Smart Cellulose-Based Janus Fabrics with Switchable Liquid Transportation for Personal Moisture and Thermal Management
Bibliographic record
Abstract
The Janus fabrics designed for personal moisture/thermal regulation have garnered significant attention for their potential to enhance human comfort. However, the development of smart and dynamic fabrics capable of managing personal moisture/thermal comfort in response to changing external environments remains a challenge. Herein, a smart cellulose-based Janus fabric was designed to dynamically manage personal moisture/heat. The cotton fabric was grafted with N-isopropylacrylamide to construct a temperature-stimulated transport channel. Subsequently, hydrophobic ethyl cellulose and hydrophilic cellulose nanofiber were sprayed on the bottom and top sides of the fabric to obtain wettability gradient. The fabric exhibits anti-gravity directional liquid transportation from hydrophobic side to hydrophilic side, and can dynamically and continuously control the transportation time in a wide range of 3-66 s as the temperature increases from 10 to 40 °C. This smart fabric can quickly dissipate heat at high temperatures, while at low temperatures, it can slow down the heat dissipation rate and prevent the human from becoming too cold. In addition, the fabric has UV shielding and photodynamic antibacterial properties through depositing graphitic carbon nitride nanosheets on the hydrophilic side. This smart fabric offers an innovative approach to maximizing personal comfort in environments with significant temperature variations.
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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".