Optical Penetration of Shape-Controlled Metallic Nanosensors across Membrane Barriers.

Precise nanostructure geometry that enables the optical biomolecular delivery of nanosensors to the living intracellular environment is highly desirable for precision biological and clinical therapies. However, the optical delivery through membrane barriers utilizing nanosensors remains difficult due to a lack of design guidelines to avoid inherent conflict between optical force and photothermal heat generation in metallic nanosensors during the process. Here, we present a numerical study reporting significantly enhanced optical penetration of nanosensors by engineering nanostructure geometry with minimized photothermal heating generation for penetrating across membrane barriers. We show that by varying the nanosensor geometry, penetration depths can be maximized while heat generated during the penetration process can be minimized. We demonstrate the effect of lateral stress induced by an angularly rotating nanosensor on a membrane barrier by theoretical analysis. Furthermore, we show that by varying the nanosensor geometry, maximized local stress fields at the nanoparticle-membrane interface enhanced the optical penetration process by four-fold. Owing to the high efficiency and stability, we anticipate that precise optical penetration of nanosensors to specific intracellular locations will be beneficial for biological and therapeutic applications.

Intelligent Fusion Imaging Photonics for Real-Time Lighting Obstructions.

Dynamic detection in challenging lighting environments is essential for advancing intelligent robots and autonomous vehicles. Traditional vision systems are prone to severe lighting conditions in which rapid increases or decreases in contrast or saturation obscures objects, resulting in a loss of visibility. By incorporating intelligent optimization of polarization into vision systems using the iNC (integrated nanoscopic correction), we introduce an intelligent real-time fusion algorithm to address challenging and changing lighting conditions. Through real-time iterative feedback, we rapidly select polarizations, which is difficult to achieve with traditional methods. Fusion images were also dynamically reconstructed using pixel-based weights calculated in the intelligent polarization selection process. We showed that fused images by intelligent polarization selection reduced the mean-square error by two orders of magnitude to uncover subtle features of occluded objects. Our intelligent real-time fusion algorithm also achieved two orders of magnitude increase in time performance without compromising image quality. We expect intelligent fusion imaging photonics to play increasingly vital roles in the fields of next generation intelligent robots and autonomous vehicles.