Top-Down and Bottom-Up Fabrication of Key Components in Miniature Energy Storage Devices

The advent of miniature electronic devices demands power sources of commensurate form factors. This spurs the research of micro energy storage devices, e.g., 3D microbatteries. A 3D microbattery contains nonplanar microelectrodes with high aspect ratio and high surface area, separated by a nanoscale electrolyte. The device takes up a total volume as small as 10 mm3, allowing it to serve on a chip and to provide power in-situ. The marriage of nanotechnology and electrochemical energy storage makes microbattery research a fascinating field with both scientific excitement and application prospect. However, successful fabrication of well-functioned key components and the assembly of them require careful choice of both materials and processing technologies, which explains the rarity of reports on fully assembled 3D microbattery devices. In this Thesis, we exploited both top-down and bottom-up methods to produce nanostructured functional materials as either microelectrodes or nanoscale electrolytes. Project 1 introduces nanoimprinting as a promising strategy toward scalable fabrication of woodpile-like 3D microelectrodes out of well-dispersed TiO2 nanoparticles. Using sequential imprinting, we created electrode structures with different aspect ratios and correlated them to the improved charge storage capacity. One step forward, we applied imprinting to other electrode materials. In Project 2, we imprinted microelectrode using customized, ultrafine LiMn2O4 (LMO) and Li4Ti5O12 (LTO) nanoparticles. A dopamine-containing copolymer electrolyte was developed to enable the layer-by-layer assembly of microbattery full device. The synergistic effect of nanosized materials and micropatterning resulted in batteries with very high volumetric energy and power densities. Project 3 explores using vapor phase chemistry to deposit copolymer thin films onto 3D nanostructures and subsequently doping the neat dielectric films into “shrink-wrap” electrolytes. Correlations between deposition parameters, copolymer composition and the resultant dielectric and conducting properties were built. In the last project, we harnessed the self-assembly of bottlebrush block copolymers to template phenolic resin precursor and obtained nanoporous carbon electrodes that show promising performance in electrostatic double layer capacitors (EDLCs). By mixing electroactive Fe2O3 nanoparticles into the precursors, the electrodes become high-capacity lithium-ion battery anodes and more importantly, the precursor can be imprinted and undergo rapid photothermal curing. The combination of bottom-up assembly, top-down patterning and rapid curing makes them attractive for a variety of applications

Iteratively Optimized Patch Label Inference Network for Automatic Pavement Disease Detection

We present a novel deep learning framework named the Iteratively Optimized Patch Label Inference Network (IOPLIN) for automatically detecting various pavement diseases that are not solely limited to specific ones, such as cracks and potholes. IOPLIN can be iteratively trained with only the image label via the Expectation-Maximization Inspired Patch Label Distillation (EMIPLD) strategy, and accomplish this task well by inferring the labels of patches from the pavement images. IOPLIN enjoys many desirable properties over the state-of-the-art single branch CNN models such as GoogLeNet and EfficientNet. It is able to handle images in different resolutions, and sufficiently utilize image information particularly for the high-resolution ones, since IOPLIN extracts the visual features from unrevised image patches instead of the resized entire image. Moreover, it can roughly localize the pavement distress without using any prior localization information in the training phase. In order to better evaluate the effectiveness of our method in practice, we construct a large-scale Bituminous Pavement Disease Detection dataset named CQU-BPDD consisting of 60,059 high-resolution pavement images, which are acquired from different areas at different times. Extensive results on this dataset demonstrate the superiority of IOPLIN over the state-of-the-art image classification approaches in automatic pavement disease detection. The source codes of IOPLIN are released on \url{https://github.com/DearCaat/ioplin}.Comment: Revision on IEEE Trans on IT

Allocating Divisible Resources on Arms with Unknown and Random Rewards

We consider a decision maker allocating one unit of renewable and divisible resource in each period on a number of arms. The arms have unknown and random rewards whose means are proportional to the allocated resource and whose variances are proportional to an order $b$ of the allocated resource. In particular, if the decision maker allocates resource $A_i$ to arm $i$ in a period, then the reward $Y_i$ is$Y_i(A_i)=A_i \mu_i+A_i^b \xi_{i}$, where $\mu_i$ is the unknown mean and the noise $\xi_{i}$ is independent and sub-Gaussian. When the order $b$ ranges from 0 to 1, the framework smoothly bridges the standard stochastic multi-armed bandit and online learning with full feedback. We design two algorithms that attain the optimal gap-dependent and gap-independent regret bounds for $b\in [0,1]$, and demonstrate a phase transition at $b=1/2$. The theoretical results hinge on a novel concentration inequality we have developed that bounds a linear combination of sub-Gaussian random variables whose weights are fractional, adapted to the filtration, and monotonic