Ultraviolet (UV) optical devices have plenteous applications in the fields of nanofabrication, military, medical, sterilization, and others. Traditional optical components utilize gradual phase accumulation phenomena to alter the wave-front of the light, making them bulky, expensive, and inefficient. A dielectric metasurface could provide an auspicious approach to precisely control the amplitude, phase, and polarization of the incident light by abrupt, discrete phase changing with high efficiency due to low absorption losses. Metalenses, being one of the most attainable applications of metasurfaces, can extremely reduce the size and complexity of the optical systems. We present the design of a high-efficiency transmissive UV metalens operating in a broadband range of UV light (250–400 nm) with outstanding focusing characteristics. The polarization conversion efficiency of the nano-rod unit and the focusing efficiency of the metasurface are optimized to be as high as 96% and 77%, respectively. The off-axis focusing characteristics at different incident angles are also investigated. The designed metalens that is composed of silicon nitride nanorods will significantly uphold the advancement of UV photonic devices and can provide opportunities for the miniaturization and integration of the UV nanophotonics and its applications.

Smart fish farming faces critical challenges in achieving comprehensive automation, real-time decision-making, and adaptability to diverse environmental conditions and multi-species aquaculture. This study presents a novel Internet of Things (IoT)-driven intelligent decision-making system that dynamically monitors and optimizes water quality parameters to enhance fish survival rates across various regions and species setups. The system integrates advanced sensors connected to an ESP32 microcontroller, continuously monitoring key water parameters such as pH, temperature, and turbidity which are increasingly affected by climate-induced variability. A custom-built dataset comprising 43,459 records, covering ten distinct fish species across diverse pond environments, was meticulously curated. The data were stored as a comma-separated values (CSV) file on the IoT cloud platform ThingSpeak and synchronized with Firebase, enabling seamless remote access, control, and real-time updates. Advanced machine learning techniques, with feature transformation and balancing, were applied to preprocess the dataset, which includes water quality metrics and species-specific parameters. Multiple algorithms were trained and evaluated, with the Decision Tree classifier emerging as the optimal model, achieving remarkable performance metrics: 99.8% accuracy, precision, recall, and F1-score, a 99.6% Matthews Correlation Coefficient (MCC), and the highest Area Under the Curve (AUC) score for multi-class classification. Our framework’s capability to manage complex, multi-species fishpond environments was validated across diverse setups, showcasing its potential to transform fish farming practices by ensuring sustainable climate-adaptive management through real-time water quality optimization. This study marks a significant step forward in climate-smart aquaculture, contributing to enhanced fish health, survival, and yield while mitigating the risks posed by climate change on aquatic ecosystems.

Metasurfaces in the ultraviolet spectrum have stirred up prevalent research interest due to the increasing demand for ultra-compact and wearable UV optical systems. The limitations of conventional plasmonic metasurfaces operating in transmission mode can be overcome by using a suitable dielectric material. A metalens holds promising wavefront engineering for various applications. Metalenses have developed a breakthrough technology in the advancement of integrated and miniaturized optical devices. However, metalenses utilizing the Pancharatnam–Berry (PB) phase or resonance tuning methodology are restricted to polarization dependence and for various applications, polarization-insensitive metalenses are highly desirable. We propose the design of a high-efficiency dielectric polarization-insensitive UV metalens utilizing cylindrical nanopillars with strong focusing ability, providing full phase delay in a broadband range of Ultraviolet light (270–380 nm). The designed metalens comprises Silicon nitride cylindrical nanopillars with spatially varying radii and offers outstanding polarization-insensitive operation in the broadband UV spectrum. It will significantly promote and boost the integration and miniaturization of the UV photonic devices by overcoming the use of Plasmonics structures that are vulnerable to the absorption and ohmic losses of the metals. The focusing efficiency of the designed metalens is as high as 40%.

Bessel beam arrays are progressively attracting attention in recent years due to their remarkable non‐diffracting nature and parallel manipulation capabilities in diverse applications. However, the poor phase discretization of conventional approaches such as spatial light modulators leads to low numerical aperture (NA) beam arrays due to the limitation imposed by the Nyquist sampling theorem and poor uniformity of the beam intensity. The key contribution of this study is to experimentally demonstrate the generation of high‐uniformity and high‐resolution Bessel beam arrays by utilizing all‐dielectric metasurfaces. This is attained by optimizing the design of the supercell of a Dammann grating, particularly decreasing each supercell of the grating to a proper size. We demonstrate a 4 × 4 array of Bessel beams with a subwavelength transverse dimension (570 nm, ∼0.9 λ ) and a large NA of 0.4 for each beam in the array, while maintaining a relatively high uniformity intensity (52.40%) for the array. Additionally, the Bessel beam arrays are generated in a broadband range through the proposed all‐dielectric metasurfaces. Our results are of great significance and particularly useful for applications of metasurface‐based Bessel beam arrays in multidisciplinary fields such as laser fabrication, biomedical imaging, data storage, and multi‐particle trapping.