A Metal–Insulator–Metal Humidity Sensor Using Albumin–WO<sub>3</sub> Composites for Enhanced Responses
Bibliographic record
Abstract
The growing demand for precise healthcare monitoring has fueled advancements in humidity sensor technology, which is crucial for applications in meteorology, environmental monitoring, industrial control, agriculture, and biomedical devices. In the biomedical realm, humidity sensors play a vital role in point-of-care testing devices for detecting respiratory conditions, such as bronchitis, asthma, and pneumonia. This study presents a two-terminal metal–insulator–metal (MIM) device utilizing a composite of chicken egg albumin and tungsten trioxide (WO3) as the active hygroscopic film, with aluminum and copper as the bottom and top electrodes, respectively. Albumin, a biocompatible and hydrophilic protein, efficiently absorbs moisture, while WO3nanoparticles, known for their excellent electrical conductivity, enhance the device's sensitivity to humidity changes. Experimental results demonstrate that increasing the WO3content in the composite film from 0% to 50% significantly improves the sensor's performance. Specifically, the maximum current response to humidity increased up to 15 times for pure albumin, 17.3 times for the albumin–WO3(25%) nanocomposite, and 21 times for the albumin–WO3(50%) nanocomposite, as the relative humidity varied from 20% to 95%. This work highlights the potential of albumin–WO3composites for diverse applications in environmental monitoring, healthcare, and industrial processes.
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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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".