Gradient free active subspace construction using Morris screening elementary effects
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Shape-persistent arylene ethynylene molecular cages have been investigated as transmembrane channels for ions and small molecules. The molecular cages were obtained starting from tetrayne monomers through alkyne metathesis cyclooligomerization. We found these porphyrin-based rigid molecular cages can insert into the lipid bilayer and efficiently transport ions and small molecules (e.g., calcein). Our study reveals longer hydrophobic alkyl chains on the cage molecule promote the channeling efficiency, while shorter and/or more polar side chains impair such activity. Kinetic analysis shows linear correlation between the rate of proton transport and the concentration of the cage, suggesting the active species is likely a monomeric cage. We found that C70-encapsulated cages are nearly inactive for transmembrane ion transportation, indicating that ions are likely transported through the internal cavity of the cage. Discrete shape-persistent organic cages represent highly stable synthetic ion channels or pores, which could have interesting applications in biomimetic signaling and drug delivery.
Lithium-rich layered cathode materials are considered to be research focus of cathode candidates for next generation lithium-ion batteries due to their high specific capacity and low cost. However, lattice deoxidation associated with elemental migration and internal local shrinkage usually results in deteriorated cyclic performance and notorious voltage attenuation, severely limiting its application. In this paper, we have successfully injected uniform oxygen defects into surface region of Li 1.2 Ni 0.13 Co 0.13 Mn 0.54 O 2 primary particles under the high-pressure and weak carbonate environment. Various experimental investigations indicate that the injected robust oxygen defects can not only mitigate detrimental interfacial reactions but also suppress unfavorable lattice variation and particle breakage. More importantly, theoretical calculations unravel the critical roles of oxygen defects in regulating energy band structure for strengthened anionic reversibility. Owing to stabilization effects of unique oxygen defect engineering, the modified Li 1.2 Ni 0.13 Co 0.13 Mn 0.54 O 2 cathode has harvested dramatically enhanced electrochemical performance including a high initial coulombic efficiency of 96.6%, an outstanding capacity retention of 91.96% (1C, 200 cycles) and suppressed voltage decay of only 1.62 mV per cycle. Therefore, this facile and effective defect engineering strategy could establish new guidance for promoting practical application of Li-rich Mn-based cathode material.
Nanofiltration membanes play a critical role in alleviating global water scarcity, affecting more than 4 billion people. Here, a new metal organic framework (MOF) sacrificicial strategy is designed to synthesize flux-enhancing nanofiltration membrane for efficient dye removal. With the aid of polyphenol chemistry, ZIF-67 MOF was first coated onto a porous support and in situ transformed into a selective layer by virtue of pyrrole polymerization. Trigerred by ammonium persulfate, polypyrrole selective layer functioalized nanofiltration membrane was fabricated when accompanying pore regulation by MOF self-sacrifice. The membrane demonstrated a flux enhancement of 745% than pristine polypyrrole (PPy) one with maintained rejection. Furthermore, the synergistic interactions among polypyrrole, polyphenol, and porous support endowed membrane with excellent stability during long term test. The designed MOF sacrificed route may open a new avenue for fabricating advanced energetic-efficient membranes with the reconstructed architecture towards diverse separation applications.
Surrogate model development is a critical step for uncertainty quantification or other sample-intensive tasks for complex computational models. Here, in this work, we develop a multi-output surrogate form using a class of neural networks (NNs) that employ shortcut connections, namely Residual NNs (ResNets). ResNets are known to regularize the surrogate learning problem and improve the efficiency and accuracy of the resulting surrogate. Inspired by the continuous, Neural ODE analogy, we augment ResNets with weight parameterization strategy with respect to ResNet depth. Weight-parameterized ResNets regularize the NN surrogate learning problem and allow better generalization with a drastically reduced number of learnable parameters. We demonstrate that weight-parameterized ResNets are more accurate and efficient than conventional feed-forward multi-layer perceptron networks. We also compare various options for parameterization of the weights as functions of ResNet depth. We demonstrate the results on both synthetic examples and a large scale earth system model of interest.
The Quasi-Steady State Approximation (QSSA) can be an effective tool for reducing the size and stiffness of chemical mechanisms for implementation in computational reacting flow solvers. However, for many applications, the resulting model still requires implicit methods for efficient time integration. Here, in this paper, we outline an approach to formulating the QSSA reduction that is coupled with a strategy to generate C++ source code to evaluate the net species production rates, and the chemical Jacobian. The code-generation component employs a symbolic approach enabling a simple and effective strategy to analytically compute the chemical Jacobian. For computational tractability, the symbolic approach needs to be paired with common subexpression elimination which can negatively affect memory usage. Several solutions are outlined and successfully tested on a 3D multipulse ignition problem, thus allowing portable application across chemical model sizes and GPU capabilities. The implementation of the proposed method is available at https://github.com/AMReX-Combustion/PelePhysics under an open-source license.
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