In situ growth of a cobalt porphyrin-based covalent organic framework on multi-walled carbon nanotubes for ultrasensitive real-time monitoring of living cell-released nitric oxide.

Nitric oxide (NO), as a critical transcellular messenger, participates in a variety of physiological and pathological processes. However, its real-time detection still faces challenges due to its short half-life and trace amounts. Here, MWCNTs@COF-366-Co was prepared by in situ growth of a cobalt porphyrin-based covalent organic framework (COF-366-Co) on multi-walled carbon nanotubes (MWCNTs), and a unique biosensing platform for ultrasensitive real-time NO determination was established. Remarkably, MWCNTs@COF-366-Co contains plenty of atomically arranged M-N4 active sites for electrocatalysis, which provides more efficient electron transfer pathways and resolves the random arrangement issue of active sites. COF-366-Co with a high surface area contains a large number of exposed active M-N4 sites, providing faster NO transport/diffusion and more efficient electron transfer pathways. Due to the synergy of atomic-level periodic structural features of COF-366-Co and high conductivity of MWCNTs, the MWCNTs@COF-366-Co electrochemical biosensor exhibited excellent NO determination performance in a wide range from 0.09 to 400 µM, with high sensitivity (8.9 µA µM-1 cm-2) and a low limit of detection (16 nM). Moreover, the biosensor has been successfully used to sensitively monitor NO molecules released from human umbilical vein endothelial cells (HUVECs). This research not only designed a multifunctional intelligent biosensor platform, but also provided a broad prospect for continuous dynamic monitoring of the activity of living cells and their released metabolites.

Secrecy Energy Efficiency Enhancement in UAV-Assisted MEC System.

A secrecy energy efficiency optimization scheme for a multifunctional unmanned aerial vehicle (UAV) assisted mobile edge computing system is proposed to solve the computing power and security issues in the Internet-of-Things scenario. The UAV can switch roles between a computing UAV and jamming UAV based on the channel conditions. To ensure the security of the content and the system energy efficiency in the process of offloading computing tasks, the UAV trajectory, uplink transmit power, user scheduling, and offload task are jointly optimized, and an updated-rate assisted block coordinate descent (BCD) algorithm is used. Simulation results show that this scheme efficiently improves the secrecy performance and energy efficiency of the system. Compared with the benchmark scheme, the secrecy energy efficiency of the scheme is improved by 38.5%.

Secrecy Capacity Enhancement in Active IRS-Assisted UAV Communication System.

As a new technology for reconstructing communication environments, intelligent reflecting surfaces (IRSs) can be applied to UAV communication systems. However, some challenges exist in IRS-assisted UAV communication system design, such as physical layer security issues, IRS design, and power consumption issues owing to the limitation of the hardware. Therefore, a secrecy capacity optimization scheme for an active IRS-assisted unmanned aerial vehicle (UAV) communication system is proposed to solve multi-user security issues. In particular, controllable power amplifiers are integrated into reflecting units to solve the problem of blocked links, and the UAV can dynamically select the served user according to the channel quality. In order to maximize the system average achievable secrecy capacity and ensure the power constraints of the UAV and active IRS, user scheduling, UAV trajectory, beamforming vector, and reflection matrix are jointly optimized, and the block coordinate descent (BCD) algorithm is applied to solve this non-convex problem. Simulation results show that the active IRS-assisted UAV communication scheme can significantly weaken the "multiplicative fading" effect and enhance the system secrecy capacity by 55.4% and 11.9% compared with the schemes with passive IRS and without optimal trajectory, respectively.

Self-Assembled Monolayer of Metal Oxide Nanosheet and Structure and Gas-Sensing Property Relationship.

Semiconducting 2D metal oxides have attracted great research interests for gas-sensing applications because of their considerable specific surface area and highly homogeneous surface. Developing a method for fabricating thin films of 2D metal oxides is crucial for minimizing the negative effects on sensing performance caused by slow diffusion. In this work, a simple, versatile, and highly reproducible self-assembly method is developed for fabricating monolayer film sensors made from metal oxide nanosheets with much superior sensing performance compared with their thick film counterparts. To prepare the monolayer film sensors, a monolayer film of metal oxide nanosheets, self-assembled at the air-water interface, is transferred onto a sensor substrate. The CuO monolayer sensors prepared with this self-assembly method show much improved gas sensitivity (sensor signal: 350% vs 100% at 5 ppm of H2S) and faster response and recovery rate (τres: 20 s vs 110 s; τrec: 120 s vs 320 s) than the thick film sensors prepared from the same sensing material. The enhanced sensing performance demonstrated by the monolayer film of CuO nanosheets is explained quantitively with a modified coupled reaction-diffusion model. Similar enhancement on gas-sensing performance is also observed for the ZnO-nanosheet-based monolayer sensors prepared by the same self-assembly method.