Physical Sensors & Devices

In this study, we report on the synthesis of the NiO/ZnO heterostructured nanowires by a facile two-step liquid phase route and their gas sensing characteristics employing Au interdigitated electrodes integrated on a miniature ceramic heater. Microstructural characterizations indicate that flocculent NiO particles are uniformly assembled on the outer surfaces of the single-crystalline ZnO nanowires, with diameters around 50 nm and lengths ranging from 500 nm to several μm. The gas sensing investigation indicates that the sensors based on NiO/ZnO heterostructured nanowires exhibit high...

While metal oxides and metal sulfides have been extensively studied for gas sensing applications, there are no extensive reports on gas sensing properties of metal diborides. Here, for the first time, we have investigated the conductometric gas sensing behavior of HfB2 nanoparticles. The HfB2 nanoparticles is synthesized via a sol-gel method and characterized using X-ray diffraction, field emission scanning electron microscopy, transmission electron microscopy, and X-ray photoelectron spectroscopy. The gas sensor is fabricated by drop casting the HfB2 nanoparticles on interdigitated Ag/Pd...

As the demand for faster, more efficient training of neural networks continues to grow, specialized photonic hardware has emerged as a potential alternative to classical computers for AI applications. Reservoir Computing (RC), a lightweight alternative to computationally-intensive Recurrent Neural Networks, has been demonstrated to be possible using simple delay dynamical systems. We propose an optoelectronic implementation of this architecture through a Mach-Zehnder modulator driven by delayed feedback from a laser. We introduce a new optoelectronic scheme in which input data is first pre...

We demonstrate a design scheme for weakly coupled resonators (WCRs) by integrating the supervised learning (SL) with the genetic algorithm (GA). In this work, three distinctive achievements have been accomplished: 1) the precise prediction of coupling characteristics of WCRs with an accuracy of 98.7% via SL; 2) the stepwise evolutionary optimization of WCR geometries while maintaining their geometric connectivity via GA; and 3) the highly efficient generation of WCR designs with a mean coupling factor down to 0.0056, which outperforms 98% of random designs. The coupling behavior analysis...