Alkali-resistant low-temperature atomic-layer-deposited oxides for optical fiber sensor overlays
Bibliographic record
Abstract
Abstract This paper presents an investigation of properties of selected metallic oxides deposited at a low temperature (100 °C) by atomic layer deposition (ALD) technique, relating to their applicability as thin overlays for optical fiber sensors resistant in alkaline environments. Hafnium oxide (Hf x O y with y / x approx. 2.70), tantalum oxide (Ta x O y with y / x approx. 2.75) and zirconium oxide (Zr x O y with y / x approx. 2.07), which deposition was based, respectively, on tetrakis(ethylmethyl)hafnium, tantalum pentachloride and tetrakis(ethylmethyl)zirconium with deionized water, were tested as thin layers on planar Si (100) and glass substrates. Growth per cycle (GPC) in the ALD processes was 0.133–0.150 nm/cycle. Run-to-run GPC reproducibility of the ALD processes was best for Hf x O y (0.145 ± 0.001 nm/cycle) and the poorest for Ta x O y (0.133 ± 0.003 nm/cycle). Refractive indices n of the layers were 2.00–2.10 (at the wavelength λ = 632 nm), with negligible k value (at λ for 240–930 nm). The oxides examined by x-ray diffractometry proved to be amorphous, with only small addition of crystalline phases for the Zr x O y . The surfaces of the oxides had grainy but smooth topographies with root-mean square roughness ∼0.5 nm (at 10 × 10 μ m 2 area) according to atomic force microscopy. Ellipsometric measurements, by contrast, suggest rougher surfaces for the Zr x O y layers. The surfaces were also slightly rougher on the glass-based samples than on the Si-based ones. Nanohardness and Young modules were 4.90–8.64 GPa and 83.7–104.4 GPa, respectively. The tests of scratch resistance revealed better tribological properties for the Hf x O y and the Ta x O y than for the Zr x O y . The surfaces were hydrophilic, with wetting angles of 52.5°–62.9°. The planar oxides on Si, being resistive even to concentrated alkali (pH 14), proved to be significantly more alkali-resistive than Al 2 O 3 . The Ta x O y overlay was deposited on long-period grating sensor induced in optical fiber. Thanks to such an overlay the sensor proved to be long-lasting resistant when exposed to alkaline environment with a pH 9. Thereby, it also proved that it has a potential to be repeatedly reused as a regenerable optical fiber biosensor.
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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".