Nanomorphology tuning of the thermal response of TiO<sub>2</sub>/SiO<sub>2</sub> Bragg stacks
Bibliographic record
Abstract
Herein, we present a comparative study of thermo- and environmentally responsive TiO2/SiO2 one-dimensional photonic crystals (Bragg stacks) fabricated by different deposition methods and fabrication schemes, featuring various multilayer nanomorphologies. These include dense multilayer systems processed by physical vapor deposition and wet-chemistry protocols, as well as porous systems, namely, nanoparticle-based optical filters exhibiting textural porosity, and evaporation-induced self-assembled mesoporous Bragg stacks exhibiting predominantly structural porosity, as well as hybrid structures comprising both dense and porous layers. We investigate the spectral shift of the photonic stop band for the different Bragg stack nanomorphologies induced by the humidity-enhanced thermo-optic effect in a temperature range from 15 to 60 °C. We also demonstrate the response and recovery kinetics of the multilayer systems during external changes in ambient humidity. Notably, the choice of fabrication method plays a significant role in the thermal and humidity response of the system. Taking advantage of different material nanomorphologies we can tune the thermal shift of the photonic stop band in the range 0.2–32.9 nm for the Bragg stacks at ambient relative humidity. In addition, we can design dense multilayer systems nonresponsive to humidity and achieve time responses of the porous systems to external changes in humidity ranging from about 1 to 3 s.
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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) | 0.001 | 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".