Superelastic and Ultralight Aerogel Assembled from Hemp Microfibers
Bibliographic record
Abstract
Abstract Aerogels with both high elastic strain and fast shape recovery after compression have broad application potentials as thermal regulation, absorbents, and electrical devices. However, creating such aerogels from cellulosic materials requires complicated preparation processes. Herein, a simple strategy for scalable production of hemp microfibers using a top‐down method is reported, which can further be assembled into aerogels with interconnected porous structures via ice‐templating technique. With density as low as 2.1 mg cm −3 , these aerogels demonstrate isotropic superelasticity, as exhibited by their fast shape restoration from over 80% compressive strain. Due to the high porosity (99.87%) and structural tortuosity, these aerogels show a low thermal conductivity of 0.0215 ± 0.0002 W m −1 K −1 , suggesting their potential in thermal insulation application. Certain hydrophobic modification using silane derivative further endows these aerogels with reduced water affinity. Overall, the proposed strategy to prepare bio‐based microfibers using scalable technology, as well as the assembled aerogels, provides new insights into the design and fabrication of multifunctional bio‐based aerogels for value‐added applications.
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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 | 0.000 |
| 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 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".