Atomic Layer Deposition on an Optical Fibre: Enhancing the Evanescent Field of a Tilted Fibre Bragg Grating Optical Sensor
Bibliographic record
Abstract
Tilted fibre Bragg gratings (TFBGs) are a class of optical fibre based sensor that are presently employed for temperature and pressure detection in transportation, building, and other infrastructure applications. The working principle of these sensors is that a Bragg grating in the core both diffracts light modes to the surface of the fibre, and breaks their symmetry. This produces very surface-sensitive modes that can interact with the fibre surface. Our discovery of several gold metal deposition processes has permitted the coating of a TFBG with gold: this allows a surface plasmon resonance (SPR) to exist in the gold, and can sense the surface in two important ways: firstly, the shift from a localised plasmon in gold nanoparticles to a continuous plasmon in a closed metal film allows the detection of the thinnest conductive layer of gold. Gold metal grown by atomic layer deposition (ALD) using (Me 3 P) Me 3 Au(III) has a growth per cycle of 0.50 Å with oxygen plasma and water as co-precursors. The fibre sensor showed that a continuous plasmon formed at 84 ALD cycles, suggesting that the thinnest optically conductive film of gold metal formed at 4.2 nm (which is demonstrably better than a sputtered film, which showed this shift at 6.5 nm). This ALD gold coated fibre also showed significant sensitivity to the surrounding refractive index (SRI). The SPR can reach several nanometers into the space surrounding the fibre, and the SRI can invoke a shift in the wavelength of the cladding mode. With an ALD grown stack of 50 nm Al 2 O 3 /50 nm Au 0 , the SPR was 150% more sensitive to an external refractive index, and the nature of the surroundings could be interrogated through changing the polarization of the affected modes. This contribution will discuss the two aspects of sensing that a TFBG can provide, and show the effect of alumina, gold, and an alumina/gold stack on the extent and sensitivity of the SPR.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 teacher head, 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".