Development and thermal performance of aerogel-based cellulose composites for thermal insulation applications
Bibliographic record
Abstract
Cellulose, a sustainable and eco-friendly material comprising around 85% recycled waste paper, could be utilized as a thermal insulation material. However, its low moisture resistance limits its feasibility under extremely harsh weather. Furthermore, it also requires a high thickness to achieve certain thermal resistance, which reduces the interior space in small buildings. To improve the performance of cellulose, this study aims to develop a thermally efficient, sustainable, and low-cost composite material utilizing cellulose and aerogel particles along with selective additives such as surfactant, binder, and rheology modifier. A detailed experimental study using the modified transient plane source (MTPS) method was conducted by systematically assessing the effect of each component on the thermal conductivity of the composite, based on 265 test specimens. The study was initiated by studying the thermal conduction of homogenous cellulose, followed by the addition of aerogel particles, surfactant, binder, and finally rheology modifier at varying concentrations. The effect of density and moisture content on the thermal conductivity of the composite was also investigated. It was observed that increasing aerogel from 0% to 50% by volume resulted in a thermal conductivity of 0.0551 and 0.0321 W/m.K, representing around a 42% reduction. Using 4.5% surfactant volume, the conductivity was further reduced to 0.0307 W/m.K, whereas adding up to 4% binder and 2.5% rheology modifier has a minimal effect on thermal conduction. Utilizing the super-insulating properties of aerogel and the low-cost and sustainability of cellulose, the proposed composite insulation material could be a robust option for housing in extreme environments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".