The uniqueness of flexible and mouldable thermal insulation materials in thermal protection systems—A comprehensive review
Bibliographic record
Abstract
Abstract In thermal control and safety systems, thermal insulation materials which are lightweight and flexible with hierarchical microstructures are commonly used nowadays. Flexible thermal insulation materials are designed to prevent heat transfer between two surfaces. These materials have various applications, from building insulation to automotive components, aerospace, and industrial processes. This review aims to provide an overview of flexible thermal insulation materials, their properties, and their applications. The most commonly used materials used for flexible thermal insulation are aerogels, ceramic fibres, and polymers. These materials are lightweight, durable, and have excellent thermal insulation properties and are also gaining popularity due to their unique characteristics. The insulation performance of flexible thermal insulation materials is influenced by thickness, density, porosity, and thermal conductivity factors. The choice of insulation material and its properties depend on the application site and the desired thermal insulation. The literature shows that nanofibrils‐based insulating materials have low thermal conductivity values and can be excellent flexible thermal insulating materials. Using flexible thermal insulation materials is crucial in reducing energy consumption and dissipation, enhancing thermal efficiency, and improving sustainability in various industries.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".