Human-like smelling of a rose scent using an olfactory receptor nanodisc-based bioelectronic nose
Bibliographic record
Abstract
We report a strategy for the human-like smelling of a rose scent utilizing olfactory receptor nanodisc (ND)-based bioelectronic nose devices. In this strategy, a floating electrode (FE)-based carbon nanotube (CNT) field effect transistor (FET) was functionalized with human olfactory receptor 1A2 (hOR1A2)-embedded NDs (hOR1A2NDs). The hOR1A2NDs responded to rose scent molecules specifically, which were monitored electrically using the underlying CNT-FET. This strategy allowed us to quantitatively assess the contents of geraniol and citronellol, the main components of a rose scent, as low as 1 fM and 10 fM, respectively. In addition, it enabled us to selectively discriminate a specific rose odorant from other odorants. Significantly, we also demonstrated that the responses of hOR1A2NDs to a rose scent could be strongly enhanced by enhancer materials like a human nose. Furthermore, the method provided a means to quantitatively evaluate rose scent components in real samples such as rose oil. Since our method allows one to quantitatively evaluate general rose scent ingredients just like a human nose, it could be a powerful strategy for versatile basic research and various applications such as fragrance development.
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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".