ENVIRONMENTAL AND SUBSTRATE EFFECT ON THE SURFACE PROPERTIES OF GRAPHENE AND GRAPHITE

This dissertation is focused on understanding and controlling of surface properties of graphene and graphite. Four specific topics are presented: 1) study the intrinsic wettability of graphene; 2) minimize the airborne hydrocarbon contamination on graphitic surface during storage; 3) investigate the anti-corrosion performance of graphene during a long-term ambient oxidation process at room temperature; 4) study the catalytic effect of copper substrate during the atmospheric oxidation of graphene at high temperature. All the results have important implications for the characterization, processing, and storage of graphene (graphite) samples and related devices. Specifically, chapter 2 reports the intrinsic wettability of graphene and the effect of airborne hydrocarbon contamination during its storage. This work overturned the long-held view that graphitic surfaces (including graphene and graphite) are hydrophobic. In chapter 3, efforts have been made to minimize the airborne hydrocarbon adsorption during the storage of graphitic surfaces, this work aimed at maintaining the intrinsic property of graphene and graphite surfaces over a long period of air exposure. Chapter 4 and 5 aimed to elucidate the mutual interactions between graphene and copper substrate during ambient air exposure as well as atmospheric oxidation at high temperature. This work is closely related to the potential application of graphene as an anti-corrosion film for metallic substrates

Text Alignment Is An Efficient Unified Model for Massive NLP Tasks

Large language models (LLMs), typically designed as a function of next-word prediction, have excelled across extensive NLP tasks. Despite the generality, next-word prediction is often not an efficient formulation for many of the tasks, demanding an extreme scale of model parameters (10s or 100s of billions) and sometimes yielding suboptimal performance. In practice, it is often desirable to build more efficient models -- despite being less versatile, they still apply to a substantial subset of problems, delivering on par or even superior performance with much smaller model sizes. In this paper, we propose text alignment as an efficient unified model for a wide range of crucial tasks involving text entailment, similarity, question answering (and answerability), factual consistency, and so forth. Given a pair of texts, the model measures the degree of alignment between their information. We instantiate an alignment model (Align) through lightweight finetuning of RoBERTa (355M parameters) using 5.9M examples from 28 datasets. Despite its compact size, extensive experiments show the model's efficiency and strong performance: (1) On over 20 datasets of aforementioned diverse tasks, the model matches or surpasses FLAN-T5 models that have around 2x or 10x more parameters; the single unified model also outperforms task-specific models finetuned on individual datasets; (2) When applied to evaluate factual consistency of language generation on 23 datasets, our model improves over various baselines, including the much larger GPT-3.5 (ChatGPT) and sometimes even GPT-4; (3) The lightweight model can also serve as an add-on component for LLMs such as GPT-3.5 in question answering tasks, improving the average exact match (EM) score by 17.94 and F1 score by 15.05 through identifying unanswerable questions.Comment: NeurIPS 2023 Camera Ready, Code available at https://github.com/yuh-zha/Alig

Phonon Transmission Across Silicon Grain Boundaries by Atomistic Green's Function Method

Nanostructured materials are of great interest for many applications because of their special properties. Understanding the effect of grain boundaries on phonon transport in polycrystals is important for engineering nanomaterials with desired thermal transport properties. The phonon transport properties of Σ3 grain boundaries in silicon are investigated by employing atomistic Green's function method. Results show that similar to electron transport, the perfect grain boundary does not significantly reduce the thermal conductance, while defective grain boundaries can dramatically reduce the thermal conductance. This work may be helpful for the understanding of the underlying thermal transport mechanism across grain boundaries and the design of grain boundaries for energy applications

Thermal Interface Conductance between Aluminum and Silicon by Molecular Dynamics Simulations

The thermal interface conductance between Al and Si was simulated by a non-equilibrium molecular dynamics method. In the simulations, the coupling between electrons and phonons in Al are considered by using a stochastic force. The results show the size dependence of the interface thermal conductance and the effect of electron-phonon coupling on the interface thermal conductance. To understand the mechanism of interface resistance, the vibration power spectra are calculated. We find that the atomic level disorder near the interface is an important aspect of interfacial phonon transport, which leads to a modification of the phonon states near the interface. There, the vibrational spectrum near the interface greatly differs from the bulk. This change in the vibrational spectrum affects the results predicted by AMM and DMM theories and indicates new physics is involved with phonon transport across interfaces. Keywords:Comment: Journal of Computational and Theoretical Nanoscience 201

PromptAgent: Strategic Planning with Language Models Enables Expert-level Prompt Optimization

Highly effective, task-specific prompts are often heavily engineered by experts to integrate detailed instructions and domain insights based on a deep understanding of both instincts of large language models (LLMs) and the intricacies of the target task. However, automating the generation of such expert-level prompts remains elusive. Existing prompt optimization methods tend to overlook the depth of domain knowledge and struggle to efficiently explore the vast space of expert-level prompts. Addressing this, we present PromptAgent, an optimization method that autonomously crafts prompts equivalent in quality to those handcrafted by experts. At its core, PromptAgent views prompt optimization as a strategic planning problem and employs a principled planning algorithm, rooted in Monte Carlo tree search, to strategically navigate the expert-level prompt space. Inspired by human-like trial-and-error exploration, PromptAgent induces precise expert-level insights and in-depth instructions by reflecting on model errors and generating constructive error feedback. Such a novel framework allows the agent to iteratively examine intermediate prompts (states), refine them based on error feedbacks (actions), simulate future rewards, and search for high-reward paths leading to expert prompts. We apply PromptAgent to 12 tasks spanning three practical domains: BIG-Bench Hard (BBH), as well as domain-specific and general NLP tasks, showing it significantly outperforms strong Chain-of-Thought and recent prompt optimization baselines. Extensive analyses emphasize its capability to craft expert-level, detailed, and domain-insightful prompts with great efficiency and generalizability.Comment: 34 pages, 10 figure

Two types of zero Hall phenomena in few-layer MnBi2_2Te4_4

The van der Waals antiferromagnetic topological insulator MnBi$_2$Te$_4$ represents a promising platform for exploring the layer-dependent magnetism and topological states of matter. Despite the realization of several quantized phenomena, such as the quantum anomalous Hall effect and the axion insulator state, the recently observed discrepancies between magnetic and transport properties have aroused controversies concerning the topological nature of MnBi$_2$Te$_4$ in the ground state. Here, we demonstrate the existence of two distinct types of zero Hall phenomena in few-layer MnBi$_2$Te$_4$. In addition to the robust zero Hall plateau associated with the axion insulator state, an unexpected zero Hall phenomenon also occurs in some odd-number-septuple layer devices. Importantly, a statistical survey of the optical contrast in more than 200 MnBi$_2$Te$_4$ reveals that such accidental zero Hall phenomenon arises from the reduction of effective thickness during fabrication process, a factor that was rarely noticed in previous studies of 2D materials. Our finding not only resolves the controversies on the relation between magnetism and anomalous Hall effect in MnBi$_2$Te$_4$, but also highlights the critical issues concerning the fabrication and characterization of devices based on 2D materials.Comment: 21 pages, 4 figure