Theoretical Studies of Titanium Dioxide for Dye-Sensitized Solar Cell and Photocatalytic Reaction

This chapter aims to provide researchers in the field of photovoltaics with the valuable information and knowledge needed to understand the physics and modeling of titanium dioxide for dye-sensitized solar cell and photocatalytic reaction. The electronic band structure of titanium dioxide, the treatment of the excited state of titanium dioxide, the molecular dynamics and ultrafast quantum dynamics simulations, and several promising photocatalytic schemes and important considerations for theoretical study are addressed and reviewed. The advanced computational strategies and methods and optimized models to achieve exact simulation are described and discussed, including first principle calculations, nonadiabatic molecular and quantum dynamics, wave function propagation methods, and surface construction of titanium dioxide. These advanced theoretical investigations have become highly active areas of photovoltaics research and powerful tools for the supplement and prediction of related experimental efforts

Three-dimensional numerical study of flow characteristic and membrane fouling evolution in an enzymatic membrane reactor

In order to enhance the understanding of membrane fouling mechanism, the hydrodynamics of granular flow in a stirred enzymatic membrane reactor was numerically investigated in the present study. A three-dimensional Euler-Euler model, coupled with k-e mixture turbulence model and drag function for interphase momentum exchange, was applied to simulate the two-phase (fluid-solid) turbulent flow. Numerical simulations of single- or two-phase turbulent flow under various stirring speed were implemented. The numerical results coincide very well with some published experimental data. Results for the distributions of velocity, shear stress and turbulent kinetic energy were provided. Our results show that the increase of stirring speed could not only enlarge the circulation loops in the reactor, but it can also increase the shear stress on the membrane surface and accelerate the mixing process of granular materials. The time evolution of volumetric function of granular materials on the membrane surface has qualitatively explained the evolution of membrane fouling.Comment: 10 panges, 8 figure

Quantized Feedback Control of Network Empowerment Ammunition with Data-Rate Limitations

This paper investigates quantized feedback control problems for network empowerment ammunition, where the sensors and the controller are connected by a digital communication network with data-rate limitations. Different from the existing ones, a new bit-allocation algorithm on the basis of the singular values of the plant matrix is proposed to encode the plant states. A lower bound on the data rate is presented to ensure stabilization of the unstable plant. It is shown in our results that, the algorithm can be employed for the more general case. An illustrative example is given to demonstrate the effectiveness of the proposed algorithm

2′,10′-Dibromo­spiro­[cylohexane-1,6-di­benzo[d,f][1,3]dioxepine]

In the title compound, C18H16Br2O2, the dihedral angle between the aromatic rings is 35.55 (17)° and the cyclohexyl ring adopts a chair-like conformation. In the crystal, molecules are linked by van der Waals forces

CycleAlign: Iterative Distillation from Black-box LLM to White-box Models for Better Human Alignment

Language models trained on large-scale corpus often generate content that is harmful, toxic, or contrary to human preferences, making their alignment with human values a critical concern. Reinforcement learning from human feedback (RLHF) with algorithms like PPO is a prevalent approach for alignment but is often complex, unstable, and resource-intensive. Recently, ranking-based alignment methods have emerged, offering stability and effectiveness by replacing the RL framework with supervised fine-tuning, but they are costly due to the need for annotated data. Considering that existing large language models (LLMs) like ChatGPT are already relatively well-aligned and cost-friendly, researchers have begun to align the language model with human preference from AI feedback. The common practices, which unidirectionally distill the instruction-following responses from LLMs, are constrained by their bottleneck. Thus we introduce CycleAlign to distill alignment capabilities from parameter-invisible LLMs (black-box) to a parameter-visible model (white-box) in an iterative manner. With in-context learning (ICL) as the core of the cycle, the black-box models are able to rank the model-generated responses guided by human-craft instruction and demonstrations about their preferences. During iterative interaction, the white-box models also have a judgment about responses generated by them. Consequently, the agreement ranking could be viewed as a pseudo label to dynamically update the in-context demonstrations and improve the preference ranking ability of black-box models. Through multiple interactions, the CycleAlign framework could align the white-box model with the black-box model effectively in a low-resource way. Empirical results illustrate that the model fine-tuned by CycleAlign remarkably exceeds existing methods, and achieves the state-of-the-art performance in alignment with human value