Hydrogel-to-Aerogel Transitions in Polymer–Particle Hydrogels Expand the Wildfire Defense Window
Bibliographic record
Abstract
The 2025 Los Angeles wildfires caused widespread urban destruction and displacement, and severe economic losses, highlighting the urgent need for better fire retardants. Current fire suppression strategies rely heavily on water, chemical fire retardants, and water-enhancing gels, which use superabsorbent polymers to retain water and adhere to substrates, offering extended fire protection compared to water alone. However, their effectiveness is limited by evaporation and degradation under extreme heat and wind conditions. This study investigates the thermal properties, evaporation dynamics, and fire retardancy mechanisms of a novel polymer-particle (PP) hydrogel with aerogel-forming capabilities. The boiling-induced water vapor expansion and bubble nucleation drive the transformation of the hydrogel into a highly porous, foam-like fire-retardant coating upon rapid heat desiccation, enhancing thermal insulation. By evaluating the retardancy window across different evaporation stages under high heat and wind conditions, this study aims to determine the duration, effectiveness, and governing physical mechanisms of this unique retardant system. These findings provide a framework for designing the next generation of fire retardants with optimized thermal stability and extended protection for wildfire mitigation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".