No abstract

Label-free nanopore sensors have emerged as a new generation technology of DNA sequencing and have been widely used for single molecule analysis. Since the first α-hemolysin biological nanopore, various types of nanopores made of different materials have been under extensive development. Noise represents a common challenge among all types of nanopore sensors. The nanopore noise can be decomposed into four components in the frequency domain (1/f noise, white noise, dielectric noise, and amplifier noise). In this work, we reviewed and summarized the physical models, origins, and reduction methods for each of these noise components. For the first time, we quantitatively benchmarked the root mean square (RMS) noise levels for different types of nanopores, demonstrating a clear material-dependent RMS noise. We anticipate this review article will enhance the understanding of nanopore sensor noises and provide an informative tutorial for developing future nanopore sensors with a high signal-to-noise ratio.

We introduce a new efficient framework, the Unified Context Network (UniCon), for robust active speaker detection (ASD). Traditional methods for ASD usually operate on each candidate's precropped face track separately and do not sufficiently consider the relationships among the candidates. This potentially limits performance, especially in challenging scenarios with low-resolution faces, multiple candidates, etc. Our solution is a novel, unified framework that focuses on jointly modeling multiple types of contextual information: spatial context to indicate the position and scale of each candidate's face, relational context to capture the visual relationships among the candidates and contrast audio-visual affinities with each other, and temporal context to aggregate longterm information and smooth out local uncertainties. Based on such information, our model optimizes all candidates in a unified process for robust and reliable ASD. A thorough ablation study is performed * Both authors contributed equally to this research.

A novel flexible room temperature ethanol gas sensor was fabricated and demonstrated in this paper. The polyimide (PI) substrate-based sensor was formed by depositing a mixture of SnO2 nanopowder and poly-diallyldimethylammonium chloride (PDDAC) on as-patterned interdigitated electrodes. PDDAC acted both as the binder, promoting the adhesion between SnO2 and the flexible PI substrate, and the dopant. We found that the response of SnO2-PDDAC sensor is significantly higher than that of SnO2 alone, indicating that the doping with PDDAC effectively improved the sensor performance. The SnO2-PDDAC sensor has a detection limit of 10 ppm at room temperature and shows good selectivity to ethanol, making it very suitable for monitoring drunken driving. The microstructures of the samples were examined by scanning electron microscopy (SEM), X-ray diffraction (XRD), transmission electron microscope (TEM) and Fourier transform infrared spectra (FT-IR), and the sensing mechanism is also discussed in detail.