Gate voltage induced injection and shift currents in AA- and AB-stacked bilayer graphene

Generating photogalvanic effects in centrosymmetric materials can provide new opportunities for developing passive photodetectors and energy harvesting devices. In this work, we investigate the photogalvanic effects in centrosymmetric two-dimensional materials, AA- and AB-stacked bilayer graphene, by applying an external gate voltage to break the symmetry. Using a tight-binding model to describe the electronic states, the injection coefficients for circular photogalvanic effects and shift conductivities for linear photogalvanic effects are calculated for both materials with light wavelengths ranging from THz to visible. We find that gate voltage induced photogalvanic effects can be very significant for AB-stacked bilayer graphene, with generating a maximal dc current in the order of mA for a 1 $\mu$m wide sample illuminated by a light intensity of 0.1 GW/cm$^2$, which is determined by the optical transition around the band gap and van Hove singularity points. Although such effects in AA-stacked bilayer graphene are about two orders of magnitude smaller than those in AB-stacked bilayer graphene, the spectrum is interestingly limited in a very narrow photon energy window, which is associated with the interlayer coupling strength. A detailed analysis of the light polarization dependence is also performed. The gate voltage and chemical potential can be used to effectively control the photogalvanic effects

High-fidelity Person-centric Subject-to-Image Synthesis

Current subject-driven image generation methods encounter significant challenges in person-centric image generation. The reason is that they learn the semantic scene and person generation by fine-tuning a common pre-trained diffusion, which involves an irreconcilable training imbalance. Precisely, to generate realistic persons, they need to sufficiently tune the pre-trained model, which inevitably causes the model to forget the rich semantic scene prior and makes scene generation over-fit to the training data. Moreover, even with sufficient fine-tuning, these methods can still not generate high-fidelity persons since joint learning of the scene and person generation also lead to quality compromise. In this paper, we propose Face-diffuser, an effective collaborative generation pipeline to eliminate the above training imbalance and quality compromise. Specifically, we first develop two specialized pre-trained diffusion models, i.e., Text-driven Diffusion Model (TDM) and Subject-augmented Diffusion Model (SDM), for scene and person generation, respectively. The sampling process is divided into three sequential stages, i.e., semantic scene construction, subject-scene fusion, and subject enhancement. The first and last stages are performed by TDM and SDM respectively. The subject-scene fusion stage, that is the collaboration achieved through a novel and highly effective mechanism, Saliency-adaptive Noise Fusion (SNF). Specifically, it is based on our key observation that there exists a robust link between classifier-free guidance responses and the saliency of generated images. In each time step, SNF leverages the unique strengths of each model and allows for the spatial blending of predicted noises from both models automatically in a saliency-aware manner. Extensive experiments confirm the impressive effectiveness and robustness of the Face-diffuser.Comment: Accepted by CVPR2024. Code: https://github.com/CodeGoat24/Face-diffuse

Interfacial thermal conductance in graphene/black phosphorus heterogeneous structures

Graphene, as a passivation layer, can be used to protect the black phosphorus from the chemical reaction with surrounding oxygen and water. However, black phosphorus and graphene heterostructures have low efficiency of heat dissipation due to its intrinsic high thermal resistance at the interfaces. The accumulated energy from Joule heat has to be removed efficiently to avoid the malfunction of the devices. Therefore, it is of significance to investigate the interfacial thermal dissipation properties and manipulate the properties by interfacial engineering on demand. In this work, the interfacial thermal conductance between few-layer black phosphorus and graphene is studied extensively using molecular dynamics simulations. Two critical parameters, the critical power Pcr to maintain thermal stability and the maximum heat power density Pmax with which the system can be loaded, are identified. Our results show that interfacial thermal conductance can be effectively tuned in a wide range with external strains and interracial defects. The compressive strain can enhance the interfacial thermal conductance by one order of magnitude, while interface defects give a two-fold increase. These findings could provide guidelines in heat dissipation and interfacial engineering for thermal conductance manipulation of black phosphorus-graphene heterostructure-based devices.Comment: 33 pages, 22 figure

Nanoparticle manipulation by thermal gradient

A method was proposed to manipulate nanoparticles through a thermal gradient. The motion of a fullerene molecule enclosed inside a (10, 10) carbon nanotube with a thermal gradient was studied by molecular dynamics simulations. We created a one-dimensional potential valley by imposing a symmetrical thermal gradient inside the nanotube. When the temperature gradient was large enough, the fullerene sank into the valley and became trapped. The escaping velocities of the fullerene were evaluated based on the relationship between thermal gradient and thermophoretic force. We then introduced a new way to manipulate the position of nanoparticles by translating the position of thermostats with desirable thermal gradients. Compared to nanomanipulation using a scanning tunneling microscope or an atomic force microscope, our method for nanomanipulation has a great advantage by not requiring a direct contact between the probe and the object

Aggregated Text Transformer for Scene Text Detection

This paper explores the multi-scale aggregation strategy for scene text detection in natural images. We present the Aggregated Text TRansformer(ATTR), which is designed to represent texts in scene images with a multi-scale self-attention mechanism. Starting from the image pyramid with multiple resolutions, the features are first extracted at different scales with shared weight and then fed into an encoder-decoder architecture of Transformer. The multi-scale image representations are robust and contain rich information on text contents of various sizes. The text Transformer aggregates these features to learn the interaction across different scales and improve text representation. The proposed method detects scene texts by representing each text instance as an individual binary mask, which is tolerant of curve texts and regions with dense instances. Extensive experiments on public scene text detection datasets demonstrate the effectiveness of the proposed framework

Structural and electronic properties of Al nanowires: an ab initio pseudopotential study

The stability and electronic structure of a single monatomic Al wire has been studied using the ab initio pseudopotential method. The Al wire undergoes two structural rearrangements under compression, i.e., zigzag configurations at angles of $140^o$ and $60^o$. The evolution of electronic structures of the Al chain as a function of structural phase transition has been investigated. The relationship between electronic structure and geometric stability is also discussed. The 2p bands in the Al nanowire are shown to play a critical role in its stability. The effects of density functionals (GGA and LDA) on cohesive energy and bond length of Al nanostructures (dimmer, chains, and monolayers) are also examined. The link between low dimensional 0D structure (dimmer) to high dimensional 3D bulk Al is estimated. An example of optimized tip-suspended finite atomic chain is presented to bridge the gap between hypothetical infinite chains and experimental finite chains.Comment: 11 pages, 5 figure