jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Resma Madhu Paruthipulli Kalarikkal | S. Jayalalitha | Kannan Krithivasan
Source: Asia-Pacific Journal of Chemical Engineering (article, 2021-07-16)
Field: Materials Science — Biomaterials

RF‐LSTM‐based method for prediction and diagnosis of fouling in heat exchanger Abstract Fouling degrades the thermal and hydraulic performances of the heat exchanger (HE), leading to failure if undetected. It occurs due to the accumulation of undesired material on the heat transfer surface. Knowledge about the HE fouling dynamics is required to plan mitigation strategies, ensuring a sustainable and safe operation. This paper aims to propose a feature‐based technique to predict the fouling status of the HE based on historical data. Three thermal and two hydraulic features are extracted from the HE. Random forest (RF) is employed to detect the dominant features using the Gini index. These dominant features are used to predict the fouling resistance using a deep neural network based on the Long Short‐Term Memory (LSTM) model. Also, these dominant features provide reliable inferences to reason out the fouling dynamics. A diagnostic flag is derived based on the dominant feature and is used to diagnose the ongoing fouling phenomena, which is vital to formulate mitigation. The proposed technique is investigated on the data acquired from the HEs used in a thermal power plant (Case 1) and petroleum refinery (Case 2). Prediction accuracy of 99% and 97% is observed for Cases 1 and 2, respectively. Experimental results illustrate that RF enables the LSTM to achieve faster training and reliable prediction of fouling. Fouling ; Heat exchanger ; Computer science ; Refinery ; Artificial intelligence ; Artificial neural network ; Environmental science ; Feature (linguistics) ; Process engineering ; Random forest ; Machine learning ; Engineering ; Environmental engineering ; Mechanical engineering Calcium Carbonate Crystallization and Inhibition ; Neural Networks and Applications
Authors: Yanfang Wu | Sina S. Jamali | Richard D. Tilley | J. Justin Gooding
Source: Faraday Discussions (article, 2021-11-20)
Field: Chemistry — Electrochemistry

Spiers Memorial Lecture. Next generation nanoelectrochemistry: the fundamental advances needed for applications Nanoelectrochemistry, where electrochemical processes are controlled and investigated with nanoscale resolution, is gaining more and more attention because of the many potential applications in energy and sensing and the fact that there is much to learn about fundamental electrochemical processes when we explore them at the nanoscale. The development of instrumental methods that can explore the heterogeneity of electrochemistry occurring across an electrode surface, monitoring single molecules or many single nanoparticles on a surface simultaneously, have been pivotal in giving us new insights into nanoscale electrochemistry. Equally important has been the ability to synthesise or fabricate nanoscale entities with a high degree of control that allows us to develop nanoscale devices. Central to the latter has been the incredible advances in nanomaterial synthesis where electrode materials with atomic control over electrochemically active sites can be achieved. After introducing nanoelectrochemistry, this paper focuses on recent developments in two major application areas of nanoelectrochemistry; electrocatalysis and using single entities in sensing. Discussion of the developments in these two application fields highlights some of the advances in the fundamental understanding of nanoelectrochemical systems really driving these applications forward. Looking into our nanocrystal ball, this paper then highlights: the need to understand the impact of nanoconfinement on electrochemical processes, the need to measure many single entities, the need to develop more sophisticated ways of treating the potentially large data sets from measuring such many single entities, the need for more new methods for characterising nanoelectrochemical systems as they operate and the need for material synthesis to become more reproducible as well as possess more nanoscale control. Nanotechnology ; Computer science ; Engineering ethics ; Engineering ; Materials science Electrochemical Analysis and Applications ; Electrochemical sensors and biosensors ; Electrocatalysts for Energy Conversion
Authors: Christopher D. Taylor | Huibin Ke
Source: Corrosion Reviews (article, 2021-05-10)
Field: Materials Science — Materials Chemistry

Investigations of the intrinsic corrosion and hydrogen susceptibility of metals and alloys using density functional theory Abstract Mechanisms for materials degradation are usually inferred from electrochemical measurements and characterization performed before, during, and after exposure testing and/or failure analysis of service materials. Predicting corrosion and other materials degradation modes, such as hydrogen-assisted cracking, from first-principles has generally been limited to thermodynamic predictions from Pourbaix or Ellingham diagrams and the Galvanic series. Using electronic structure calculations, modern first-principles methods can predict ab initio the key rate-controlling processes for corrosion and hydrogen susceptibility as a function of pH, potential, and solution chemistry, and materials composition and microstructure. Herein we review density functional theory (DFT) approaches for studying the electrochemical reactions occurring on fresh metal and alloy surfaces related to environmentally assisted cracking and localized corrosion/pitting. Predicted changes in surface chemistry as a function of the environment were correlated against experimental crack growth rate data obtained for alloys 718, 725, and pipeline steel under electrochemical control. We also review the application of the method to study the effects of alloying on the chloride susceptibility of stainless steels and Ni–Cr-based corrosion-resistant alloys. Perspectives for improving the model are given, and extending it to future fields of application in corrosion science and engineering. Pourbaix diagram ; Materials science ; Corrosion ; Density functional theory ; Alloy ; Electrochemistry ; Metallurgy ; Galvanic corrosion ; Microstructure ; Hydrogen ; Characterization (materials science) ; Cracking ; Anaerobic corrosion ; Hydrogen embrittlement ; Physical chemistry ; Composite material ; Computational chemistry ; Nanotechnology ; Electrode ; Chemistry Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals ; Concrete Corrosion and Durability
Authors: Han Guo | Patricia Poths | Philippe Sautet | Anastassia N. Alexandrova
Source: ACS Catalysis (article, 2021-12-28)
Field: Materials Science — Materials Chemistry

Oxidation Dynamics of Supported Catalytic Cu Clusters: Coupling to Fluxionality Copper oxide nanoclusters have a wide range of catalytic applications, such as the selective oxidation of hydrocarbons. O2 binding to the catalyst, activation, and release upon reagent oxidation are key events in these catalytic chemistries. These events are expected to be accompanied by significant structural changes of the Cu clusters, because O atoms integrate into the cluster, rather than bind to its surface. Topping the complexity of the problem, partially oxidized Cu clusters are known to exhibit strong fluxionality and feature diverse and interconverting structures and oxygen contents in conditions of oxidative dehydrogenation (ODH). Hence, a significant dynamic coupling between the “hot” O2 molecule impacting the cluster at reaction temperatures and the cluster fluxionality can be expected. In this work, we focus on the dynamics of dioxygen integration into a partially oxidized Cu cluster supported on hydroxylated amorphous alumina–a system recently reported to be an exceptionally selective catalyst for cyclohexane ODH with very little CO produced, whose mechanistic underpinnings are of utmost interest. The statistics over a swarm of adsorption and scattering trajectories where O2 hits various sites on the cluster at reaction temperature shows that the O2 binding does not only follow the minimal energy paths. O2 also rarely integrates into the cluster in a single step and instead first binds to a single Cu atom via either an η1-O2 or an η2-O2 mode. Surprisingly, this step often has a higher barrier than the subsequent O2 integration and dissociation, which in turn take multiple steps and complete the oxidation process. Dynamic trajectories starting from the key transition state of integration of the adsorbed O2 can also lead to different intermediate structures during or right after the dissociation, due to the energy released from the transition state and the thermal intracluster effects. From these activated O2 chemisorbed structures, O2 dissociation occurs with moderate barriers (∼0.5 eV), producing multiple final oxidized Cu4O4 states. Hence, a diversity of reaction profiles for the attack of supported Cu cluster by O2 emerges due to the dynamic effects, with implications for mechanisms, kinetic models, and catalyst design principles. Catalysis ; Chemistry ; Dehydrogenation ; Cluster (spacecraft) ; Nanoclusters ; Dissociation (chemistry) ; Copper ; Photochemistry ; Adsorption ; Heterogeneous catalysis ; Crystallography ; Physical chemistry ; Organic chemistry Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Nanocluster Synthesis and Applications
Authors: Khaled Muftah Elsherif | Abdelmeneim El-Dali | Asma Amar Alkarewi | Abdunaser Mabrok Ewlad-Ahmed | Abdullah Treban
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2021-04-01)
Field: Chemistry — Analytical Chemistry

Adsorption of crystal violet dye onto olive leaves powder: Equilibrium and kinetic studies Adsorption of crystal violet dye from aqueous solutions applying olive leaves powder (OLP) as a biosorbent has been examined under various experimental circumstances. The influence of contact time, pH, initial concentration of studied dye and adsorbent dose on the adsorption process has been investigated applying batch experiments. The concentration of remaining dye has been determined using molecular absorption spectrometry at wave length of 580 nm. The maximum removal of studied dye has been realized at pH 7.5 with a percent removal of 99.2% after 20 min of agitation time. Langmuir, Freundlich, and Temkin isotherm models exemplify the best fit for the experimental data; while the elevated adsorption capacity was 181.1 mg.g<sup>1</sup>. Adsorption kinetics of crystal violet was expected sufficiently with the empirical pseudo-second-order model. Corresponding to the adsorption capacity, olive leaves powder thought as a low cost, effective, and environmentally friendly biosorbent for the removal of crystal violet dye from aqueous solutions. Crystal violet ; Adsorption ; Aqueous solution ; Freundlich equation ; Langmuir ; Absorption (acoustics) ; Kinetics ; Chemistry ; Crystal (programming language) ; Nuclear chemistry ; Materials science ; Chromatography ; Organic chemistry ; Composite material Dye analysis and toxicity ; Edible Oils Quality and Analysis ; Adsorption and biosorption for pollutant removal
Authors: Kasra Momeni | Yanzhou Ji | Long‐Qing Chen
Source: Journal of materials research/Pratt's guide to venture capital sources (article, 2021-10-12)
Field: Materials Science — Materials Chemistry

Computational synthesis of 2D materials grown by chemical vapor deposition Abstract The exotic properties of 2D materials made them ideal candidates for applications in quantum computing, flexible electronics, and energy technologies. A major barrier to their adaptation for industrial applications is their controllable and reproducible growth at a large scale. A significant effort has been devoted to the chemical vapor deposition (CVD) growth of wafer-scale highly crystalline monolayer materials through exhaustive trial-and-error experimentations. However, major challenges remain as the final morphology and growth quality of the 2D materials may significantly change upon subtle variation in growth conditions. Here, we introduced a multiscale/multiphysics model based on coupling continuum fluid mechanics and phase-field models for CVD growth of 2D materials. It connects the macroscale experimentally controllable parameters, such as inlet velocity and temperature, and mesoscale growth parameters such as surface diffusion and deposition rates, to morphology of the as-grown 2D materials. We considered WSe 2 as our model material and established a relationship between the macroscale growth parameters and the growth coverage. Our model can guide the CVD growth of monolayer materials and paves the way to their synthesis-by-design. Graphic abstract Materials science ; Chemical vapor deposition ; Monolayer ; Nanotechnology ; Wafer ; Deposition (geology) ; Electronics ; Multiscale modeling ; Engineering physics ; Chemical physics ; Computational chemistry 2D Materials and Applications ; Graphene research and applications ; Thermal properties of materials
Authors: Aleksandar Y. Mehandzhiyski | Igor Zozoulenko
Source: Polysaccharides (review, 2021-04-12)
Field: Materials Science — Biomaterials

A Review of Cellulose Coarse-Grained Models and Their Applications Cellulose is the most common biopolymer and widely used in our daily life. Due to its unique properties and biodegradability, it has been attracting increased attention in the recent years and various new applications of cellulose and its derivatives are constantly being found. The development of new materials with improved properties, however, is not always an easy task, and theoretical models and computer simulations can often help in this process. In this review, we give an overview of different coarse-grained models of cellulose and their applications to various systems. Various coarse-grained models with different mapping schemes are presented, which can efficiently simulate systems from the single cellulose fibril/crystal to the assembly of many fibrils/crystals. We also discuss relevant applications of these models with a focus on the mechanical properties, self-assembly, chiral nematic phases, conversion between cellulose allomorphs, composite materials and interactions with other molecules. Cellulose ; Biopolymer ; Materials science ; Process (computing) ; Fibril ; Nanotechnology ; Computer science ; Polymer science ; Chemical engineering ; Polymer ; Chemistry ; Composite material ; Engineering Advanced Cellulose Research Studies ; Liquid Crystal Research Advancements ; Advanced Materials and Mechanics
Authors: Anja Aarva | Sami Sainio | Volker L. Deringer | A. Miguel | Tomi Laurila
Source: The Journal of Physical Chemistry C (article, 2021-08-16)
Field: Materials Science — Materials Chemistry

X-ray Spectroscopy Fingerprints of Pristine and Functionalized Graphene In this work, we demonstrate how to identify and characterize the atomic structure of pristine and functionalized graphene materials from a combination of computational simulation of X-ray spectra, on the one hand, and computer-aided interpretation of experimental spectra, on the other. Despite the enormous scientific and industrial interest, the precise structure of these 2D materials remains under debate. As we show in this study, a wide range of model structures from pristine to heavily oxidized graphene can be studied and understood with the same approach. We move systematically from pristine to highly oxidized and defective computational models, and we compare the simulation results with experimental data. Comparison with experiments is valuable also the other way around; this method allows us to verify that the simulated models are close to the real samples, which in turn makes simulated structures amenable to several computational experiments. Our results provide <i>ab initio</i> semiquantitative information and a new platform for extended insight into the structure and chemical composition of graphene-based materials. Graphene ; Ab initio ; Materials science ; Interpretation (philosophy) ; Nanotechnology ; Spectral line ; Range (aeronautics) ; Chemical physics ; Computer science ; Computational physics ; Chemistry ; Physics Graphene research and applications ; Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques
Authors: Joshua J. Goings | Hang Hu | Chao Yang | Xiaosong Li
Source: Journal of Chemical Theory and Computation (article, 2021-08-23)
Field: Materials Science — Materials Chemistry

Reinforcement Learning Configuration Interaction Selected configuration interaction (sCI) methods exploit the sparsity of the full configuration interaction (FCI) wave function, yielding significant computational savings and wave function compression without sacrificing the accuracy. Despite recent advances in sCI methods, the selection of important determinants remains an open problem. We explore the possibility of utilizing reinforcement learning approaches to solve the sCI problem. By mapping the configuration interaction problem onto a sequential decision-making process, the agent learns on-the-fly which determinants to include and which to ignore, yielding a compressed wave function at near-FCI accuracy. This method, which we call reinforcement-learned configuration interaction, adds another weapon to the sCI arsenal and highlights how reinforcement learning approaches can potentially help solve challenging problems in electronic structure theory. Reinforcement learning ; Computer science ; Exploit ; Function (biology) ; Artificial intelligence ; Process (computing) ; Machine learning ; Reinforcement ; Engineering Machine Learning in Materials Science ; Molecular Junctions and Nanostructures ; Advanced Memory and Neural Computing
Authors: Atreyee Banerjee | Dipti Jasrasaria | Samuel P. Niblett | David J. Wales
Source: The Journal of Physical Chemistry A (article, 2021-04-21)
Field: Materials Science — Materials Chemistry

Crystal Structure Prediction for Benzene Using Basin-Hopping Global Optimization Organic molecules can be stable in distinct crystalline forms, known as polymorphs, which have significant consequences for industrial applications. Here, we predict the polymorphs of crystalline benzene computationally for an accurate anisotropic model parametrized to reproduce electronic structure calculations. We adapt the basin-hopping global optimization procedure to the case of crystalline unit cells, simultaneously optimizing the molecular coordinates and unit cell parameters to locate multiple low-energy structures from a variety of crystal space groups. We rapidly locate all the well-established experimental polymorphs of benzene, each of which corresponds to a single local energy minimum of the model. Our results show that basin-hopping can be both an efficient and effective tool for polymorphic crystal structure prediction, requiring no <i>a priori</i> experimental knowledge of cell parameters or symmetry. Crystal (programming language) ; Crystal structure ; Anisotropy ; Benzene ; Crystal structure prediction ; Molecule ; A priori and a posteriori ; Materials science ; Chemical physics ; Symmetry (geometry) ; Crystallography ; Computer science ; Biological system ; Statistical physics ; Chemistry ; Physics ; Mathematics ; Geometry ; Optics Machine Learning in Materials Science ; Crystallography and molecular interactions ; Crystallization and Solubility Studies
Authors: L. H. Tabares | Juan G. Darias-Gonzalez | Frank J. Chao‐Mujica | Luis Miguel Ledo Pereda | Manuel Antuch | Ernesto Carrillo Barroso | J. Enrique Chong-Quero | E. Reguera | Luis Felipe Desdín García
Source: Journal of Nanomaterials (article, 2021-11-08)
Field: Materials Science — Materials Chemistry

Stabilization Methods in the Submerged Arc Discharge Synthesis of Carbon Nanostructures Many papers, in which the submerged arc discharge (SAD) method in nanoparticle synthesis was used, reported similar operating parameters, but different electrode erosion rate values, different yields and purities of the obtained nanostructures, and a different sort of contaminants present in the synthesis. Analyzing these articles, we found insufficient attention to ensure the arc power stability, which is a key factor guaranteeing the product homogeneity and quality. This paper presents an analysis of different control strategies, remarks their advantages and drawbacks, and proposes the most appropriate technique to be used in SAD. The most appropriate technique is proposed from the SAD stabilization method analysis. Materials science ; Homogeneity (statistics) ; Electric arc ; Nanostructure ; Process engineering ; Electrode ; sort ; Arc (geometry) ; Key (lock) ; Nanotechnology ; Control theory (sociology) ; Computer science ; Control (management) ; Mechanical engineering ; Artificial intelligence ; Machine learning Diamond and Carbon-based Materials Research ; Carbon Nanotubes in Composites ; Graphene research and applications
Authors: Joseph Glover | Elena Besley
Source: Faraday Discussions (article, 2021-01-01)
Field: Chemistry — Inorganic Chemistry

A high-throughput screening of metal–organic framework based membranes for biogas upgrading Applications of biomethane as a source of renewable energy and transport fuel rely heavily on successful implementation of purification methods capable of removing undesirable impurities from biogas and increasing its calorific content. Metal-organic frameworks (MOFs) are competitive candidates for biogas upgrading due to a versatile range of attractive physical and chemical properties which can be utilised in membrane materials. In this work, we present a high-throughput computational screening methodology for efficient identification of MOF structures with promising gas separation performance. The proposed screening strategy is based on initial structural analysis and predictions of the single-component permeation of CO<sub>2</sub>, CH<sub>4</sub> and H<sub>2</sub>S from adsorption and diffusion calculations at infinite dilution. The identified top performing candidates are subject to further analysis of their gas separation performance at the operating conditions of 10 bar and 298 K, using grand canonical Monte Carlo and equilibrium molecular dynamics simulations on equimolar CO<sub>2</sub>/CH<sub>4</sub> and H<sub>2</sub>S/CH<sub>4</sub> mixtures. The Henry constant for the adsorption of H<sub>2</sub>O was also calculated to determine the hydrophobicity of MOF structures, as the presence of H<sub>2</sub>O often leads to membrane instability and performance limitations. For the considered gas mixtures, the top MOF candidates exhibit superior separation capabilities over polymer-, zeolite-, and mixed matrix-based membranes as indicated by the predicted values of selectivity and permeability. The proposed screening protocol offers a powerful tool for the rational design of novel MOFs for biogas upgrading. Biogas ; Membrane ; Gas separation ; Permeation ; Metal-organic framework ; Process engineering ; Adsorption ; Renewable energy ; Materials science ; Chemistry ; Chemical engineering ; Waste management ; Organic chemistry ; Engineering Metal-Organic Frameworks: Synthesis and Applications ; Membrane Separation and Gas Transport ; Catalytic Processes in Materials Science