MECHANICAL PROPERTIES AND DEGRADATION OF HIGH CAPACITY BATTERY ELECTRODES: FUNDAMENTAL UNDERSTANDING AND COPING STRATEGIES

Rechargeable lithium ion and lithium (Li) metal batteries with high energy density and stability are in high demand for the development of electric vehicles and smart grids. Intensive efforts have been devoted to developing high capacity battery electrodes. However, the known high capacity electrode materials experience fast capacity fading and have limited cycle life due to electromechanical degradations, such as fracture of Si-based electrodes and dendrite growth in Li metal electrodes. A fundamental understanding of electromechanical degradation mechanisms of high capacity electrodes will provide insights into strategies for improving their electrochemical performance. Thus, this dissertation focuses on mechanical properties, microstructure changes, and degradation mechanisms of Si composite electrodes and Li metal electrodes. Based on these findings, possible coping strategies are proposed to improve the cycling stability of both electrodes. The poor cycling life of Si-based electrodes is caused by the repeated lithiation/delithiation-induced huge volumetric change in Si particles, which leads to the fracture of particles, excessive formation of solid electrolyte interphase on the newly exposed surface, as well as the loss of electronic conductivity between Si particles and the conductive matrix. The expansion/contraction of Si particles during cycling also causes the changes in the mechanical properties, microstructure, and porosity of Si composite electrodes. Understanding the relationship between mechanical property evolution, microstructure degradation, and capacity fading is essential for the design of Si composite electrodes. Using an environmental nanoindentation system, in situ microscope cell, and electrochemical impedance spectroscopy, I investigated the mechanical properties, cracking behavior, and lithiation/delithiation kinetics of Si composite electrodes made with different polymeric binders, including polyvinylidene fluoride, Nafion, sodium-carboxymethyl cellulose, and sodium-alginate, in their realistic working environment. The mechanical property evolution is determined by the state-of-charge, porosity, irreversible volume change, and mechanical behavior of binders. Periodical crack opening and closing happens in Si composite electrodes prepared with binders that have strong adhesion with Si. Mechanical degradations, e.g., irreversible volume change, cracking, and debonding between binders and Si particles, are correlated with the evolution of lithiation/delithiation kinetics and the capacity fading of Si composite electrodes. Based on these findings, a partial charging approach is proposed and confirmed experimentally to improve the cycling stability of Si composite electrodes. Li metal electrodes suffer from the low Coulombic efficiency, high electrochemical reactivity with the electrolytes, and the safety hazards caused by the uncontrollable dendrite growth during cycling. Mechanical suppression by using solid electrolytes and artificial SEI is a promising strategy to inhibit the formation of Li dendrites. Mechanical properties of bulk and mossy Li are required for designing mechanical inhibitors and improving the stability of the Li | inhibitor interface. Using an environmental nanoindentation system, I studied the mechanical behavior, especially the time-dependent behavior, of bulk Li and porous mossy Li at ambient temperature. By combining finite element (FE) modeling with experiments, a constitutive law was determined for the viscoplastic deformation of Li metal. FE modeling also demonstrates that the elasticity has a negligible influence on the indentation deformation of bulk Li. Flat punch indentation measurements showed that mossy Li has significantly higher deformation and creep resistance than bulk Li despite of its porous microstructure. The mechanical parameters of bulk and mossy Li may be helpful to develop of dendrite-free Li metal electrodes

LOCUS: A Novel Decomposition Method for Brain Network Connectivity Matrices using Low-rank Structure with Uniform Sparsity

Network-oriented research has been increasingly popular in many scientific areas. In neuroscience research, imaging-based network connectivity measures have become the key for understanding brain organizations, potentially serving as individual neural fingerprints. There are major challenges in analyzing connectivity matrices including the high dimensionality of brain networks, unknown latent sources underlying the observed connectivity, and the large number of brain connections leading to spurious findings. In this paper, we propose a novel blind source separation method with low-rank structure and uniform sparsity (LOCUS) as a fully data-driven decomposition method for network measures. Compared with the existing method that vectorizes connectivity matrices ignoring brain network topology, LOCUS achieves more efficient and accurate source separation for connectivity matrices using low-rank structure. We propose a novel angle-based uniform sparsity regularization that demonstrates better performance than the existing sparsity controls for low-rank tensor methods. We propose a highly efficient iterative Node-Rotation algorithm that exploits the block multi-convexity of the objective function to solve the non-convex optimization problem for learning LOCUS. We illustrate the advantage of LOCUS through extensive simulation studies. Application of LOCUS to Philadelphia Neurodevelopmental Cohort neuroimaging study reveals biologically insightful connectivity traits which are not found using the existing method

A Novel Joint Brain Network Analysis Using Longitudinal Alzheimer's Disease Data.

There is well-documented evidence of brain network differences between individuals with Alzheimer's disease (AD) and healthy controls (HC). To date, imaging studies investigating brain networks in these populations have typically been cross-sectional, and the reproducibility of such findings is somewhat unclear. In a novel study, we use the longitudinal ADNI data on the whole brain to jointly compute the brain network at baseline and one-year using a state of the art approach that pools information across both time points to yield distinct visit-specific networks for the AD and HC cohorts, resulting in more accurate inferences. We perform a multiscale comparison of the AD and HC networks in terms of global network metrics as well as at the more granular level of resting state networks defined under a whole brain parcellation. Our analysis illustrates a decrease in small-worldedness in the AD group at both the time points and also identifies more local network features and hub nodes that are disrupted due to the progression of AD. We also obtain high reproducibility of the HC network across visits. On the other hand, a separate estimation of the networks at each visit using standard graphical approaches reveals fewer meaningful differences and lower reproducibility

Knockoffs-SPR: Clean Sample Selection in Learning with Noisy Labels

A noisy training set usually leads to the degradation of the generalization and robustness of neural networks. In this paper, we propose a novel theoretically guaranteed clean sample selection framework for learning with noisy labels. Specifically, we first present a Scalable Penalized Regression (SPR) method, to model the linear relation between network features and one-hot labels. In SPR, the clean data are identified by the zero mean-shift parameters solved in the regression model. We theoretically show that SPR can recover clean data under some conditions. Under general scenarios, the conditions may be no longer satisfied; and some noisy data are falsely selected as clean data. To solve this problem, we propose a data-adaptive method for Scalable Penalized Regression with Knockoff filters (Knockoffs-SPR), which is provable to control the False-Selection-Rate (FSR) in the selected clean data. To improve the efficiency, we further present a split algorithm that divides the whole training set into small pieces that can be solved in parallel to make the framework scalable to large datasets. While Knockoffs-SPR can be regarded as a sample selection module for a standard supervised training pipeline, we further combine it with a semi-supervised algorithm to exploit the support of noisy data as unlabeled data. Experimental results on several benchmark datasets and real-world noisy datasets show the effectiveness of our framework and validate the theoretical results of Knockoffs-SPR. Our code and pre-trained models are available at https://github.com/Yikai-Wang/Knockoffs-SPR.Comment: update: refined theory and analysis, release cod

The Politico-Economic Dynamics of China’s Growth

China's rapid growth has been driven by policy reforms that significantly reduce market frictions. Policy reforms are determined by the government according to its own politico-economic considerations. This paper embeds these politico-economic considerations in a macro model of China to endogenously study government policies, market frictions, and economic growth. In the model, an elite runs the government and maximizes its own incomes, facing a political constraint: getting enough supporters. The government provides high enough incomes to state workers in order to gain their support. It also controls capital allocations in the state and the private sector to balance between keeping enough supporters and extracting more taxes from the private sector. These policies initially generate rapid growth accompanied by declining labor and capital market frictions but in the long run, keep the frictions persistent, which are harmful to growth. The calibrated model can quantitatively account for salient aspects of China's recent development and provide predictions for future dynamics

AMP in the wild: Learning robust, agile, natural legged locomotion skills

The successful transfer of a learned controller from simulation to the real world for a legged robot requires not only the ability to identify the system, but also accurate estimation of the robot's state. In this paper, we propose a novel algorithm that can infer not only information about the parameters of the dynamic system, but also estimate important information about the robot's state from previous observations. We integrate our algorithm with Adversarial Motion Priors and achieve a robust, agile, and natural gait in both simulation and on a Unitree A1 quadruped robot in the real world. Empirical results demonstrate that our proposed algorithm enables traversing challenging terrains with lower power consumption compared to the baselines. Both qualitative and quantitative results are presented in this paper.Comment: Video: https://youtu.be/7Ggcj6Izfh

Studies on Application of Silyl Groups in Ring-Closing Metathesis Reactions and Fragment-Based Probe Discovery

In efforts to search for tool compounds that are capable of probing normal and disease-associated biological processes, both quality and identity of the screening collection are very important. Towards this goal, diversity-oriented synthesis (DOS) has been explored for a decade, which aims to populate the chemical space with diverse sets of small molecules distinct from the traditional ones obtained via combinatorial chemistry. In the practice of DOS, macrocyclic ring-closing metathesis (RCM) reactions have been widely used. However, the prediction and control of stereoselectivity of the reaction is often challenging; chemical transformation of the olefin moiety within the product is in general limited. Chapter I of this thesis describes a methodology that addresses both problems simultaneously and thus extends the utility of the RCM reactions. By installing a silyl group at the internal position of one of the olefin termini, the RCM reaction could proceed with high stereoselectivity to afford the (E)-alkenylsiloxane regardless of the intrinsic selectivity of the substrate. The resulting alkenylsiloxane can be transformed to a variety of functionalities in a regiospecific fashion. The conversion of the (E)-alkenylsiloxanes to alkenyl bromides could proceed with inversion of stereochemistry for some substrates allowing the selective access of both the E- and Z-trisubstituted macrocyclic alkenes. It was also found that the silyl group could trap the desired mono-cyclized product by suppressing nonproductive pathways. Chapter II of this thesis describes the application of the concept of DOS in the area of fragment-based drug discovery. Most fragment libraries used to date have been limited to aromatic heterocycles with an underrepresentation of chiral, enantiopure, $sp^3$-rich compounds. In order to create a more diverse fragment collection, the build/couple/pair algorithm was adopted. Starting from proline derivatives, a series of bicyclic compounds were obtained with complete sets of stereoisomers and high $sp^3$ ratio. Efforts are also described toward the generation of diverse fragments using methodology described in Chapter I. The glycogen synthase kinase $(GSK3\beta)$ was selected as the proof-of-concept target for screening the DOS fragments.Chemistry and Chemical Biolog