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Oxidation of n-alkanes using TS-1 and H 2 O 2 : Effects of chain length and solvents
The selective oxidation of n-C 8 H 18 , n-C 12 H 26 , n-C 16 H 34 , n-C 20 H 42 , and n-C 36 H 74 was studied with a goal of using these as models to provide insight into how to functionalize polyolefins. Reactions were carried out using a TS-1 catalyst and H 2 O 2 in a batch reactor with different cosolvents, including methanol, acetone, acetonitrile, methyl ethyl ketone, and methyl butyl ketone. Rates decreased with increasing alkane size, possibly due to the reduced solubility of larger alkanes into the water-rich phases. Cosolvents that promote the partitioning of alkanes in the aqueous phase increased the rates. 1 H NMR spectroscopy demonstrated that ketones were the primary products, although some alcohols also formed. There was preferential reaction at the 2 position in the alkanes, but reaction at central carbons was also observed. The results of this study suggest strategies for using this catalytic chemistry to functionalize polyolefins.
Three-stage design of high-resolution microalgae-based biofuel supply chain using geographic information system
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Ensemble models of feedstock blend ratios to minimize supply chain risk in bio-based manufacturing
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Structural basis for the stabilization of amyloidogenic immunoglobulin light chains by hydantoins
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Dehydration of polymer chains initiates graphene folding in water
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Fabrication of carbon chains (pseudo carbynes) by stabilization with gold-thiol complex
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The light chain of the L9 antibody is critical for binding circumsporozoite protein minor repeats an
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A chain stretch-based gradient-enhanced model for damage and fracture in elastomers
Similar to quasi-brittle materials, it has been recently shown that elastomers can exhibit a macroscopically diffuse damage zone that accompanies the fracture process. In this study, we introduce a stretch-based gradient-enhanced damage (GED) model that allows the fracture to localize and also captures the development of a physically diffuse damage zone. This capability contrasts with the paradigm of the phase field method for fracture, where a sharp crack is numerically approximated in a diffuse manner. Capturing fracture localization and diffuse damage in our approach is achieved by considering nonlocal effects that encompass network topology, heterogeneity, and imperfections. These considerations motivate the use of a statistical damage function dependent upon the nonlocal deformation state. From this model, fracture toughness is realized as an output. While GED models have been classically utilized for damage modeling of structural engineering materials (e.g., concrete), they face challenges when trying to capture the cascade from damage to fracture, often leading to damage zone broadening (de Borst and Verhoosel, 2016). This deficiency contributed to the popularity of the phase-field method over the GED model for elastomers and other quasi-brittle materials. Other groups have proceeded with damage-based GED formulations that prove identical to the phase-field method (Lorentz et al., 2012), but these inherit the aforementioned limitations. To address this issue in a thermodynamically consistent framework, we implement two modeling features (a nonlocal driving force bound and a simple relaxation function) specifically designed to capture the evolution of a physically meaningful damage field and the simultaneous localization of fracture, thereby overcoming a longstanding obstacle in the development of these nonlocal strain- or stretch-based approaches. Here, we discuss several numerical examples to understand the features of the approach at the limit of incompressibility, and compare them to the phase-field method as a benchmark for the macroscopic response and fracture energy predictions.
Towards integrated landscape design and biofuel supply chain optimization
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Single-residue engineering of lambda (λ) antibody light chains reduces conformational flexibility and enhances thermal stability
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A database and framework for carbon ore resources and associated supply chain data
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