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Thermal radiation with narrow bandwidth and well-defined emission directions is highly sought after for a variety of applications, ranging from infrared sensing and thermal imaging to thermophotovoltaics. Here, a large-area (4-inch-diameter) long-wavelength infrared thermal emitter is presented, which is spectrally selective, highly directional, and easily fabricated. The basic structure of the proposed thermal emitter is composed of a truncated one-dimensional photonic crystal and a continuous metallic film separated by a dielectric spacer. Experimental results show that the emitter exhibits a narrowband thermal emittance peak of 92% in the normal direction at the wavenumber of 943.4 cm−1 with a bandwidth of 12.5 cm−1 and a narrow angular emission lobe with a limited solid angle of 0.325 sr (0.115 sr) for s (p) polarization. Numerical simulation analyses are performed to corroborate the experimental observations. Temporal coupled-mode theory combined with transfer matrix method is employed to analytically investigate the emission properties of the structure, which not only can be used to understand the experimental results, but also plays a certain guidance role in designing a thermal emitter with the desired properties. The present thermal emitter can be implemented for thermal photonics management, allowing applications in thermal imaging and medical systems, etc.

Two-dimensional (2D) semiconductors of graphene, as well as transition–metal dichalcogenides, have performed strong interaction with light. Here the strong light–matter interaction between monolayer tungsten disulphide ( W S 2 ) excitons and microcavity photons at room temperature is well studied by the introduction of a gain material embedded dielectric optical microcavity structure. A Rabi splitting of about 36 meV is observed in angle-resolved reflectance spectra at room temperature, which agrees well with the theoretical results simulated by using the transfer matrix method. Since the cavity structures and 2D semiconductors can be prepared, the cavity and the gain materials, respectively, can be optimized separately in this platform. An all-dielectric Fabry–Pérot microcavity provides a simple but effective way to study the room temperature strong coupling between cavity photons and 2D excitons.

As we know, simulated annealing algorithm with large neighborhoods has greater probability of arriving at a global optimum than a small one has, if the other conditions, i.e., the initial configuration, initial temperature and temperature decreasing rate, are the same. However, the large neighborhood is not always beneficial, such as when the distance between the global optimum and the current solution is smaller than the step size. Therefore a simulated annealing algorithm with adaptive neighborhood is proposed in this paper. The adaptive non-uniform mutation operation borrowed from evolutionary algorithm is incorporated into simulated annealing for new solution generation. The neighborhood size reduces in probability with the progress of algorithm. It nearly covers the whole search space in the initial stage of algorithm in the sense of probability. The search engine only searches a very local neighborhood at the later stage of algorithm. Why to hybridize non-uniform mutation with simulated annealing is also analyzed and demonstrated. The numerical experiments show that the hybridization can greatly enhance the performance and the reliability of simulated annealing algorithm. Further experiments are made for benchmarks with expanding search domains. Satisfiable results are obtained again even if the variable bounds are enlarged to 1000 times. Theoretical analysis and simulation experiments illustrate the consistent excellent performance and the possible application of nonu-SA algorithm.

The canonical particle swarm optimization (PSO) has its own disadvantages, such as the high speed of convergence which often implies a rapid loss of diversity during the optimization process, which inevitably leads to undesirable premature convergence. In order to overcome the disadvantage of PSO, a perturbed particle swarm algorithm (pPSA) is presented based on the new particle updating strategy which is based upon the concept of perturbed global best to deal with the problem of premature convergence and diversity maintenance within the swarm. A linear model and a random model together with the initial max-min model are provided to understand and analyze the uncertainty of perturbed particle updating strategy. pPSA is validated using 12 standard test functions. The preliminary results indicate that pPSO performs much better than PSO both in quality of solutions and robustness and comparable with GCPSO. The experiments confirm us that the perturbed particle updating strategy is an encouraging strategy for stochastic heuristic algorithms and the max-min model is a promising model on the concept of possibility measure.