New technique for fabrication of low loss high temperature stable high reflectivity FBG sensor arrays
Bibliographic record
Abstract
Fiber Bragg gratings (FBG) arrays in silica based optical fibers are increasingly used in applications involving system monitoring in extreme high temperature environments. Where operational temperatures are < 600 °C, traditional UVlaser inscribed FBGs are not appropriate since the induced Type I index change is erased. Instead two competing FBG technologies exist: 1) regenerative FBGs resulting from high temperature annealing of a UV-laser written grating in a hydrogen loaded fiber and 2) FBGs written with femtosecond infrared pulse duration radiation (fs-IR), either using the point-by-point method or using the phase mask approach. Regenerative gratings possess low reflectivity and are cumbersome to produce, requiring high temperature processing in an oxygen free environment. Multiple pulse Type II femtosecond IR laser induced gratings made with a phase mask, while having very good thermal stability, also tend to have high insertion loss (~ 1dB/grating) limiting the number of gratings that can be concatenated in a sensor array. Recently it has been shown that during multiple pulse type II thermally stable fs-IR FBG production, two competing process occur: an initial induced fs-IR type I FBG followed by a thermally stable high insertion loss type II FBG. In this paper, we show that if only a type I FBG is written using type II intensity conditions but limited numbers of pulses and then annealed above 600 °C, the process results in a type II grating that is stable up to 1000 °C with very low insertion loss ideal for an FBG sensor array.
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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".