In-situ synthesis of zeolitic imidazolate framework-8 on an optical microbubble cavity for enhanced ethanol sensing
Bibliographic record
Abstract
Optical whispering-gallery-mode microcavities have been applied for ultra-sensitive detection of physical parameters and nano-scale analytes due to strong light-matter interactions. Surface treatments to enhance mode confinement or increase analyte interaction with the optical field are effective methods to improve the sensing performance. In this work, an optical microbubble cavity fabricated via heat-and-pull and arc discharge techniques is functionalized with zeolitic imidazolate framework-8 (ZIF-8), a subclass of metal-organic frameworks (MOFs), for concentration sensing of ethanol solutions. The bubble-shaped structure forms an internal microfluidic channel to sustain the analyte solution, which provides an excellent platform for trace detection. By growing a multilayer of MOFs on the inner surface of the microcavity, the sensitivity is significantly enhanced, with distinct behaviors observed when detecting ethanol solution concentrations lower or higher than 0.1%. Compared to a pure silica microbubble cavity, the sensitivity is enhanced by 47 times and 7 times at lower and higher concentrations, respectively. The knowledge gained on sensitivity enhancement of microcavities induced by surface functionalization will guide the combinations of novel materials and optical sensors for superior detection demands.
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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) | 0.000 | 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".