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Mid‐infrared spectroscopy is often used to identify material. Thousands of spectral points are measured in a time‐consuming process using expensive table‐top instrument. However, material identification is a sparse problem, which in theory can be solved with just a few measurements. Here the sparsity of the problem is exploited to develop an ultra‐fast, portable, and inexpensive method to identify materials. In a single‐shot, a mid‐infrared camera can identify materials based on their spectroscopic signatures. This method does not require prior calibration, making it robust and versatile in handling a broad range of materials.

 

Acoustic topological systems explore topological behaviors of phononic crystals. Currently, most of the experimentally demonstrated acoustic topological systems are for airborne acoustic waves and work at or below the kHz frequency range. Here, we report an underwater acoustic topological waveguide that works at the MHz frequency range. The 2D topological waveguide was formed at the interface of two hexagonal lattices with different pillar radii that were fabricated with metal additive manufacturing. We demonstrated the existence of edge stages both numerically and in underwater experiments. Our work has potential applications in underwater/biomedical sensing, energy transport, and acoustofluidics. 

There is a long history of using angle sensors to measure wavefront. The best example is the Shack-Hartmann sensor. Compared to other methods of wavefront sensing, angle-based approach is more broadly used in industrial applications and scientific research. Its wide adoption is attributed to its fully integrated setup, robustness, and fast speed. However, there is a long-standing issue in its low spatial resolution, which is limited by the size of the angle sensor. Here we report a angle-based wavefront sensor to overcome this challenge. It uses ultra-compact angle sensor built from flat optics. It is directly integrated on focal plane array. This wavefront sensor inherits all the benefits of the angle-based method. Moreover, it improves the spatial sampling density by over two orders of magnitude. The drastically improved resolution allows angle-based sensors to be used for quantitative phase imaging, enabling capabilities such as video-frame recording of high-resolution surface topography.

 

Adjoint optimization is an effective method in the inverse design of nanophotonic devices. In order to ensure the manufacturability, one would like to have control over the minimal feature sizes. Here we propose utilizing a level-set method based on b-spline surfaces in order to control the feature sizes. This approach is first used to design a wavelength demultiplexer. It is also used to implement a nanophotonic structure for artificial neural computing. In both cases, we show that the minimal feature sizes can be easily parameterized and controlled.

 

A radiative vapor condenser sheds heat in the form of infrared radiation and cools itself to below the ambient air temperature to produce liquid water from vapor. This effect has been known for centuries, and is exploited by some insects to survive in dry deserts. Humans have also been using radiative condensation for dew collection. However, all existing radiative vapor condensers must operate during the nighttime. Here, we develop daytime radiative condensers that continue to operate 24 h a day. These daytime radiative condensers can produce water from vapor under direct sunlight, without active consumption of energy. Combined with traditional passive cooling via convection and conduction, radiative cooling can substantially increase the performance of passive vapor condensation, which can be used for passive water extraction and purification technologies.

 

Metasurfaces have been used to realize optical functions such as focusing and beam steering. They use subwavelength nanostructures to control the local amplitude and phase of light. Here we show that such control could also enable a new function of artificial neural inference. We demonstrate that metasurfaces can directly recognize objects by focusing light from an object to different spatial locations that correspond to the class of the object.