Generation of green second harmonic radiation in LBO, BiBO, KTP, and PPLN crystals using passively Q-switched sub-nanosecond microchip laser
Bibliographic record
Abstract
A sub-nanosecond green laser source has big demand in such fields like spectroscopy, micromachining, fluorescence imaging, and laser displays. Most often green light is produced by frequency doubling of pulsed laser sources based on Nd3+-ion or Yb3+-ion doped gain media that oscillate in the near-IR range. For creating compact and relatively small source of green light suitable for broad type of applications a passively Q-switched Nd:YAG microchip laser (Teem Photonics) operating at 1064 nm with 6.9 kHz repetition rate was chosen. It delivers 560 ps long pulses with 10 μJ energy, corresponding to an average output power of 69 mW. Crystals of BiBO, KTP, LBO, and PPLN were chosen for frequency doubling. Main goal of this work was to study the characteristics of these crystals under similar experimental conditions and to select the most efficient one for this task. To optimize second harmonic generation (SHG) process, different focusing conditions were used during the experiments. In this work we measured the second harmonic output power as a function of the incident power, beam profile for fundamental and second harmonic radiation for all crystals, and second harmonic output power as a function of temperature for PPLN crystal. Crystal of PPLN was found to be the most suitable for SHG process and produced it with up to 60% conversion efficiency.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".