Temporal coupled-mode theory for thermal emission from multiple arbitrarily coupled resonators

Controlling the spectral response of thermal emitters has become increasingly important for a range of energy and sensing applications. Conventional approaches to achieving arbitrary spectrum selectivity in photonic systems have entailed combining multiple resonantly emissive elements together to achieve a range of spectral profiles through numerical optimization, with a universal theoretical framework lacking. Here, we develop a temporal coupled mode theory for thermal emission from multiple, arbtirarily-coupled resonators. We validate our theory against numerical simulations of complex two- and three-dimensional nanophotonic thermal emitters, highlighting the anomalous thermal emission spectra that can emerge when multiple resonators with arbitrary properties couple to each other with varying strengths

Perturbation Theory for Plasmonic Modulation and Sensing

We develop a general perturbation theory to treat small parameter changes in dispersive plasmonic nanostructures and metamaterials. We specifically apply it to dielectric refractive index, and metallic plasma frequency modulation in metal- dielectric nanostructures. As a numerical demonstration, we verify the theory's accu- racy against direct calculations, for a system of plasmonic rods in air where the metal is defined by a two-pole fit of silver's dielectric function. We also discuss new optical behavior related to plasma frequency modulation in such systems. Our approach provides new physical insight for the design of plasmonic devices for biochemical sensing and optical modulation, and future active metamaterial applications.Comment: 17 pages, 6 figure

Fundamental Limit of Nanophotonic Light-trapping in Solar Cells

Establishing the fundamental limit of nanophotonic light-trapping schemes is of paramount importance and is becoming increasingly urgent for current solar cell research. The standard theory of light trapping demonstrated that absorption enhancement in a medium cannot exceed a factor of 4n^2/ sin^2(\theta), where n is the refractive index of the active layer, and \theta is the angle of the emission cone in the medium surrounding the cell. This theory, however, is not applicable in the nanophotonic regime. Here we develop a statistical temporal coupled-mode theory of light trapping based on a rigorous electromagnetic approach. Our theory reveals that the standard limit can be substantially surpassed when optical modes in the active layer are confined to deep-subwavelength scale, opening new avenues for highly efficient next-generation solar cells

Free Subcooling with the Sky: Improving the efficiency of air conditioning systems

Radiative sky cooling is a passive process that can be harnessed to subcool refrigerants in air conditioning and refrigeration systems, thereby increasing the cooling capacity of the refrigerant, and improving the underlying efficiency of the base cooling system. Here, we demonstrate for the first time the use of a radiative sky cooling-enabled passive fluid cooling panel to improve the efficiency of an air conditioning system by subcooling. The panel’s passive cooling capability is enabled by a multilayer optical film that enables the sky cooling effect 24-hours a day. The film is simultaneously a good reflector of solar energy and a strong emitter of infrared heat in the 8 to 13 micron wavelength range. Multiple such panels were built and then connected in a closed fluid loop to two 1-ton split air conditioning units in a field trial in Davis, CA. The panels were used to subcool refrigerant out of the condenser by rejecting heat to the sky via a closed fluid loop. Refrigerant R410A was passed through a counterflow plate heat exchanger, where the cold fluid source was the circulating water/glycol solution in the panels. As much as 15˚F of additional subcooling was observed during the hottest time of the day. This resulted in calculated net efficiency improvements up to 8%. The only added operating electricity required was to run a small circulating water pump, which consumed less than \u3c 1% of total compressor power. These results reveal the remarkable ability of radiative sky cooling to markedly improve the efficiency of vapor compression systems.as an add-on technology

DeepAdjoint: An All-in-One Photonic Inverse Design Framework Integrating Data-Driven Machine Learning with Optimization Algorithms

In recent years, hybrid design strategies combining machine learning (ML) with electromagnetic optimization algorithms have emerged as a new paradigm for the inverse design of photonic structures and devices. While a trained, data-driven neural network can rapidly identify solutions near the global optimum with a given dataset's design space, an iterative optimization algorithm can further refine the solution and overcome dataset limitations. Furthermore, such hybrid ML-optimization methodologies can reduce computational costs and expedite the discovery of novel electromagnetic components. However, existing hybrid ML-optimization methods have yet to optimize across both materials and geometries in a single integrated and user-friendly environment. In addition, due to the challenge of acquiring large datasets for ML, as well as the exponential growth of isolated models being trained for photonics design, there is a need to standardize the ML-optimization workflow while making the pre-trained models easily accessible. Motivated by these challenges, here we introduce DeepAdjoint, a general-purpose, open-source, and multi-objective "all-in-one" global photonics inverse design application framework which integrates pre-trained deep generative networks with state-of-the-art electromagnetic optimization algorithms such as the adjoint variables method. DeepAdjoint allows a designer to specify an arbitrary optical design target, then obtain a photonic structure that is robust to fabrication tolerances and possesses the desired optical properties - all within a single user-guided application interface. Our framework thus paves a path towards the systematic unification of ML and optimization algorithms for photonic inverse design