Highly porous and flexible capacitive humidity sensor based on self-assembled graphene oxide sheets on a paper substrate
Bibliographic record
Abstract
This paper reports the fabrication of capacitive humidity sensors by integrating a graphene oxide sensing layer inside paper substrates. Graphene oxide sheets were self-assembled on the papers’ fibers. A comparative study between several sensors with different concentrations of graphene oxide and different processing times in the graphene oxide suspension is reported. Its aim is to optimize the sensing layer in terms of concentration and thickness towards the fabrication of highly sensitive and porous sensors. The morphology of the fabricated sensors was characterized using scanning electron microscopy, their structure and chemical composition using Raman and infrared spectroscopies. The washability and mechanical strength of the graphene oxide coated paper were tested in water and in an ultrasonic bath. Last, the sensing capabilities of the fabricated devices were tested for a relative humidity ranging from 30% to 90% RH. The optimal sensor is highly porous, hydrophobic and exhibits a good response towards humidity with a low hysteresis. This work presents a low cost alternative for the use of polymers and coated-papers as substrates for flexible electronics. It is also a first step towards the integration of flexible electronics into substrates, which enables the fabrication of highly porous, economical and flexible devices ideal for air flow monitoring, e-dressings and e-textiles.
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) | 0.000 | 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 | 0.001 | 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".