A spectroscopic comparison of femtosecond-laser-modified fused silica using kilohertz and megahertz laser systems
Bibliographic record
Abstract
Waveguides were written in fused silica using both a femtosecond fiber laser with a 1MHz pulse repetition rate and a femtosecond amplified Ti:sapphire laser with a 1kHz repetition rate. Confocal Raman and fluorescence microscopies were used to study structural changes in the waveguides written with both systems. A broad fluorescence band, centered at 650nm, associated with nonbridging oxygen hole center (NBOHC) defects was observed after waveguide fabrication with the megahertz laser. With the kilohertz laser system these defects were only observed for pulse energies above 1μJ. Far fewer NBOHC defects were formed with the megahertz laser than with kilohertz writing, possibly due to thermal annealing driven by heat accumulation effects at 1MHz. When the kilohertz laser was used with pulse energies below 1μJ, the predominant fluorescence was centered at 550nm, a band assigned to the presence of silicon clusters (Eδ′). We also observed an increase in the intensity of the 605cm−1 Raman peak relative to the total Raman intensity, corresponding to an increase in the concentration of three-membered rings in the lines fabricated with both laser systems.
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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".