Electrospun Lignin/ZnO Nanofibrous Membranes for Self‐Powered Ultrasensitive Flexible Airflow Sensor and Wearable Device
Bibliographic record
Abstract
Abstract The interest and demand for flexible sensors and wearable devices are rapidly growing. The added benefit of electricity generation, enabling gas sensors to be self‐powered, increases the applicability of these devices for flexible and wearable airflow sensors. Inspired by water evaporation‐induced power generation, this study explores its potential in sensing applications, which has not yet been explored in detail. Electrospinning technology is used to prepare superhydrophilic lignin/ZnO nanofibrous membranes with a ZnO nanoparticle layer, capable of generating at least 100 mV (which allows it to power its own signal transduction). The membrane is highly sensitive to variations in airflow, enabling its use as an ultrasensitive and flexible airflow sensor. This sensor demonstrates exceptional performance, including a fast response time (0.65 s), broad detection range (with lower detection limit down to 0.25 and upper detection limit of 3 m s −1 ), and extremely high airflow velocity detection accuracy. Beyond these, it can serve as a wearable sensor for sweat monitoring, motion detection, and breath monitoring (to accurately detect breathing rate, intensity and variations in speech). Such self‐powered, ultrasensitive, and flexible lignin/ZnO airflow sensors provide novel potential to advance the development of smart textiles and wearable electronics.
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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".