Novel Nickel Oxide/Graphene Composite Sensor: A Low‐Temperature Approach to Flexible Temperature Sensing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 it