Reactive Ion Etching of Columnar Nanostructured ${\rm TiO}_{2}$ Thin Films for Modified Relative Humidity Sensor Response Time
Bibliographic record
Abstract
A CF4dry etch recipe for TiO2was optimized for nanostructured thin films. The impact of our etching process and ultraviolet irradiation of nanostructured relative humidity (RH) sensors was studied. Reactive ion etching of titanium dioxide decreased device adsorption response time by opening high diffusivity channels while retaining the high surface area and high dynamic range of the interdigitated electrode device. The full electrical response (impedance and phase) and response time of our sensors was studied as a function of etch duration. Using a TiO2etch recipe consisting of CF4produced large changes to RH sensor electrical response and introduced a large hysteresis. As a result of significant microstructural change, the adsorption response time of the RH sensors is greatly improved from ap 150 ms to an instrument-limited 50 ms. The adsorption times are at least six times faster than previous, thinner sensors. However, the current sensors do not recover as well as previous sensors, possibly due to nodular defects observed here and which are absent in previous devices. Although sensor step desorption times improved from an unetched ap 130 ms to the instrument limit of 50 ms, full recovery times increased beyond ap 3 s. When the etch treatment was followed by a 48 h ultraviolet treatment, the hysteresis introduced by the CF4etch was significantly reduced, without reducing the improvement in response time.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".