Sensing the future with graphene-based wearable sensors: A review
Bibliographic record
Abstract
In this current era, the demand for wearable sensors is increasing in full swing due to their multiphase applications, from the human body to soft robotics. Different materials, including carbon nanotubes, nanowires, nanoparticles, graphene, and more, have been studied to develop cost-efficient, enhanced sensing-capable, multifunctional, easy-to-operate, and easy-to-process wearable sensors. It is a suitable choice for the wearable sensor due to its excellent sensing ability along with myriad suitable mechanical, physical, electrical, and thermal properties. Through the proper utilization of these characteristics of graphene, wearable sensors containing graphene and its derivatives are now widely studied in healthcare, environment protection, and artificial intelligence sectors. This review has comprehensively discussed the current progression of multiple types of graphene and its derivative-based wearable sensors. The specific applications of these different types of graphene wearable sensors, including pressure, strain, gas, electrophysiological sensors, etc., in different fields are broadly described. The design, fabrication process, working, and sensing mechanisms are elaborately discussed. The challenges and limitations of graphene-based wearable sensors, along with potential opportunities, are thoroughly described for further research direction to develop, upgrade, and continue the progression of graphene wearable sensors. • Graphene's exceptional properties make it a promising material for wearable sensors; hence, demand for it increases rapidly. • Sensors are being used in areas like health monitoring, textiles, environmental sensing, and more. • Graphene-based sensors are superior due to their effective applications, such as high sensitivity, low power consumption, and rapid response times. • Its high surface area and biocompatibility make it suitable for wearables. • Challenges and future of graphene-based wearables, including potential breakthroughs and emerging applications.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".