Adaptive Thermal Management Radiative Cooling Smart Window with Perfect Near-Infrared Shielding.

The architectural window with spectrally selective features and radiative cooling is an effective way to save building energy consumption. However, architectural windows that combine both functions are currently based on micro-nano photonic structures, which undoubtedly hinder their commercial application due to the complexity of manufacture. Herein, a novel tunable visible light transmittance radiative cooling smart window (TTRC smart window) with perfect near-infrared (NIR) shielding ability is manufactured via a mass-producible scraping method. TTRC smart window presents high luminous transmittance (Tlum = 56.8%), perfect NIR shielding (TNIR = 3.4%), bidirectional transparency adjustment ability unavailable in other transparent radiative coolers based on photonic structures (ΔTlum = 54.2%), and high emittance in the atmospheric window (over 94%). Outdoor measurements confirm that smart window can reduce 8.2 and 6.6 °C, respectively, compared to ordinary glass and indium tin oxide (ITO) glass. Moreover, TTRC smart window can save over 20% of annual energy in the tropics compared to ITO and ordinary glass. The simple preparation method employed in this work and the superior optical properties of the smart window have significantly broadened the scope of application of architectural windows and advanced the commercialization of architectural windows.

Machine learning-assisted design of polarization-controlled dynamically switchable full-color metasurfaces.

Dynamic color tuning has significant application prospects in the fields of color display, steganography, and information encryption. However, most methods for color switching require external stimuli, which increases the structural complexity and hinders the applicability of front-end dynamic display technology. In this study, we propose polarization-controlled hybrid metal-dielectric metasurfaces to realize full-color display and dynamic color tuning by altering the polarization angle of incident light without changing the structure and properties of the material. A bidirectional neural network is trained to predict the colors of mixed metasurfaces and inversely design the geometric parameters for the desired colors, which is less dependent on design experience and reduces the computational cost. According to the color recognition ability of human eyes, the accuracy of color prediction realized in our study is 93.18% and that of inverse parameter design is 92.37%. This study presents a simple method for dynamic structural color tuning and accelerating the design of full-color metasurfaces, which can offer further insight into the design of color filters and promote photonics research.

Evaluation and Design of Colored Silicon Nanoparticle Systems Using a Bidirectional Deep Neural Network.

Silicon nanoparticles (SiNPs) with lowest-order Mie resonance produce non-iridescent and non-fading vivid structural colors in the visible range. However, the strong wavelength dependence of the radiation pattern and dielectric function makes it very difficult to design nanoparticle systems with the desired colors. Most existing studies focus on monodisperse nanoparticle systems, which are unsuitable for practical applications. This study combined the Lorentz-Mie theory, Monte Carlo, and deep neural networks to evaluate and design colored SiNP systems. The effects of the host medium and particle size distribution on the optical and color properties of the SiNP systems were investigated. A bidirectional deep neural network achieved accurate prediction and inverse design of structural colors. The results demonstrated that the particle size distribution flattened the Mie resonance peak and influenced the reflectance and brightness of the SiNP system. The SiNPs generated vivid colors in all three of the host media. Meanwhile, our proposed neural network model achieved a near-perfect prediction of colors with high accuracy of the designed geometric parameters. This work accurately and efficiently evaluates and designs the optical and color properties of SiNP systems, thus accelerating the design process and contributing to the practical production design of color inks, decoration, and printing.

Needle-Free Jet Injectors' Geometry Design and Drug Diffusion Process Analysis.

In order to study the injection and diffusion process of the drug in the subcutaneous tissue of a needle-free jet injectors (NFJIs) in detail and understand the influence of different nozzle geometry on the diffusion process of the drug, in this paper, numerical simulations were performed to study the diffusion process of the drug in the subcutaneous tissue of NFJIs with cylindrical nozzle. On this basis, the differences of the drug diffusion process with different nozzle geometries were analyzed. The results show that the drug diffused in the shape of ellipsoid in the subcutaneous tissue. The penetration of the drug into the subcutaneous tissue is deeper under the condition of conical nozzle and conical cylindrical nozzle at the same time. However, it takes longer to spread to the interface between skin and subcutaneous tissue in reverse.

A Hierarchical Bayesian Model for Differential Connectivity in Multi-trial Brain Signals.

There is a strong interest in the neuroscience community to measure brain connectivity and develop methods that can differentiate connectivity across patient groups and across different experimental stimuli. The development of such statistical tools is critical to understand the dynamics of functional relationships among brain structures supporting memory encoding and retrieval. However, the challenge comes from the need to incorporate within-condition similarity with between-conditions heterogeneity in modeling connectivity, as well as how to provide a natural way to conduct trial- and condition-level inference on effective connectivity. A Bayesian hierarchical vector autoregressive (BH-VAR) model is proposed to characterize brain connectivity and infer differences in connectivity across conditions. Within-condition connectivity similarity and between-conditions connectivity heterogeneity are accounted for by the priors on trial-specific models. In addition to the fully Bayesian framework, an alternative two-stage computation approach is also proposed which still allows straightforward uncertainty quantification of between-trial conditions via MCMC posterior sampling, but provides a fast approximate procedure for the estimation of trial-specific VAR parameters. A novel aspect of the approach is the use of a frequency-specific measure, partial directed coherence (PDC), to characterize effective connectivity under the Bayesian framework. More specifically, PDC allows inferring directionality and explaining the extent to which the present oscillatory activity at a certain frequency in a sender channel influences the future oscillatory activity in a specific receiver channel relative to all possible receivers in the brain network. The proposed model is applied to a large electrophysiological dataset collected as rats performed a complex sequence memory task. This unique dataset includes local field potentials (LFPs) activity recorded from an array of electrodes across hippocampal region CA1 while animals were presented with multiple trials from two main conditions. The proposed modeling approach provided novel insights into hippocampal connectivity during memory performance. Specifically, it separated CA1 into two functional units, a lateral and a medial segment, each showing stronger functional connectivity to itself than to the other. This approach also revealed that information primarily flowed in a lateral-to-medial direction across trials (within-condition), and suggested this effect was stronger on one trial condition than the other (between-conditions effect). Collectively, these results indicate that the proposed model is a promising approach to quantify the variation of functional connectivity, both within- and between-conditions, and thus should have broad applications in neuroscience research.