jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yuhua Wang | Weihua Wang | Shuyun Zhu | Ge Yang | Zhiqiang Zhang | Ping Li
Source: RSC Advances (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

Theoretical studies on the structures and properties of doped graphenes with and without an external electrical field To expand the applications of graphene in optoelectronic devices, B, Al, Si, Ge, As, and Sb doped graphenes (marked as B-G, Al-G, Si-G, Ge-G, As-G, and Sb-G, respectively) were synthesised. The geometric structures, population analyses, and also electronic and optical properties of these doped graphene materials were investigated employing the density functional theory (DFT) method. It was shown that the band gaps of doped graphenes were opened and their absorption spectra were red-shifted by the addition of doping atoms, and their dielectric functions and refractive indexes of low frequency were decreased compared with those of pure graphene. Moreover, the electronic and optical properties of doped graphenes under an external electrical field ranging from -0.4 to 1.2 eV Å<sup>-1</sup> have been explored. It was found that the band gaps of As-G and Sb-G were increased to 0.864 and 1.841 eV under a 1.2 eV Å<sup>-1</sup> external electrical field, respectively. On the contrary, the band gaps of B-G, Al-G, Si-G, and Ge-G were decreased with the increase of the external electrical field intensity. Additionally, the absorption peaks of B-G, Al-G, Si-G, and Ge-G were red-shifted upon applying the external electrical field. Correspondingly, their dielectric functions and refractive indexes of low frequency were increased. Surprisingly, the absorption spectra, dielectric functions, and refractive indexes of As-G and Sb-G have no significant changes. Doping ; Materials science ; Field (mathematics) ; Nanotechnology ; Engineering physics ; Chemical physics ; Condensed matter physics ; Optoelectronics ; Chemistry ; Physics ; Mathematics Graphene research and applications ; Carbon Nanotubes in Composites ; Graphene and Nanomaterials Applications
Authors: Mehmet Özdoğan | Serap Yiğen | Cem Çelebi | G. Utlu
Source: Thin Solid Films (article, 2019-04-19)
Field: Materials Science — Materials Chemistry

The comparison of transient photocurrent spectroscopy measurements of Pulsed Electron Deposited ZnO thin film for air and vacuum ambient conditions Photocurrent ; Photoconductivity ; Thin film ; Desorption ; Materials science ; Atmosphere (unit) ; Spectroscopy ; Deposition (geology) ; Analytical Chemistry (journal) ; Ultra-high vacuum ; Adsorption ; Optoelectronics ; Chemistry ; Nanotechnology ZnO doping and properties ; Gas Sensing Nanomaterials and Sensors ; Copper-based nanomaterials and applications
Authors: Andreas Himmelsbach | Alexandra Schneider‐Chaabane | Karen Lienkamp
Source: Macromolecular Chemistry and Physics (article, 2019-11-27)
Field: Chemistry — Organic Chemistry

Asymmetrically Substituted Poly(diitaconates) Obtained by Reversible Addition‐Fragmentation Chain Transfer (RAFT) Polymerization: Synthesis, Copolymerization Parameters, and Antimicrobial Activity Abstract Asymmetrically substituted poly(diitaconate) copolymers are synthesized from 1‐(( N ‐ tert ‐butoxycarbonyl)‐2‐aminoethyl)‐4‐propyl diitaconate (PrIA) and different comonomers ( N,N ‐dimethyl‐acrylamide, DMAA; acrylic acid; or (( N ‐ tert‐ butoxycarbonyl)‐2‐aminoethyl)methacrylate) by reversible addition–fragmentation chain transfer polymerization (RAFT). The RAFT copolymerization parameters of PrIA and DMAA are r DMAA = 0.49 and r PrIA = 0.17, compared to r DMAA = 0.52 and r PrIA = 0.54 obtained by free radical copolymerization (FRP). Thus, the RAFT process has a stronger trend to alternating polymerization than the FRP process. The polydispersity index of the RAFT copolymers is around 1.2–1.8, compared to 2.8–2.9 for the corresponding FRP copolymers. After removal of the tert ‐butoxycarbonyl protective groups, antimicrobially active synthetic mimics of antimicrobial peptides are obtained. The thus activated poly(PrIA‐ co ‐DMAA) copolymers (repeat unit ratio 1:1) have an increasing activity against Escherichia coli and Staphylococcus aureus with increasing molar mass. The RAFT copolymers are slightly more active and less toxic than comparable FRP polymers, leading to a higher selectivity for bacteria over mammalian cells. Higher molar fractions of PrIA in poly(PrIA‐ co ‐DMAA) copolymers (up to 80 mol%) do not increase their antimicrobial activity; reduction of the BuIA content in poly(BuIA‐DMAA) (down to 10 mol%) leads to a loss of activity against both E. coli and S. aureus . Copolymer ; Chain transfer ; Chemistry ; Polymer chemistry ; Raft ; Reversible addition−fragmentation chain-transfer polymerization ; Dispersity ; Polymerization ; Molar mass ; Methacrylate ; Polymer ; Radical polymerization ; Organic chemistry Antimicrobial agents and applications ; Advanced Polymer Synthesis and Characterization ; Click Chemistry and Applications
Authors: Paweł Pakulski | Mirosław Arczyński | Dawid Pinkowicz
Source: Crystals (article, 2019-01-07)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Bis(triphenylphosphine)iminium Salts of Dioxothiadiazole Radical Anions: Preparation, Crystal Structures, and Magnetic Properties Phenanthroline dioxothiadiazoles are redox active molecules that form stable radical anions suitable for the construction of supramolecular magnetic materials. Herein, the preparation, structures and magnetic properties of bis(triphenylphosphine)iminium (PPN) salts of [1,2,5]thiadiazole[3,4-f][1,10]phenanthroline 1,1-dioxide (L), [1,2,5]thiadiazole[3,4-f][4,7]phenanthroline 1,1-dioxide (4,7-L), 5-bromo-[1,2,5]thiadiazolo[3,4-f][1,10]phenanthroline 2,2-dioxide (BrL), and 5,10-dibromo-[1,2,5]thiadiazolo[3,4-f][1,10]phenanthroline 2,2-dioxide (diBrL) are reported. The preparation of new bromo derivatives of the L: 5-bromo-[1,2,5]thiadiazolo[3,4-f][1,10]phenanthroline 2,2-dioxide (BrL) and 5,10-dibromo-[1,2,5]thiadiazolo[3,4-f][1,10]phenanthroline 2,2-dioxide (diBrL)—suitable starting materials for further derivatization—are described starting from a commercially available and cheap 1,10-phenanthroline. All PPN salts show antiferromagnetic interactions between the pairs of radical anions, which in the case of PPN(diBrL) are very strong (−116 cm−1; using Ĥ = −2JSS type of exchange coupling Hamiltonian) due to a different crystal packing of the anion radicals as compared to PPN(L), PPN(4,7-L), and PPN(BrL). Chemistry ; Phenanthroline ; Iminium ; Crystal structure ; Triphenylphosphine ; Triphenylphosphine oxide ; Medicinal chemistry ; Molecule ; Crystallography ; Inorganic chemistry ; Ion ; Organic chemistry ; Catalysis Magnetism in coordination complexes ; Organic and Molecular Conductors Research ; Organometallic Complex Synthesis and Catalysis
Authors: Jan Patrick Calupitan | Olivier Galangau | Takuya Nakashima | Tsuyoshi Kawai | Gwénaël Rapenne
Source: ChemPlusChem (review, 2019-02-07)
Field: Materials Science — Materials Chemistry

Photochromic Diarylethenes Designed for Surface Deposition: From Self‐Assembled Monolayers to Single Molecules The efficient switching that can occur between two stable isomers of diarylethenes makes them particularly promising targets for opto- and molecular electronics. To examine these classes of molecules for electronics applications, they have been subjected to a series of scanning tunneling microscopy (STM) experiments, which are the focus of this Review. A brief introduction to the chemical design of diarylethenes in terms of their switching capabilities along with the basics of STM are presented. Next, initial STM studies on these compounds under ambient conditions are discussed. An overview of how molecular design affects the isomerization and self-assembly of diarylethenes at the solid-liquid interface as investigated by STM is then presented, as well as single-molecule studies under ultrahigh vacuum. The last section presents further prospects for molecular design in the field. Photochromism ; Scanning tunneling microscope ; Self-assembled monolayer ; Molecular electronics ; Nanotechnology ; Monolayer ; Molecule ; Materials science ; Isomerization ; Quantum tunnelling ; Molecular switch ; Chemistry ; Chemical physics ; Optoelectronics ; Organic chemistry ; Catalysis Photochromic and Fluorescence Chemistry ; Surface Chemistry and Catalysis ; Molecular Junctions and Nanostructures
Authors: Wen Xi Chia | Mayu Nishijo | Seongsoo Kang | Juwon Oh | Tsubasa Nishimura | Hiroto Omori | Jean‐François Longevial | Yoshihiro Miyake | Dongho Kim | Hiroshi Shinokubo
Source: Chemistry - A European Journal (article, 2019-12-03)
Field: Materials Science — Materials Chemistry

Site‐Selective N‐Methylation of 5,15‐Diazaporphyrins: Reactive Cationic Porphyrinoids that Provide Isoporphyrin Analogues N-Alkylation significantly changes the electronic and optical properties, as well as the reactivity of nitrogen-containing π-conjugated molecules. In this study, it is found that treating 5,15-diazaporphyrins with methyl triflate selectively affords the corresponding N-methyl-5,15-diazaporphyrinium cations in good yield. N-Methylation substantially alters the electronic properties and reactivity of diazaporphyrins. The electron-accepting properties of the N-methyl-5,15-diazaporphyrinium cations are enhanced due to their lowered LUMO level. Stabilization of the LUMO energy enables regio- and stereoselective Diels-Alder reactions of the cationic diazaporphyrin with cyclopentadiene. N-Methylation also enhances the acidity of the inner NH protons, and thus, allows facile deprotonation to provide nitrogen-substituted isoporphyrin analogues with only one NH group in the central cavity. Cationic polymerization ; Methylation ; Chemistry ; Stereochemistry ; Combinatorial chemistry ; Organic chemistry ; Biochemistry ; DNA Porphyrin and Phthalocyanine Chemistry ; Porphyrin Metabolism and Disorders ; Metal-Catalyzed Oxygenation Mechanisms
Authors: Pablo A. R. Muñoz | Cedric Noordam | Richard J. M. Egberink | Jurriaan Huskens | Sonia M. García‐Blanco
Source: Applied Optics (article, 2019-11-20)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Comparative study of multiple thiol-based self-assembled monolayer coatings for the SERS detection of nitrite, nitrate, and perchlorate anions in water Raman-based sensors represent a promising solution to enable both detection and fingerprinting of anionic pollutants in the water distribution network. Due to the weak nature of Raman scattering, a signal intensity enhancement mechanism, such as surface enhanced Raman spectroscopy (SERS), is required. Given the combination of SERS being a first layer effect and the low affinity for metallic surfaces shown by anions, functionalization of the SERS substrates using positively charged self-assembled monolayers (SAMs) is required to guarantee a strong SERS signal. In this work, the performance of three thiol-based coatings, namely, 2-mercapto-4-methyl-pyrimidine, cysteamine, and 2-dimethyl-amino-ethanethiol, is systematically compared for the detection of nitrite, nitrate, and perchlorate ions in water. For each coating, the limit of detection of those analytes is studied in combination with commercial SERS substrates. Cysteamine-coated SERS substrates are shown to provide the lowest limit of detection for the three analytes of this study. Evaluation of this coating on real drinking water samples is reported. Detection limit ; Monolayer ; Raman spectroscopy ; Raman scattering ; Materials science ; Nitrite ; Inorganic chemistry ; Coating ; Nitration ; Surface-enhanced Raman spectroscopy ; Photochemistry ; Analytical Chemistry (journal) ; Chemistry ; Nitrate ; Nanotechnology ; Organic chemistry ; Chromatography ; Optics Gold and Silver Nanoparticles Synthesis and Applications ; Advanced Nanomaterials in Catalysis ; Electrochemical Analysis and Applications
Authors: M. O’Donnell | Jonathan P. Stacey | Isaac V. Chenchiah | Alberto Pirrera
Source: Journal of the Mechanics and Physics of Solids (article, 2019-08-30)
Field: Materials Science — Materials Chemistry

Multiscale tailoring of helical lattice systems for bespoke thermoelasticity Bespoke ; Thermoelastic damping ; Materials science ; Length scale ; Shape-memory alloy ; Lattice (music) ; Structural material ; Statistical physics ; Phase transition ; Thermal ; Computer science ; Mechanical engineering ; Mechanics ; Condensed matter physics ; Physics ; Composite material ; Thermodynamics Thermal Expansion and Ionic Conductivity ; Thermal properties of materials ; Shape Memory Alloy Transformations
Authors: J. E. Lang | David A. Broadway | Gregory A. L. White | Liam T. Hall | Alastair Stacey | Lloyd C. L. Hollenberg | T. S. Monteiro | Jean‐Philippe Tetienne
Source: Physical Review Letters (article, 2019-11-22)
Field: Materials Science — Materials Chemistry

Quantum Bath Control with Nuclear Spin State Selectivity via Pulse-Adjusted Dynamical Decoupling Dynamical decoupling (DD) is a powerful method for controlling arbitrary open quantum systems. In quantum spin control, DD generally involves a sequence of timed spin flips (π rotations) arranged to either average out or selectively enhance coupling to the environment. Experimentally, errors in the spin flips are inevitably introduced, motivating efforts to optimize error-robust DD. Here we invert this paradigm: by introducing particular control "errors" in standard DD, namely, a small constant deviation from perfect π rotations (pulse adjustments), we show we obtain protocols that retain the advantages of DD while introducing the capabilities of quantum state readout and polarization transfer. We exploit this nuclear quantum state selectivity on an ensemble of nitrogen-vacancy centers in diamond to efficiently polarize the ^{13}C quantum bath. The underlying physical mechanism is generic and paves the way to systematic engineering of pulse-adjusted protocols with nuclear state selectivity for quantum control applications. Dynamical decoupling ; Physics ; Quantum sensor ; Quantum ; Quantum computer ; Quantum error correction ; Quantum state ; Quantum technology ; Spin (aerodynamics) ; Quantum mechanics ; Quantum network ; Open quantum system Diamond and Carbon-based Materials Research ; High-pressure geophysics and materials ; Advanced Fiber Laser Technologies
Authors: Taqi ur Rahman | Salman Amin | Hammad Shaukat | Shaikh Saaqib Haroon | Intisar Ali Sajjad | Muhammad Awais
Source: SN Applied Sciences (article, 2019-09-14)
Field: Materials Science — Materials Chemistry

Effect of nano filler concentration on leakage current and partial discharge properties of zepoxy nano composites Materials science ; Composite material ; Nano- ; Epoxy ; Partial discharge ; Filler (materials) ; Leakage (economics) ; Natural rubber ; Insulation system ; Composite number ; Voltage ; Electrical engineering ; Engineering High voltage insulation and dielectric phenomena ; Dielectric materials and actuators ; Electrical Fault Detection and Protection
Authors: Hao Wen | Xiaoxing Zhang | Rong Xia | Guoxiong Hu | Yunjian Wu
Source: Materials (article, 2019-04-30)
Field: Materials Science — Materials Chemistry

Decomposition Characteristics of SF6 under Flashover Discharge on the Epoxy Resin Surface In this paper, the flashover discharging experiment was carried out on epoxy resin surface in an SF<sub>6</sub> atmosphere under pin-plate electrodes, with the electrodes distance from 5 mm to 9 mm. The concentration of seven characteristic gases was detected, indicating that the concentration of SOF<sub>2</sub> and CF<sub>4</sub> was the two highest, followed by SO<sub>2</sub>, CO<sub>2</sub>, SO<sub>2</sub>F<sub>2</sub>, CS<sub>2</sub>, and H<sub>2</sub>S. Based on the changes in the concentration of the characteristic gases, a preliminary rule was proposed to predict the occurrence of flashover discharge on epoxy resin: When the concentration of SOF<sub>2</sub> reaches twice of CF<sub>4</sub> concentration, and the total concentration of both SOF<sub>2</sub> and CF<sub>4</sub> is much higher than that of H<sub>2</sub>S, a possible flashover discharge on the epoxy resin surface in SF<sub>6</sub>-infused electrical equipment occurs. Through the simulation of decomposition of epoxy resin, it has been revealed that H<sub>2</sub>O has different generation paths that can facilitate the formation of SOF<sub>2</sub>, finally influencing the concentration variation of the seven characteristic gases. Epoxy ; Arc flash ; Materials science ; Composite material ; Decomposition ; Electrode ; Sulfur hexafluoride ; Chemistry ; Organic chemistry ; Insulator (electricity) ; Physical chemistry High voltage insulation and dielectric phenomena ; Electrostatic Discharge in Electronics ; Power Transformer Diagnostics and Insulation
Authors: Hiroyuki Shindo | Yūji Matsumoto
Source: arXiv (Cornell University) (preprint, 2019-08-31)
Field: Materials Science — Materials Chemistry

Gated Graph Recursive Neural Networks for Molecular Property Prediction Molecule property prediction is a fundamental problem for computer-aided drug discovery and materials science. Quantum-chemical simulations such as density functional theory (DFT) have been widely used for calculating the molecule properties, however, because of the heavy computational cost, it is difficult to search a huge number of potential chemical compounds. Machine learning methods for molecular modeling are attractive alternatives, however, the development of expressive, accurate, and scalable graph neural networks for learning molecular representations is still challenging. In this work, we propose a simple and powerful graph neural networks for molecular property prediction. We model a molecular as a directed complete graph in which each atom has a spatial position, and introduce a recursive neural network with simple gating function. We also feed input embeddings for every layers as skip connections to accelerate the training. Experimental results show that our model achieves the state-of-the-art performance on the standard benchmark dataset for molecular property prediction. Computer science ; Molecular graph ; Scalability ; Artificial neural network ; Property (philosophy) ; Benchmark (surveying) ; Graph ; Theoretical computer science ; Simple (philosophy) ; Artificial intelligence ; Quantum chemical ; Graph theory ; Machine learning ; Topology (electrical circuits) ; Molecule ; Mathematics ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Chao Feng | Edward Sharman | Sheng Ye | Yi Luo | Jun Jiang
Source: Science China Chemistry (article, 2019-11-06)
Field: Materials Science — Materials Chemistry

A neural network protocol for predicting molecular bond energy Flexibility (engineering) ; Artificial neural network ; Computer science ; Protocol (science) ; Bond energy ; Chemical bond ; Triple bond ; Energy (signal processing) ; Molecule ; Chemistry ; Biological system ; Computational chemistry ; Artificial intelligence ; Physics ; Double bond ; Mathematics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Shuangjia Zheng | Jiahua Rao | Zhongyue Zhang | Jun Xu | Yuedong Yang
Source: ChemRxiv (preprint, 2019-07-03)
Field: Materials Science — Materials Chemistry

Predicting Retrosynthetic Reaction using Self-Corrected Transformer Neural Networks Synthesis planning is the process of recursively decomposing target molecules into available precursors. Computer-aided retrosynthesis can potentially assist chemists in designing synthetic routes, but at present it is cumbersome and provides results of dissatisfactory quality. In this study, we develop a template-free self-corrected retrosynthesis predictor (SCROP) to perform a retrosynthesis prediction task trained by using the Transformer neural network architecture. In the method, the retrosynthesis planning is converted as a machine translation problem between molecular linear notations of reactants and the products. Coupled with a neural network-based syntax corrector, our method achieves an accuracy of 59.0% on a standard benchmark dataset, which increases >21% over other deep learning methods, and >6% over template-based methods. More importantly, our method shows an accuracy 1.7 times higher than other state-of-the-art methods for compounds not appearing in the training set. Retrosynthetic analysis ; Transformer ; Computer science ; Artificial neural network ; Artificial intelligence ; Benchmark (surveying) ; Machine translation ; Notation ; Machine learning ; Engineering ; Mathematics ; Voltage ; Arithmetic Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemistry and Chemical Engineering
Authors: Mihail Bogojeski | Leslie Vogt-Maranto | Mark E. Tuckerman | Klaus-Robert Müller | Kieron Burke
Source: ChemRxiv (preprint, 2019-05-06)
Field: Materials Science — Materials Chemistry

Density Functionals with Quantum Chemical Accuracy: From Machine Learning to Molecular Dynamics Kohn-Sham density functional theory (DFT) is a standard tool in most branches of chemistry, but accuracies for many molecules are limited to 2-3 kcal/mol with presently-available functionals. Ab initio methods, such as coupled-cluster, routinely produce much higher accuracy, but computational costs limit their application to small molecules. We create density functionals from coupled-cluster energies, based only on DFT densities, via machine learning. These functionals attain quantum chemical accuracy (errors below 1 kcal/mol). Moreover, density-based ∆-learning (learning only the correction to a standard DFT calculation, ∆-DFT) significantly reduces the amount of training data required. We demonstrate these concepts for a single water molecule, and then illustrate how to include molecular symmetries with ethanol. Finally, we highlight the robustness of ∆-DFT by correcting DFT simulations of resorcinol on the fly to obtain molecular dynamics (MD) trajectories with coupled-cluster accuracy. Thus ∆-DFT opens the door to running gas-phase MD simulations with quantum chemical accuracy, even for strained geometries and conformer changes where standard DFT is quantitatively incorrect. Density functional theory ; Coupled cluster ; Quantum chemistry ; Quantum chemical ; Molecular dynamics ; Ab initio ; Molecule ; Robustness (evolution) ; Cluster (spacecraft) ; Quantum ; Statistical physics ; Computer science ; Computational chemistry ; Chemistry ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Catalysis and Oxidation Reactions