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Unlike noble metals, refractory plasmonic materials can maintain resilient and attractive optical properties even at comparatively extreme temperatures and high current densities. One refractory plasmonic material of interest is TiN, which exhibits an extremely high melting temperature of about 3000 K and noble-metal-like optical properties in the visible and near-infrared regime. Using lithographically fabricated TiN nanowires and leveraging their ability to host plasmon modes, we have examined plasmonic photothermal heating and photothermoelectric response whose anisotropy and magnitude depend on the width of the nanowires. The photothermoelectric response is consistent with changes in the Seebeck coefficient where the wire fans out to wider contact pads. Upon electrically biasing the structures, Joule heating of the TiN wires can produce detectable thermal emission within the visible and near-IR range, with emission intensity growing rapidly with increasing bias. This emission is consistent with local temperatures exceeding 2000 K, as expected from a finite element model of the Joule heating. 

Multilayer films with continuously varying indices for each layer have attracted great deal of attention due to their superior optical, mechanical, and thermal properties. However, difficulties in fabrication have limited their application and study in scientific literature compared to multilayer films with fixed index layers. In this work we propose a neural network based inverse design technique enabled by a differentiable analytical solver for realistic design and fabrication of single material variable-index multilayer films. This approach generates multilayer films with excellent performance under ideal conditions. We furthermore address the issue of how to translate these ideal designs into practical useful devices which will naturally suffer from growth imperfections. By integrating simulated systematic and random errors just as a deposition tool would into the optimization process, we demonstrated that the same neural network that produced the ideal device can be retrained to produce designs compensating for systematic deposition errors. Furthermore, the proposed approach corrects for systematic errors even in the presence of random fabrication imperfections. The results outlined in this paper provide a practical and experimentally viable approach for the design of single material multilayer film stacks for an extremely wide variety of practical applications with high performance. 

We report a 250-fold photoluminescence enhancement of V_B^-spin-defects in hBN by coupling them to nanopatch antennas (NPA). Considering the relative size of the NPAs and laser-spot, an actual enhancement of 1695 times is determined.