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Chlorophyll-a measurement is important in algal growth and water quality monitoring in natural waters. A portable pulsed LED fluorescence lidar system based on the preliminary algal organic matter and pigments excitation–emission matrix (EEM) of commercialized AZTEC Spirulina powder at varying concentrations was developed. Fluorescence peaks from EEMs showed increasing intensity as the Spirulina concentration increases. Using this information, an LED fluorescence lidar with a wavelength of 385 nm, pulse width of 10 ns, and repetition frequency of 500 kHz was constructed for chlorophyll detection at 680 nm. Turbidity measurements were also conducted at 700 nm emission wavelength at the same excitation wavelength. Range-resolved fluorescence lidar signals from the portable pulsed LED fluorescence lidar system are highly correlated with the standard methods such as optical density at 680 nm (R2 = 0.87), EEM fluorescence chlorophyll-a pigment at 680 nm (R2 = 0.89), and corrected chlorophyll-a concentration (R2 =0.92). The F680/F700 lidar ratio was measured to provide a linear relationship of chlorophyll-a and turbidity in waters. The F680/F700 measurement showed strong correlations with Spirulina concentration (R2 = 0.94), absorbance at 680 nm (R2 = 0.84), EEM chlorophyll-a pigment at 680 nm (R2 = 0.83), and corrected chlorophyll-a concentration (R2 = 0.86). Results revealed that this new technique of chlorophyll-a measurement can be used as an alternative to other standard methods in algal growth monitoring.

The real-time chlorophyll-a pigment monitoring of C. reinhardtii is studied using our developed LED fluorescence light detection and ranging (LiDAR) system. It features a portable set-up that uses a pulsed LED module with an excitation wavelength of 385 nm. We were able to monitor the different growth phases of C. reinhardtii with specific cultivation parameters. The developed fluorescence LiDAR system showed the linear correlation of its chlorophyll-a signal with the optical density and EEM fluorescence measurements at 680 nm emission wavelength. Water quality and weather parameters were also measured, which explains the variation in the growth dynamics of C. reinhardtii during the sampling period. The results from the monitoring demonstrated a different technique that can be used in estimating algal biomass in the environment.

Taking inspiration from beautiful colors in nature, structural colors produced from nanostructured metasurfaces have shown great promise as a platform for bright, highly saturated, and high-resolution colors. Both plasmonic and dielectric materials have been employed to produce static colors that fulfil the required criteria for high-performance color printing, however, for practical applications in dynamic situations, a form of tunability is desirable. Combinations of the additive color palette of red, green, and blue enable the expression of further colors beyond the three primary colors, while the simultaneous intensity modulation allows access to the full color gamut. Here, we demonstrate an electrically tunable metasurface that can represent saturated red, green, and blue pixels that can be dynamically and continuously controlled between on and off states using liquid crystals. We use this to experimentally realize ultrahigh-resolution color printing, active multicolor cryptographic applications, and tunable pixels toward high-performance full-color reflective displays.Ellipsoidal-shaped Mie resonators are integrated with a liquid crystal cell to demonstrate electrically tunable reflective structural color metasurfaces that can be modulated continuously from bright spectral colors to dark blacks.

We investigate the performance of Opticks, a NVIDIA OptiX API 7.5 GPU-accelerated photon propagation tool compared with a single-threaded Geant4 simulation. We compare the simulations using an improved model of the NEXT-CRAB-0 gaseous time projection chamber. Performance results suggest that Opticks improves simulation speeds by between 58.47 ± 0.02 and 181.39 ± 0.28 times relative to a CPU-only Geant4 simulation and these results vary between different types of GPU and CPU. A detailed comparison shows that the number of detected photons, along with their times and wavelengths, are in good agreement between Opticks and Geant4.

A single-step printable platform for ultraviolet (UV) metasurfaces is introduced to overcome both the scarcity of low-loss UV materials and manufacturing limitations of high cost and low throughput. By dispersing zirconium dioxide (ZrO 2 ) nanoparticles in a UV-curable resin, ZrO 2 nanoparticle-embedded-resin (nano-PER) is developed as a printable material which has a high refractive index and low extinction coefficient from near-UV to deep-UV. In ZrO 2 nano-PER, the UV-curable resin enables direct pattern transfer and ZrO 2 nanoparticles increase the refractive index of the composite while maintaining a large bandgap. With this concept, UV metasurfaces can be fabricated in a single step by nanoimprint lithography. As a proof of concept, UV metaholograms operating in near-UV and deep-UV are experimentally demonstrated with vivid and clear holographic images. The proposed method enables repeat and rapid manufacturing of UV metasurfaces, and thus will bring UV metasurfaces more close to real life. Facile nanostamping of the high-refractive index nanoparticle-embedded-resin for the direct fabrication of highly efficient metaholographic devices down to the deep-ultraviolet region.

Position and time measurements of scintillation events encode information about the radiation source. Single photon avalanche diode (SPAD) arrays offer multiple-megapixel spatial resolution and tens of picoseconds temporal resolution for detecting single photons. Current lensless designs for measuring scintillation events use sensors that are lower in spatial resolution. Camera-based designs use sensors that are lower in temporal resolution or readout rate and cannot image individual interactions. Here we propose to image scintillation events in a thick, monolithic scintillator using a high-resolution SPAD camera. We demonstrate that a commercial SPAD camera is able to gather sufficient signal to image individual scintillation events and observe 3D shifts in their spatial distribution. Simulations show that a SPAD camera can localize individual scintillation events in 3D. We report direct imaging of gamma-ray interactions in a scintillator with a SPAD camera. The proposed design may allow to measure complex signatures of individual particles interacting in the scintillator.

Wavelength-selective thermal emitters (WS-TEs) have been frequently designed to achieve desired target emissivity spectra, as a typical emissivity engineering, for broad applications such as thermal camouflage, radiative cooling, and gas sensing, etc. However, previous designs require prior knowledge of materials or structures for different applications and the designed WS-TEs usually vary from applications to applications in terms of materials and structures, thus lacking of a general design framework for emissivity engineering across different applications. Moreover, previous designs fail to tackle the simultaneous design of both materials and structures, as they either fix materials to design structures or fix structures to select suitable materials. Herein, we employ the deep Q-learning network algorithm, a reinforcement learning method based on deep learning framework, to design multilayer WS-TEs. To demonstrate the general validity, three WS-TEs are designed for various applications, including thermal camouflage, radiative cooling and gas sensing, which are then fabricated and measured. The merits of the deep Q-learning algorithm include that it can (1) offer a general design framework for WS-TEs beyond one-dimensional multilayer structures; (2) autonomously select suitable materials from a self-built material library and (3) autonomously optimize structural parameters for the target emissivity spectra. The present framework is demonstrated to be feasible and efficient in designing WS-TEs across different applications, and the design parameters are highly scalable in materials, structures, dimensions, and the target functions, offering a general framework for emissivity engineering and paving the way for efficient design of nonlinear optimization problems beyond thermal metamaterials. We proposed a general deep reinforcement learning framework to design materials and structures simultaneously for wavelength-selective thermal emitters with broad applications in emissivity engineering.

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Grain boundaries in organic–inorganic halide perovskite solar cells (PSCs) have been found to be detrimental to the photovoltaic performance of devices. Here, we develop a unique approach to overcome this problem by modifying the edges of perovskite grain boundaries with flakes of high-mobility two-dimensional (2D) materials via a convenient solution process. A synergistic effect between the 2D flakes and perovskite grain boundaries is observed for the first time, which can significantly enhance the performance of PSCs. We find that the 2D flakes can conduct holes from the grain boundaries to the hole transport layers in PSCs, thereby making hole channels in the grain boundaries of the devices. Hence, 2D flakes with high carrier mobilities and short distances to grain boundaries can induce a more pronounced performance enhancement of the devices. This work presents a cost-effective strategy for improving the performance of PSCs by using high-mobility 2D materials.