Eliminating cracking during drying

When colloidal suspensions dry, stresses build up and cracks often occur - a phenomenon undesirable for important industries such as paint and ceramics. We demonstrate an effective method which can completely eliminate cracking during drying: by adding emulsion droplets into colloidal suspensions, we can systematically decrease the amount of cracking, and eliminate it completely above a critical droplet concentration. Since the emulsion droplets eventually also evaporate, our technique achieves an effective function while making little changes to the component of final product, and may therefore serve as a promising approach for cracking elimination. Furthermore, adding droplets also varies the speed of air invasion and provides a powerful method to adjust drying rate. With the effective control over cracking and drying rate, our study may find important applications in many drying and cracking related industrial processes.Comment: 5 pages, 5 figure

Conceptual Study and Performance Analysis of Tandem Dual-Antenna Spaceborne SAR Interferometry

Multi-baseline synthetic aperture radar interferometry (MB-InSAR), capable of mapping 3D surface model with high precision, is able to overcome the ill-posed problem in the single-baseline InSAR by use of the baseline diversity. Single pass MB acquisition with the advantages of high coherence and simple phase components has a more practical capability in 3D reconstruction than conventional repeat-pass MB acquisition. Using an asymptotic 3D phase unwrapping (PU), it is possible to get a reliable 3D reconstruction using very sparse acquisitions but the interferograms should follow the optimal baseline design. However, current spaceborne SAR system doesn't satisfy this principle, inducing more difficulties in practical application. In this article, a new concept of Tandem Dual-Antenna SAR Interferometry (TDA-InSAR) system for single-pass reliable 3D surface mapping using the asymptotic 3D PU is proposed. Its optimal MB acquisition is analyzed to achieve both good relative height precision and flexible baseline design. Two indicators, i.e., expected relative height precision and successful phase unwrapping rate, are selected to optimize the system parameters and evaluate the performance of various baseline configurations. Additionally, simulation-based demonstrations are conducted to evaluate the performance in typical scenarios and investigate the impact of various error sources. The results indicate that the proposed TDA-InSAR is able to get the specified MB acquisition for the asymptotic 3D PU, which offers a feasible solution for single-pass 3D SAR imaging.Comment: 16 pages, 20 figure

One-dimensional phosphorus chain and two-dimensional blue phosphorene grown on Au(111) by molecular-beam epitaxy

Single layer (SL) phosphorus (phosphorene) has drawn considerable research attention recently as a two-dimensional (2D) material for application promises. It is a semiconductor showing superior transport and optical properties. Few-layer or SL black phosphorus has been successfully isolated by exfoliation from bulk crystals and extensively studied thereof for its electronic and optical properties. Blue phosphorus (blueP), an allotrope of black phosphorus where atoms are arranged in a more flat atomic configuration, has been recently suggested by theory to exist in the SL form on some substrates. In this work, we report the formation of a blueP-like epilayer on Au(111) by molecular-beam epitaxy. In particular, we uncover by scanning tunneling microscopy (STM) one-dimensional (1D) atomic chains at low coverage, which develop into more compact islands or patches of $(\sqrt{3}\times\sqrt{3})R30^\circ$ structure with increasing coverage before blueP-like islands nucleate and grow. We also note an interesting growth characteristic where the $(\sqrt{3}\times\sqrt{3})R30^\circ$ surface at intermediate coverage tends to phase-separate into locally low-coverage 1D chain and high-coverage blueP-like structures, respectively. This experiment thus not only lends a support of the recently proposed half-layer by half-layer (HLBHL) growth mechanism but also reveals the kinetic details of blueP growth processes

Eocene Podocarpium (Leguminosae) from South China and its biogeographic implications

Podocarpium A. Braun ex Stizenberger is one of the most common legumes in the Neogene of Eurasia, including fossil fruits, seeds, leaves, and possible flower and pollen grains. This genus is not completely consistent with any extant genera according to gross morphological characters and poorly preserved cuticular structures reported in previous studies. The fossil pods collected from the coal-bearing series of the Changchang Basin of Hainan Island and Maoming Basin of Guangdong, South China, are examined by morphologically comparative work, with special reference to venation patterns and placental position. These distinctive features, as well as the ovule development of pods from different growing stages and the epidermal structure of the pods, as distinguished from previous records lead to the conclusion that these fossils can be recognized as a new species of Podocarpium, P. eocenicum sp. nov. This new discovery indicates that Podocarpium had arrived in South China by the Eocene. Investigation on the fossil records of this extinct genus shows that P. eocenicum is the earliest and lowest latitude fossil data. The possible occurrence pattern of this genus is revealed as follows: Podocarpium had distributed in the South China at least in the middle Eocene, and then migrated to Europe during the Oligocene; in the Miocene this genus reached its peak in Eurasia, spreading extensively across subtropical areas to warm temperate areas; finally, Podocarpium shrank rapidly and became extinct in Eurasia during the Pliocene

SAR-NeRF: Neural Radiance Fields for Synthetic Aperture Radar Multi-View Representation

SAR images are highly sensitive to observation configurations, and they exhibit significant variations across different viewing angles, making it challenging to represent and learn their anisotropic features. As a result, deep learning methods often generalize poorly across different view angles. Inspired by the concept of neural radiance fields (NeRF), this study combines SAR imaging mechanisms with neural networks to propose a novel NeRF model for SAR image generation. Following the mapping and projection pinciples, a set of SAR images is modeled implicitly as a function of attenuation coefficients and scattering intensities in the 3D imaging space through a differentiable rendering equation. SAR-NeRF is then constructed to learn the distribution of attenuation coefficients and scattering intensities of voxels, where the vectorized form of 3D voxel SAR rendering equation and the sampling relationship between the 3D space voxels and the 2D view ray grids are analytically derived. Through quantitative experiments on various datasets, we thoroughly assess the multi-view representation and generalization capabilities of SAR-NeRF. Additionally, it is found that SAR-NeRF augumented dataset can significantly improve SAR target classification performance under few-shot learning setup, where a 10-type classification accuracy of 91.6\% can be achieved by using only 12 images per class