Designing Metasurfaces for Efficient Solar Energy Conversion.

Metasurfaces have recently emerged as a promising technological platform, offering unprecedented control over light by structuring materials at the nanoscale using two-dimensional arrays of subwavelength nanoresonators. These metasurfaces possess exceptional optical properties, enabling a wide variety of applications in imaging, sensing, telecommunication, and energy-related fields. One significant advantage of metasurfaces lies in their ability to manipulate the optical spectrum by precisely engineering the geometry and material composition of the nanoresonators' array. Consequently, they hold tremendous potential for efficient solar light harvesting and conversion. In this Review, we delve into the current state-of-the-art in solar energy conversion devices based on metasurfaces. First, we provide an overview of the fundamental processes involved in solar energy conversion, alongside an introduction to the primary classes of metasurfaces, namely, plasmonic and dielectric metasurfaces. Subsequently, we explore the numerical tools used that guide the design of metasurfaces, focusing particularly on inverse design methods that facilitate an optimized optical response. To showcase the practical applications of metasurfaces, we present selected examples across various domains such as photovoltaics, photoelectrochemistry, photocatalysis, solar-thermal and photothermal routes, and radiative cooling. These examples highlight the ways in which metasurfaces can be leveraged to harness solar energy effectively. By tailoring the optical properties of metasurfaces, significant advancements can be expected in solar energy harvesting technologies, offering new practical solutions to support an emerging sustainable society.

Machine learning assisted quantum super-resolution microscopy.

One of the main characteristics of optical imaging systems is spatial resolution, which is restricted by the diffraction limit to approximately half the wavelength of the incident light. Along with the recently developed classical super-resolution techniques, which aim at breaking the diffraction limit in classical systems, there is a class of quantum super-resolution techniques which leverage the non-classical nature of the optical signals radiated by quantum emitters, the so-called antibunching super-resolution microscopy. This approach can ensure a factor of [Formula: see text] improvement in the spatial resolution by measuring the n -th order autocorrelation function. The main bottleneck of the antibunching super-resolution microscopy is the time-consuming acquisition of multi-photon event histograms. We present a machine learning-assisted approach for the realization of rapid antibunching super-resolution imaging and demonstrate 12 times speed-up compared to conventional, fitting-based autocorrelation measurements. The developed framework paves the way to the practical realization of scalable quantum super-resolution imaging devices that can be compatible with various types of quantum emitters.

The diagnostic value of neurogranin in patients with carbon monoxide poisoning: Can it show early neurological damage?

BACKGROUND AND AIM: Carbon monoxide poisoning is a toxicological emergency that causes neurological complications. High serum neurogranin can be detected in acute or chronic conditions where brain tissue is damaged. This study aimed to investigate the diagnostic value of serum neurogranin level and its role in demonstrating neurological damage in patients admitted to the emergency department with carbon monoxide poisoning. MATERIALS AND METHODS: The study was conducted prospectively on patients with carbon monoxide poisoning (patient group) and healthy volunteers (control group). Demographic characteristics and serum neurogranin level of all participants and symptoms at admission, neurological examination findings, laboratory results, and Diffusion-Weighted Magnetic Resonance Imaging results of the patient group were recorded. We used an independent sample t-test to compare neurogranin levels and bivariate correlation analysis to compare the relationship between serum neurogranin levels and data belonging to the patient group. RESULTS: Sixty eight participants (patient group, n = 36; control group, n = 32) were included in the study. Serum neurogranin level was significantly higher in patients with carbon monoxide poisoning (0.31 ± 0.16 ng/ml) compared to control group (0.22 ± 0.10 ng/ml) (p = 0.015). The mean Glasgow Coma Scale of the patients with carbon monoxide poisoning was 14.59 ± 0.23, and of Diffusion Weighted Magnetic Resonance Imaging results were completely normal in 94.4% (n = 34). There was no correlation between serum neurogranin level and Diffusion Weighted Magnetic Resonance Imaging results (r = -0.011; p = 0.953). CONCLUSION: Serum neurogranin level may be a new diagnostic biomarker in patients admitted to the emergency department with carbon monoxide poisoning. The high serum neurogranin levels detected in patients with normal diffusion-weighted imaging after carbon monoxide poisoning suggest that there is neurological damage in these patients, even if imaging methods cannot detect it.