Superelastic Cellulose Sub‐Micron Fibers/Carbon Black Aerogel for Highly Sensitive Pressure Sensing
Bibliographic record
Abstract
Superelastic aerogels with rapid response and recovery times, as well as exceptional shape recovery performance even from large deformation, are in high demand for wearable sensor applications. In this study, a novel conductive and superelastic cellulose-based aerogel is successfully developed. The aerogel incorporates networks of cellulose sub-micron fibers and carbon black (SMF/CB) nanoparticles, achieved through a combination of dual ice templating assembly and electrostatic assembly methods. The incorporation of assembled cellulose sub-micron fibers imparts remarkable superelasticity to the aerogel, enabling it to retain 94.6% of its original height even after undergoing 10 000 compression/recovery cycles. Furthermore, the electrostatically assembled CB nanoparticles contribute to exceptional electrical conductivity in the cellulose-based aerogel. This combination of electrical conductivity and superelasticity results in an impressive response time of 7.7 ms and a recovery time of 12.8 ms for the SMF/CB aerogel, surpassing many of the aerogel sensors reported in previous studies. As a proof of concept, the SMF/CB aerogel is utilized to construct a pressure sensor and a sensing array, which exhibit exceptional responsiveness to both minor and substantial human motions, indicating its significant potential for applications in human health monitoring and human-machine interaction.
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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) | 0.001 | 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".