345 research outputs found
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
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
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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, -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 ratio. Efforts are also described toward the generation of diverse fragments using methodology described in Chapter I. The glycogen synthase kinase was selected as the proof-of-concept target for screening the DOS fragments.Chemistry and Chemical Biolog
Performance Investigation of Two-stage Heat Pump with Vapor Injection Using R410A as Working Fluid
The heating capacity and coefficient of performance (COP) will significantly decrease when the conventional air source heat pump (ASHP) system is operated under low ambient temperature conditions, with the high discharge temperature. The vapor injection technique with an internal heat exchanger (IHX) has been proposed as an effective way to acquire better performance. In this paper, the performance of a R410A two-stage air source heat pump system with the scroll compressor was studied experimentally. According to the experimental results, the heating capacity increased with the vapor injection at the fixed ambient temperature and water temperature, however the discharge temperature decreased. One interesting founding was that the discharge temperature at the ambient temperature of -6°C and -12°C increased slightly when a little refrigerant was injected into the compressor. The optimal point always occurred when the system COP was maximized with the other constant parameters. Furthermore, the peak points of each COP curve were shifted to the right if the ambient temperature was elevated. With variation of the ambient temperature, the peak points of the heat pump with vapor injection technique were compared with those of the conventional system. The results showed that the vapor injection technique could improve the heating performance and enhance the stability and reliability of the compressor. At the ambient temperature of -20°C, the system could have up to 17.01% improved COP and 13.73% decreased discharge temperature, respectively. In addition, the variation of injection ratio with intermediate pressure and expansion valve operating reliability were also investigated
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