A fabric-based hydrovoltaic electricity generator with multi-component carbon black for sustainable energy output.

Water evaporation-induced electricity generators are considered a promising green energy-harvesting technology to alleviate the increasingly serious fossil energy crisis. Previous water evaporation-induced electricity generators mainly focused on single component carbon black, limiting the improvements in energy output. At present, there are relatively few studies on multi-component carbon black for improving electricity-generation performance. Herein, inspired by plant transpiration, we designed a fabric-based water evaporation-induced electricity generator (FWEG) based on multi-component carbon black, which can maintain a voltage of 0.65 V for more than 48 h. Through the synergistic effect of multi-component carbon black-enhanced oxygen-containing functional density, the FWEG can generate an enhanced output current of 61.61 µA without any additional energy input. Moreover, we show that the FWEG can be integrated readily to charge commercial capacitors or directly power LED lights and calculators for a long time. This work provides new insights for designing high-performance hydrovoltaic electricity generators for sustainable green energy harvesting.

Highly Tough, Stretchable, and Solvent-Resistant Cellulose Nanocrystal Photonic Films for Mechanochromism and Actuator Properties.

Cellulose nanocrystals (CNCs)-derived photonic materials have confirmed great potential in producing renewable optical and engineering areas. However, it remains challenging to simultaneously possess toughness, strength, and multiple responses for developing high-performance sensors, intelligent coatings, flexible textiles, and multifunctional devices. Herein, the authors report a facile and robust strategy that poly(ethylene glycol) dimethacrylate (PEGDMA) can be converged into the chiral nematic structure of CNCs by ultraviolet-triggered free radical polymerization in an N,N-dimethylformamide solvent system. The resulting CNC-poly(PEGDMA) composite exhibits impressive strength (42 MPa), stretchability (104%), toughness (31 MJ m-3 ), and solvent resistance. Notably, it preserves vivid optical iridescence, displaying stretchable variation from red, yellow, to green responding to the applied mechanical stimuli. More interestingly, upon exposure to spraying moisture, it executes sensitive actuation (4.6° s-1 ) and multiple complex 3D deformation behaviors, accompanied by synergistic iridescent appearances. Due to its structural anisotropy of CNC with typical left-handedness, the actuation shows the capability to generate a high probability (63%) of right-handed helical shapes, mimicking a coiled tendril. The authors envision that this versatile system with sustainability, robustness, mechanochromism, and specific actuating ability will open a sustainable avenue in mechanical sensors, stretchable optics, intelligent actuators, and soft robots.

Robust Spacecraft Component Detection in Point Clouds.

Automatic component detection of spacecraft can assist in on-orbit operation and space situational awareness. Spacecraft are generally composed of solar panels and cuboidal or cylindrical modules. These components can be simply represented by geometric primitives like plane, cuboid and cylinder. Based on this prior, we propose a robust automatic detection scheme to automatically detect such basic components of spacecraft in three-dimensional (3D) point clouds. In the proposed scheme, cylinders are first detected in the iteration of the energy-based geometric model fitting and cylinder parameter estimation. Then, planes are detected by Hough transform and further described as bounded patches with their minimum bounding rectangles. Finally, the cuboids are detected with pair-wise geometry relations from the detected patches. After successive detection of cylinders, planar patches and cuboids, a mid-level geometry representation of the spacecraft can be delivered. We tested the proposed component detection scheme on spacecraft 3D point clouds synthesized by computer-aided design (CAD) models and those recovered by image-based reconstruction, respectively. Experimental results illustrate that the proposed scheme can detect the basic geometric components effectively and has fine robustness against noise and point distribution density.

3D Reconstruction of Space Objects from Multi-Views by a Visible Sensor.

In this paper, a novel 3D reconstruction framework is proposed to recover the 3D structural model of a space object from its multi-view images captured by a visible sensor. Given an image sequence, this framework first estimates the relative camera poses and recovers the depths of the surface points by the structure from motion (SFM) method, then the patch-based multi-view stereo (PMVS) algorithm is utilized to generate a dense 3D point cloud. To resolve the wrong matches arising from the symmetric structure and repeated textures of space objects, a new strategy is introduced, in which images are added to SFM in imaging order. Meanwhile, a refining process exploiting the structural prior knowledge that most sub-components of artificial space objects are composed of basic geometric shapes is proposed and applied to the recovered point cloud. The proposed reconstruction framework is tested on both simulated image datasets and real image datasets. Experimental results illustrate that the recovered point cloud models of space objects are accurate and have a complete coverage of the surface. Moreover, outliers and points with severe noise are effectively filtered out by the refinement, resulting in an distinct improvement of the structure and visualization of the recovered points.