Optomechanical spring enhanced mass sensing
Bibliographic record
Abstract
On-chip nano-optomechanical systems (NOMS) have demonstrated a zeptogram-level mass sensitivity and are promising candidates for low-cost implementations in areas such as metabolite quantitation and chemical analysis. High responsivity and sensitivity call for substantial optomechanical coupling and cavity finesse, resulting in detuning-dependent stiffness and mechanical damping via optomechanical back-action. Since mass loading (or temperature or force change) can alter both mechanical and cavity properties, mechanical frequency shifts induced by loading can encompass both effects. Precision sensing requires understanding and quantifying the source of the frequency tuning. Here, we show the deconvolution of direct loading and optomechanical stiffness change on the mechanical eigenfrequency as a function of detuning for a nano-optomechanical sensor in gaseous sensing experiments. Responses were generally dominated by shifts in optical stiffness and resulted in a mass loading signal amplification by as much as a factor of 2.5. This establishes an alternative possible route toward better mass sensitivity in NOMS while confirming the importance of incorporating optical stiffness effects for precision mass sensing.
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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".