A nano-photonic filter for near infrared radiative heater

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

Interpretable Forward and Inverse Design of Particle Spectral Emissivity Using Common Machine-Learning Models

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

Gradient Polarity Solvent Wash for Separation and Analysis of Electrolyte Decomposition Products on Electrode Surfaces

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

Gradient Polarity Solvent Wash for Separation and Analysis of Electrolyte Decomposition Products on Electrode Surfaces

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

Fluidic and mechanical thermal control devices

In recent years, intensive studies on thermal control devices have been conducted for the thermal management of electronics and computers as well as for applications in energy conversion, chemistry, sensors, buildings, and outer space. Conventional cooling or heating techniques realized using traditional thermal resistors and capacitors cannot meet the thermal requirements of advanced systems. Therefore, new thermal control devices are being investigated to satisfy these requirements. These devices include thermal diodes, thermal switches, thermal regulators, and thermal transistors, all of which manage heat in a manner analogous to how electronic devices and circuits control electricity. To design or apply these novel devices as well as thermal control principles, this paper presents a systematic and comprehensive review of the state-of-the-art of fluidic and mechanical thermal control devices that have already been implemented in various applications for different size scales and temperature ranges. Operation principles, working parameters, and limitations are discussed and the most important features for a particular device are identified