2D Plasmonic Tungsten Oxide Enabled Ultrasensitive Fiber Optics Gas Sensor
Bibliographic record
Abstract
Abstract Functional materials coated on optical fibers have demonstrated great potential for optical gas sensing applications. However, their sensitivity is typically limited to the sub‐parts per million (sub‐ppm) range. Here, for the first time a 2D near‐infrared plasmonic tungsten oxide (WOx) enabled ultrasensitive fiber optics gas sensor on a side‐polished D‐shape single mode optical fiber is presented. The plasmon resonance wavelength range of 2D WOx is matched with a conventional telecommunications wavelength of 1550 nm for driving the optical fiber, therefore inducing a strong light–matter interaction. Upon the surface adsorption of gas molecules, free electrons in the 2D WOx body are redistributed changing the plasmon resonance properties and hence the transmission through the optical fiber. The sensor is selectively responsive to NO2 at concentrations down to 44 parts per billion (ppb) with a limit of detection of 8 ppb at a relatively low elevated temperature. Such an excellent sensing performance is significantly improved over the previously reported fiber optics NO2 sensors, which suggests the integration of 2D plasmonic degenerated semiconductors as a viable approach to develop high‐performance fiber optics gas sensors.
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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 | 0.000 | 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".