???????????? ?????? ???????????? ????????? ?????? ??? ?????? ?????????????????? ??????

Department of ChemistryThe environmental issues caused by the hydrocarbon energy sources have emerged as one of the most urgent challenges in 21st century. The development of clean and renewable energy technologies is critical to meet both the environment regulations and to circumvent dependence upon the fossil fuels. This situation has brought a new idea about the future society solely driven by hydrogen-based energy infrastructures, so-called hydrogen economy. The production and utilization of hydrogen via water electrolysis and fuel cells, respectively, are key ingredients to realize the hydrogen economy. However, the high cost of those devices hinders their wide adoption, which can be attributed primarily to the use of precious metal electrocatalysts such as Pt and Ir that are required for efficient operation. In this context, the development of active non-precious metal catalysts (NPMCs) is of great significance. In this dissertation, new NPMCs based on carbon nanotube (CNT) have been designed and prepared for oxygen reduction reaction (ORR), oxygen evolution reaction (OER), and hydrogen evolution reaction (HER), where the ORR is an important half-reaction that critically affects the fuel cell performance, while the OER & HER are involved in water electrolysis. CNT was selected as the carbon support owing to its high conductivity, chemical stability, and surface tunability, advantageous for electrocatalysis. In Chapter 2, we developed a facile and scalable synthetic method for carbon nanostructures comprising active heteroatom-doped carbon (HDC) layers coated on CNT (CNT/HDC), which was exploited as a metal-free ORR electrocatalyst. The preparation involves the adsorption of heteroatom-containing ionic liquid (IL) on the CNT walls via van der Waals and cationic-?? interactions and subsequent carbonization, yielding CNT/HDC core???sheath nanostructures. The design enables both the efficient utilization of surface active sites of HDC layers and high electric conductivity of the CNT core. The CNT/HDC catalyst exhibited high ORR activity and reaction kinetics comparable to a commercial Pt/C catalyst in alkaline media, and an excellent anion exchange membrane fuel cell (AEMFC) performance. The IL-derived CNT/HDC catalysts could be prepared using various types of IL precursors. Iron and nitrogen codoped carbon (Fe???N/C) catalysts have emerged as the most promising electrocatalysts for the ORR among various classes of NPMCs. A growing body of literature suggests that Fe???Nx species are major active sites in a Fe???N/C catalyst. Chapter 3 presents a general ???silica-protective-layer-assisted??? approach that can preferentially generate the catalytically active Fe???Nx sites in Fe???N/C catalysts while suppressing the formation of less-active large Fe-based particles. The catalyst preparation consisted of the adsorption of iron porphyrin precursor on CNT, silica layer overcoating, high-temperature pyrolysis, and silica layer etching, which yielded CNTs coated with thin layers of porphyrinic carbon (CNT/PC) catalysts. We found that the silica-coating step plays a decisive role in preferentially generating catalytically active Fe???Nx coordination sites, as revealed by temperature-controlled in situ X-ray absorption spectroscopy (XAS). The CNT/PC catalyst contained higher concentration of active Fe???Nx sites compared to the CNT/PC prepared without silica coating. The CNT/PC showed very high ORR activity and excellent stability in alkaline media. Importantly, an alkaline AEMFC with a CNT/PC-based cathode exhibited the highest current and power densities among NPMC-based AEMFCs. In addition, a CNT/PC-based cathode exhibited a high volumetric current density of 320 A cm???3 in acidic proton exchange membrane fuel cell. We also demonstrated the general applicability of this synthetic strategy to other carbon supports. Chapter 4 describes the investigation of active site structures of bifunctional oxygen electrode catalysts based on cobalt oxide (CoOx) under reaction conditions. Size-controlled (3???10 nm) cobalt oxide nanoparticles (CoOx NPs) supported on CNT were prepared, and served as model catalysts. Electrochemical in situ XAS suggested that the initial Co3O4 or CoO phase was transformed to Co3O4???CoOOH core???shell structures under the ORR and OER conditions regardless of particle sizes. Combined with the in situ XAS, cyclic voltammetry study revealed that Co2+/Co3+ and Co3+/Co4+ redox transitions are involved in the ORR and OER, respectively. We further examined the size-dependent electrocatalytic activities. The OER activity increased with decreasing NP size, which correlated to the larger amount of Co(III) species and larger surface area in smaller NPs. For the ORR, no particle size dependence was foundthe CoOx NPs mainly played an auxiliary role, promoting the reduction or disproportionation of peroxide generated from the two-electron reduction of O2 by CNT. In Chapter 5, we investigated the active site structure of NPMC comprising cobalt- and nitrogen-codoped carbon supported on CNT for the HER. For this purpose, CNT hybridized with cobalt phthalocyanic carbons (CNT/Co-PcC) were prepared via the silica coating strategy. A suite of Co???N/C catalysts that contain different concentrations of cobalt-based species (Co???Nx and Co@C) were prepared by controlling experimental parameters. The catalytic role of two Co-based sites for the HER in both acidic and alkaline media was investigated, which revealed that the HER activity in both media was linearly increased with the portion of the Co???Nx sites. This structure???activity relationship suggests that the Co???Nx sites are the major active sites while Co@C species have a minimal catalytic effect for the HER. In addition, reaction kinetics study over the CNT/Co-PcC catalyst allowed us to acquire a better understanding of the Co???Nx active sites for the HER.ope

Speeding up Context-based Sentence Representation Learning with Non-autoregressive Convolutional Decoding

Context plays an important role in human language understanding, thus it may also be useful for machines learning vector representations of language. In this paper, we explore an asymmetric encoder-decoder structure for unsupervised context-based sentence representation learning. We carefully designed experiments to show that neither an autoregressive decoder nor an RNN decoder is required. After that, we designed a model which still keeps an RNN as the encoder, while using a non-autoregressive convolutional decoder. We further combine a suite of effective designs to significantly improve model efficiency while also achieving better performance. Our model is trained on two different large unlabelled corpora, and in both cases the transferability is evaluated on a set of downstream NLP tasks. We empirically show that our model is simple and fast while producing rich sentence representations that excel in downstream tasks

Rethinking Skip-thought: A Neighborhood based Approach

We study the skip-thought model with neighborhood information as weak supervision. More specifically, we propose a skip-thought neighbor model to consider the adjacent sentences as a neighborhood. We train our skip-thought neighbor model on a large corpus with continuous sentences, and then evaluate the trained model on 7 tasks, which include semantic relatedness, paraphrase detection, and classification benchmarks. Both quantitative comparison and qualitative investigation are conducted. We empirically show that, our skip-thought neighbor model performs as well as the skip-thought model on evaluation tasks. In addition, we found that, incorporating an autoencoder path in our model didn't aid our model to perform better, while it hurts the performance of the skip-thought model

Multijet topology in high-energy nuclear collisions: jet broadening

This work presents the first theoretical investigation of the medium modification of jet broadening as an event-shape observable in multijet final states due to jet quenching in high-energy nuclear collisions. The partonic spectrum of $pp$ collisions with next-to-leading order (NLO) accuracy at $\sqrt{s_{\mathrm{NN}}} = 5.02$ TeV is provided by the POWHEG$+$PYTHIA8 event generator, while the linear Boltzmann transport (LBT) model is utilized to investigate the energy loss of fast partons as they traverse through the hot and dense QCD medium. We present the jet broadening distributions in multijet final states for both $pp$ and PbPb collisions at $\sqrt{s_{\mathrm{NN}}} = 5.02$ TeV, then observe an enhancement at the small jet broadening region and suppression at the large jet broadening region in PbPb collisions relative to that in $pp$. This suggests that medium modification with parton energy loss in the QGP leads to a more concentrated energy flow in all observed multijet events in PbPb reactions. We also demonstrate that the intertwining of two effects, the jet number reduction and the restructured contribution, results in the novel behavior of nuclear modification of the jet broadening observable in PbPb collisions.Comment: 9 pages, 6 figures, 2 table

Quantitative assessment of image motion blur in diffraction images of moving biological cells

Motion blur (MB) presents a significant challenge for obtaining high-contrast image data from biological cells with a polarization diffraction imaging flow cytometry (p-DIFC) method. A new p-DIFC experimental system has been developed to evaluate the MB and its effect on image analysis using a time-delay-integration (TDI) CCD camera. Diffraction images of MCF-7 and K562 cells have been acquired with different speed-mismatch ratios and compared to characterize MB quantitatively. Frequency analysis of the diffraction images shows that the degree of MB can be quantified by bandwidth variations of the diffraction images along the motion direction. The analytical results were confirmed by the p-DIFC image data acquired at different speed-mismatch ratios and used to validate a method of numerical simulation of MB on blur-free diffraction images, which provides a useful tool to examine the blurring effect on diffraction images acquired from the same cell. These results provide insights on the dependence of diffraction image on MB and allow significant improvement on rapid biological cell assay with the p-DIFC method

Order-disorder layering transitions of a spin-1 Ising model in a variable crystal field

The magnetic order-disorder layering transitions of a spin-1 Ising model are investigated, under the effect of a variable surface crystal field $\Delta_{s}$, using the mean field theory. Each layer $k$, of the film formed with $N$ layers, disorders at a finite surface crystal field distributed according to the law $\Delta_k=\Delta_s/k^\alpha$, $k=1,2,...,N$ and $\alpha$ being a positive constant. We have established the temperature-crystal field phase diagrams and found a constant tricritical point and a reentrant phenomenon for the first $k_0$ layers. This reentrant phenomenon is absent for the remaining $N-k_0$ layers, but the tricritical points subsist and depend not only on the film thickness but also on the exponent $\alpha$. On the other hand, the thermal behaviour of the surface magnetisation for a fixed value of the surface crystal field $\Delta_{s}$ and selected values of the parameter $\alpha$ are established.Comment: 10 Pages Latex, 9 Figures Postscript. To appear in JMMM (2002