Complex-valued neural networks have many advantages over their real-valued counterparts. Conventional digital electronic computing platforms are incapable of executing truly complex-valued representations and operations. In contrast, optical computing platforms that encode information in both phase and magnitude can execute complex arithmetic by optical interference, offering significantly enhanced computational speed and energy efficiency. However, to date, most demonstrations of optical neural networks still only utilize conventional real-valued frameworks that are designed for digital computers, forfeiting many of the advantages of optical computing such as efficient complex-valued operations. In this article, we highlight an optical neural chip (ONC) that implements truly complex-valued neural networks. We benchmark the performance of our complex-valued ONC in four settings: simple Boolean tasks, species classification of an Iris dataset, classifying nonlinear datasets (Circle and Spiral), and handwriting recognition. Strong learning capabilities (i.e., high accuracy, fast convergence and the capability to construct nonlinear decision boundaries) are achieved by our complex-valued ONC compared to its real-valued counterpart.

Surmounting the inhomogeniety issue of gas sensors and realizing their reproducible ppb‐level gas sensing are highly desirable for widespread deployments of sensors to build networks in applications of industrial safety and indoor/outdoor air quality monitoring. Herein, a strategy is proposed to substantially improve the surface homogeneity of sensing materials and gas sensing performance via chip‐level pyrolysis of as‐grown ZIF‐L (ZIF stands for zeolitic imidazolate framework) films to porous and hierarchical zinc oxide (ZnO) nanosheets. A novel approach to generate adjustable oxygen vacancies is demonstrated, through which the electronic structure of sensing materials can be fine‐tuned. Their presence is thoroughly verified by various techniques. The sensing results demonstrate that the resultant oxygen vacancy‐abundant ZnO nanosheets exhibit significantly enhanced sensitivity and shortened response time toward ppb‐level carbon monoxide (CO) and volatile organic compounds encompassing 1,3‐butadiene, toluene, and tetrachloroethylene, which can be ascribed to several reasons including unpaired electrons, consequent bandgap narrowing, increased specific surface area, and hierarchical micro–mesoporous structures. This facile approach sheds light on the rational design of sensing materials via defect engineering, and can facilitate the mass production, commercialization, and large‐scale deployments of sensors with controllable morphology and superior sensing performance targeted for ultratrace gas detection.

A novel and sensitive electrochemical DNA biosensor based on multi-walled carbon nanotubes functionalized with a carboxylic acid group (MWNTs-COOH) for covalent DNA immobilization and enhanced hybridization detection is described. The MWNTs-COOH-modified glassy carbon electrode (GCE) was fabricated and oligonucleotides with the 5'-amino group were covalently bonded to the carboxyl group of carbon nanotubes. The hybridization reaction on the electrode was monitored by differential pulse voltammetry (DPV) analysis using an electroactive intercalator daunomycin as an indicator. Compared with previous DNA sensors with oligonucleotides directly incorporated on carbon electrodes, this carbon nanotube-based assay with its large surface area and good charge-transport characteristics dramatically increased DNA attachment quantity and complementary DNA detection sensitivity. This is the first application of carbon nanotubes to the fabrication of an electrochemical DNA biosensor with a favorable performance for the rapid detection of specific hybridization.