jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Keke Meng | Tingyan Li | Chunbing Yu | Cong Shen | Jian Zhang | Guofu Zhong
Source: Nature Communications (article, 2019-11-08)
Field: Chemistry — Organic Chemistry

Geminal group-directed olefinic C-H functionalization via four- to eight-membered exo-metallocycles Great efforts have been made in the activation of a C(alkenyl)-H bond vicinal to the directing group to proceed via five- or six-membered endo-metallocycles. In stark contrast, functionalization of a C(alkenyl)-H bond geminal to the directing group via exo-metallocycle pathway continued to be elusive. Here we report the selective transformation of an olefinic C-H bond that is geminal to the directing group bearing valuable hydroxyl, carbamate or amide into a C-C bond, which proceeds through four- to eight-membered exo-palladacycles. Compared to the reported mechanisms proceeding only through six-membered exo-palladacycles via N,N-bidentate chelation, our weak and O-monodentate chelation-assisted C(alkenyl)-H activations tolerate longer or shorter distances between the olefinic C-H bonds and the coordinating groups, allowing for the functionalizations of many olefinic C-H bonds in alkenyl alcohols, carbamates and amides. The synthetic applicability has been demonstrated by the preparative scale and late-stage C-H functionalization of steroid and ricinoleate derivatives. Geminal ; Chemistry ; Vicinal ; Amide ; Surface modification ; Stereochemistry ; Chelation ; Denticity ; Medicinal chemistry ; Double bond ; Organic chemistry ; Metal Catalytic C–H Functionalization Methods ; Synthesis and Catalytic Reactions ; Cyclopropane Reaction Mechanisms
Authors: Van‐Dung Mai | Dae-Il Lee | Jun Hong Park | Dai-Soo Lee
Source: Polymers (article, 2019-04-02)
Field: Materials Science — Materials Chemistry

Rheological Properties and Thermal Conductivity of Epoxy Resins Filled with a Mixture of Alumina and Boron Nitride Electronic packaging materials with high thermal conductivity and suitable viscosity are necessary in the manufacturing of highly integrated electronic devices for efficient heat dissipation during operation. This study looked at the effect of boron nitride (BN) platelets on the rheology and thermal conductivity of composites based on alumina (Al₂O₃) and epoxy resin (EP) for the potential application as electronic packaging. The viscosity and thermal conductivity of the composite were increased upon increasing filler content. Furthermore, thermal conductivity of the BN/Al₂O₃/EP was much higher than that of Al₂O₃/EP at almost the same filler loadings. These unique properties resulted from the high thermal conductivity of the BN and the synergistic effect of the spherical and plate shapes of these two fillers. The orientation of BN platelets can be controlled by adjusting their loading to facilitate the formation of higher thermally conductive pathways. The optimal content of the BN in the Al₂O₃/EP composites was confirmed to be 5.3 vol %, along with the maximum thermal conductivity of 4.4 W/(m·K). Thermal conductivity ; Materials science ; Composite material ; Boron nitride ; Epoxy ; Rheology ; Viscosity ; Composite number ; Thermal conduction ; Filler (materials) ; Conductivity ; Chemistry Thermal properties of materials ; High voltage insulation and dielectric phenomena ; Epoxy Resin Curing Processes
Authors: Jeffrey J. Urban | Akanksha K. Menon | Zhiting Tian | Anubhav Jain | Kedar Hippalgaonkar
Source: Journal of Applied Physics (article, 2019-05-14)
Field: Materials Science — Materials Chemistry

New horizons in thermoelectric materials: Correlated electrons, organic transport, machine learning, and more Thermoelectrics represent a unique opportunity in energy to directly convert thermal energy or secondary waste heat into a primary resource. The development of thermoelectric materials has improved over the decades in leaps, rather than by increments—each leap forward has recapitulated the science of its time: from the crystal growth of semiconductors, to controlled doping, to nanostructuring, and to 2D confinement. Each of those leaps forward was, arguably, more a result of materials science than physics. Thermoelectrics is now ripe for another leap forward, and many probable advances rely on new physics outside of the standard band transport model of thermoelectrics. This perspective will cover a limited selection of how thermoelectrics can benefit from new discoveries in physics: wave effects in phonon transport, correlated electron physics, and unconventional transport in organic materials. We also highlight recent developments in thermoelectrics discovery aided by machine learning that may be needed to realize some of these new concepts practically. Looking ahead, developing new thermoelectric physics will also have a concomitant domino effect on adjacent fields, furthering the understanding of nonequilibrium thermal and electronic transport in novel materials. Thermoelectric materials ; LEAPS ; Thermoelectric effect ; Engineering physics ; Nanotechnology ; Materials science ; Physics Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; Thermal properties of materials
Authors: Xiaokun Gu | Changying Zhao
Source: Computational Materials Science (article, 2019-04-24)
Field: Materials Science — Materials Chemistry

Thermal conductivity of single-layer MoS2(1−x)Se2x alloys from molecular dynamics simulations with a machine-learning-based interatomic potential Interatomic potential ; Phonon ; Thermal conductivity ; Molecular dynamics ; Materials science ; Multiscale modeling ; Condensed matter physics ; Superlattice ; Chemical physics ; Chemistry ; Computational chemistry ; Optoelectronics ; Physics ; Composite material Thermal properties of materials ; 2D Materials and Applications ; Machine Learning in Materials Science
Authors: Jatin Kumar | Qianxiao Li | Jun Ye
Source: MRS Communications (article, 2019-05-03)
Field: Materials Science — Materials Chemistry

Challenges and opportunities of polymer design with machine learning and high throughput experimentation Throughput ; Perspective (graphical) ; Status quo ; Materials science ; Polymer ; Scale (ratio) ; Computer science ; Nanotechnology ; Systems engineering ; Machine learning ; Artificial intelligence ; Engineering Machine Learning in Materials Science ; Advanced Polymer Synthesis and Characterization ; Software Engineering Research
Authors: Christopher M. Childs | Newell R. Washburn
Source: MRS Communications (article, 2019-07-10)
Field: Materials Science — Materials Chemistry

Embedding domain knowledge for machine learning of complex material systems Interpretability ; Embedding ; Inference ; Domain (mathematical analysis) ; Computer science ; Machine learning ; Domain knowledge ; Artificial intelligence ; Complex system ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Neural Networks and Applications
Authors: Niklas W. A. Gebauer | Michael Gastegger | Kristof T. Schütt
Source: arXiv (Cornell University) (preprint, 2019-06-02)
Field: Materials Science — Materials Chemistry

Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules Deep learning has proven to yield fast and accurate predictions of quantum-chemical properties to accelerate the discovery of novel molecules and materials. As an exhaustive exploration of the vast chemical space is still infeasible, we require generative models that guide our search towards systems with desired properties. While graph-based models have previously been proposed, they are restricted by a lack of spatial information such that they are unable to recognize spatial isomerism and non-bonded interactions. Here, we introduce a generative neural network for 3d point sets that respects the rotational invariance of the targeted structures. We apply it to the generation of molecules and demonstrate its ability to approximate the distribution of equilibrium structures using spatial metrics as well as established measures from chemoinformatics. As our model is able to capture the complex relationship between 3d geometry and electronic properties, we bias the distribution of the generator towards molecules with a small HOMO-LUMO gap - an important property for the design of organic solar cells. Cheminformatics ; Chemical space ; Computer science ; Generative grammar ; Theoretical computer science ; Graph ; Artificial intelligence ; Point (geometry) ; Generative model ; Maxima and minima ; Biological system ; Mathematics ; Drug discovery ; Computational chemistry ; Chemistry ; Geometry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Guorong Chen | Tiange Li | Qijun Chen | Shaofei Ren | Chao Wang | Shaofan Li
Source: Computational Mechanics (article, 2019-04-17)
Field: Materials Science — Materials Chemistry

Application of deep learning neural network to identify collision load conditions based on permanent plastic deformation of shell structures Artificial neural network ; Shell (structure) ; Deformation (meteorology) ; Structural engineering ; Collision ; Computational Science and Engineering ; Computer science ; Artificial intelligence ; Materials science ; Engineering ; Mechanical engineering ; Machine learning ; Composite material Material Properties and Failure Mechanisms ; High-Velocity Impact and Material Behavior ; Structural Integrity and Reliability Analysis
Authors: Chia-Hao Liu | Yunzhe Tao | Daniel Hsu | Qiang Du | Simon J. L. Billinge
Source: Acta Crystallographica Section A Foundations and Advances (article, 2019-06-26)
Field: Materials Science — Materials Chemistry

Using a machine learning approach to determine the space group of a structure from the atomic pair distribution function A method is presented for predicting the space group of a structure given a calculated or measured atomic pair distribution function (PDF) from that structure. The method utilizes machine learning models trained on more than 100 000 PDFs calculated from structures in the 45 most heavily represented space groups. In particular, a convolutional neural network (CNN) model is presented which yields a promising result in that it correctly identifies the space group among the top-6 estimates 91.9% of the time. The CNN model also successfully identifies space groups for 12 out of 15 experimental PDFs. Interesting aspects of the failed estimates are discussed, which indicate that the CNN is failing in similar ways as conventional indexing algorithms applied to conventional powder diffraction data. This preliminary success of the CNN model shows the possibility of model-independent assessment of PDF data on a wide class of materials. Convolutional neural network ; Space (punctuation) ; Group (periodic table) ; Artificial neural network ; Function (biology) ; Distribution (mathematics) ; Pattern recognition (psychology) X-ray Diffraction in Crystallography ; Thermal Expansion and Ionic Conductivity ; Machine Learning in Materials Science
Authors: Musen Zhou | Anthony Vassallo | Jian Wu
Source: Journal of Membrane Science (article, 2019-11-17)
Field: Chemistry — Inorganic Chemistry

Toward the inverse design of MOF membranes for efficient D2/H2 separation by combination of physics-based and data-driven modeling Isotopologue ; Gas separation ; Separation (statistics) ; Membrane ; Nanoporous ; Metal-organic framework ; Process engineering ; Isotope separation ; Hydrogen ; Materials science ; Adsorption ; Computer science ; Nanotechnology ; Chemistry ; Isotope ; Physics ; Engineering ; Machine learning ; Molecule ; Nuclear physics ; Physical chemistry ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Membrane Separation and Gas Transport ; Hydrogen Storage and Materials
Authors: Mojtaba Arjomandi | Hamid Shirkhanloo
Source: Analytical Methods in Environmental Chemistry Journal (article, 2019-09-01)
Field: Chemistry — Electrochemistry

A review: Analytical methods for heavy metals determination in environment and human samples A Review: Analytical methods for heavy metals determination in environment and human samples Many different techniques have been applied for determination of heavy metal in different samples. Atomic absorption spectrometry (AAS) is a quantitative method of metals analysis which was suitable for the determination of 70 elements by three option accessory (flame, furnace, Hydride generation). AAS measures the concentration of the metals by passing light (wave length emitted) by a radiation source of Heavy metals ; Environmental science ; Environmental chemistry ; Chemistry Electrochemical Analysis and Applications ; Analytical chemistry methods development ; Extraction and Separation Processes
Authors: Fengjie Zhao | Xiangsheng Liu | Annie Kong | Yuxin Zhao | Fan Xu | Ting Ma | Weixia Gao | Shufang Wang | Chao Yang
Source: Scientific Reports (article, 2019-02-12)
Field: Materials Science — Biomaterials

Screening of endogenous strong promoters for enhanced production of medium-chain-length polyhydroxyalkanoates in Pseudomonas mendocina NK-01 Polyhydroxyalkanoate (PHA) can be produced by microorganisms from renewable resources and is regarded as a promising bioplastic to replace petroleum-based plastics. Pseudomonas mendocina NK-01 is a medium-chain-length PHA (mcl-PHA)-producing strain and its whole-genome sequence is currently available. The yield of mcl-PHA in P. mendocina NK-01 is expected to be improved by applying a promoter engineering strategy. However, a limited number of well-characterized promoters has greatly restricted the application of promoter engineering for increasing the yield of mcl-PHA in P. mendocina NK-01. In this work, 10 endogenous promoters from P. mendocina NK-01 were identified based on RNA-seq and promoter prediction results. Subsequently, 10 putative promoters were characterized for their strength through the expression of a reporter gene gfp. As a result, five strong promoters designated as P4, P6, P9, P16 and P25 were identified based on transcriptional level and GFP fluorescence intensity measurements. To evaluate whether the screened promoters can be used to enhance transcription of PHA synthase gene (phaC), the three promoters P4, P6 and P16 were separately integrated into upstream of the phaC operon in the genome of P. mendocina NK-01, resulting in the recombinant strains NKU-4C1, NKU-6C1 and NKU-16C1. As expected, the transcriptional levels of phaC1 and phaC2 in the recombinant strains were increased as shown by real-time quantitative RT-PCR. The phaZ gene encoding PHA depolymerase was further deleted to construct the recombinant strains NKU-∆phaZ-4C1, NKU-∆phaZ-6C1 and NKU-∆phaZ-16C1. The results from shake-flask fermentation indicated that the mcl-PHA titer of recombinant strain NKU-∆phaZ-16C1 was increased from 17 to 23 wt% compared with strain NKU-∆phaZ. This work provides a feasible method to discover strong promoters in P. mendocina NK-01 and highlights the potential of the screened endogenous strong promoters for metabolic engineering of P. mendocina NK-01 to increase the yield of mcl-PHA. Polyhydroxyalkanoates ; Endogeny ; Promoter ; Pseudomonas ; Production (economics) ; Microbiology ; Chemistry ; Biology ; Genetics ; Bacteria ; Biochemistry ; Gene ; Gene expression biodegradable polymer synthesis and properties ; Microplastics and Plastic Pollution ; Enzyme Catalysis and Immobilization
Authors: Janne Soininen | Anette Teittinen
Source: Freshwater Biology (article, 2019-08-12)
Field: Materials Science — Biomaterials

Fifteen important questions in the spatial ecology of diatoms Abstract Recently, studies about the spatial distribution of microbial species and communities have increased substantially. Freshwater diatoms are used as one of the most popular model taxa in such macroecological studies, but the most exciting patterns in the spatial ecology of diatoms have not been reviewed. Here, we identify 15 important questions about freshwater diatom ecology, review the major findings, and suggest novel research avenues. We found that diatoms do not support classical latitudinal diversity gradient, species–area relationship or Rapoport's rule. We found some, albeit inconsistent support for an elevational diversity gradient, Bergmann's rule and niche conservation. Diatoms do support a positive interspecific abundance–occupancy relationship like nearly all other taxa. With regards to metacommunity dynamics, diatom communities are typically structured by species sorting, but neutral dynamics or mass effects are also evident at large or small spatial scale, respectively. Taken together, this review suggests that diatoms follow some classical biogeographical patterns similarly to larger taxa, but patterns are typically weaker than for macro‐organisms. This may result because diatoms are typically relatively strongly controlled by local factors and often exhibit efficient stochastic dispersal processes both at small and large scales. More research is needed to build a more comprehensive view of diatom species–area relationships and latitudinal gradients in diversity, for example. In the future, well‐replicated macroecological field experiments, diatom studies with intercontinental or global coverage, trait‐based approaches, and DNA ‐based species identification would be fruitful avenues to shed more light into the spatial ecology of diatoms. Metacommunity ; Ecology ; Macroecology ; Diatom ; Biological dispersal ; Biology ; Spatial ecology ; Niche ; Abundance (ecology) ; Beta diversity ; Interspecific competition ; Taxon ; Biodiversity ; Population Diatoms and Algae Research ; Freshwater macroinvertebrate diversity and ecology ; Aquatic Invertebrate Ecology and Behavior
Authors: Lujun Wang | Jiong Guo | Chen Xu | Tiezhou Wu | Huipin Lin
Source: Energies (article, 2019-06-04)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Hybrid Model Predictive Control Strategy of Supercapacitor Energy Storage System Based on Double Active Bridge In order to solve the problem of which the dynamic response of a supercapacitor (SC) is limited due to the mismatch dynamic characteristics between the DC/DC converter and supercapacitor in an energy storage system, this paper proposes a hybrid model predictive control strategy based on a dual active bridge (DAB). The hybrid model predictive control model considers the supercapacitor and DAB in a unified way, including the equivalent series resistance and capacitance parameters of the SC. The method can obtain a large charging and discharging current of the SC, thereby not only improving the overall response speed of the system, but also expanding the actual capacity utilization range of the SC. The simulation results show that compared with the model prediction method of the dual active bridge converter, the proposed control method can effectively improve the overall response speed of the system, which can be improved by at least 0.4 ms. In addition, the proposed method increases the actual upper limit of the SC voltage, reduces the actual lower limit of the SC voltage, and then expands the actual capacity utilization range of the SC by 18.63%. The proposed method has good application prospects in improving the dynamic response performance of energy storage systems. Supercapacitor ; Model predictive control ; Energy storage ; Control theory (sociology) ; Voltage ; Limit (mathematics) ; Capacitance ; Dual (grammatical number) ; Computer science ; Range (aeronautics) ; Engineering ; Control (management) ; Power (physics) ; Electrical engineering ; Chemistry ; Mathematics Supercapacitor Materials and Fabrication ; Microgrid Control and Optimization ; Electric and Hybrid Vehicle Technologies
Authors: Ángel Morales‐García | Marc Mayans-Llorach | Francesc Viñes | Francesc Illas
Source: Physical Chemistry Chemical Physics (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

Thickness biased capture of CO<sub>2</sub> on carbide MXenes The synthesis of two-dimensional transition metal carbides (MXenes) with a predefined number of atomic layers offers a possible way to tune these nanomaterials chemical activity. MXenes have been theoretically predicted to be able to store CO2 even at high temperatures and low CO2 partial pressures, a prediction which has been experimentally confirmed afterwards. In the present work, the influence of the number of atomic layers on CO2 adsorption is systematically investigated by means of density functional theory based calculations, using suitable periodic models representing the (0001) surface of a series of these materials with formula Mn+1Cn (M = Ti, Zr, Hf, V, Nb, Ta, Mo, W) and n = 1-3. The interaction of CO2 with the MXene surfaces is always favorable with the adsorption energy decreasing as the transition metal electronic configuration goes from d2 through d3 to d4, in agreement with previous work for n = 1. The influence of the thickness is found to be rather small, yet noticeable, although somewhat erratic. Nevertheless, the adsorption energy seems to converge to a defined clear limit for sufficiently thick MXenes. Interestingly, this value is close to that corresponding to the (111) surface of bulk Transition Metal Carbides (TMCs). The close structural similarity between the MXene (0001) and TMC (111) surfaces strongly suggests that the former provide a practical way to approach this otherwise unstable surface. The possibility to tune the CO2 interaction based on the MXene thickness is further investigated by means of kinetic phase diagrams. These provide additional evidence that carbide MXene surfaces are promising materials for CO2 capture even at low CO2 partial pressures, and that the MXene thickness can be used to fine tune this appealing behavior. MXenes ; Carbide ; MAX phases ; Titanium carbide ; Materials science ; Nanotechnology ; Metallurgy MXene and MAX Phase Materials ; Graphene research and applications ; Advanced Photocatalysis Techniques
Authors: Xiao‐Tong He | Yang‐Hui Luo | Zi‐Yue Zheng | Cong Wang | Jiaying Wang | Dan‐Li Hong | Lihai Zhai | Lihong Guo | Bai‐Wang Sun
Source: ACS Applied Nano Materials (article, 2019-10-30)
Field: Chemistry — Inorganic Chemistry

Porphyrin-Based Hydrogen-Bonded Organic Frameworks for the Photocatalytic Degradation of 9,10-Diphenylanthracene Fine tuning of the structures and properties of hydrogen-bonded organic frameworks (HOFs) has been considered to be a huge challenge. Herein, we reported the controlled synthesis of meso-tetrakis(carboxyphenyl)porphyrin (TCPP)-based HOFs via a “counterstrategy” of blocking the strong hydrogen-bonded building units on the TCPP backbones. Mediated by the temperatures, both the kinetically and thermodynamically favored HOF products TCPP-2(DMF), TCPP-4(DMF), and TCPP-6(DMF) can be obtained under 120, 130, and 140 °C, respectively. Results revealed that the numbers and binding positions of end-capped N,N-dimethylformamide (DMF) molecules not only played a key role in the thermal stability and permanent porosity of the present HOFs but also affected significantly the photocatalytic activities for the degradation of 9,10-diphenylanthracene, where a positive correlation between the numbers of DMF molecules and the photocatalytic activities has been established, which can be attributed to variation of the intermolecular van der Waals forces. In addition, upon removal of the lattice water and extra DMF molecules, respectively, HOFs TCPP-2(DMF) and TCPP-6(DMF) can be transformed into a polymorph TCPP-4(DMF)* in the solid state, suggesting a dynamic TCPP-DMF skeleton. This work will promote significantly the development of porphyrin-based HOFs and porphyrin-based photocatalysis materials. Porphyrin ; Photocatalysis ; van der Waals force ; Molecule ; Photochemistry ; Intermolecular force ; Hydrogen bond ; Chemistry ; Materials science ; Catalysis ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Luminescence and Fluorescent Materials