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Radiative particles are ubiquitous in nature and in various technologies. Calculating radiative properties from known geometry and designs can be computationally expensive, and trying to invert the problem to come up with designs specific to desired radiative properties is even more challenging. Here, we report a machine-learning (ML)-based method for both the forward and inverse problem for dielectric and metallic particles. Our decision-tree-based model is able to provide explicit design rules for inverse problems. Furthermore, we can use the same trained model for both the forward and the inverse problem, which greatly simplifies the computation. Our methodology shows the promise of augmenting optical design optimizations by providing interpretable and actionable design rules for rapidly finding approximate solutions for the inverse design problem. Inverse design is usually done by expensive, slow, iterative optimization. Elzouka et al. show how a simple machine-learning model (decision trees) can efficiently perform inverse design in one shot, while recovering design rules understandable by humans. Inverse design of the spectral emissivity of particles is used as an example.

The solid electrolyte interphase (SEI) formed during the cycling of lithium-ion batteries (LIBs) by decomposition of electrolyte molecules has key impact on device performance. However, the detailed decomposition process and distribution of products remain a mystery due to the wide variety of electrochemical pathways and the lack of facile analytical methods for chemical characterization of SEIs. In this report, a gradient polarity solvent wash technique involving the use of solvents with gradually increased polarities is employed to sequentially remove different SEI components from electrode surfaces. Fourier transform infrared (FTIR) spectroscopy is utilized to characterize the SEI composition. The impacts of electrolyte additives and discharge rates over SEI formation are illustrated. This study presents a new concept of rationally controlled solvent wash technique for electrode surface analysis that can selectively remove targeted components. The findings in this study provide experimental support for the slow charge formation processes commonly employed for LIBs in industry.

Infrared (IR) radiative heating is a highly desirable method for heating as compared to convective heating due to the unprecedented control of radiative energy transfer, leading to a significant increase in energy efficiency. The greatest challenge however with IR radiative heating is its low penetration depth due to the strong IR absorption by the water content in the substance to be heated. Near IR (NIR) heating can circumvent this problem as it has greater penetration depths. The proposed nano-photonic design for NIR filter (or effective selective emitter) has transmissivity of more than 70% in NIR and less than 15% in both visible and IR wavelengths as opposed to currently available IR heaters, which have high emissivity across all wavelengths. This NIR filter can be applied to any radiative heating source to transform it into a NIR radiative heater. We demonstrate this with a simple prototype by applying it in front of tungsten-based incandescent lamp where significant reduction in white glow (glare) was observed. Potential application of this NIR filter would be in heating in both building and industrial sectors where the ability to provide localized heating could lead to significant energy savings. In addition, NIR selective emitters can be applied for power generation by supplying thermophotovoltaics (TPV) with photons at the right wavelengths, which will increase the efficiency of TPV.

The development of a colloidal synthesis procedure to produce nanomaterials with high shape and size purity is often a time-consuming, iterative process. This is often due to quantitative uncertainties in the required reaction conditions and the time, resources, and expertise intensive characterization methods required for quantitative determination of nanomaterial size and shape. Absorption spectroscopy is often the easiest method for colloidal nanomaterial characterization. However, due to the lack of a reliable method to extract nanoparticle shapes from absorption spectroscopy, it is generally treated as a more qualitative measure for metal nanoparticles. This work demonstrates a gold nanorod (AuNR) spectral morphology analysis tool, called AuNR-SMA, which is a fast and accurate method to extract quantitative structural information from colloidal AuNR absorption spectra. To demonstrate the practical utility of this model, we apply it to three distinct applications. First, we demonstrate this model's utility as an automated analysis tool in a high-throughput AuNR synthesis procedure by generating quantitative size information from optical spectra. Second, we use the predictions generated by this model to train a machine learning model to predict the resulting AuNR size distributions under specified reaction conditions. Third, we apply this model to spectra extracted from the literature where no size distributions are reported and impute unreported quantitative information on AuNR synthesis. This approach can potentially be extended to any other nanocrystal system where absorption spectra are size dependent, and accurate numerical simulation of absorption spectra is possible. In addition, this pipeline could be integrated into automated synthesis apparatuses to provide interpretable data from simple measurements, help explore the synthesis science of nanoparticles in a rational manner, or facilitate closed-loop workflows.

This work demonstrates a method to design photonic surfaces by combining femtosecond laser processing with the inverse design capabilities of tandem neural networks that directly link laser fabrication parameters to their resulting textured substrate optical properties. High throughput fabrication and characterization platforms are developed that generate a dataset comprising 35280 unique microtextured surfaces on stainless steel with corresponding measured spectral emissivities. The trained model utilizes the nonlinear one-to-many mapping between spectral emissivity and laser parameters. Consequently, it generates predominantly novel designs, which reproduce the full range of spectral emissivities (average root-mean-squared-error < 2.5%) using only a compact region of laser parameter space 25 times smaller than what is represented in the training data. Finally, the inverse design model is experimentally validated on a thermophotovoltaic emitter design application. By synergizing laser-matter interactions with neural network capabilities, the approach offers insights into accelerating the discovery of photonic surfaces, advancing energy harvesting technologies., (© 2024 The Authors. Advanced Science published by Wiley‐VCH GmbH.)

Optical device design is typically an iterative optimization process based on a good initial guess from prior reports. Optical properties databases are useful in this process but difficult to compile because their parsing requires finding relevant papers and manually converting graphical emissivity curves to data tables. Here, we present two contributions: one is a dataset of thermal emissivity records with design-related parameters, and the other is a software tool for automated colored curve data extraction from scientific plots. We manually collected 64 papers with 176 figures reporting thermal emissivity and automatically retrieved 153 colored curve data records. The automated figure analysis software pipeline uses Faster R-CNN for axes and legend object detection, EasyOCR for axes numbering recognition, and k-means clustering for colored curve retrieval. Additionally, we manually extracted geometry, materials, and method information from the text to add necessary metadata to each emissivity curve. Finally, we analyzed the dataset to determine the dominant classes of emissivity curves and determine the underlying design parameters leading to a type of emissivity profile., (© 2022. The Author(s).)

Coherent thermal emission deviates from the Planckian blackbody emission with a narrow spectrum and strong directionality. While far-field thermal emission from polaritonic resonance has shown the deviation through modelling and optical characterizations, an approach to achieve and directly measure dominant coherent thermal emission has not materialised. By exploiting the large disparity in the skin depth and wavelength of surface phonon polaritons, we design anisotropic SiO 2 nanoribbons to enable independent control of the incoherent and coherent behaviours, which exhibit over 8.5-fold enhancement in the emissivity compared with the thin-film limit. Importantly, this enhancement is attributed to the coherent polaritonic resonant effect, hence, was found to be stronger at lower temperature. A thermometry platform is devised to extract, for the first time, the thermal emissivity from such dielectric nanoemitters with nanowatt-level emitting power. The result provides new insight into the realisation of spatial and spectral distribution control for far-field thermal emission.

The rate of heat transfer by thermal radiation is a function of the number of channels that carry the electromagnetic energy, and the capacity of each channel to convey the electromagnetic energy. In this research, we show that we can increase the number of these channels for a given emitter volume, and accordingly, we can enhance both near- and far-field thermal radiation exchange. We increase the number of channels by carving a variety of slots with different sizes. Using a modified finite-difference time-domain simulation, we show that the interweaved L slots achieved higher rates of heat transfer than the flat slab and straight slots (all having the same volume) by 15 and 2.5 times, respectively, for far-field thermal radiation (separation gap dc = 30 μm), and 5.6730 and 1.145 times for near-field thermal radiation (dc = 0.5 μm).

Limited performance and reliability of electronic devices at extreme temperatures, intensive electromagnetic fields, and radiation found in space exploration missions (i.e., Venus &Jupiter planetary exploration, and heliophysics missions) and earth-based applications requires the development of alternative computing technologies. In the pursuit of alternative technologies, research efforts have looked into developing thermal memory and logic devices that use heat instead of electricity to perform computations. However, most of the proposed technologies operate at room or cryogenic temperatures, due to their dependence on material's temperature-dependent properties. Here in this research, we show experimentally-for the first time-the use of near-field thermal radiation (NFTR) to achieve thermal rectification at high temperatures, which can be used to build high-temperature thermal diodes for performing logic operations in harsh environments. We achieved rectification through the coupling between NFTR and the size of a micro/nano gap separating two terminals, engineered to be a function of heat flow direction. We fabricated and tested a proof-of-concept NanoThermoMechanical device that has shown a maximum rectification of 10.9% at terminals' temperatures of 375 and 530 K. Experimentally, we operated the microdevice in temperatures as high as about 600 K, demonstrating this technology's suitability to operate at high temperatures.