Sensor Array for Volatile Organic Compounds Based on Doped Poly (2,5‐dimethyl aniline)
Bibliographic record
Abstract
Summary A sensor array containing five different sensing materials has been created that is able to distinguish between six different volatile organic compounds (VOCs) at an analyte concentration of 5 ppm. While none of the sensing materials, poly (2,5‐dimethyl aniline) (P25DMA) and P25DMA doped with four different metal oxide nanoparticles (Al 2 O 3 , NiO, TiO 2 , and ZnO), were selective towards any single analyte on their own, through the use of a filtering algorithm, this partial selectivity was exploited. Principal component analysis (PCA) was used as that a filtering algorithm, which was able to separate the responses from the sensing materials and identify six different gas analytes (acetaldehyde, acetone, benzene, ethanol, formaldehyde, and methanol). Both unknown gas analytes and a gas mixture were evaluated to determine the effectiveness of this sensor array.
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 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) | 0.000 | 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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".