Exploring the Mechanisms of LiNiO 2 Cathode Degradation by the Electrolyte Interfacial Deprotonation Reaction
Not Available
25 ENERGY STORAGE↗
Engineering topics
Zheng, Yu
Publications and source records attributed to Zheng, Yu.
Protonation Stimulates the Layered to Rock Salt Phase Transition of Ni‐Rich Sodium Cathodes
Abstract Protonation of oxide cathodes triggers surface transition metal dissolution and accelerates the performance degradation of Li‐ion batteries. While strategies are developed to improve cathode material surface stability, little is known about the effects of protonation on bulk phase transitions in these cathode materials or their sodium‐ion battery counterparts. Here, using NaNiO 2 in electrolytes with different proton‐generating levels as model systems, a holistic picture of the effect of incorporated protons is presented. Protonation of lattice oxygens stimulate transition metal migration to the alkaline layer and accelerates layered‐rock‐salt phase transition, which leads to bulk structure disintegration and anisotropic surface reconstruction layers formation. A cathode that undergoes severe protonation reactions attains a porous architecture corresponding to its multifold performance fade. This work reveals that interactions between electrolyte and cathode that result in protonation can dominate the structural reversibility/stability of bulk cathodes, and the insight sheds light for the development of future batteries.
25 ENERGY STORAGE↗
Data-driven Vulnerability Analysis of Networked Pipeline System
This paper introduces an attack generation framework for evaluating the vulnerability of nonlinear networked pipeline systems. The vulnerability analysis is formulated as determining the presence of feasible attack sets, defined by boundary functions representing the effectiveness and stealthiness of attack signals with respect to the objective and attack detection module. The framework utilizes three data-driven models, including two discriminative models that learn the boundary functions and a generative model that produces elements of the feasible attack set. A new loss function ensures successful attack generation with high probability.
03 NATURAL GAS↗
Robust Resilient Signal Reconstruction under Adversarial Attacks
We consider the problem of signal reconstruction for a system under sparse signal corruption by a malicious agent. The reconstruction problem follows the standard error coding problem that has been studied extensively in the literature. We include a new challenge of robust estimation of the attack support. The problem is then cast as a constrained optimization problem merging promising techniques in the area of deep learning and estimation theory. A pruning algorithm is developed to reduce the "false positive" uncertainty of data-driven attack localization results, thereby improving the probability of correct signal reconstruction. Sufficient conditions for the correct reconstruction and the associated reconstruction error bounds are obtained for both exact and inexact attack support estimation. Moreover, a simulation of a water distribution system is presented to validate the proposed techniques.
Robust, Signal reconstruction, Resilient estimator↗