Biological organisms, organs and tissues have evolved through natural selection diverse functional and structural traits to accomplish complex tasks. For example, small insects with tiny thermal capacitance have developed tailored spectral properties and behavioral tactics to mitigate rapid changes of body temperatures caused by environmental electromagnetic radiations; neural networks in the brain, through changing the efficacy of synapses, can recognize hidden patterns and correlations in raw data, cluster and classify them, and continuously learn and improve over time. These biological systems are a rich source of bio-inspiration for developing solutions to address engineering challenges. My thesis work focuses on the intersection between photonics and biology and explores three unique biological systems and their technological implications.
Beginning with the investigation of butterfly wings, we observed that the wings contain a matrix of living structures, including mechanical and thermal sensory neural cells, hemocytes, pheromone producing organs, , and even “wing hearts”, and that these living structures carry out their specific functions over the entire life span of butterflies but are vulnerable to sustained high temperatures. We discovered that butterflies have evolved heterogeneously thickened wing cuticles and special nanostructured wing scales to locally enhance thermal emissivity so that the regions of the wings containing living structures can better dissipate heat through thermal radiation. Furthermore, we discovered that butterfly wings almost always possess enhanced reflectivity in the near-infrared, which can significantly reduce heating caused by solar radiation. This enhanced near-infrared reflectivity is found to originate from optical scattering at the porous wing scales, especially pale-colored scales underneath the surface layer of colorful ones.
Besides these structural adaptations, our bioassays showed that butterflies utilize a number of behavioral strategies to avoid overheating or overcooling of their wings. We found that butterflies can use their wings as a fast and sensitive temperature monitor to detect the direction and strength of sunlight or artificial light applied onto the wings; as such, they can adapt the most suitable postures to minimize overheating of the wings if the illumination is too strong and to warm up the wings when ambident temperatures are insufficient for taking flight. Drawing inspiration from the multi-layered wing scales, which impart coloration to the wings while maintaining their high near-infrared reflectivity, we developed a double-layered, radiative-cooling coating that is able to minimize solar heating while still stay colorful.
The second part of my thesis work explored nanostructured fibers and textiles as a novel solution for radiative cooling. The work was motivated by our discovery that the silk fibers produced by the caterpillars of the Madagascan moon moth (Argema mittrei) contain a high density of filamentary air voids, which enable individual fibers of the moth to strongly reflect light over the solar spectrum. This, in combination with natural polymers’ intrinsic high mid-infrared emissivity, provides the cocoons of the moth with remarkable passive radiative-cooling properties.
We developed fabrication platforms to produce synthetic fibers with filamentary air voids by modifying both wet spinning and melt extrusion techniques. The melt extrusion approach, in particular, is implemented in an industry-scale fiber extrusion machine for high-throughput, high-yield production. The fabricated nanostructured fibers reproduce the prominent solar reflectivity of the Madagascan moon moth silk fibers and possess high emissivity due to the variety of chemical bonds in the synthetic polymers used. The melt-extruded fibers were twisted into yarns, which were subsequently woven and knitted into fabrics. The finished fabric samples were demonstrated to perform as effective radiative cooling devices compared to conventional white fabrics.
Lastly, inspired by how neural networks in the brain form the basis of learning and motivated by how artificial neural networks are implemented in computers, we develop a novel platform of optical neural computing, a smart glass, for object recognition. Our optical neural network takes advantage of strong light-matter interactions with sub-wavelength resolutions in metasurfaces to emulate the layered computations in a biological or artificial neural network. In the simplest implementation of a single-layer smart glass, a metasurface was trained to provide 2D phase modulations that can transform the complex optical wave scattered from an input object into a characteristic intensity distribution pattern on the output plane corresponding to the identity of the object.
We experimentally demonstrated the recognition of handwritten numerical digits and letters with different fonts with high accuracies using the smart glass and explored the capability of a polarization-multiplexing smart glass based on birefringent metasurfaces for performing distinct recognition tasks at orthogonal incident polarizations. This optical neural computing platform represents a new paradigm of computation operating at the speed of light with no power consumption and this physical-wave-based computation guarantees data security beyond digital encryption.
| Identifer | oai:union.ndltd.org:columbia.edu/oai:academiccommons.columbia.edu:10.7916/d8-hgez-1r37 |
| Date | January 2022 |
| Creators | Tsai, Cheng-Chia |
| Source Sets | Columbia University |
| Language | English |
| Detected Language | English |
| Type | Theses |
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