Flexible physical sensors made from paper substrates integrated with zinc oxide nanostructures
Bibliographic record
Abstract
Abstract Paper-based physical sensors represent an emerging research direction in the field of flexible sensors, which offers a low-cost alternative to current silicon-based sensors. The ultralow cost and excellent biodegradability of paper substrates contribute to the major advantages of paper-based sensors. To enhance the sensor performance, a variety of functional nanomaterials have been utilized for developing paper-based physical sensors, among which zinc oxide (ZnO) nanostructures (e.g. nanoparticles, nanowires, and nanotrees) are popular choices because of their multiple physical sensing modalities and ease of synthesis on paper. This article reviews the recent advances of paper-based physical sensors integrating zinc oxide nanostructures. First, we summarize the methods for synthesizing ZnO nanostructures on paper, with a focus on the low-cost facile hydrothermal approach. We then discuss the physical properties (e.g. piezoelectricity, piezotronics, and ultraviolet (UV) sensitivity) of ZnO nanostructures that have been used for physical sensing applications. We review the representative designs of paper-based ZnO physical sensors and their applications such as nanogenerators, strain sensors, touch pads, and UV sensors. Finally, we conclude the current progress, and envision the future trends and research opportunities.
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 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".