178 research outputs found
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 MnBiTe
The van der Waals antiferromagnetic topological insulator MnBiTe
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
MnBiTe in the ground state. Here, we demonstrate the existence of two
distinct types of zero Hall phenomena in few-layer MnBiTe. 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 MnBiTe 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 MnBiTe, but also highlights the critical issues
concerning the fabrication and characterization of devices based on 2D
materials.Comment: 21 pages, 4 figure
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