Advanced Cellulose‐Based Gels for Wearable Physiological Monitoring: From Fiber Modification to Application Optimization
Bibliographic record
Abstract
Abstract Cellulose‐based hydrogels have emerged as an important player in the smart health monitoring sector due to their excellent biocompatibility, tunable properties, and sustainability. The interface modification and structural regulation can effectively improve key properties further, such as mechanical strength and conductivity of cellulose‐based hydrogels. Especially with the utilization of green and sustainable chemical modification techniques, the structure of cellulose can be optimized, providing new solutions for its applications in health monitoring, wound care, and intelligent response systems. Furthermore, the combination of cellulose‐based hydrogels with other polymers, as well as their integration with 3D printing technology and artificial intelligence (AI), further expands their potential applications in complex architectures and intelligent functionalities. This review discusses modification strategies and performance optimization methods for cellulose‐based hydrogels, analyzes their application progress in physiological signal monitoring, and explores the effects of pretreatment, crosslinking, and molding methods on gel performance. The paper aims to provide valuable insights into the efficient utilization of plant fibers and the environmentally friendly development of next‐generation wearable electronic devices.
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 | 0.000 | 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 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".