Erasing and Correction of Liquid Metal Printed Electronics Made of Gallium Alloy Ink from the Substrate

Gallium-based liquid metals have recently been found important in a variety of newly emerging applications such as room temperature metal 3D printing, direct writing of electronics and biomedicine etc. In all these practices, one frequently encounters the situations that a printed circuit or track needs to be corrected or the unwanted parts of the device should be removed as desired. However, few appropriate strategies are currently available to tackle such important issues at this stage. Here we have identified several low cost ways toward this goal by comparatively investigating three typical strategies spanning from mechanical, chemical, to electrochemical principles, for removing the gallium-based liquid metal circuits or thin films. Regarding the mechanical approach, we constructed an eraser for removing the liquid metal thin films. It was shown that ethanol (CH3CH2OH) could serve as a good candidacy material for the mechanical eraser. In the chemical category, we adopted alkalis and acids to remove the finely printed liquid metal circuits and sodium hydroxide (NaOH) solution was particularly revealed to be rather efficient in making a chemical eraser. In the electrochemical strategy, we applied a 15 V voltage to a liquid metal thin film (covered with water) and successfully removed the target metal part. These methods were comparatively evaluated with each of the merits and shortcomings preliminarily clarified in the end. The present work is expected to be important for the increasing applications of the liquid metal enabled additive manufactures.Comment: 16 pages, 4 figure

Unveiling Correlated Topological Insulators through Fermionic Tensor Network States -- Classification, Edge Theories and Variational Wavefunctions

The study of topological band insulators has revealed fascinating phases characterized by band topology indices, harboring extraordinary boundary modes protected by anomalous symmetry actions. In strongly correlated systems, where the traditional notion of electronic bands becomes obsolete, it has been established that topological insulator phases persist as stable phases, separate from trivial insulators. However, due to the inability to express the ground states of such systems as Slater determinants, the formulation of generic variational wavefunctions for numerical simulations is highly desirable. In this paper, we tackle this challenge by developing a comprehensive framework for fermionic tensor network states. Starting from simple assumptions, we obtain possible sets of tensor equations for any given symmetry group, capturing consistent relations governing symmetry transformation rules on tensor legs. We then examine the connections between these tensor equations and topological insulators by construing edge theories and extracting quantum anomaly data from each set of tensor equations. By exhaustively exploring all possible sets of equations, we achieve a systematic classification of topological insulator phases. Imposing the solutions of a given set of equations onto local tensors, we obtain generic variational wavefunctions for corresponding topological insulator phases. Our methodology provides a crucial first step towards simulating topological insulators in strongly correlated systems. We discuss the limitations and potential generalizations of our results, paving the way for further advancements in this field.Comment: 32+20 pages, 11 figure

Variational Tensor Wavefunctions for the Interacting Quantum Spin Hall Phase

The quantum spin hall (QSH) phase, also known as the 2D topological insulator, is characterized by protected helical edge modes arising from time reversal symmetry. While initially proposed for band insulators, this phase can also manifest in strongly-correlated systems where conventional band theory fails. To overcome the challenge of simulating this phase in realistic correlated models, we propose a novel framework utilizing fermionic tensor network states. Our approach involves constructing a tensor representation of the fixed-point wavefunction based on an exact solvable model, enabling us to derive a set of tensor equations governing the transformation rules of local tensors under symmetry operations. These tensor equations lead to the anomalous edge theory, which provides a comprehensive description of the QSH phase. By solving these tensor equations, we obtain variational ansatz for the QSH phase, which we subsequently verify through numerical calculations. This method serves as an initial step towards employing tensor algorithms to simulate the QSH phase in strongly-correlated systems, opening new avenues for investigating and understanding topological phenomena in complex materials.Comment: 6+15 pages,12 figures. Numerical calculations are adde

THE STUDIES OF CONFINED FLUID PHASE BEHAVIOR IN SHALE RESOURCES

Fluid phase behavior in porous media is governed by not only fluid-fluid interactions but also fluid-wall interactions. In shale formations, a large amount of hydrocarbon fluid is stored within the organic matters where the pore sizes are in the order of nanometer scales. Inside these nanopores, the interactions between the fluid molecules and porous walls play such an important role that can change the fluid physical properties of the stored hydrocarbons. The first part of this work is to focus on investigating the effects of pore proximity in shale formations on phase behavior of the reservoir fluids by modifying the cubic equations of state (EOS), e.g. van der Waals EOS and Peng-Robinson EOS. Effects of both fluid-fluid and fluid-wall interactions are included in the modified EOS. Such effects were averaged for any particular pore sizes. Correlations based upon the available molecular simulation results were developed to include the effects of fluid-wall interactions into the modified EOS. The relationships between the binary interaction coefficients and pore sizes of a C1/nC5 binary mixture were obtained based on experimental data using modified Peng-Robinson EOS. The vapor-liquid equilibrium (VLE) calculations were performed on a C1/nC4/C10 ternary mixture using the modified Peng-Robinson EOS. The results showed that smaller pores caused the fluid mixture to behave similar to dry gas, which results in reduction in condensate banking and delay in condensate dropout during production in comparison to conventional reservoirs. Although the fluid phase behavior was calculated based on an average point of view for different pore sizes. It is believed that the fluids inside the nanoscale pores are notuniformly distributed due to the fluid-wall interactions. The fluid density is higher near the wall than the center region of the pore. The second part of this work is concentrated on obtaining the fluid density profiles across the pore. The fluids in nanoscale pores were considered to form bulk phase, transition phase and adsorbed phase depended on the distances to the pore walls. Simplified Local Density (SLD) theory coupled with the modified Peng-Robinson EOS was used to calculate the density profiles for both single- component fluids and mixtures in different pore sizes. Both the fluid density profiles of single component fluids (e.g. methane, ether, propane and n-butane) and binary mixtures were investigated using the SLD-PR model. The results showed that the fluid density near the wall is much higher than that in the center of the pore. On the other hand, pressure, temperature, pore size, fluid type and fluid composition all have impacts on the fluid distributions. Higher pressure can shift the fluid density profile to a higher value while increase in temperature can shift down the density profile. For heavy components, such as n-butane, the adsorbed region is larger than that for light components, such as methane. For fluid mixtures, the composition of the fluid changes across the pore and the composition of the heavier component is much higher close to the pore compare to the bulk fluid. The third part of this work is the application of the SLD theory to couple with a new Gas- In-Place (GIP) model on a case study of predicting the phase behavior of real reservoir fluids in condensate window of Eagle Ford shale and estimating the adsorbed gas content and the total GIP of the reservoir at high temperature and pressure. The preliminary computation results showed that the adsorbed gas could take more than 30% of gas in place in Eagle Ford shale. By using the introduced method, the adsorbed gas content and xviii the total GIP in unconventional reservoirs were calculated with good accuracy under short computational time. This makes the model useful when implemented into reservoir simulators. The fourth part of this work investigates the occurrence of capillary condensation inside nanoporous shales and the way to quantify the condensed fluid contents and the behaviors using the SLD model combined with modified Young-Laplace equation. It is the first attempt to quantitatively consider both adsorption and capillary condensation for hydrocarbon mixtures in shale media. For a retrograde mixture, the effects of capillary condensation reduce the lower dew point pressure and increase the upper dew point pressure. The shift is larger for the lower dew point pressure. This finding is consistent with the results calculated using the modified EOS to consider the pore size effects in the first part of this dissertation, which gives a cross-validation to both models. The last part of this dissertation work focuses on molecular dynamic (MD) simulations of hydrocarbons in nanopores. A universal molecular simulator called LAMMPS is used to perform MD simulations of hydrocarbon mixtures. From these MD simulations, we are able to investigate how hydrocarbon fluids are arranged under the effects of the pore wall. The results show that the pore wall attracts hydrocarbon molecules to form a high- density adsorbed region. This agrees with the findings from SLD model described in previous chapters

Large Language Model Is Not a Good Few-shot Information Extractor, but a Good Reranker for Hard Samples!

Large Language Models (LLMs) have made remarkable strides in various tasks. Whether LLMs are competitive few-shot solvers for information extraction (IE) tasks, however, remains an open problem. In this work, we aim to provide a thorough answer to this question. Through extensive experiments on nine datasets across four IE tasks, we demonstrate that current advanced LLMs consistently exhibit inferior performance, higher latency, and increased budget requirements compared to fine-tuned SLMs under most settings. Therefore, we conclude that LLMs are not effective few-shot information extractors in general. Nonetheless, we illustrate that with appropriate prompting strategies, LLMs can effectively complement SLMs and tackle challenging samples that SLMs struggle with. And moreover, we propose an adaptive filter-then-rerank paradigm to combine the strengths of LLMs and SLMs. In this paradigm, SLMs serve as filters and LLMs serve as rerankers. By prompting LLMs to rerank a small portion of difficult samples identified by SLMs, our preliminary system consistently achieves promising improvements (2.4% F1-gain on average) on various IE tasks, with an acceptable time and cost investment.Comment: Accepted by EMNLP 2023 Finding

Statistical and Computational Trade-offs in Variational Inference: A Case Study in Inferential Model Selection

Variational inference has recently emerged as a popular alternative to the classical Markov chain Monte Carlo (MCMC) in large-scale Bayesian inference. The core idea of variational inference is to trade statistical accuracy for computational efficiency. It aims to approximate the posterior, reducing computation costs but potentially compromising its statistical accuracy. In this work, we study this statistical and computational trade-off in variational inference via a case study in inferential model selection. Focusing on Gaussian inferential models (a.k.a. variational approximating families) with diagonal plus low-rank precision matrices, we initiate a theoretical study of the trade-offs in two aspects, Bayesian posterior inference error and frequentist uncertainty quantification error. From the Bayesian posterior inference perspective, we characterize the error of the variational posterior relative to the exact posterior. We prove that, given a fixed computation budget, a lower-rank inferential model produces variational posteriors with a higher statistical approximation error, but a lower computational error; it reduces variances in stochastic optimization and, in turn, accelerates convergence. From the frequentist uncertainty quantification perspective, we consider the precision matrix of the variational posterior as an uncertainty estimate. We find that, relative to the true asymptotic precision, the variational approximation suffers from an additional statistical error originating from the sampling uncertainty of the data. Moreover, this statistical error becomes the dominant factor as the computation budget increases. As a consequence, for small datasets, the inferential model need not be full-rank to achieve optimal estimation error. We finally demonstrate these statistical and computational trade-offs inference across empirical studies, corroborating the theoretical findings.Comment: 56 pages, 8 figure