Trevon Badloe

Pancharatnam‐Berry phase gradients generated by spatially rotated meta atoms are widely used to control optical wavefronts with metasurfaces. Here, we develop a weak measurement method for characterizing the effective Jones matrix parameters of fabricated PB phase gradient metasurfaces. In the specified effective Jones model, the local polarization response is described by the amplitude ratio and phase difference, while the spatial rotation of the meta atoms determines the PB phase gradient. By scanning the imaginary part angle and real part angle, the spin dependent deflection is converted into a two dimensional centroid map. The amplitude ratio, phase difference, and phase gradient produce different spatial features in this map, providing information for their distinction and retrieval. For a fabricated metasurface with a known phase gradient, the amplitude ratio and phase difference are obtained by fitting the measured centroid response with the theoretical model. When all three parameters are unknown, three independently trained machine learning models retrieve the amplitude ratio, phase difference, and phase gradient from the same complete centroid map and its angular coordinates. These results establish two dimensional weak measurement as an optical approach for characterizing the effective parameters of PB phase gradient metasurfaces.

Daytime radiative cooling is an environmentally friendly approach that reaches below ambient temperature by simultaneously reflecting solar irradiance and dissipating heat to the cold universe through the atmospheric window (AW). Conventional daytime radiative coolers typically exhibit white appearances due to their broadband solar reflection, limiting their aesthetic applicability. Here, we propose colored daytime radiative coolers (CRCs) comprising an optimized top thermal emitter and an inverse designed metal–insulator–metal (MIM) metasurface reflector. The multilayer top emitter is optimized using a genetic algorithm, achieving a high mean emissivity of 98.9% within the AW. The bottom elliptic MIM metasurface is inverse designed using a deep neural network to penalize solar absorption while enabling on-demand structural color generation with polarization control. By appropriately specifying the color targets, the framework enables the inverse design of both conventional CRCs operating under unpolarized illumination and polarization-dependent CRCs that exhibit distinct visible appearances under orthogonal x- and y-polarized illumination, while remaining fully functional under unpolarized light within a single device geometry, enabling color modulation without physical reconfiguration. The proposed CRCs achieve cooling powers of up to 25 W m–2 and demonstrate positive cooling performance across diverse global climates, offering a versatile solution for visually appealing and energy-efficient thermal management.

Radiative cooling (RC) is a sustainable technology that passively lowers the temperature of terrestrial objects through thermal radiation, dissipating heat into outer space via the atmospheric window, unlike conventional cooling systems that require electricity and release waste heat to the environment. In recent years, RC technologies have advanced rapidly, moving steadily toward practical implementation and commercialization. Here, this mega-review offers a comprehensive overview of RC, encompassing its fundamental background, the underlying photonic and thermal principles, and fabrication approaches for constructing efficient cooling devices. We discuss a variety of RC structures along with their design strategies and explore the diverse functionalities that adapt RC toward particular requirements. We further review emerging applications of RC, highlighting practical implementations. Finally, we outline the key challenges and future directions, offering perspectives and insights to guide continued progress in this rapidly advancing field.

Dynamic switching between edge detection and bright‐field imaging modes is advantageous in optical imaging, particularly for biomedical diagnostics and material characterization. However, conventional approaches necessitate complex setups or intricate fabrication processes, limiting their practicality. This study demonstrates a humidity‐responsive optical imaging system, enabling reversible and tunable transitions between edge‐enhanced and Gaussian‐like bright‐field imaging modes by leveraging humidity‐induced variations in the spin Hall effect of light. Utilizing polyvinyl alcohol films that exhibit reversible humidity‐dependent changes in thickness and refractive index, the Fresnel reflection coefficients ( and ) are effectively modulated, leading to asymmetric spin‐dependent beam splitting in both x‐ and y‐directions. These humidity‐driven variations disrupt the initial symmetric vortex conditions, namely that the in‐plane and out‐of‐plane shifts induced by the spin Hall effect of light are equal, transforming the topological vortex beam into a quasi‐Gaussian distribution. Consequently, the imaging performance shifts from edge‐enhanced mode to quasibright‐field mode as the relative humidity increases. Experimental validation using customized resolution targets and biological tissue samples (planaria and small intestine) demonstrates reliable and reproducible imaging mode switching without requiring mechanical adjustments or complex fabrication. Thus, the proposed system offers improved simplicity, operational convenience, and cost‐effectiveness compared to existing methods (e.g., metasurface‐based techniques), underscoring the potential of humidity‐tunable spin Hall effect of light‐based polymer optics for practical and versatile imaging applications.

We demonstrate an electrically tunable metalens that combines hydrogenated amorphous silicon (a-Si:H) meta-atoms with a liquid crystal (LC) layer for rapid polarization-controlled depth sensing and imaging. Two independent focusing profiles for left-circularly polarized (LCP) light and right-circularly polarized (RCP) light are encoded via propagation and geometric phases in a single metasurface. Adjusting the LC voltage and the incident polarization among the LCP, RCP, and their superposition enables millisecond-scale reconfiguration. Under pure circular polarization, the metalens yields a single high-fidelity focal spot or an image. Linearly polarized light produces two rotating images, whose relative orientation encodes object depth. Experiments match theory and confirm a full π rotation of the image during focal scanning. The device offers a compact, real-time platform for imaging and depth sensing in microscopy, holography, and adaptive optics.

Artificial intelligence (AI) has emerged as a transformative tool in nanophotonics, revolutionizing the field of inverse design of nanoscale devices. This perspective delves into the advancing trend of AI-driven approaches in the field with a particular focus on hybrid frameworks. These hybrid models synergistically combine deep learning with classical optimization techniques, such as adjoint methods and evolutionary-based algorithms, effectively addressing the limitations of standalone approaches. By leveraging the computational efficiency and generalization capabilities of deep learning alongside the robustness of classical optimization, hybrid frameworks enable faster convergence, higher design efficiency, and the exploration of diverse, fabrication-feasible solutions. Additionally, methods such as a physics-informed neural network are also discussed for their significant role by embedding governing physical laws into the learning process to reduce data dependency and enhance interpretability. These advancements, demonstrated in applications such as metasurfaces and other nanophotonic devices, are driving scalable and practical innovations, paving the way for the next generation of nanophotonic technologies and advancements in functional material engineering.

Measurements of the refractive index of liquids are in high demand in numerous fields such as agriculture, food and beverages, and medicine. However, conventional ellipsometric refractive index measurements are too expensive and labor‐intensive for consumer devices, while Abbe refractometry is limited to the measurement at a single wavelength. Here, a new approach is proposed using machine learning to unlock the potential of colorimetric metasurfaces for the real‐time measurement of the dispersive refractive index of liquids over the entire visible spectrum. The platform with a proof‐of‐concept experiment for measuring the concentration of glucose is further demonstrated, which holds a profound impact in non‐invasive medical sensing. High‐index‐dielectric metasurfaces are designed and fabricated, while their experimentally measured reflectance and reflected colors, through microscopy and a standard smartphone, are used to train deep‐learning models to provide measurements of the dispersive background refractive index with a resolution of ≈10⁻⁴, which is comparable to the known index as measured with ellipsometry. These results show the potential of enabling the unique optical properties of metasurfaces with machine learning to create a platform for the quick, simple, and high‐resolution measurement of the dispersive refractive index of liquids, without the need for highly specialized experts and optical procedures.