Superwetting Capillary Tubes: Surface Science under Confined Space.

Capillarity is a crucial and pervasive phenomenon in nature and has found important applications in wearable electronics, medical devices, and miniature energy systems. Capillary tubes are the transport vessels in which the surface wettability plays an essential role in efficient and accurate liquid delivery. However, it remains a challenging issue to tailor and measure the surface wettability inside the tubes in view of the confined space. Herein, recent progress on the surface science under confined space is discussed, with a particular focus on surface modification, wettability evaluation, and advanced applications of the superwetting capillary tubes. This Perspective aims to highlight the emerging opportunities in surface science with spatial confinement toward flexible and portable devices.

TiO2 Nanotube Arrays Inside Ultrafine Ti Tubes with an Aspect Ratio of 2500 for Sensing Needles: The Bubble Effect in a Confined Space.

Capillary metal tubes have attracted considerable interest for flexible electronics, portable devices, trace sampling, and detection. Tailoring the microstructure and wettability inside the capillary tubes is of paramount importance, yet it presents great difficulty because of the spatial confinement. Here, the coupling effect is revealed between the fluidic and electric field induced by bubble motion in a confined space during anodic oxidation. By controlling the bubble regeneration and flow rate, uniform and superhydrophilic TiO2 nanotube arrays are developed throughout the inner surface of an ultrafine Ti tube with a diameter of 0.4 mm and length of 1000 mm, equivalent to an aspect ratio of 2500 that is the largest value being ever reported. The inner surface of a capillary tube is further coated with a polytetrafluoroethylene layer and explored as a sensing needle for liquid detection in terms of concentration and species. This study provides an innovative approach to tailor the microstructure and wettability in a confined space for functional capillary tubes.

SPA-Net: A Deep Learning Approach Enhanced Using a Span-Partial Structure and Attention Mechanism for Image Copy-Move Forgery Detection.

With the wide application of visual sensors and development of digital image processing technology, image copy-move forgery detection (CMFD) has become more and more prevalent. Copy-move forgery is copying one or several areas of an image and pasting them into another part of the same image, and CMFD is an efficient means to expose this. There are improper uses of forged images in industry, the military, and daily life. In this paper, we present an efficient end-to-end deep learning approach for CMFD, using a span-partial structure and attention mechanism (SPA-Net). The SPA-Net extracts feature roughly using a pre-processing module and finely extracts deep feature maps using the span-partial structure and attention mechanism as a SPA-net feature extractor module. The span-partial structure is designed to reduce the redundant feature information, while the attention mechanism in the span-partial structure has the advantage of focusing on the tamper region and suppressing the original semantic information. To explore the correlation between high-dimension feature points, a deep feature matching module assists SPA-Net to locate the copy-move areas by computing the similarity of the feature map. A feature upsampling module is employed to upsample the features to their original size and produce a copy-move mask. Furthermore, the training strategy of SPA-Net without pretrained weights has a balance between copy-move and semantic features, and then the module can capture more features of copy-move forgery areas and reduce the confusion from semantic objects. In the experiment, we do not use pretrained weights or models from existing networks such as VGG16, which would bring the limitation of the network paying more attention to objects other than copy-move areas.To deal with this problem, we generated a SPANet-CMFD dataset by applying various processes to the benchmark images from SUN and COCO datasets, and we used existing copy-move forgery datasets, CMH, MICC-F220, MICC-F600, GRIP, Coverage, and parts of USCISI-CMFD, together with our generated SPANet-CMFD dataset, as the training set to train our model. In addition, the SPANet-CMFD dataset could play a big part in forgery detection, such as deepfakes. We employed the CASIA and CoMoFoD datasets as testing datasets to verify the performance of our proposed method. The Precision, Recall, and F1 are calculated to evaluate the CMFD results. Comparison results showed that our model achieved a satisfactory performance on both testing datasets and performed better than the existing methods.

Backscattering enhancements by partially exposed spheres due to reflected subsonic Rayleigh waves at air-water interfaces.

Air-water interfaces can enable distinct target scattering mechanisms different from the mechanism under free field conditions. In this study, backscattering experiments are performed by lowering an acrylic or polymethylmethacrylate sphere through the air-water interface into the water and insonifying the sphere from below at grazing incidence. Pronounced backscattering enhancements associated with the subsonic Rayleigh wave propagation mechanism are observed before the specular reflection point of the sphere reaches the water. The results indicate that, for a partially exposed sphere, subsonic Rayleigh waves can pass through the air-water interface and circumnavigate the sphere multiple times. The phase velocities of Rayleigh waves are different when propagating above and below the air-water interface. Moreover, subsonic Rayleigh waves are partially reflected when passing through the air-water interface, generating wavefronts that propagate in the reverse direction.

Dissecting the Chain Length Effect on Separation of Alkane-in-Water Emulsions with Superwetting Microchannels.

Oil/water separation is an essential process in the petrochemical industry, environmental remediation, and water treatment. Alkanes are the major components of crude oil and are difficult to separate once they form emulsions in water. Much less attention has been focused on the feature of liquid alkanes that could, in turn, influence the separation process. The role of chain length is systematically studied herein by separating the alkane-in-water emulsions with superwetting titanium microchannels of 14-55 µm. The chain length covers the entire liquid alkane spectrum with carbon numbers ranging from 6 to 16. The separation efficiency decreases while the TOC content increases with the chain length of liquid alkanes for a given channel. This is attributed to the small Ostwald ripening rate with the long chains, which stabilize the oil droplets of small sizes that could pass through the zigzag channels. Accordingly, a high separation efficiency of >99.97% and a low TOC content of <5 ppm are achieved with superhydrophilic channels of 14 µm for alkanes with less than 12 carbons. The metallic microchannels surpass the conventional organic membranes and inorganic frameworks over the entire liquid n-alkane spectrum, paving the way for the future development of oil/water separation using porous metals.