ZIF-67-incorporated multifunctional nanocomposite organohydrogel for wearable pressure and temperature sensing applications
Bibliographic record
Abstract
Pressure and temperature are vital physiological parameters for human health assessment and monitoring, which provide essential insights into critical functions and overall well-being. Pressure sensing aids in tracking cardiovascular health and physical activity, while temperature monitoring helps detect fever, inflammation, and other health anomalies. However, achieving efficient and reliable pressure and temperature measurements with robust physical properties for healthcare applications remains challenging. Herein, we demonstrate a multifunctional, high-performance nanocomposite organohydrogel synthesized by incorporating zeolitic imidazolate framework-67 (ZIF-67) into a poly(acrylamide)–co-hydroxyethyl acrylate polymer network. The nanocomposite organohydrogel is synthesized via a one-step free-radical polymerization using a binary solvent system of water, glycerol, and choline chloride to enhance environmental stability and thermal resilience. The resulting nanocomposite organohydrogel exhibits remarkable stretchability, high toughness, strong adhesion to different substrates, and rapid self-healing within 10 s. Furthermore, it demonstrates excellent stability across a broad temperature range from − 80 to 80 °C. The low stiffness, high dielectric constant, and temperature-dependent ionic conductivity of the ZIF-67-reinforced organohydrogel enable effective detection of external stimuli. It achieves capacitive pressure sensitivity of 0.75 kPa −1 and reliable thermosensitivity with a temperature coefficient of resistance of 1.1 %/°C, demonstrating exceptional performance under dynamic conditions. Integrating advanced properties and sensing functionalities, this work presents a flexible, multifunctional wearable material designed for gait analysis and temperature monitoring, offering potential applications in healthcare and sports science.
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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".