Investigating the synthesis and application of phase change material composites in firefighting clothing
Bibliographic record
Abstract
Firefighters respond to emergencies and tackle accidents and fires. Currently, efforts are being made to enhance the protection provided by firefighting clothing. Embedding phase change materials (PCMs) in firefighting clothing can lead to the absorption of external heat flow and flame heat, thereby preventing burns by offering enhanced thermal protection. Considering the importance of maintaining occupational safety and health for firefighters, this systematic review aims to investigate the use of PCMs in firefighting clothing and evaluate their effectiveness in providing thermal protection. The research draws on studies obtained from a systematic search of the Web of Science and Scopus databases. The following keywords were utilized: "phase change materials", "firefighting clothing", "firefighting vest", and "firefighting garment". Out of 225 articles identified, 13 numerical and experimental studies met our eligibility criteria. The melting temperature of PCM used in the reviewed studies ranged from 25 to 450 °C, with enthalpy values between 55 and 430 kJ/kg. The results highlighted the potential impact of PCM on enhancing the thermal resistance of firefighting clothing, as well as extending the time it takes for second-degree burns to occur. Additionally, it was concluded that the effectiveness of PCM is influenced by its type, melting temperature, enthalpy, and mass. Environmental conditions, fire scenarios, and exposure time also play significant roles in this context.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".