Moisture adsorption by porous terahertz optical materials: a case study of artificial SiO<sub>2</sub> opals
Bibliographic record
Abstract
Artificial opals fabricated by sedimentation and self-assembly of colloidal SiO 2 nanoparticles and annealed at different temperatures were recently considered favorable terahertz (THz) optical materials with manageable optical properties. However, interactions between such a porous material and water vapour in a humid atmosphere can hamper their THz applications due to the related changes in the material parameters and additional power loss. To quantify such an effect, in this paper, moisture adsorption by artificial SiO2 opals is studied using THz pulsed spectroscopy. Particularly, opals of two kinds were sedimented from the colloidal suspension of 300-nm-diameter SiO 2 nanoparticles with different intraglobular structures and porosity. They were annealed at temperatures of 200–800°C aimed at changing their internal structure, porosity, and THz optical properties. Opals were dehydrated in a vacuum and then exposed to a humid atmosphere with 82.0 ± 2.0% relative humidity, while their THz complex dielectric permittivity was evaluated in situ in the 0.5–2.5 THz range. The observed changes in the THz dielectric curves were analyzed using the sum rule and the adsorption kinetics models. Our findings reveal a strong dependence of the THz dielectric response, amount of adsorbed water, and adsorption time constant on the opal type and annealing conditions. This effect has a general character: it can hamper real-live applications of a variety of porous THz optical materials and, thus, should be taken into account during their synthesis.
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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".