Novel Nickel Oxide/Graphene Composite Sensor: A Low‐Temperature Approach to Flexible Temperature Sensing
Bibliographic record
Abstract
Abstract Accurate temperature monitoring is crucial in sectors such as food safety, healthcare, and environmental management, where precise monitoring is crucial. While integrated circuit (IC) based sensors are known for their high sensitivity, they suffer from limitations, including high production costs, complex fabrication, and poor performance in humid environments. This study developed a novel Nickel Oxide (NiO)‐based temperature sensor using the doctor blade technique, incorporating a nanocomposite of polystyrene (PS) and graphene to enhance flexibility, stability, and conductivity. The sensor demonstrates a rapid response time of ≈30 s and high sensitivity with a B‐value of 2354 K. Long‐term stability tests show minimal drift and consistent performance over 50 days, with a low coefficient of variation of 1.19%. The sensor also exhibits robust performance under varying relative humidity conditions (RH≈10%–65%) and mechanical strain, maintaining functionality after repeated 80 bending cycles at a bending radius of 2.1 cm. The results indicate that this NiO‐based sensor is a promising candidate for applications requiring reliable and flexible temperature monitoring, providing a cost‐effective and scalable alternative to traditional IC‐based sensors.
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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 | 0.001 | 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".