jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Alex J. Plajer | Annie L. Colebatch | Felix J. Rizzuto | Patrick Pröhm | Andrew D. Bond | Raúl García‐Rodríguez | Dominic S. Wright
Source: Angewandte Chemie International Edition (article, 2018-03-31)
Field: Chemistry — Organic Chemistry

How Changing the Bridgehead Can Affect the Properties of Tripodal Ligands Although a multitude of studies have explored the coordination chemistry of classical tripodal ligands containing a range of main-group bridgehead atoms or groups, it is not clear how periodic trends affect ligand character and reactivity within a single ligand family. A case in point is the extensive family of neutral tris-2-pyridyl ligands E(2-py)<sub>3</sub> (E=C-R, N, P), which are closely related to archetypal tris-pyrazolyl borates. With the 6-methyl substituted ligands E(6-Me-2-py)<sub>3</sub> (E=As, Sb, Bi) in hand, the effects of bridgehead modification alone on descending a single group in the periodic table were assessed. The primary influence on coordination behaviour is the increasing Lewis acidity (electropositivity) of the bridgehead atom as Group 15 is descended, which not only modulates the electron density on the pyridyl donor groups but also introduces the potential for anion selective coordination behaviour. Chemistry ; Ligand (biochemistry) ; Stereochemistry ; Tris ; Reactivity (psychology) ; Coordination complex ; Tripodal ligand ; Group (periodic table) ; Crystallography ; Crystal structure ; Metal ; Organic chemistry ; Receptor Organometallic Complex Synthesis and Catalysis ; Metal complexes synthesis and properties ; Asymmetric Hydrogenation and Catalysis
Authors: Shengtao Yin | Dong Xiao | Jianxun Liu | Ke Li | Huilin He | Shouzhen Jiang | Dan Luo | Xiao Wei Sun | Wei Ji | Yan Jun Liu
Source: IEEE photonics journal (article, 2018-11-06)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Reconfigurable Chiral Metasurface Absorbers Based on Liquid Crystals We propose a liquid-crystal-based reconfigurable chiral metasurface absorber and numerically investigate its chiro-optical properties. The chiral metasurface absorber consists of a metal-insulator-metal structure with the substrate, which can strongly absorb a circularly polarized wave of one spin state and reflects that of the opposite spin, resulting a strong circular dichroism (CD). A birefringent liquid crystal (LC) is exploited to serve as the insulator layer in the metal-insulator-metal structure. We could then vary the circular state of the incident light by controlling the alignment of the LC molecules, hence inversing the CD. The simulation results show that the CD will change the sign as the LC molecules are realigned from 0° to 90°. The absorption efficiency for the specific circularly polarized wave is larger than 80% and the CD is nearly 70%. The simple and compact design of our proposed chiral metasurface absorber is especially favorable for integration, and such a reconfigurable chiral absorber could find many potential applications in biological detection/sensing, polarimetric imaging, and optical communications. Liquid crystal ; Birefringence ; Circular polarization ; Materials science ; Circular dichroism ; Polarimetry ; Optoelectronics ; Polarization (electrochemistry) ; Optics ; Metal ; Absorption (acoustics) ; Physics ; Crystallography ; Chemistry Metamaterials and Metasurfaces Applications ; Electromagnetic Scattering and Analysis ; Plasmonic and Surface Plasmon Research
Authors: Julia Hain | Patrick Rollin | Werner Klaffke | Thisbe K. Lindhorst
Source: Beilstein Journal of Organic Chemistry (review, 2018-06-29)
Field: Chemistry — Organic Chemistry

Anomeric modification of carbohydrates using the Mitsunobu reaction The Mitsunobu reaction basically consists in the conversion of an alcohol into an ester under inversion of configuration, employing a carboxylic acid and a pair of two auxiliary reagents, mostly triphenylphosphine and a dialkyl azodicarboxylate. This reaction has been frequently used in carbohydrate chemistry for the modification of sugar hydroxy groups. Modification at the anomeric position, leading mainly to anomeric esters or glycosides, is of particular importance in the glycosciences. Therefore, this review focuses on the use of the Mitsunobu reaction for modifications of sugar hemiacetals. Strikingly, unprotected sugars can often be converted regioselectively at the anomeric center, whereas in other cases, the other hydroxy groups in reducing sugars have to be protected to achieve good results in the Mitsunobu procedure. We have reviewed on the one hand the literature on anomeric esterification, including glycosyl phosphates, and on the other hand glycoside synthesis, including S- and N-glycosides. The mechanistic details of the Mitsunobu reaction are discussed as well as this is important to explain and predict the stereoselectivity of anomeric modifications under Mitsunobu conditions. Though the Mitsunobu reaction is often not the first choice for the anomeric modification of carbohydrates, this review shows the high value of the reaction in many different circumstances. Anomer ; Mitsunobu reaction ; Chemistry ; Glycosyl ; Anomeric effect ; Glycoside ; Organic chemistry ; Combinatorial chemistry ; Reagent Carbohydrate Chemistry and Synthesis ; Glycosylation and Glycoproteins Research ; Chemical Synthesis and Analysis
Authors: Nedunchezhian Srinivasan | Lalith Kumar Bhaskar | Ravi Kumar | Sergio Baragetti
Source: Materials & Design (article, 2018-09-12)
Field: Materials Science — Materials Chemistry

Residual stress gradient and relaxation upon fatigue deformation of diamond-like carbon coated aluminum alloy in air and methanol environments Amorphous diamond-like carbon coating (DLC) of 2 μm in thickness was deposited over the aluminum alloy substrate using magnetron sputtering deposition technique. In order to understand the efficacy of coating deposition, coated specimens were subjected to rotating bending fatigue in air and methanol environments respectively. Raman spectroscopy was used in conjunction with grazing incidence X-diffraction technique to obtain depth-resolved residual stress gradients of coated-aluminum substrate. The residual stress generated due to coating deposition was calculated using Raman spectroscopy and it was −1.13 ± 0.16 GPa (compressive in nature). Furthermore, Raman spectroscopy was utilized for the quantification of stress relaxation upon fatigue loading in air and methanol environments. It was observed that the irrespective of the testing environment, good correlation exists between the stress relaxation magnitude and number of cycles endured before failure. Materials science ; Residual stress ; Raman spectroscopy ; Composite material ; Diamond-like carbon ; Sputter deposition ; Deposition (geology) ; Alloy ; Substrate (aquarium) ; Stress relaxation ; Coating ; Relaxation (psychology) ; Stress (linguistics) ; Metallurgy ; Sputtering ; Thin film ; Optics ; Nanotechnology Diamond and Carbon-based Materials Research ; Metal and Thin Film Mechanics ; Tribology and Wear Analysis
Authors: Takuya Inokuchi | Na Li | Kei Morohoshi | Noriyoshi Arai
Source: Nanoscale (article, 2018-01-01)
Field: Materials Science — Materials Chemistry

Multiscale prediction of functional self-assembled materials using machine learning: high-performance surfactant molecules Various physical properties of functional materials can be induced by controlling their chemical molecular structures. Therefore, molecular design is crucial in the fields of engineering and materials science. With its remarkable development in various fields, machine learning combined with molecular simulation has recently been found to be effective at predicting the electronic structure of materials (Nat. Commun., 2017, 8, 872 and Nat. Commun., 2017, 8, 13890). However, previous studies have used similar microscale information as input and output data for machine learning, i.e., molecular structures and electronic structures. In this study, we determined whether multiscale data can be predicted using machine learning via a self-assembly functional material system. In particular, we investigated whether machine learning can be used to predict dispersion and viscosity, as the representative physical properties of a self-assembled surfactant solution, from the chemical molecular structures of a surfactant. The results showed that relatively accurate information on these physical properties can be predicted from the molecular structure, suggesting that machine learning can be used to predict multiscale systems, such as surfactant molecules, self-assembled micelle structures, and physical properties of solutions. The results of this study will aid in further development of the application of machine learning to materials science and molecular design. Pulmonary surfactant ; Materials science ; Molecule ; Nanotechnology ; Computer science ; Chemical engineering ; Chemistry ; Organic chemistry ; Engineering Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Surface Chemistry and Catalysis
Authors: І. V. Konovalenko | Pavlo Maruschak | Olegas Prentkovskis | Raimundas Junevičius
Source: Materials (article, 2018-12-05)
Field: Materials Science — Materials Chemistry

Investigation of the Rupture Surface of the Titanium Alloy Using Convolutional Neural Networks The research of fractographic images of metals is an important method that allows obtaining valuable information about the physical and mechanical properties of a metallic specimen, determining the causes of its fracture, and developing models for optimizing its properties. One of the main lines of research in this case is studying the characteristics of the dimples of viscous detachment, which are formed on the metal surface in the process of its fracture. This paper proposes a method for detecting dimples of viscous detachment on a fractographic image, which is based on using a convolutional neural network. Compared to classical image processing algorithms, the use of the neural network significantly reduces the number of parameters to be adjusted manually. In addition, when being trained, the neural network can reveal a lot more characteristic features that affect the quality of recognition in a positive way. This makes the method more versatile and accurate. We investigated 17 models of convolutional neural networks with different structures and selected the optimal variant in terms of accuracy and speed. The proposed neural network classifies image pixels into two categories: "dimple" and "edge". A transition from a probabilistic result at the output of the neural network to an unambiguously clear classification is proposed. The results obtained using the neural network were compared to the results obtained using a previously developed algorithm based on a set of filters. It has been found that the results are very similar (more than 90% similarity), but the neural network reveals the necessary features more accurately than the previous method. Convolutional neural network ; Artificial neural network ; Artificial intelligence ; Dimple ; Computer science ; Pattern recognition (psychology) ; Enhanced Data Rates for GSM Evolution ; Similarity (geometry) ; Probabilistic neural network ; Pixel ; Materials science ; Image (mathematics) ; Time delay neural network ; Composite material Material Properties and Failure Mechanisms ; Engineering Diagnostics and Reliability ; Industrial Engineering and Technologies
Authors: Qing Zhang | Dongping Chang | Xiuyun Zhai | Wencong Lu
Source: Chemometrics and Intelligent Laboratory Systems (article, 2018-04-10)
Field: Materials Science — Materials Chemistry

OCPMDM: Online computation platform for materials data mining Computer science ; Analytics ; Computation ; Data analysis ; Data science ; Machine learning ; Data mining ; Artificial intelligence ; Algorithm Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Guangqing Cai | Zhefu Liu | Linzhou Zhang | Suoqi Zhao | Chunming Xu
Source: Energy & Fuels (article, 2018-02-07)
Field: Chemistry — Spectroscopy

Quantitative Structure–Property Relationship Model for Hydrocarbon Liquid Viscosity Prediction The liquid viscosity of hydrocarbon compounds is essential in the chemical engineering process design and optimization. In this paper, we developed a quantitative structure–property relationship (QSPR) model to predict the hydrocarbon viscosity at different temperatures from the chemical structure. We collected viscosity data at different temperatures of 261 hydrocarbon compounds (C3–C64), covering n-paraffins, isoparaffins, olefins, alkynes, monocyclic and polycyclic cycloalkanes, and aromatics. We regressed the experimental data using an improved Andrade equation at first. Hydrocarbon viscosity versus temperature curves were characterized by only two parameters (named B and T0). The QSPR model was then built to capture the complex dependence of the Andrade equation parameters upon the chemical structures. A total of 36 key chemical features (including 15 basic groups, 20 united groups, and molecular weights) were manually selected through the trial-and-error process. An artificial neural network was trained to correlate the Andrade model parameters to the selected chemical features. The average relative errors for B and T0 predictions are 2.87 and 1.05%, respectively. The viscosity versus temperature profile was calculated from the predicted Andrade model parameters, reaching the mean absolute error at a value of 0.10 mPa s. We also proved that the established QSPR model can describe the viscosity versus temperature profile of different isomers, such as isoparaffins, with different branch degrees and aromatic hydrocarbons with different substituent positions. At last, we applied the QSPR model to predict gasoline and diesel viscosities based on the measured molecular composition. A good agreement was observed between predicted and experimental data (absolute mean deviation equals 0.21 mPa s), demonstrating that it has capacity to calculate viscosity of hydrocarbon mixtures. Hydrocarbon ; Quantitative structure–activity relationship ; Viscosity ; Chemistry ; Thermodynamics ; Approximation error ; Aromatic hydrocarbon ; Organic chemistry ; Applied mathematics ; Mathematics ; Stereochemistry Analytical Chemistry and Chromatography ; Phase Equilibria and Thermodynamics ; Computational Drug Discovery Methods
Authors: Seok Yoon | Jun-Seo Jeon | Geon-Young Kim | Joo-Hyun Seong | Min-Hoon Baik
Source: Annals of Nuclear Energy (article, 2018-10-28)
Field: Materials Science — Materials Chemistry

Specific heat capacity model for compacted bentonite buffer materials Bentonite ; Radioactive waste ; Buffer (optical fiber) ; Environmental science ; Groundwater ; Decay heat ; Materials science ; High-level waste ; Spent nuclear fuel ; Heat capacity ; Waste management ; Nuclear engineering ; Geotechnical engineering ; Geology Graphite, nuclear technology, radiation studies ; Nuclear and radioactivity studies ; Soil and Unsaturated Flow
Authors: Kun Luo | Jiangkuan Xing | Yun Bai | Jianren Fan
Source: Combustion and Flame (article, 2018-04-24)
Field: Materials Science — Materials Chemistry

Prediction of product distributions in coal devolatilization by an artificial neural network model Coal ; Artificial neural network ; Combustion ; Range (aeronautics) ; Process engineering ; Coal combustion products ; Product distribution ; Energy value of coal ; Particle (ecology) ; Residence time (fluid dynamics) ; Percolation (cognitive psychology) ; Biological system ; Environmental science ; Computer science ; Materials science ; Chemistry ; Artificial intelligence ; Engineering ; Waste management ; Composite material ; Geology ; Organic chemistry Catalytic Processes in Materials Science ; Thermochemical Biomass Conversion Processes ; Iron and Steelmaking Processes
Authors: Tuo He | Lichao Jiao | Min Yu | Juan Guo | Xiaomei Jiang | Yafang Yin
Source: Holzforschung (article, 2018-09-18)
Field: Chemistry — Organic Chemistry

DNA barcoding authentication for the wood of eight endangered <i>Dalbergia</i> timber species using machine learning approaches Abstract Reliable wood identification and proof of the provenance of trees is the first step for combating illegal logging. DNA barcoding belongs to the promising tools in this regard, for which reliable methods and reference libraries are needed. Machine learning approaches (MLAs) are tailored to the necessities of DNA barcoding, which are based on mathematical multivaried analysis. In the present study, eight Dalbergia timber species were investigated in terms of their DNA sequences focusing on four barcodes (ITS2, mat K, trn H- psb A and trn L) by means of the MLAs BLOG and WEKA for wood species identification. The data material downloaded from NCBI (288 sequences) and taken from a previous study of the authors (153 DNA sequences) was taken as dataset for calibration. The MLAs’ effectivity was verified through identification of non-vouchered wood specimens. The results indicate that the SMO classifier as part of the WEKA approach performed the best (98%~100%) for discriminating the eight Dalbergia timber species. Moreover, the two-locus combination ITS2+ trn H- psb A showed the highest success rate. Furthermore, the non-vouchered wood specimens were successfully identified by means of ITS2+ trn H- psb A with the SMO classifier. The MLAs are successful in combi- nation with DNA barcode reference libraries for the identification of endangered Dalbergia timber species. DNA barcoding ; Dalbergia ; Endangered species ; Barcode ; Species identification ; Biology ; Classifier (UML) ; Identification (biology) ; Botany ; Artificial intelligence ; Computer science ; Evolutionary biology ; Ecology Wood and Agarwood Research ; Identification and Quantification in Food ; Genomics and Phylogenetic Studies
Authors: Sarita J. Charde
Source: Chemical and Biochemical Engineering Quarterly (article, 2018-07-06)
Field: Materials Science — Materials Chemistry

Degradation Kinetics of Polycarbonate Composites: Kinetic Parameters and Artificial Neural Network In order to design a reactor, kinetics of degradation of polycarbonate/CaCO 3 composites was investigated here by thermogravimetric analysis (TGA), applying model-free and modelistic methods together, to obtain E, A, S*, H* and G* (kinetic parameters). The system was tested with all the mechanisms available using non-isothermal modelistic method (Coats-Redfern). This approach allowed choosing the models, which are otherwise difficult to decide upon simply based on regression fit methods. The mechanism proposed was a simple n th order. Application of artificial neural network supported in designing a neural network could lead to a quick determination of kinetic parameters. Thermogravimetric analysis ; Polycarbonate ; Kinetics ; Isothermal process ; Artificial neural network ; Kinetic energy ; Degradation (telecommunications) ; Materials science ; Biological system ; Composite material ; Computer science ; Chemical engineering ; Thermodynamics ; Artificial intelligence ; Engineering ; Physics Thermal and Kinetic Analysis ; Polymer Nanocomposites and Properties ; Polymer Science and PVC
Authors: Maurílio Gustavo Nespeca | Rafael Rodrigues Hatanaka | Danilo Luiz Flumignan | José Eduardo de Oliveira
Source: Journal of Analytical Methods in Chemistry (article, 2018-01-01)
Field: Chemistry — Analytical Chemistry

Rapid and Simultaneous Prediction of Eight Diesel Quality Parameters through ATR-FTIR Analysis Quality assessment of diesel fuel is highly necessary for society, but the costs and time spent are very high while using standard methods. Therefore, this study aimed to develop an analytical method capable of simultaneously determining eight diesel quality parameters (density; flash point; total sulfur content; distillation temperatures at 10% (T10), 50% (T50), and 85% (T85) recovery; cetane index; and biodiesel content) through attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy and the multivariate regression method, partial least square (PLS). For this purpose, the quality parameters of 409 samples were determined using standard methods, and their spectra were acquired in ranges of 4000-650 cm<sup>-1</sup>. The use of the multivariate filters, generalized least squares weighting (GLSW) and orthogonal signal correction (OSC), was evaluated to improve the signal-to-noise ratio of the models. Likewise, four variable selection approaches were tested: manual exclusion, forward interval PLS (FiPLS), backward interval PLS (BiPLS), and genetic algorithm (GA). The multivariate filters and variables selection algorithms generated more fitted and accurate PLS models. According to the validation, the FTIR/PLS models presented accuracy comparable to the reference methods and, therefore, the proposed method can be applied in the diesel routine monitoring to significantly reduce costs and analysis time. Diesel fuel ; Fourier transform infrared spectroscopy ; Quality (philosophy) ; Environmental science ; Chromatography ; Materials science ; Chemistry ; Engineering ; Chemical engineering ; Organic chemistry ; Physics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Water Quality Monitoring and Analysis
Authors: Cathrin Veenaas | Anna Linusson | Peter Haglund
Source: Analytical and Bioanalytical Chemistry (article, 2018-10-25)
Field: Chemistry — Spectroscopy

Retention-time prediction in comprehensive two-dimensional gas chromatography to aid identification of unknown contaminants Comprehensive two-dimensional (2D) gas chromatography (GC×GC) coupled to mass spectrometry (MS, GC×GC-MS), which enhances selectivity compared to GC-MS analysis, can be used for non-directed analysis (non-target screening) of environmental samples. Additional tools that aid in identifying unknown compounds are needed to handle the large amount of data generated. These tools include retention indices for characterizing relative retention of compounds and prediction of such. In this study, two quantitative structure-retention relationship (QSRR) approaches for prediction of retention times (<sup>1</sup>t<sub>R</sub> and <sup>2</sup>t<sub>R</sub>) and indices (linear retention indices (LRIs) and a new polyethylene glycol-based retention index (PEG-<sup>2</sup>I)) in GC × GC were explored, and their predictive power compared. In the first method, molecular descriptors combined with partial least squares (PLS) analysis were used to predict times and indices. In the second method, the commercial software package ChromGenius (ACD/Labs), based on a "federation of local models," was employed. Overall, the PLS approach exhibited better accuracy than the ChromGenius approach. Although average errors for the LRI prediction via ChromGenius were slightly lower, PLS was superior in all other cases. The average deviations between the predicted and the experimental value were 5% and 3% for the <sup>1</sup>t<sub>R</sub> and LRI, and 5% and 12% for the <sup>2</sup>t<sub>R</sub> and PEG-<sup>2</sup>I, respectively. These results are comparable to or better than those reported in previous studies. Finally, the developed model was successfully applied to an independent dataset and led to the discovery of 12 wrongly assigned compounds. The results of the present work represent the first-ever prediction of the PEG-<sup>2</sup>I. Graphical abstract ᅟ. Kovats retention index ; Retention time ; Partial least squares regression ; Chemistry ; Gas chromatography ; Chromatography ; Mass spectrometry ; Gas chromatography–mass spectrometry ; Analytical Chemistry (journal) ; Statistics ; Mathematics Analytical Chemistry and Chromatography ; Advanced Chemical Sensor Technologies ; Computational Drug Discovery Methods