Developing High-Performance 2D Heterostructured Electrocatalysts and Photocatalysts for Hydrogen Production and Utilizationsts and Photocatalysts for Hydrogen Production and Utilization

H2 is a pivotal chemical in modern society, not only as a clean energy carrier but also as a versatile chemical reactant. However, traditional hydrogen production and utilization heavily rely on thermocatalysis, which is highly energy-intensive and can result in heavy carbon emission and severe environmental problems. Photocatalysis and electrocatalysis are greener alternatives to thermocatalysis that can capitalize on the renewable sunlight and electricity and thus dramatically reduce energy requirements. However, heterogeneous electro/photocatalysts are still far from application to hydrogen economy due to the lack of design principles that can lead to sufficient efficiency. To address this challenge, the dissertation primarily focuses on developing high-performance electrocatalysts and photocatalysts by understanding the impact of surface defects and interactions between different phases on catalytic performance. With the obtained understanding, electro/photocatalysts with high efficiency in H2 production and utilization (herein, transfer hydrogenation) can be facilely fabricated. To better achieve an in-depth understanding of fabricating electro/photocatalysts used for the hydrogen economy, my thesis work starts with the research on H2 evolution reaction (HER) via electrocatalysis, and then moves to the HER using the more challenging photocatalytic approach and finally proceeds to the most challenging part, photocatalytic transfer hydrogenation. For electrocatalytic HER, MoS2 nanosheets are in situ grown on carbon fiber paper for the fabrication of the proton exchange membrane (PEM) cell electrode. Impressively, this integrated electrode with an ultralow MoS2 loading of 0.14 mg/cm2 can achieve small cell voltages of 1.96 and 2.25 V under 1 and 2 A/cm2, respectively, in a practical PEM cell, which is superior to most cells using noble-metal-free HER electrocatalysts even with extremely high catalyst loadings of 3~6 mg/cm2 under the similar cell operation conditions. The ultrahigh activity of the as-synthesized electrode is attributed to the intimate contact between MoS2 and CFP, vertical alignment of MoS2 nanosheets on CFP, the coexistence of 1T and 2H multiphase MoS2 and the existence of various defects on MoS2. For photocatalytic HER, an Au nanocage/MoS2 system is investigated to understand the effect of localized surface plasmon resonance (LSPR) on photocatalysis. The match between the LSPR wavelength of Au nanocages and the optical absorption edge of MoS2 is found to be critical to the activity of the composite. When the match is achieved, a 40-fold activity increase over the bare MoS2 is observed, while the other unmatched counterparts show much less activity enhancement (~15-fold). The near field enhancement (NFE) is proposed to govern the LSPR process with the energy of surface plasmon transferred from Au to MoS2 to promote electron excitation in MoS2, the efficiency of which maximized when the LSPR wavelength of Au matches the MoS2 absorption edge. In the photocatalytic transfer hydrogenation case, phenylacetylene (PA) semi-hydrogenation is selected as a model reaction to understand how vacancies in 2D semiconductors may be utilized to manipulate photocatalytic efficiency. 2D g-C3N4 nanosheets loaded with Ni single-atoms (SAs) are used as the catalyst for this reaction. By controlling both the Ni loading and the density of surface vacancies on g-C3N4, it is found that the numbers of vacancies and Ni SAs had a synergistic impact on the activity of the catalyst. Therefore, a fine tuning of both factors should be important to achieve an optimal hydrogenation activity. Overall, all research examples highlight the important role played by surface defects and metal-semiconductor interactions, and the findings from the research can be potentially used to guide the design of high-performance photocatalysts for hydrogen evolution and hydrogenation reactions

EU member states and major external powers: is China's engagement in Central and Eastern Europe politically dividing the EU?

China is increasing its engagement with the countries of Central and Eastern Europe. There are growing concerns in Europe that China’s economic engagement in this region may translate into political influence, which may politically divide the European Union and hamper the Union’s ability to speak to China with one voice. Against this backdrop, this thesis seeks answers to the question of what factors account for variation in adherence to the EU’s common policy on China across EU member states. The potential explanatory factors that the thesis focuses on include the degree of member states’ economic involvement with China and the degree of their normative compliance with EU rules and norms. The thesis employs a small-N research design with Estonia, Poland, Hungary and the Czech Republic as cases. By analyzing variation across the observed countries, the thesis concludes that both factors affect member states’ adherence to the EU’s common policy on China: there is an inverse correlation between a member state’ economic involvement with China and its adherence to the EU’s common policy on China, while a positive correlation exists between a member state’s normative compliance with the EU and its adherence to the EU’s common policy on China. Driven by different motives – economic gains or normative values, the member states respond to China’s engagement differently, which results in internal vertical incoherence in the EU and growing difficulties for the EU to formulate a unified policy approach to China and to adhere to it. Based on these empirical findings, this thesis gives suggestions to the EU in response to China’s increasing engagement that the EU should primarily focus on fixing the problem of internal non-compliance with EU norms and rules, so that the EU can deal with China as a truly unified Community in both economic and political realms.https://www.ester.ee/record=b5243306*es

A Socioeconomic Well-Being Index

An annual well-being index constructed from thirteen socioeconomic factors is proposed in order to dynamically measure the mood of the US citizenry. Econometric models are fitted to the log-returns of the index in order to quantify its tail risk and perform option pricing and risk budgeting. By providing a statistically sound assessment of socioeconomic content, the index is consistent with rational finance theory, enabling the construction and valuation of insurance-type financial instruments to serve as contracts written against it. Endogenously, the VXO volatility measure of the stock market appears to be the greatest contributor to tail risk. Exogenously, "stress-testing" the index against the politically important factors of trade imbalance and legal immigration, quantify the systemic risk. For probability levels in the range of 5% to 10%, values of trade below these thresholds are associated with larger downward movements of the index than for immigration at the same level. The main intent of the index is to provide early-warning for negative changes in the mood of citizens, thus alerting policy makers and private agents to potential future market downturns

In-Sample Policy Iteration for Offline Reinforcement Learning

Offline reinforcement learning (RL) seeks to derive an effective control policy from previously collected data. To circumvent errors due to inadequate data coverage, behavior-regularized methods optimize the control policy while concurrently minimizing deviation from the data collection policy. Nevertheless, these methods often exhibit subpar practical performance, particularly when the offline dataset is collected by sub-optimal policies. In this paper, we propose a novel algorithm employing in-sample policy iteration that substantially enhances behavior-regularized methods in offline RL. The core insight is that by continuously refining the policy used for behavior regularization, in-sample policy iteration gradually improves itself while implicitly avoids querying out-of-sample actions to avert catastrophic learning failures. Our theoretical analysis verifies its ability to learn the in-sample optimal policy, exclusively utilizing actions well-covered by the dataset. Moreover, we propose competitive policy improvement, a technique applying two competitive policies, both of which are trained by iteratively improving over the best competitor. We show that this simple yet potent technique significantly enhances learning efficiency when function approximation is applied. Lastly, experimental results on the D4RL benchmark indicate that our algorithm outperforms previous state-of-the-art methods in most tasks

RepVGG:Making VGG-style ConvNets Great Again

We present a simple but powerful architecture of convolutional neural network, which has a VGG-like inference-time body composed of nothing but a stack of 3x3 convolution and ReLU, while the training-time model has a multi-branch topology. Such decoupling of the training-time and inference-time architecture is realized by a structural re-parameterization technique so that the model is named RepVGG. On ImageNet, RepVGG reaches over 80% top-1 accuracy, which is the first time for a plain model, to the best of our knowledge. On NVIDIA 1080Ti GPU, RepVGG models run 83% faster than ResNet-50 or 101% faster than ResNet-101 with higher accuracy and show favorable accuracy-speed trade-off compared to the state-of-the-art models like EfficientNet and RegNet. The code and trained models are available at https://github.com/megvii-model/RepVGG.Comment: CVPR 202

Mini-PointNetPlus: a local feature descriptor in deep learning model for 3d environment perception

Common deep learning models for 3D environment perception often use pillarization/voxelization methods to convert point cloud data into pillars/voxels and then process it with a 2D/3D convolutional neural network (CNN). The pioneer work PointNet has been widely applied as a local feature descriptor, a fundamental component in deep learning models for 3D perception, to extract features of a point cloud. This is achieved by using a symmetric max-pooling operator which provides unique pillar/voxel features. However, by ignoring most of the points, the max-pooling operator causes an information loss, which reduces the model performance. To address this issue, we propose a novel local feature descriptor, mini-PointNetPlus, as an alternative for plug-and-play to PointNet. Our basic idea is to separately project the data points to the individual features considered, each leading to a permutation invariant. Thus, the proposed descriptor transforms an unordered point cloud to a stable order. The vanilla PointNet is proved to be a special case of our mini-PointNetPlus. Due to fully utilizing the features by the proposed descriptor, we demonstrate in experiment a considerable performance improvement for 3D perception