Fs laser written volume Raman–Nath grating for integrated spectrometer on smartphone
Bibliographic record
Abstract
Abstract In this work we demonstrate the integration of a spectrometer directly into smartphone screen by femtosecond laser inscription of a weak Raman–Nath volume grating either into the Corning Gorilla glass screen layer or in the tempered aluminosilicate glass protector screen placed in front of the phone camera. Outside the thermal accumulation regime, a new writing regime yielding positive refractive index change was found for both glasses which is fluence dependent. The upper-bound threshold for this thermal-accumulation-less writing regime was found for both glasses and were, respectively at a repetition rate less than 150 kHz and 101 kHz for fluence of 8.7 × 106 J/m2and 1.4 × 107 J/m2. A weak volume Raman–Nath grating of dimension 0.5 by 3 mm and 3 μm pitch was placed in front of a Samsung Galaxy S21 FE cellphone to record the spectrum using the 2nd diffraction order. This spectrometer covers the visible band from 401 to 700 nm with a 0.4 nm/pixel detector resolution and 3 nm optical resolution. It was used to determine the concentration detection limit of Rhodamine 6G in water which was found to be 0.5 mg/L. This proof of concept paves the way to in-the-field absorption spectroscopy for quick information gathering.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".