Engineering mesoporous silica for superior optical and thermal properties

Butts, Danielle M.; McNeil, Patricia E.; Marszewski, Michal; Lan, Esther H.; Galy, Tiphaine; Li, Man; Kang, Joon Sang; Ashby, David S.; King, Sophia C.; Tolbert, Sarah H.; Hu, Yongjie; Pilon, Laurent; Dunn, Bruce

doi:10.1557/mre.2020.40

Cited by 13 publications

(18 citation statements)

References 54 publications

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“…The fraction microporosity was also calculated and is described in the Supporting Information (S3). Nitrogen porosimetry isotherms were used to determine the surface fractal dimension, D s , which should be equal to the mass fractal dimension in fractal solids, ,, utilizing the Frenkel–Halsey–Hill method ln ⁡ n = C − false( 3 − D normals false) ln true( R T nobreak0em.25em⁡ normalln nobreak0em.25em⁡ P 0 P true) where n is the quantity of N 2 gas adsorbed in cm 3 STP g –1 , P is the pressure, and P 0 is the saturation vapor pressure . Sample calculations are given in the Supporting Information (S4).…”

Section: Experimental Methodsmentioning

confidence: 99%

Characterization of Fragility in Silica-Based Ionogels

et al. 2022

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The present study examines the ionic conductivity and transport properties for ionic liquids confined within mesoporous materials. The fragility index was determined for several samples with varying microstructures and used to characterize performance as a pseudosolid electrolyte. The degree of microstructure complexity was characterized via the mass fractal dimension, which correlates with the fragility index in the pore size domain studied. We demonstrate for the materials studied here that the presence of the microporous matrix serves to inhibit ion mobility, and as the complexity of the matrix increases, the fragility index also increases, leading to lower conductivity materials. The microstructural features influencing conductivity were examined using Archie’s law when assuming the matrix conductivity to be negligible. This type of analysis, previously validated for rock matrices, has now been extended to pseudosolid electrolyte materials. The results indicate that ionogel samples can approach the conductivity of the unconfined ionic liquid. These conclusions are applicable to a variety of pseudosolid electrolyte systems and have important implications for the design of electrochemical devices.

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Section: Experimental Methodsmentioning

confidence: 99%

Characterization of Fragility in Silica-Based Ionogels

et al. 2022

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“…The properties of composites are determined by the percentage, shape, size, and arrangement of each component, and sometimes the interface interaction between them, as illustrated in Figure . For porous structures like aerogels, the key descriptors are porosity, pore size, wall thickness, pore arrangement, pore shape, and so on. − Effective medium approximation was the most used tool to predict the thermal conductivity of composites, but unsatisfied due to the lack of consideration of the detailed fillers’ structures. Finite element analysis of the heat diffusion equation is a common tool to consider the exact structures of composites.…”

Section: Thermal Energy Materials Genealogymentioning

confidence: 99%

“…The philosophy of material discovery will be revolutionized from the traditional and slow trial and error strategy to modern efficient high-throughput screening from millions of candidate materials. On the other hand, the structure and composition of materials are also decisive to their thermal performance, such as the porous structure of thermal insulation materials and components of composite materials. − It is labor-intensive and time-consuming to scan all the compositional or structural parameters to find the optimal material synthesis and manufacturing recipes. By taking advantage of the ML’s power on optimization problems with limited knowledge, the efforts on material design can be significantly reduced.…”

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confidence: 99%

Machine Learning for Harnessing Thermal Energy: From Materials Discovery to System Optimization

Dai

2022

ACS Energy Lett.

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Recent advances in machine learning (ML) have impacted research communities based on statistical perspectives and uncovered invisibles from conventional standpoints. Though the field is still in the early stage, this progress has driven the thermal science and engineering communities to apply such cutting-edge toolsets for analyzing complex data, unraveling abstruse patterns, and discovering non-intuitive principles. In this work, we present a holistic overview of the applications and future opportunities of ML methods on crucial topics in thermal energy research, from bottom-up materials discovery to top-down system design across atomistic levels to multi-scales. In particular, we focus on a spectrum of impressive ML endeavors investigating the state-of-the-art thermal transport modeling (density functional theory, molecular dynamics, and Boltzmann transport equation), different families of materials (semiconductors, polymers, alloys, and composites), assorted aspects of thermal properties (conductivity, emissivity, stability, and thermoelectricity), and engineering prediction and optimization (devices and systems). We discuss the promises and challenges of current ML approaches and provide perspectives for future directions and new algorithms that could make further impacts on thermal energy research.

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“…Optical transparency and thermal insulation are two important aspects of solar thermal systems and window materials. [9][10][11][12][13] Compared with conventional transparent glass materials, silica aerogels have high-volume nanoporous structures, which bring unique properties such as lightweight (density of 0.01-0.1 g cm À3 ) and ultra-low thermal conductivity (as low as 15 mW m À1 K À1 under ambient conditions). Thus, silica aerogels are ideal for thermal and acoustic insulators.…”

Section: Introductionmentioning

confidence: 99%