Carbon-Nanotube Loaded Antenna-Based Ammonia Gas Sensor
Bibliographic record
Abstract
Carbon nanotubes (CNTs) have been researched extensively for gas-sensing applications due to their unique electrical, chemical, and structural properties. Single-walled carbon nanotubes (SWNTs) have been predominantly used due to their superior electrical conductivity and higher sensitivity relative to multiwalled CNTs. This paper presents the design and characterization of a novel planar sensor fabricated on paper substrate to detect small concentrations of ammonia gas, using the shift in resonance frequency of a patch antenna as the discriminator. We have investigated three main design issues in depth. First, functionalization of the SWNTs with a polymer is studied in order to enhance the gas detection sensitivity. Second, a thin film of the functionalized SWNT is characterized to create a surface impedance model for the explanation and prediction of the resonance shift due to different gas concentrations. Finally, as a proof of concept, functionalized SWNTs are integrated into a patch antenna design and the return loss is measured in a closed-system environment to show high sensitivity for low concentrations of ammonia gas. The proposed antenna-based wireless gas sensor can be utilized in several applications, given its small form factor, light weight, and little to no power requirements.
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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 | 0.001 | 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".