Graphene oxide coated optical fiber Mach-Zehnder \ninterferometers
Bibliographic record
Abstract
Optical fibers are extensively utilized in the telecommunication industry for their exceptional light-guiding capabilities. Furthermore, their remarkable attributes, including high flexibility, low loss, compact size, immunity to electromagnetic interference, and operation in harsh environments, have sparked extensive research into their applications across diverse sensor fields. \nThe emergence of nanomaterials with unique physical and chemical properties offers many new applications. As one of the graphene derivatives with various oxygen-containing functional groups, graphene oxide (GO) based materials enable a wide range of sensing applications owing to their interaction with external water molecules and various organic solvents. This study investigates the spectral properties of two GO-coated symmetric fiber Mach-Zehnder interferometers (FMZIs), i.e., tapered and bulge-fused structures, under varying environmental conditions, in comparison with uncoated FMZIs. The light energy density distribution along those microstructures is simulated by COMSOL Multiphysics software. The study begins by examining the sensitivity of FMZIs to refractive index, temperature, and humidity by observing the shift of dip wavelength at different environmental conditions. Then, the interferometers are coated with GO using the in-situ layer self-assembly method, which are silanization-treated fibers to create a positively charged surface, enabling the attraction and accumulation of negatively charged materials. Next, coating effects on different fiber structures and sensitivities to environmental conditions are compared. Additionally, two different GO-based coatings (graphene oxide-sodium alginate composite, and graphene oxide-fullerenol nano- \ncomposite encapsulated by the hydrogel) are applied to the tapered structure to assess their effects on the optical properties of the sensor under different environmental conditions. Finally, the spectra of the tapered FMZIs with and without different coatings are measured at various curvatures. The sensitivity of the interferometer in curvature measurement is analyzed using the conformal mapping technique to explore the properties of the coatings under different curvatures. This study demonstrates the effectiveness and great potentials of the graphene oxide-based nanomaterials in fiber-optic sensing.
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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) | 0.001 | 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".