Hardware-accelerated integrated optoelectronic platform towards real-time high-resolution hyperspectral video understanding.

Recent advancements in artificial intelligence have significantly expanded capabilities in processing language and images. However, the challenge of comprehensively understanding video content still needs to be solved. The main problem is the requirement to process real-time multidimensional video information at data rates exceeding 1 Tb/s, a demand that current hardware technologies cannot meet.

Light-Induced Quantum Reconfiguration of Oxyhydroxides for Photoanodes with 4.24% Efficiency and Stability Beyond 250 Hours.

Photoelectrochemical (PEC) water splitting is attracting significant research interest in addressing sustainable development goals in renewable energy. Current state-of-the-art, however, cannot provide photoanodes with simultaneously high efficiency and long-lasting lifetime. Here, large-scale NiFe oxyhydroxides-alloy hybridized co-catalyst layer that exhibits an applied bias photon-to-current efficiency (ABPE) of 4.

Pixelated High- Metasurfaces for in Situ Biospectroscopy and Artificial Intelligence-Enabled Classification of Lipid Membrane Photoswitching Dynamics.

Nanophotonic devices excel at confining light into intense hot spots of electromagnetic near fields, creating exceptional opportunities for light-matter coupling and surface-enhanced sensing. Recently, all-dielectric metasurfaces with ultrasharp resonances enabled by photonic bound states in the continuum (BICs) have unlocked additional functionalities for surface-enhanced biospectroscopy by precisely targeting and reading out the molecular absorption signatures of diverse molecular systems. However, BIC-driven molecular spectroscopy has so far focused on end point measurements in dry conditions, neglecting the crucial interaction dynamics of biological systems.

Innovative Strategies for Photons Management on Ultrathin Silicon Solar Cells.

Silicon (Si), the eighth most common element in the known universe by mass and widely applied in the industry of electronics chips and solar cells, rarely emerges as a pure element in the Earth's crust. Optimizing its manufacturing can be crucial in the global challenge of reducing the cost of renewable energy modules and implementing sustainable development goals in the future. In the industry of solar cells, this challenge is stimulating studies of ultrathin Si-based architectures, which are rapidly attracting broad attention.

Real-time simultaneous refractive index and thickness mapping of sub-cellular biology at the diffraction limit.

Mapping the cellular refractive index (RI) is a central task for research involving the composition of microorganisms and the development of models providing automated medical screenings with accuracy beyond 95%. These models require significantly enhancing the state-of-the-art RI mapping capabilities to provide large amounts of accurate RI data at high throughput. Here, we present a machine-learning-based technique that obtains a biological specimen's real-time RI and thickness maps from a single image acquired with a conventional color camera.