Water-Induced Fused Silica Glass Surface Alterations Monitored Using Long-Period Fiber Gratings
Bibliographic record
Abstract
Long-period gratings (LPGs) induced in optical fibers show exceptional refractive index (RI) sensitivity and thus they are often applied for label-free biosensing. However, during measurements at changing environmental conditions, the surface of the fiber made of fused silica is subjected to stresses that can cause alterations in its structure. The problem is particularly important when long-term or biosensing measurements are considered and when sensor surface may be exposed to subsequent drying and immersion in aqueous solutions. The aim of this paper was to investigate the influence of sequential drying in air and soaking in water of the fused silica fiber cladding surface on the optical response of LPGs. We demonstrated that transmission spectrum of LPG measured in water before and after drying in air differed significantly and further changed with subsequent drying steps indicating increase of the local RI. All these changes correspond to the alterations in the cladding surface, i.e., its fracturing and pores rearrangement, caused by the stress induced during drying and then immersing in water, as well as possible corrosion and formation of nanosized objects on the cladding surface. The effect can be additionally influenced by washing the samples in organic solvents. We confirmed that measurements done in a flow-cell system, where the sensor was kept wet during the sensing experiment may eliminate the water-induced fiber surface alteration effect and thus minimize the amount of false results. Described findings are highly important for biosensing applications of any optical-fiber-based devices.
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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".