Printed Flexible Sensors for Low-Cost Temperature and Humidity Monitoring: Materials, Processes, and Applications
Bibliographic record
Abstract
Printed flexible temperature and humidity sensors have emerged as essential components for diverse applications requiring low-cost, scalable, and conformable environmental monitoring. This chapter presents a comprehensive overview of recent advancements in printed flexible sensors for temperature and humidity, emphasizing material selection, additive fabrication techniques, and practical applications. The chapter analyzes critical printing processes such as screen printing, inkjet printing, aerosol jet printing, gravure, and roll-to-roll methods, assessing their impact on feature resolution, ink properties, and processing temperatures. Comparative analyses highlight performance trends across metal nanoparticle inks, conductive polymers, nanocomposites, and two-dimensional materials, clearly linking these materials' stability, response time, and mechanical durability to specific fabrication conditions and substrate characteristics. Additionally, the chapter explores strategies for effective encapsulation, calibration, and artificial intelligence (AI)-supported drift correction to enhance sensor accuracy and reliability. By summarizing practical design guidelines and market outlooks, the chapter provides clear pathways for scaling laboratory prototypes into fully deployable sensor system configurations. The insights presented demonstrate the significant potential of printed flexible sensors in healthcare wearables, environmental monitoring networks, and industrial internet of things (IoT) solutions, contributing to transformative advancements in sensing technology.
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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.014 |
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".