Accompanied by the increasing requirements of the probing micro/nanoscopic structures of biological samples, various image-processing algorithms have been developed for visualization or to facilitate data analysis. However, it remains challenging to enhance both the signal-to-noise ratio and image resolution using a single algorithm. In this investigation, we propose a composite image processing method by combining discrete wavelet transform (DWT) and the Lucy–Richardson (LR) deconvolution method, termed the DWDC method. Our results demonstrate that the signal-to-noise ratio and resolution of live cells’ microtubule networks are considerably improved, allowing the recognition of features as small as 120 nm. The method shows robustness in processing the high-noise images of filament-like biological structures, e.g., the cytoskeleton networks captured by fluorescent microscopes.

To date, live-cell imaging at the nanometer scale remains challenging. Even though super-resolution microscopy methods have enabled visualization of sub-cellular structures below the optical resolution limit, the spatial resolution is still far from enough for the structural reconstruction of biomolecules in vivo (i.e., ~24 nm thickness of microtubule fiber). In this study, a deep learning network named A-net was developed and shows that the resolution of cytoskeleton images captured by a confocal microscope can be significantly improved by combining the A-net deep learning network with the DWDC algorithm based on a degradation model. Utilizing the DWDC algorithm to construct new datasets and taking advantage of A-net neural network’s features (i.e., considerably fewer layers and relatively small dataset), the noise and flocculent structures which originally interfere with the cellular structure in the raw image are significantly removed, with the spatial resolution improved by a factor of 10. The investigation shows a universal approach for exacting structural details of biomolecules, cells and organs from low-resolution images.

Electrokinetic flow can be generated as a highly coupled phenomenon among velocity fields, electric conductivity fields, and electric fields. It can exhibit different responses to AC electric fields in different frequency regimes, according to different instability/receptivity mechanisms. In this investigation, by both flow visualization and single-point laser-induced fluorescence (LIF) method, the response of AC electrokinetic flow and the transition routes towards chaos and turbulence have been experimentally investigated. It is found, when the AC frequency Hz, the interface responds at both the neutral frequency of the basic flow and the AC frequency. However, when Hz, the interface responds only at the neutral frequency of the basic flow. Both periodic doubling and subcritical bifurcations have been observed in the transition of AC electrokinetic flow. We hope the current investigation can promote our current understanding of the ultrafast transition process of electrokinetic flow from laminar state to turbulence.

Multi-focal laser direct writing (LDW) based on phase-only spatial light modulation (SLM) can realize flexible and parallel nanofabrication with high-throughput potential. In this investigation, a novel approach of combining two-photon absorption, SLM, and vector path-guided by scalable vector graphics (SVGs), termed SVG-guided SLM LDW, was developed and preliminarily tested for fast, flexible, and parallel nanofabrication. Three laser focuses were independently controlled with different paths, which were optimized according to the SVG to improve fabrication and promote time efficiency. The minimum structure width could be as low as 81 nm. Accompanied by a translation stage, a carp structure of 18.10 μm × 24.56 μm was fabricated. This method shows the possibility of developing LDW techniques toward fully electrical systems, and provides a potential way to efficiently engrave complex structures on nanoscales.