Capacitive Humidity Sensing using Carbon Nanotube Enabled Capillary Condensation
Bibliographic record
Abstract
This electronic document is a "live" A capacitive humidity sensor, fabricated by depositing multi-wall carbon nanotubes (MWCNTs) on one of the stainless steel substrates, is presented for moisture detection at room temperature. When compared to a sensor without CNTs, CNT-enhanced sensor has a capacitance response of 60-200% more when the humidity is under 70% relative humidity (RH), and 300%-3000% more if RH level goes over 70%. The detection and recovery response times are on the order of seconds. The performance is comparable to a commercial sensor from Honeywell that is used as a benchmark throughout the experiments. Our results demonstrate that nano-materials like MWCNTs, can naturally form porous nano-structures, which can potentially realize a miniature capacitive humidity sensor with a higher sensing resolution. The gain in performance is attributed to capillary condensation effect. The capillary condensation effect, that is facilitated by the porous nanostructures of random aligned MWCNTs, is discussed in this paper template. The various components of your paper [title, text, heads, etc.] are already defined on the style sheet, as illustrated by the portions given in this document.
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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 | 0.001 |
| 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".