Measuring the cantilever-position-sensitive detector distance and cantilever curvature for cantilever sensor applications
Bibliographic record
Abstract
Measuring cantilever sensor deflections using an optical beam deflection system is more complicated than often assumed. The direction of the reflected beam is dependent on the surface normal of the cantilever, which in turn is dependent on the state of the cantilever. It is often assumed that the cantilever is both straight and perfectly level before the onset of sensing experiments although this assumption, especially the former, is rarely true. Failure to characterize the initial state of the cantilever can lead to irreproducibility in cantilever sensor measurements. We have developed three new methods for characterizing the initial state of the cantilever. In the first case we show how to define the initial angle of inclination beta of the chip on which the cantilever is attached. This method was tested using an aluminum block with a known angle of inclination. A new method for determining the initial distance L(o) between the cantilever and the position-sensitive detector (PSD) is also presented. This parameter which behaves as an amplification factor of the PSD signal is critical for obtaining precise cantilever sensor data. Lastly, we present a method for determining the initial curvature of the cantilever which often results from depositing the sensing platform on the lever. Experiments conducted using deflected cantilevers showed the model to be accurate. The characterization methods presented in this work are simple to use, easy to implement, and can be incorporated into most cantilever sensor setups.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".