(Invited) Metallic Nanocoatings on Optical Fibers as a Sensor Platform
Bibliographic record
Abstract
Optical fibres are excellent platforms on which to build a sensor: they substrate is inexpensive, and produced in tonnes per year. The technology effectively couples light and materials, and employs inexpensive (telecommunication) tools to interrogate the results. We are working on a tilted fibre Bragg grating (TFBG) sensor using atomic layer deposition (ALD) to modify the surface interface (Figure 1). By printing a FBG into the core of an optical fibre, light “modes" that were confined to the core can be directed to the cladding interface. These light modes can then sense changes in this interface, and these changes can be read if the light is directed back into the core. Depositing nanoparticulate films onto the cladding surface alters the effect on these cladding modes. They undergo a wavelength shift and a "peak-to-peak" amplitude change that is significantly different than is seen in uncoated fibres. We have been exploring the deposition of copper, silver, and gold metal films on optical fibre surfaces and investigating the changes in the light modes. The changes in the light modes that interact at the fibre metal interface are sensitive to the surrounding index of refraction. So, by using a high-permittivity dielectric material between the fibre and the metal coating, the sensitivity can be changed. Thus, the sensor platform that will be discussed consists of an optical fibre coated in a high-K dielectric material, and then further decorated with metal nanoparticles. The dielectric material is typically deposited by ALD, and the metal is typically deposited by either chemical vapour deposition or atomic layer deposition. This presentation will cover an overview of the chemistry of the precursor compounds for thin film deposition, the film deposition processes, and an interpretation of the resulting sensing ability of the cladding light modes. The wavelength shift is stronger when the device experiences a higher surrounding refractive index (SRI). This happens at low thicknesses, and can be exploited as an SRI detector. Interestingly, there is a significant difference in the spectra for a monolayer of discrete nanoparticles compared to a connected metallic film, also making these devices useful for detecting conductive film deposition. Figure 1
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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) | 0.002 | 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".