Gigahertz-Based Visible Light Detection Enabled via CdS-Coated TiO<sub>2</sub> Nanotube Layers
Bibliographic record
Abstract
Detection of visible light is a key component in material characterization techniques and often a key component of quality or purity control analyses for health and safety applications. Here in this work, to enable visible light detection at gigahertz frequencies, a planar microwave resonator is integrated with high aspect ratio TiO 2 nanotube (TNT) layer-sensitized CdS coating using the atomic layer deposition (ALD) technique. This unique method of visible light detection with microwave-based sensing improves integration of the light detection devices with digital technology. The designed planar microwave resonator sensor was implemented and tested with resonant frequency between 8.2 and 8.4 GHz and a resonant amplitude between −15 and −25 dB, depending on the wavelength of the illuminated light illumination on the nanotubes. The ALD CdS coating sensitized the nanotubes in visible light up to ∼650 nm wavelengths, as characterized by visible spectroscopy. Furthermore, CdS-coated TNT layer integration with the planar resonator sensor allowed for development of a robust microwave sensing platform with improved sensitivity to green and red light (60 and 1300%, respectively) compared to the blank TNT layers. Moreover, the CdS coating of the TNT layer enhanced the sensor’s response to light exposure and resulted in shorter recovery times once the light source was removed. Despite having a CdS coating, the sensor was capable of detecting blue and UV light; however, refining the sensitizing layer could potentially enhance its sensitivity to specific wavelengths of light in certain applications.
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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 | 0.001 | 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".