Beihang University

About: Beihang University is a education organization based out in Beijing, China. It is known for research contribution in the topics: Control theory & Microstructure. The organization has 67002 authors who have published 73507 publications receiving 975691 citations. The organization is also known as: Beijing University of Aeronautics and Astronautics.

Fully Bioabsorbable Natural-Materials-Based Triboelectric Nanogenerators.

Abstract: Implantable medical devices provide an effective therapeutic approach for neurological and cardiovascular diseases. With the development of transient electronics, a new power source with biocompatibility, controllability, and bioabsorbability becomes an urgent demand for medical sciences. Here, various fully bioabsorbable natural-materials-based triboelectric nanogenerators (BN-TENGs), in vivo, are developed. The "triboelectric series" of five natural materials is first ranked, it provides a basic knowledge for materials selection and device design of the TENGs and other energy harvesters. Various triboelectric outputs of these natural materials are achieved by a single material and their pairwise combinations. The maximum voltage, current, and power density reach up to 55 V, 0.6 µA, and 21.6 mW m-2 , respectively. The modification of silk fibroin encapsulation film makes the operation time of the BN-TENG tunable from days to weeks. After completing its function, the BN-TENG can be fully degraded and resorbed in Sprague-Dawley rats, which avoids a second operation and other side effects. Using the proposed BN-TENG as a voltage source, the beating rates of dysfunctional cardiomyocyte clusters are accelerated and the consistency of cell contraction is improved. This provides a new and valid solution to treat some heart diseases such as bradycardia and arrhythmia.

Efficient Electrocatalytic Water Oxidation by Using Amorphous Ni–Co Double Hydroxides Nanocages

Abstract: Ni–Co amorphous double hydroxides nanomaterials with a hollow structure and tunable Ni/Co molar ratio are synthesized using a template method. The amorphous NiCo2.7(OH)x nanocages demonstrate high surface reactivity, comparable catalytic activity, and excellent stability for efficient water oxidation. Density functional theory simulations suggest that the component-dependent electrocatalytic activities are connected to the binding energies of oxygen radical on diverse hydroxides.

Wearable and Implantable Triboelectric Nanogenerators

Abstract: Triboelectric nanogenerators (TENGs) are a promising technology to convert mechanical energy to electrical energy based on coupled triboelectrification and electrostatic induction. With the rapid development of functional materials and manufacturing techniques, wearable and implantable TENGs have evolved into playing important roles in clinic and daily life from in vitro to in vivo. These flexible and light membrane-like devices have the potential to be a new power supply or sensor element, to meet the special requirements for portable electronics, promoting innovation in electronic devices. In this review, the recent advances in wearable and implantable TENGs as sustainable power sources or selfpowered sensors are reviewed. In addition, the remaining challenges and future possible improvements of wearable and implantable TENG-based self-powered systems are discussed.

Remote Sensing Image Change Detection With Transformers

Abstract: Modern change detection (CD) has achieved remarkable success by the powerful discriminative ability of deep convolutions. However, high-resolution remote sensing CD remains challenging due to the complexity of objects in the scene. Objects with the same semantic concept may show distinct spectral characteristics at different times and spatial locations. Most recent CD pipelines using pure convolutions are still struggling to relate long-range concepts in space-time. Nonlocal self-attention approaches show promising performance via modeling dense relationships among pixels, yet are computationally inefficient. Here, we propose a bitemporal image transformer (BIT) to efficiently and effectively model contexts within the spatial-temporal domain. Our intuition is that the high-level concepts of the change of interest can be represented by a few visual words, that is, semantic tokens. To achieve this, we express the bitemporal image into a few tokens and use a transformer encoder to model contexts in the compact token-based space-time. The learned context-rich tokens are then fed back to the pixel-space for refining the original features via a transformer decoder. We incorporate BIT in a deep feature differencing-based CD framework. Extensive experiments on three CD datasets demonstrate the effectiveness and efficiency of the proposed method. Notably, our BIT-based model significantly outperforms the purely convolutional baseline using only three times lower computational costs and model parameters. Based on a naive backbone (ResNet18) without sophisticated structures (e.g., feature pyramid network (FPN) and UNet), our model surpasses several state-of-the-art CD methods, including better than four recent attention-based methods in terms of efficiency and accuracy. Our code is available at https://github.com/justchenhao/BIT_CD.