jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Bijan Medi | Alireza Bahramian | Vahide Nazari
Source: JOM (article, 2020-09-09)
Field: Materials Science — Polymers and Plastics

Synthesis and Characterization of Conducting Polyaniline Nanostructured Thin Films for Solar Cell Applications Polyaniline ; Materials science ; Characterization (materials science) ; Scanning electron microscope ; Solar cell ; Thin film ; Conductivity ; Transparency (behavior) ; Organic solar cell ; Nanotechnology ; Profilometer ; Optoelectronics ; Composite material ; Computer science ; Chemistry ; Polymer Conducting polymers and applications ; Organic Electronics and Photovoltaics ; Advanced Sensor and Energy Harvesting Materials
Authors: Rafał Topolnicki | Fabien Brieuc | Christoph Schran | Dominik Marx
Source: Journal of Chemical Theory and Computation (article, 2020-09-22)
Field: Chemistry — Spectroscopy

Deciphering High-Order Structural Correlations within Fluxional Molecules from Classical and Quantum Configurational Entropy We employ the <i>k</i>th nearest-neighbor estimator of configurational entropy in order to decode within a parameter-free numerical approach the complex high-order structural correlations in fluxional molecules going much beyond the usual linear, bivariate correlations. This generic entropy-based scheme for determining many-body correlations is applied to the complex configurational ensemble of protonated acetylene, a prototype for fluxional molecules featuring large-amplitude motion. After revealing the importance of high-order correlations beyond the simple two-coordinate picture for this molecule, we analyze in detail the evolution of the relevant correlations with temperature as well as the impact of nuclear quantum effects down to the ultralow temperature regime of 1 K. We find that quantum delocalization and zero-point vibrations significantly reduce all correlations in protonated acetylene in the deep quantum regime. Even at low temperatures up to about 100 K, most correlations are essentially absent in the quantum case and only gain importance at higher temperatures. In the high temperature regime, beyond roughly 800 K, the increasing thermal fluctuations are found to exert a destructive effect on the presence of correlations. At intermediate temperatures of approximately 100-800 K, a quantum-to-classical cross-over regime is found where classical mechanics starts to correctly describe trends in the correlations whereas it even qualitatively fails below 100 K. Finally, a classical description of the nuclei provides correlations that are in quantitative agreement with the quantum ones only at temperatures exceeding 1000 K. This data-intensive analysis has been made possible due to recent developments of machine learning techniques based on high-dimensional neural network potential energy surfaces in full dimensionality that allow us to exhaustively sample both the classical and quantum ensemble of protonated acetylene at essentially converged coupled cluster accuracy from 1 to more than 1000 K. The presented non-parametric analysis of correlations beyond usual linear two-coordinate terms is transferable to other system classes. The technique is also expected to complement and guide the analysis of experimental measurements, in particular multidimensional vibrational spectroscopy, by revealing the complex coupling between various degrees of freedom. Statistical physics ; Configuration entropy ; Quantum ; Entropy (arrow of time) ; Physics ; Order (exchange) ; Quantum mechanics Molecular spectroscopy and chirality ; Machine Learning in Materials Science ; Advanced NMR Techniques and Applications
Authors: Song Wang | Yi Li | Sheng Dai | De‐en Jiang
Source: Angewandte Chemie (article, 2020-06-02)
Field: Chemistry — Inorganic Chemistry

Prediction by Convolutional Neural Networks of CO<sub>2</sub>/N<sub>2</sub> Selectivity in Porous Carbons from N<sub>2</sub> Adsorption Isotherm at 77 K Abstract Porous carbons are an important class of porous materials with many applications, including gas separation. An N 2 adsorption isotherm at 77 K is the most widely used approach to characterize porosity. Conventionally, textual properties such as surface area and pore volumes are derived from the N 2 adsorption isotherm at 77 K by fitting it to adsorption theory and then correlating it to gas separation performance (uptake and selectivity). Here the N 2 isotherm at 77 K was used directly as input (representing feature descriptors for the porosity) to train convolutional neural networks to predict gas separation performance (using CO 2 /N 2 as a test case) for porous carbons. The porosity space for porous carbons was explored for higher CO 2 /N 2 selectivity. Porous carbons with a bimodal pore‐size distribution of well‐separated mesopores (3–7 nm) and micropores (&lt;2 nm) were found to be most promising. This work will be useful in guiding experimental research of porous carbons with the desired porosity for gas separation and other applications. Porosity ; Adsorption ; Selectivity ; Mesoporous material ; Materials science ; Sorption isotherm ; Porous medium ; Chemical engineering ; Characterisation of pore space in soil ; Chemistry ; Physical chemistry ; Organic chemistry ; Catalysis ; Composite material Metal-Organic Frameworks: Synthesis and Applications ; Carbon Dioxide Capture Technologies ; Membrane Separation and Gas Transport
Authors: Dongdong Zhang | Xin Liu | Chengshun Yang | Ni Lianghua | Xiaoning Huang | Chao Zhang
Source: Science of Advanced Materials (article, 2020-10-01)
Field: Materials Science — Materials Chemistry

Study on the Lifespan Prediction of Silicon Rubber Nanocomposites Under High Humidity and High Temperature Environment In this paper, the silicon rubber nanocomposites samples cut from the composite insulators operating in high humidity and high temperature areas for 0–13 years were taken as the research object. In accordance to previous research experiences, test methods such as static contact angle method, hardness test method were employed to investigate the changing law of lifespan prediction parameters with operating time. Based on test results, some lifespan prediction parameters significantly correlated with operating time were filtered by means of correlation calculation. On this basis, a prediction method which can be used to determine the operating time of the nanocomposites was proposed based on BP neural network. Test results indicate that lifespan prediction parameters including H C , θ , A , T , H , X O were significantly correlated with the operating time of the insulation material from composite insulator, and these parameters can be used to characterize the aging degree accurately. Besides, due to the high accuracy in experimental verification, the lifespan prediction method proposed in this paper can be used to determine the operating time of composite insulators from transmission lines in future research. Materials science ; Silicon rubber ; Operating temperature ; Natural rubber ; Humidity ; Composite material ; Nanocomposite ; Research Object ; Accelerated aging ; Electrical engineering ; Engineering ; Thermodynamics High voltage insulation and dielectric phenomena
Authors: Mark P. Oxley | Jia‐Xin Yin | Nikolay Borodinov | Suhas Somnath | Maxim Ziatdinov | Andrew R. Lupini | Stephen Jesse | Rama K. Vasudevan | S.V. Kalinin
Source: Machine Learning Science and Technology (article, 2020-07-06)
Field: Materials Science — Materials Chemistry

Deep learning of interface structures from simulated 4D STEM data: cation intermixing vs. roughening <sup> <sup>∗</sup> </sup> Abstract Interface structures in complex oxides remain an active area of condensed matter physics research, largely enabled by recent advances in scanning transmission electron microscopy (STEM). Yet the nature of the STEM contrast in which the structure is projected along the given direction precludes separation of possible structural models. Here, we utilize deep convolutional neural networks (DCNN) trained on simulated 4D STEM datasets to predict structural descriptors of interfaces. We focus on the widely studied interface between LaAlO 3 and SrTiO 3 , using dynamical diffraction theory and leveraging high performance computing to simulate thousands of possible 4D STEM datasets to train the DCNN to learn properties of the underlying structures on which the simulations are based. We test the DCNN on simulated data and show that it is possible (with &gt;95% accuracy) to identify a physically rough from a chemically diffuse interface and create a DCNN regression model to predict step positions. We quantify the applicability of the model to different thicknesses and the transferability of the approach. The method shown here is general and can be applied for any inverse imaging problem where forward models are present. Transferability ; Interface (matter) ; Convolutional neural network ; Focus (optics) ; Computer science ; Deep learning ; Diffraction ; Scanning transmission electron microscopy ; Artificial intelligence ; Materials science ; Inverse problem ; Biological system ; Transmission electron microscopy ; Physics ; Machine learning ; Optics ; Nanotechnology ; Mathematics ; Parallel computing Electronic and Structural Properties of Oxides ; Machine Learning in Materials Science ; Advanced X-ray and CT Imaging
Authors: Wentao Xu | Yang Jiao | Jacob Fish
Source: Computational Mechanics (article, 2020-05-08)
Field: Materials Science — Materials Chemistry

An atomistically-informed multiplicative hyper-elasto-plasticity-damage model for high-pressure induced densification of silica glass Plasticity ; Materials science ; Peridynamics ; Mechanics ; Amorphous solid ; Molecular dynamics ; Deformation (meteorology) ; Shear (geology) ; Thermodynamics ; Composite material ; Continuum mechanics ; Physics ; Crystallography High-Velocity Impact and Material Behavior ; High-pressure geophysics and materials ; Metal and Thin Film Mechanics
Authors: Zhenpeng Yao | Benjamín Sánchez-Lengeling | N. Scott Bobbitt | Benjamin J. Bucior | Sai Govind Hari Kumar | Sean P. Collins | Thomas Burns | Tom K. Woo | Omar K. Farha | Randall Q. Snurr | Alán Aspuru‐Guzik
Source: ChemRxiv (preprint, 2020-04-27)
Field: Chemistry — Inorganic Chemistry

Inverse Design of Nanoporous Crystalline Reticular Materials with Deep Generative Models Reticular frameworks are crystalline porous materials that form via the self-assembly of molecular building blocks (i.e., nodes and linkers) in different topologies. Many of them have high internal surface areas and other desirable properties for gas storage, separation, and other applications. The notable variety of the possible building blocks and the diverse ways they can be assembled endow reticular frameworks with a near-infinite combinatorial design space, making reticular chemistry both promising and challenging for prospective materials design. Here, we propose an automated nanoporous materials discovery platform powered by a supramolecular variational autoencoder (SmVAE) for the generative design of reticular materials with desired functions. We demonstrate the automated design process with a class of metal-organic framework (MOF) structures and the goal of separating CO2 from natural gas or flue gas. Our model exhibits high fidelity in capturing structural features and reconstructing MOF structures. We show that the autoencoder has a promising optimization capability when jointly trained with multiple top adsorbent candidates identified for superior gas separation. MOFs discovered here are strongly competitive against some of the best-performing MOFs/zeolites ever reported. This platform lays the groundwork for the design of reticular frameworks for desired applications. Autoencoder ; Computer science ; Nanoporous ; Reticular connective tissue ; Nanotechnology ; Materials science ; Artificial intelligence ; Deep learning Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Zeolite Catalysis and Synthesis
Authors: Cyrille Lavigne | Gabriel dos Passos Gomes | Robert Pollice | Alán Aspuru‐Guzik
Source: ChemRxiv (preprint, 2020-11-16)
Field: Materials Science — Materials Chemistry

Automatic discovery of chemical reactions using imposed activation Computational power and quantum chemical methods have improved immensely since computers were first applied to the study of reactivity, but the de novo prediction of chemical reactions has remained challenging. We show that complex reactions can be efficiently and autonomously predicted using chemical activation imposed by simple geometrical constraints. Our approach is demonstrated on realistic and challenging chemistry, such as a triple cyclization cascade involved in the total synthesis of a natural product and several oxidative addition reactions of complex drug-like molecules. Notably and in contrast with traditional hand-guided computational chemistry calculations, our method requires minimal human involvement and no prior knowledge of products or mechanisms. Imposed activation can be a transformational tool to screen for chemical reactivity and mechanisms as well as to study byproduct formation and decomposition. Reactivity (psychology) ; Chemical reaction ; Drug discovery ; Chemistry ; Natural product ; Biochemical engineering ; Decomposition ; Computer science ; Combinatorial chemistry ; Computational chemistry ; Molecule ; Quantum chemical ; Biological system ; Organic chemistry ; Engineering Machine Learning in Materials Science ; Various Chemistry Research Topics ; Microbial Natural Products and Biosynthesis
Authors: Lin‐Qiang Mo | Xianfei Huang | Gaocai Wang | Guan Huang | Peng Liu
Source: Scientific Reports (article, 2020-08-24)
Field: Materials Science — Materials Chemistry

Full use of factors promoting catalytic performance of chitosan supported manganese porphyrin In order to make full use of the impact of internal and external factors on the performance of title catalyst for ethyl benzene oxidation, the key internal influencing factors on the catalytic performance were modulated by coordinating and grafting manganese porphyrin to mesoporous and macroporous chitosan, and the important external factors (i.e. oxidation reaction conditions) were optimized using Response Surface Methodology. Under the Response Surface Methodology optimized oxidation reaction conditions (176.56 °C, 0.59 MPa, and 0.25 mg amount of manganese porphyrin), the catalyst could be used at least five times. The ethyl benzene conversion, catalyst turnover numbers, and yields reached up to 51.2%, 4.37 × 10<sup>6</sup> and 36.4% in average, respectively. Compared with the other optimized oxidation reaction conditions, the corresponding values increased 17%, 26% and 53%. Relative to the manganese porphyrin, the catalytic performance and efficiency of the immobilized catalyst had notably increased. Porphyrin ; Catalysis ; Manganese ; Chitosan ; Benzene ; Mesoporous material ; Chemistry ; Response surface methodology ; Catalytic oxidation ; Grafting ; Combinatorial chemistry ; Nuclear chemistry ; Photochemistry ; Organic chemistry ; Chromatography Porphyrin and Phthalocyanine Chemistry ; Electrocatalysts for Energy Conversion ; Oxidative Organic Chemistry Reactions
Authors: Tomiwa Oguntade | Christiana S. Ita | Oluwasanmi Olabode | Daniel T. Oyekunle
Source: The Open Chemical Engineering Journal (article, 2020-04-22)
Field: Materials Science — Materials Chemistry

A Binary Mixture of Sesame And Castor Oil as an Ecofriendly Corrosion Inhibitor of Mild Steel In Crude Oil Background: A binary mixture of sesame and castor oil was used for reducing the corrosion rate of mild steel in crude oil environments. This study investigated the corrosion behavior of a binary mixture of sesame and castor oil as a corrosion inhibitor for mild steel in crude oil. Different parameters such as immersion time, the concentration of inhibitor and pH were investigated for corrosion of mild steel. Methods: Experimental analysis indicates that a passive layer of the inhibitor formed over the surface of mild steel thereby reducing the corrosion rate. This was demonstrated by varying different process parameters such as the concentration of binary inhibitor, pH and time using two different statistical models; the Response Surface Methodology (RSM) and the Artificial Neural Network (ANN). Results: From the results, it was observed that ANN was a better predictive tool to determine the corrosion rate of mild steel than the RSM. Conclusion: Overall, both the models prove that relative to the process parameters used, the importance level of the parameters was Time &lt; Concentration of binary inhibitor &lt; pH. Corrosion ; Response surface methodology ; Corrosion inhibitor ; Castor oil ; Materials science ; Metallurgy ; Chromatography ; Chemistry ; Organic chemistry Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals ; Concrete Corrosion and Durability
Authors: Madiha Batool | Shazia Khurshid | Zahid Muhammad Qureshi | Walid M. Daoush | Farwa Hashmi | Nida Mehboob | H Bahadar | F Maqbool | K Niaz | M Abdollahi | Z Aksu | Y Xi | C Hu | P Gao | R Yang | X He | X Wang | B Wan | G Mustafa | H Tahir | S Muhammad | A Nasir | M Somasekhara Reddy | L Sivaramakrishna | A Varada Reddy | A Binupriya | M Sathishkumar | K Swaminathan | C Kuz | S Yun | Z Hu | H Chen | F Ji | S Yuan | Z Cai | Y Sun | W Liu | F Pan | P Sun | J Fu | Y Wong | Y Szeto | W Cheung | G Mckay | T Guo | M Yao | Y Lin | C Nan | H Naika | K Lingaraju | K Manjunath | D Kumar | G Nagaraju | D Suresh | H Nagabhushana | P Sutradhar | M Saha | D Maiti | S Saranya | A Eswari | E Gayathri | S Eswari | K Vijayarani | M Ismail | S Gul | M Khan | A Asiri | S Khan | E Dil | M Ghaedi | A Asfaram | M Asemani | N Anarjan | M Asghar | E Zahir | S Shahid | J Iqbal | G Walker | J Surez-Cerda | H Espinoza-Gmez | G Alonso-Nez | I Rivero | Y Gochi-Ponce | L Flores-Lpez | H Graham
Source: Biointerface Research in Applied Chemistry (article, 2020-03-09)
Field: Materials Science — Materials Chemistry

Effective adsorptive removal of azodyes on synthesized copper oxide nanoparticles Copper oxide nanoparticles were synthesized by using Camellia Sinensis leaves extract as a reducing and capping agent of the copper ions in solutions and its azodyes adsorptive efficiency were studied. The produced copper oxide nanoparticles were subsequently characterized by SEM, TEM, XRD, FTIR, and UV spectrophotometer for investigating its particle shape, size, crystalline phase and chemical composition. The particle size of the prepared copper oxide nanoparticle was calculated from the XRD data by using the Scherrer equation was found 17.26 nm. However, the median particle size calculated from the SEM and TEM image analysis was found 25~85 nm of tetragonal particle shape. UV spectrum was obtained with maximum absorption peak at 280 nm. The FTIR spectrum indicated -OH, -C=C- and -C-H functional groups, which is due to the presence of the stabilized layer of the Camellia Sinensis leaf extract which is binded with the prepared copper oxide nanoparticles. The produced copper oxide nanoparticles were used for studying the degradation of Congo red and Malachite green azodies. Different parameters were studied to optimize the reaction conditions. Kinetic models of Langmuir, Freundlich and Elovich models were also applied. The degradation percent of the investigated azodyes on the surface of the produced copper oxide nanoparticles in aqueous solutions was observed between 70-75%. Copper ; Nanoparticle ; Particle size ; Oxide ; Fourier transform infrared spectroscopy ; Materials science ; Copper oxide ; Nuclear chemistry ; Aqueous solution ; Inorganic chemistry ; Chemical engineering ; Chemistry ; Metallurgy ; Nanotechnology ; Organic chemistry ; Physical chemistry Nanoparticles: synthesis and applications ; Water Quality Monitoring and Analysis ; Copper-based nanomaterials and applications
Authors: Fanny Lambert | Yann Danten | Carlo Gatti | Christine Frayret
Source: Physical Chemistry Chemical Physics (article, 2020-01-01)
Field: Chemistry — Electrochemistry

A tool for deciphering the redox potential ranking of organic compounds: a case study of biomass-extracted quinones for sustainable energy Carbonyl compounds have emerged as promising organic electrodes for sustainable energy storage. Accelerating the process of performant materials discovery relies on the possibility of developing methodologies to enable scanning of various sets of candidates. The genesis of this educated guess strategy must be privileged to reduce the search space of experiments, accelerate this research area and contribute to sustainable effort. To address this challenge, we built a quantitative structure-activity relationship to unveil the origin of the redox potential magnitude as a function of both structural features and complexation effects. The potential of this prediction model was demonstrated on various ortho-quinones directly derived from naturally occurring catechols. In addition to the modulation provided by substituent changes, the possibility of applying various types of alkaline(-earth)-ion electrochemistry was examined thoroughly. The power of partitioning the total molecular energy into additive atomic group contributions is highlighted, and the construction of this robust strategy provides guidance towards rational selection of the most suitable compound/metal-ion couples. An upshift/downshift of the redox potential by switching from Li to Mg/Na is revealed, while the identification of the relative role played by the various components of the systems as well as electrostatic interactions is clearly identified. These results, particularly the evidence of the different substituent effects on the single/double reduction potentials and as a function of the type of electrochemistry (Li/Na/Mg), have important implications for designing new electroactive compounds with tailored redox properties. USable ; Redox ; Identification (biology) ; Ranking (information retrieval) ; Sustainable energy ; Chemistry ; Environmental chemistry ; Biochemical engineering ; Computer science ; Organic chemistry ; Renewable energy ; Artificial intelligence ; Engineering ; Ecology ; World Wide Web ; Biology Electrochemical Analysis and Applications ; Conducting polymers and applications ; Advanced battery technologies research
Authors: Amir Samimi | Kasra Kavousi | Soroush Zarinabadi | Alireza Bozorgian
Source: Progress in Chemical and Biochemical Research (article, 2020-01-01)
Field: Chemistry — Analytical Chemistry

Optimization of the Gasoline Production Plant in order to Increase Feed Increasing the capacity of Gasoline Production Plant (G.P.P) will cause problems such as the increase of pressure downfall, reduction of output pressure of circulating gas compressor, increasing the thermal load of the re-boiler furnaces of unit towers, increase of the eventuality of formation green oil in vessels, increase of the eventuality of coke formation on the catalyst and eventually reducing the catalyst life span. In this research, operating conditions have been investigated that indicates the increase of the petrochemical feed with the design capacity of 71,000 barrels per day, to the 75,000 barrels per day. The results indicate that with increasing feed, the entrance temperature and pressure to the unit reactor should be increased. According to the obtained results the best temperature (in Feed) is 525 °C, output pressure of circulating gas compressor is more than 22 ton/hr and the circulation of catalysis range is 750-950 kg/hr, the Octane Number will be a Constant Rate 95 and the amount of Coke Formation in all Conditions 3.5-4.3%. The outcomes also show how this approach can be used to gain insight into some refineries and how to deliver results in a comprehensible and user-friendly way. Petrochemical ; Gasoline ; Coke ; Gas compressor ; Oil refinery ; Environmental science ; Waste management ; Fluid catalytic cracking ; Petroleum engineering ; Process engineering ; Chemistry ; Catalysis ; Engineering ; Mechanical engineering ; Organic chemistry Petroleum Processing and Analysis ; Process Optimization and Integration ; Global Energy and Sustainability Research