island on a surface, requiring fewer energy and force evaluations for convergence to the minimum energy path. Gaussian process ; Inverse ; Gaussian ; Kriging ; Potential energy surface ; Convergence (economics) ; Statistical physics ; Physics ; Mathematics ; Molecule ; Quantum mechanics ; Statistics ; Geometry Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; nanoparticles nucleation surface interactions
Authors: Xin Huang | Zhongwu Wang
Source: Materials (review, 2019-11-17)
Field: Materials Science — Materials Chemistry

Supercrystallography-Based Decoding of Structure and Driving Force of Nanocrystal Assembly Nanocrystal (NC) assembly appears as one promising method towards the controllable design and fabrication of advanced materials with desired property and functionality. The achievement of a "materials-by-design" requires not only a primary structural decoding of NC assembled supercrystal at a wide range of length scales, but also an improved understanding of the interactions and changeable roles of various driving forces over the course of nucleation and growth of NC superlattice. The recent invention of a synchrotron-based X-ray supercrystallographic approach makes it feasible to uncover the structural details of NC-assembled supercrystal at unprecedented levels from atomic through nano to mesoscale. Such structural documentations can be used to trace how various driving forces interact in a competitive way and thus change relatively in strength to govern the formation of individual superlattices under certain circumstances. This short review makes use of four single supercrystals typically made up of spherical, truncate, cubic and octahedral NCs, respectively, and provides a comparable description and a reasonable analysis of the use of a synchrotron-based supercrystallographic approach to reveal various degrees of translational and orientational ordering of NCs within various superlattices. In the connection of observed structural aspects with controlled environments of NC assembly, we further address how various driving forces interact each other to develop relatively changeable roles upon variation of the NC shape to respond to the nucleation and growth of various superlattices. With the guidance of such gained insights, we provide additional examples to illustrate how realistic environments are designed into delicate control of NC assembly to achieve particular interactions between NCs towards harvesting superlattice with NC translational symmetry and atomically crystallographic orientation as desired. Superlattice ; Nanocrystal ; Nucleation ; Nanotechnology ; Synchrotron ; Materials science ; Fabrication ; Constructive ; Computer science ; Process (computing) ; Physics ; Optics ; Optoelectronics Quantum Dots Synthesis And Properties ; Bacteriophages and microbial interactions ; Acne and Rosacea Treatments and Effects
Authors: Robert A. Shaw | J. Grant Hill
Source: Inorganics (article, 2019-02-10)
Field: Chemistry — Physical and Theoretical Chemistry

A Simple Model for Halogen Bond Interaction Energies Halogen bonds are prevalent in many areas of chemistry, physics, and biology. We present a statistical model for the interaction energies of halogen-bonded systems at equilibrium based on high-accuracy ab initio benchmark calculations for a range of complexes. Remarkably, the resulting model requires only two fitted parameters, X and B—one for each molecule—and optionally the equilibrium separation, R e , between them, taking the simple form E = X B / R e n . For n = 4 , it gives negligible root-mean-squared deviations of 0.14 and 0.28 kcal mol − 1 over separate fitting and validation data sets of 60 and 74 systems, respectively. The simple model is shown to outperform some of the best density functionals for non-covalent interactions, once parameters are available, at essentially zero computational cost. Additionally, we demonstrate how it can be transferred to completely new, much larger complexes and still achieve accuracy within 0.5 kcal mol − 1 . Using a principal component analysis and symmetry-adapted perturbation theory, we further show how the model can be used to predict the physical nature of a halogen bond, providing an efficient way to gain insight into the behavior of halogen-bonded systems. This means that the model can be used to highlight cases where induction or dispersion significantly affect the underlying nature of the interaction. Halogen bond ; Halogen ; Simple (philosophy) ; Computational chemistry ; Perturbation theory (quantum mechanics) ; Statistical physics ; Ab initio ; Molecule ; Chemistry ; Thermodynamics ; Materials science ; Physics ; Quantum mechanics ; Organic chemistry Crystallography and molecular interactions ; Inorganic Fluorides and Related Compounds ; Advanced Chemical Physics Studies
Authors: Matthew M. Davis | Jared D. Weidman | Adam S. Abbott | Gary E. Douberly | Justin M. Turney | Henry F. Schaefer
Source: The Journal of Chemical Physics (article, 2019-09-24)
Field: Chemistry — Organic Chemistry

Characterization of the 2-methylvinoxy radical + O2 reaction: A focal point analysis and composite multireference study Vinoxy radicals are involved in numerous atmospheric and combustion mechanisms. High-level theoretical methods have recently shed new light on the reaction of the unsubstituted vinoxy radical with O<sub>2</sub>. The reactions of 1-methylvinoxy radical and 2-methylvinoxy radical with molecular oxygen have experimental high pressure limiting rate constants, k<sub>∞</sub>, 5-7 times higher than that of the vinoxy plus O<sub>2</sub> reaction. In this work, high-level ab initio quantum chemical computations are applied to the 2-methylvinoxy radical plus O<sub>2</sub> system, namely, the formation and isomerization of the 1-oxo-2-propylperoxy radical, the immediate product of O<sub>2</sub> addition to the 2-methylvinoxy radical. Multireference methods were applied to the entrance channel. No barrier to O<sub>2</sub> addition could be located, and more sophisticated treatment of dynamic electron correlation shows that the principal difference between O<sub>2</sub> addition to the vinoxy and 2-methylvinoxy radicals is a larger steric factor for 2-methylvinoxy + O<sub>2</sub>. This is attributed to the favorable interaction between the incoming O<sub>2</sub> molecule and the methyl group of the 2-methylvinoxy radical. Via the focal point approach, energetics for this reaction were determined, in most cases, to chemical accuracy. The coupled-cluster singles, doubles, and perturbative triples [CCSD(T)] correlation energy and Hartree-Fock energies were independently extrapolated to the complete basis set limit. A correction for the effect of higher excitations was computed at the CCSDT(Q)/6-31G level. Corrections for the frozen-core approximation, the Born-Oppenheimer approximation, the nonrelativistic approximation, and the zero-point vibrational energy were included. From the 1-oxo-2-propylperoxy radical, dissociation to reactants is competitive with the lowest energy isomerization pathway. The lowest energy isomerization pathway ultimately forms acetaldehyde, CO, and <sup>·</sup>OH as the final products. Characterization (materials science) ; Composite number ; Chemistry ; Focal point ; Computational chemistry ; Materials science ; Physics ; Nanotechnology ; Optics ; Composite material Free Radicals and Antioxidants ; Atmospheric chemistry and aerosols ; Catalysis and Oxidation Reactions
Authors: Joanne Y. Yew
Source: Mass Spectrometry (article, 2019-12-06)
Field: Chemistry — Spectroscopy

Natural Product Discovery by Direct Analysis in Real Time Mass Spectrometry Direct analysis in real time mass spectrometry (DART MS) is one of the first ambient ionization methods to be introduced and commercialized. Analysis by DART MS requires minimal sample preparation, produces nearly instantaneous results, and provides detection over a broad range of compounds. These advantageous features are particularly well-suited for the inherent complexity of natural product analysis. This review highlights recent applications of DART MS for species identification by chemotaxonomy, chemical profiling, genetic screening, and chemical spatial analysis from plants, insects, microbes, and metabolites from living systems. DART ion source ; Dart ; Mass spectrometry ; Ambient ionization ; Natural product ; Profiling (computer programming) ; Computational biology ; Chemical ionization ; Environmental chemistry ; Chemistry ; Biochemical engineering ; Chromatography ; Biology ; Computer science ; Ionization ; Electron ionization Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Identification and Quantification in Food
Authors: Shawna J. Standke | Devon H. Colby | Ryan C. Bensen | Anthony W. G. Burgett | Zhibo Yang
Source: Journal of Visualized Experiments (article, 2019-06-21)
Field: Chemistry — Spectroscopy

Integrated Cell Manipulation Platform Coupled with the Single-probe for Mass Spectrometry Analysis of Drugs and Metabolites in Single Suspension Cells Single cell mass spectrometry (SCMS) enables sensitive detection and accurate analysis of broad ranges of cellular species on the individual-cell level. The single-probe, a microscale sampling and ionization device, can be coupled with a mass spectrometer for on-line, rapid SCMS analysis of cellular constituents under ambient conditions. Previously, the single-probe SCMS technique was primarily used to measure cells immobilized onto a substrate, limiting the types of cells for studies. In the current study, the single-probe SCMS technology has been integrated with a cell manipulation system, typically used for in vitro fertilization. This integrated cell manipulation and analysis platform uses a cell-selection probe to capture identified individual floating cells and transfer the cells to the single-probe tip for microscale lysis, followed by immediate mass spectrometry analysis. This capture and transfer process removes the cells from the surrounding solution prior to analysis, minimizing the introduction of matrix molecules in the mass spectrometry analysis. This integrated setup is capable of SCMS analysis of targeted patient-isolated cells present in body fluids samples (e.g., urine, blood, saliva, etc.), allowing for potential applications of SCMS analysis to human medicine and disease biology. Single-cell analysis ; Mass spectrometry ; Microscale chemistry ; Lysis ; Chemistry ; Metabolomics ; Chromatography ; Analytical Chemistry (journal) ; Cell ; Biochemistry Mass Spectrometry Techniques and Applications ; Analytical Chemistry and Chromatography ; Microfluidic and Capillary Electrophoresis Applications
Authors: Seungheon Yeon | Se‐Yeong Park | Jong-Hwa Kim | Jongchan Kim | Sang-Yun Yang | Hwanmyeong Yeo | Ohkyung Kwon | In-Gyu Choi
Source: Journal of the Korean Wood Science and Technology (article, 2019-07-01)
Field: Chemistry — Organic Chemistry

Effect of Organic Solvent Extractives on Korean Softwoods Classification Using Near-infrared Spectroscopy This study analyzed the effect of organic solvent extractives on the classification of wood species via near-infrared spectroscopy (NIR). In our previous research, five species of Korean softwood were classified into three groups (i.e., Cryptomeria japonica (cedar)/Chamaecyparis obtuse (cypress), Pinus densiflora (red pine)/Pinus koraiensis (Korean pine), and Larix kaempferi (Larch)) using an NIR-based principal component analysis method. Similar tendencies of extractive distribution were observed among the three groups in that study. Therefore, in this study, we qualitatively analyzed extractives extracted by an organic solvent and analyzed the NIR spectra in terms of the extractives’ chemical structure and band assignment to determine their effect in more detail. Cedar/cypress showed a similar NIR spectra patterns by removing the extractives at 1695, 1724, and 2291 nm. D-pinitol, which was detected in cedar, contributed to that wavelength. Red pine/Korean pine showed spectra changes at 1616, 1695, 1681, 1705, 1724, 1731, 1765, 1780, and 2300 nm. Diterpenoids and fatty acid, which have a carboxylic group and an aliphatic double bond, contributed to that wavelength. Larch showed a catechin peak in gas chromatography and mass spectroscopy analysis, but it exhibited very small NIR spectra changes. The aromatic bond in larch seemed to have low sensitivity because of the 1st overtone of the O-H bond of the sawdust cellulose. The three groups sorted via NIR spectroscopy in the previous research showed quite different compositions of extractives, in accordance with the NIR band assignment. Thus, organic solvent extractives are expected to affect the classification of wood species using NIR spectroscopy. Chemistry ; Larix kaempferi ; Larch ; Cryptomeria ; Softwood ; Chamaecyparis ; Cypress ; Pinus densiflora ; Solvent ; Pinus koraiensis ; Infrared spectroscopy ; Spectroscopy ; Botany ; Organic chemistry ; Japonica Wood and Agarwood Research ; Wood Treatment and Properties ; Spectroscopy and Chemometric Analyses
Authors: Yuan He | Yue‐De Yang | Airong Zhang | Xiaoxia Zhang | Jing Sun | Li Tian
Source: RSC Advances (article, 2019-01-01)
Field: Chemistry — Analytical Chemistry

Lipid-lowering and antioxidative effects of<i>Apium graveolens</i>L. root flavonoid extracts Flavonoids are the major components of <i>Apium graveolens</i> L. root (AR). In this study, we used a rat model of high-fat emulsion die induced hyperlipidemia (HLP) to examine the antioxidant and lipid-lowering effects of the AR flavonoid extract (ARFE), and to identify its potential targets by molecular docking and western-blotting. ARFE significantly lowered the serum lipid levels in HLP rats. The lipid-lowering targets of ARFE were predicted and validated, which showed that ARFE significantly downregulated the expression levels of cholesterol synthesis-associated target proteins, including SREBP1, HMGCR and ACC1. These findings demonstrate that ARFE has remarkable lipid-lowering and antioxidant effects, and the former is mediated by the inhibition of SREBP1, HMGCR and ACC1. Apium graveolens ; Flavonoid ; Chemistry ; Hyperlipidemia ; Traditional medicine ; Botany ; Antioxidant ; Biology ; Medicine ; Biochemistry Chromatography in Natural Products ; Pharmacological Effects of Natural Compounds ; Neurological Disease Mechanisms and Treatments
Authors: Fatma F. Abdallah | Hany W. Darwish | Ibrahim A. Darwısh | Ibrahim A. Naguib
Source: PLoS ONE (article, 2019-09-25)
Field: Chemistry — Analytical Chemistry

Orthogonal projection to latent structures and first derivative for manipulation of PLSR and SVR chemometric models' prediction: A case study Novel manipulations of the well-established multivariate calibration models namely; partial least square regression (PLSR) and support vector regression (SVR) are introduced in the presented comparative study. Two preprocessing methods comprising first derivatization and orthogonal projection to latent structures (OPLS) are implemented prior to modeling with PLSR and SVR. Quantitative determination of pyridostigmine bromide (PR) in existence of its two associated substances; impurity a (IMP A) and impurity b (IMP B); was utilized as a case study for achieving comparison. A series consisting of 16 mixtures with numerous percentages of the studied compounds was applied for implementation of a 3 factor 4 level experimental design. Additionally, a series consisting of 9 mixtures was employed in an independent test to verify the predictive power of the suggested models. Significant improvement of predictive abilities of the two studied chemometric models was attained via implementation of OPLS processing method. The root mean square error of prediction RMSEP for the test set mixtures was employed as a key comparison tool. About PLSR model, RMSEP was found 0.5283 without preprocessing method, 1.1750 when first derivative data was used and 0.2890 when OPLS preprocessing method was applied. With regard to SVR model, RMSEP was found 0.2173 without preprocessing method, 0.3516 when first derivative data was used and 0.1819 when OPLS preprocessing method was applied. Partial least squares regression ; Chemometrics ; OPLS ; Mean squared error ; Calibration ; Latent variable ; Mathematics ; Computer science ; Artificial intelligence ; Statistics ; Chemistry ; Machine learning ; Computational chemistry Spectroscopy and Chemometric Analyses ; Analytical Chemistry and Chromatography ; Computational Drug Discovery Methods