Simple optical setup for the undergraduate experimental measurement of the refractive indices and attenuation coefficient of liquid samples and characterization of laser beam profile
Bibliographic record
Abstract
An optical setup was devised for the Electrical engineering undergraduate course “Photonic devices” where students were introduced to a simple visible diode laser based method of measuring the refractive indices of liquid samples in a transparent quartz cuvette placed on a computer-controlled rotating stage. When setting the cuvette at a small angle with respect to the incident laser beam, the light transmission through the cuvette results in a small mm-scale deflection of the laser path. The evaluation of the sample’s refractive index hinges on measuring the beam displacement. Moreover, by positioning the cuvette at normal incidence and recording the optical power after passing through various liquid samples (e.g. distilled water, maple syrup) and comparing with a reference (empty cuvette), students learn to estimate the attenuation coefficients of the substances by taking into account the contribution of the multiple Fresnel reflections. Finally, the same optical setup is also used by students to perform the “knife-edge” technique for the characterization of the beam profile emitted by the visible diode laser used in the setup. The proposed setup was implemented in the fall 2018 where undergraduate students were able to practice optical alignment and implement the concepts of material refractive indices and attenuation, as well as revisit the Gaussian beam theory taught in-class.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".