Selective microwave sensors exploiting the interaction of analytes with trap states in TiO<sub>2</sub>nanotube arrays
Bibliographic record
Abstract
Sensing of molecular analytes by probing the effects of their interaction with microwaves is emerging as a cheap, compact, label-free and highly sensitive detection and quantification technique. Microstrip ring-type resonators are particularly favored for this purpose due to their planar sensing geometry, electromagnetic field enhancements in the coupling gap and compatibility with established printed circuit board manufacturing. However, the lack of selectivity in what is essentially a permittivity-sensing method is an impediment to wider adoption and implementation of this sensing platform. By placing a polycrystalline anatase-phase TiO2 nanotube membrane in the coupling gap of a microwave resonator, we engineer selectivity for the detection and differentiation of methanol, ethanol and 2-propanol. The scavenging of reactive trapped holes by aliphatic alcohols adsorbed on TiO2 is responsible for the alcohol-specific detection while the different short chain alcohols are distinguished on the basis of differences in their microwave response. Electrodeless microwave sensors which allow spectral and time-dependent monitoring of the resonance frequency and quality factor provide a wealth of information in comparison with electrode-based resistive sensors for the detection of volatile organic compounds. A high dynamic range (400 ppm-10,000 ppm) is demonstrated for methanol detection.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".