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At least 361 records · Page 20

Materials Data on Co(CO)4 by Materials Project

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

36 MATERIALS SCIENCE↗

A Blockchain Based Co-Simulation Framework for Integrating DERs to Wholesale Electricity Markets

As the number of distributed energy resources (DERs) continue to increase across energy-delivery systems, there remains a need for integrating their capabilities into traditional grid operations. In this paper, a blockchain-based solution is proposed to facilitate FERC's Order No. 2222 implementations. The presented use-case enables small-scale DERs to participate in wholesale market operations through DER aggregators, while also enabling local distribution system operators to enforce distribution system constraints in a secure and traceable manner. The presented use case is built around the Unified Testing Platform (UTP) being developed as a part of the Blockchain for Optimized Security and Energy Management (BLOSEM) project. This is a multi-lab effort intended to simplify the deployment of blockchain-powered grid solutions by enabling the integration of simulation tools, and blockchain technologies through the use of system-agnostic interfaces that provide a modular, interoperable, and reusable connectivity layer.

blockchain interoperability↗

Monte Carlo simulations of Cu/Ni–Si–Mn co-precipitation in duplex stainless steels

First-Passage Kinetic Monte Carlo (FPKMC) simulations of species migration in duplex stainless steels were performed in order to establish relationships between alloy content, segregation behavior, and the formation of Ni-Si-Mn rich particles in cast duplex stainless steels during thermal aging. The Ni-Si-Mn-rich second phase forms after extended aging at reactor operating temperatures and degrades alloy properties. Simulations of Ni-Si-Mn cluster formation were validated through comparison with experimental results obtained through Atom Probe Tomography (APT) on similar alloy compositions, identifying several trends. First, Cu promotes the formation of Ni-Si-Mn clusters, but only when Ni or Mn prefer segregation to the surface of Cu particles; without the segregation of these species, the critical composition for the clusters to form was not achieved. Second, the width of precipitate-denuded zones near ?/d interfaces increases with decreasing Cu content. This finding was in strong agreement with APT results, further validating our model. Finally, our model predicts that Ni-Si-Mn cluster formation will be the most extensive when the Si:Mn ratio is approximately 1:1 and the least extensive when one of these key elements is less concentrated (Mn = 0.5 at.% or Si = 0.5 at.%). The implications of these results on how to improve alloy properties are discussed.

Cu co-precipitation, Atom probe tomography (APT), ↗

Materials Data on Co by Materials Project

Co is gamma-brass-derived structured and crystallizes in the hexagonal P6_3mc space group. The structure is three-dimensional. there are eleven inequivalent Co sites. In the first Co site, Co is bonded in a 11-coordinate geometry to fourteen Co atoms. There are a spread of Co–Co bond distances ranging from 2.40–2.84 Å. In the second Co site, Co is bonded in a 11-coordinate geometry to eleven Co atoms. There are a spread of Co–Co bond distances ranging from 2.31–2.56 Å. In the third Co site, Co is bonded in a 12-coordinate geometry to eleven Co atoms. There are a spread of Co–Co bond distances ranging from 2.41–2.83 Å. In the fourth Co site, Co is bonded to twelve Co atoms to form a mixture of distorted face and corner-sharing CoCo12 cuboctahedra. There are a spread of Co–Co bond distances ranging from 2.42–2.62 Å. In the fifth Co site, Co is bonded in a 9-coordinate geometry to ten Co atoms. There are three shorter (2.49 Å) and one longer (3.01 Å) Co–Co bond lengths. In the sixth Co site, Co is bonded in a 11-coordinate geometry to eleven Co atoms. There are a spread of Co–Co bond distances ranging from 2.37–2.51 Å. In the seventh Co site, Co is bonded in a 10-coordinate geometry to thirteen Co atoms. There are a spread of Co–Co bond distances ranging from 2.49–2.65 Å. In the eighth Co site, Co is bonded in a 8-coordinate geometry to fourteen Co atoms. All Co–Co bond lengths are 2.68 Å. In the ninth Co site, Co is bonded in a 12-coordinate geometry to twelve Co atoms. There are one shorter (2.33 Å) and one longer (2.90 Å) Co–Co bond lengths. In the tenth Co site, Co is bonded in a 10-coordinate geometry to ten Co atoms. The Co–Co bond length is 2.85 Å. In the eleventh Co site, Co is bonded in a 6-coordinate geometry to fourteen Co atoms.

36 MATERIALS SCIENCE↗

Distinct Bimetallic Cooperativity Among Water Reduction Catalysts Containing [Co III Co III ], [Ni II Ni II ], and [Zn II Zn II ] Cores

We report three binuclear species [LCo III 2 (μ-Pz) 2 ](ClO 4 ) 3 (1), [LNi II 2 (CH 3 OH) 2 Cl 2 ]ClO 4 (2), and [LZn II 2 Cl 2 ]PF 6 (3) supported by the deprotonated form of the ligand 2,6-bis[bis(2-pyridylmethyl) amino-methyl]-4-methylphenol were synthesized, structurally characterized as solids and in solution, and had their electrochemical and spectroscopic behavior established. Species 1–3 had their water reduction ability studied aiming to interrogate the possible cooperative catalytic activity between two neighboring metal centers. Species 1 and 2 reduced H 2 O to H 2 effectively at an applied potential of -1.6 V Ag/AgCl , yielding turnover numbers of 2,820 and 2,290, respectively, after 30 minutes. Species 3 lacked activity and was used as a negative control to eliminate the possibility of ligand-based catalysis. Pre- and post-catalytic data gave evidence of the molecular nature of the process within the timeframe of the experiments. Species 1 showed structural, rather than electronic cooperativity, while species 2 displayed no obvious cooperativity. DFT methods complemented the experimental results determining plausible mechanisms.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Economic and Environmental Analysis to Evaluate the Potential Value of Co-Optima Diesel Bioblendstocks to Petroleum Refiners

The U.S. petroleum refining sector is undergoing a period of historic transformation, catalyzed by the decarbonization of the U.S. economy. Diesel-boiling-range bioblendstocks have gained traction, owing to their superior fuel properties and environmental performance as compared to traditional petroleum fuels. This work couples refinery linear programming models with life cycle assessment to quantify the potential economic and environmental benefits, and trade-offs, of blending diesel-boiling-range bioblendstocks at petroleum refineries. Linear programming models were developed in Aspen Process Industry Modeling Systems (PIMS) for three representative petroleum refinery configurations of differing complexity. Seven diesel-boiling-range bioblendstocks: 4-butoxyheptane, 5-ethyl-4-propylnonane, soy biodiesel, sludge hydrothermal liquefaction diesel, polyoxymethylene ethers, renewable diesel, and hexyl hexanoate, were investigated to identify key fuel properties that influence refineries' economics and to track the effect of adding bioblendstocks on refinery-wide cradle-to-gate greenhouse gases (GHG) emissions. These analyses considered blending levels from 10 to 30 vol% and fuel demand projections over the period 2040 to 2050. This analysis determines that bioblendstock sulfur content and cetane number are the primary fuel attributes with the potential to provide value to refiners. Life cycle assessment results indicate that the use of diesel-boiling-range bioblendstocks can reduce cradle-to-gate refinery GHG emissions by up to ~ 40 % relative to conventional refinery operations when considering carbon uptake in the supply chain of the bioblendstock. Refinery-wide marginal GHG abatement costs range from 120 to 3,600 USD2016/metric tons carbon dioxide equivalent avoided across the scenarios evaluated. Reducing the price of bioblendstocks is identified as a key to their adoption.

ADVANCED PROPULSION SYSTEMS,BIOMASS FUELS,ENERGY P↗