Improved Prediction of Elastic Modulus for Carbon-Based Aerogels Using Power-Scaling Model

The mechanical stability of carbon aerogels, particularly their thermal insulation performance, is closely linked to their elastic modulus. This property plays a crucial role in determining the material's overall mechanical stability. The objective of this study is to enhance the accuracy of elastic modulus predictions for carbon aerogels using a power-scaling model. By setting the prefactor of the Gibson and Ashby equation to 1.0, accurate predictions of the elastic modulus can be achieved if the correct scaling exponent is determined. Twelve sets of experimental data were used to fit the power-scaling model, revealing that the scaling exponent for the elastic modulus of carbon aerogels typically falls between 2.2 and 3.0. This range is narrower than the 2.0 to 4.0 range reported in the literature, with a median value of 2.6 providing reliable predictions. Additionally, a relationship between the solid thermal conductivity and the elastic modulus of carbon aerogels was established using a thermal conduction model. The study also examined the elastic modulus of carbon nanotube and graphene aerogels-both allotropes of carbon aerogel. By fitting experimental data into the power-scaling model, the scaling exponents for carbon nanotube aerogels and graphene aerogels were found to range from 2.7 to 3.5 and 2.7 to 3.7, respectively. Median exponent values of 3.1 and 3.2 were identified as optimal for predicting the elastic moduli of carbon nanotube and graphene aerogels.

Authors

Institutions

Publication Details

Journal
Gels
Published
2025-03-06
DOI
https://doi.org/10.3390/gels11030184
Citations
1
Primary Topic
Aerogels and thermal insulation
Type
article
Field-Weighted Citation Impact
0.76

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Improved Prediction of Elastic Modulus for Carbon-Based Aerogels Using Power-Scaling Model

Yuan Lu, Mingyang Yang, Ke Yun, Jie Zheng et al.
1 citations
Gels
Aerogels and thermal insulation
0.76
article

Improved Prediction of Elastic Modulus for Carbon-Based Aerogels Using Power-Scaling Model

Yuan Lu, Mingyang Yang, Ke Yun, Jie Zheng, Cheng Bi, Mu Du, Yang Xu, Ying Zhang
article en
1 citations

Abstract

The mechanical stability of carbon aerogels, particularly their thermal insulation performance, is closely linked to their elastic modulus. This property plays a crucial role in determining the material's overall mechanical stability. The objective of this study is to enhance the accuracy of elastic modulus predictions for carbon aerogels using a power-scaling model. By setting the prefactor of the Gibson and Ashby equation to 1.0, accurate predictions of the elastic modulus can be achieved if the correct scaling exponent is determined. Twelve sets of experimental data were used to fit the power-scaling model, revealing that the scaling exponent for the elastic modulus of carbon aerogels typically falls between 2.2 and 3.0. This range is narrower than the 2.0 to 4.0 range reported in the literature, with a median value of 2.6 providing reliable predictions. Additionally, a relationship between the solid thermal conductivity and the elastic modulus of carbon aerogels was established using a thermal conduction model. The study also examined the elastic modulus of carbon nanotube and graphene aerogels-both allotropes of carbon aerogel. By fitting experimental data into the power-scaling model, the scaling exponents for carbon nanotube aerogels and graphene aerogels were found to range from 2.7 to 3.5 and 2.7 to 3.7, respectively. Median exponent values of 3.1 and 3.2 were identified as optimal for predicting the elastic moduli of carbon nanotube and graphene aerogels.

GelsVol. 11(3)
Xi'an University of Architecture and Technology (CN), Shandong University (CN), Xi'an Shiyou University (CN), Anhui Special Equipment Inspection Institute (CN)
Basic and Applied Basic Research Foundation of Guangdong Province
Openalex Percentile: Top 34%
Aerogels and thermal insulation
0.76
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.