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Thermal radiation has diffusive and broad emission characteristics. Controlling emission spectrum and direction is essential for various applications. Nanoparticle arrays, supporting collective lattice resonances, can be employed for controlling optical properties. However, thermal emission characteristics remain unexplored due to the lack of a theoretical model. Here, we develop an analytical model to predict thermal radiation from a nanoparticle array using fluctuation–dissipation theorem and lattice Green's functions. Our findings reveal that the periodicity and particle size of the particle array are main parameters to control both emission spectrum and direction. The derived simple expression for thermal emission enables insightful interpretation of physics. This model will lay a foundation for analytical derivation of thermal radiation from metasurfaces. Our study can be useful in engineering infrared thermal sources and radiative cooling applications.

Metasurfaces consisting of an array of planar sub-wavelength structures have shown great potentials in controlling thermal infrared radiation, including intensity, coherence, and polarization. These capabilities together with the two-dimensional nature make thermal metasurfaces an ultracompact multifunctional platform for infrared light manipulation. Integrating the functionalities, such as amplitude, phase (spectrum and directionality), and polarization, on a single metasurface offers fascinating device responses. However, it remains a significant challenge to concurrently optimize the optical, electrical, and thermal responses of a thermal metasurface in a small footprint. In this work, we develop a center-contacted electrode line design for a thermal infrared metasurface based on a gold nanorod array, which allows local Joule heating to electrically excite the emission without undermining the localized surface plasmonic resonance. The narrowband emission of thermal metasurfaces and their robustness against temperature nonuniformity demonstrated in this work have important implications for the applications in infrared imaging, sensing, and energy harvesting.

Although social species as diverse as humans and ants are among the most abundant organisms on Earth, animals cooperate and form groups for many reasons. How these different reasons for grouping affect a species' ecological dominance remains unknown. Here we use a theoretical model to demonstrate that the different fitness benefits that animals receive by forming groups depend on the quality of their environment, which in turn impacts their ecological dominance and resilience to global change. We then test the model's key predictions using phylogenetic comparative analysis of >6500 bird species. As predicted, we find that cooperative breeders occurring in harsh and fluctuating environments have larger ranges and greater abundances than non‐cooperative breeders, but cooperative breeders occurring in benign and stable environments do not. Using our model, we further show that social species living in harsh and fluctuating environments will be less vulnerable to climate change than non‐social species.

State of health (SOH) estimation of lithium-ion batteries has typically been focused on estimating present cell capacity relative to initial cell capacity. While many successes have been achieved in this area, it is generally more advantageous to not only estimate cell capacity, but also the underlying degradation modes which cause capacity fade because these modes give further insight into maximizing cell usage. There have been some successes in estimating cell degradation modes, however, these methods either require long-term degradation data, are demonstrated solely on artificially constructed cells, or exhibit high error in estimating late-life degradation. To address these shortfalls and alleviate the need for long-term cycling data, we propose a method for estimating the capacity of a battery cell and diagnosing its primary degradation mechanisms using limited early-life degradation data. The proposed method uses simulation data from a physics-based half-cell model and early-life degradation data from 16 cells cycled under two temperatures and C rates to train a machine learning model. Results obtained from a four-fold cross validation study indicate that the proposed physics-informed machine learning method trained with only 60 early life data (five data from each of the 12 training cells) and 30 high-degradation simulated data can decreasemore »estimation error by up to a total of 9.77 root mean square error % when compared to models which were trained only on the early-life experimental data.« less

In this work, we demonstrate two types of heterogeneous irradiated-pristine polyethylene nanofiber junctions, ‘heavily-irradiated-pristine’ (HI-P) and ‘lightly-irradiated-pristine’ (LI-P) junctions, as high-performance solid-state thermal diodes. The HI-P junction rectifies heat flux in a single direction, while the LI-P junction shows dual-directional rectification under different working temperatures. We accurately model the phase transition of polyethylene nanofibers with a finite temperature range rather than a step function. The finite-temperature-range model suggests that the rectification factor increases with temperature bias and there is a minimum threshold of temperature bias for notable rectification. Besides, the finite-temperature-range model shows better prediction for the heat flow data from experiments, while the step function model tends to overestimate the rectification performance around the optimal length fraction of irradiation. Although both the models show that an optimal rectification occurs when the interface temperatures in the forward and the reverse biases are equal, the optimized rectification factor is determined by the temperature bias and the temperature range of phase transition. This work elucidates the influence of both the temperature bias and the temperature range of phase transition on thermal rectification performance, which could incredibly benefit the evaluation and design of thermal diodes.