Color from colorless nanomaterials: Bragg reflectors made of nanoparticles
Bibliographic record
Abstract
We report herein on a facile and reproducible approach to prepare mesoporous nanoparticle-based distributed Bragg reflectors (DBRs) from a diverse group of metal oxide nanoparticles including SiO2, TiO2, SnO2, and Sb:SnO2. The films prepared, regardless of the composition, and following dispersion and process engineering, demonstrate uniform color and high optical quality over large areas. Not only do the prepared NP DBRs possess high reflectivity but also significant mesoporosity, which opens up the opportunity for introduction of a variety of materials into the pores creating new opportunities in optical and optoelectronic devices. In addition, what also must be highlighted are the added-value properties to the above characteristic of the individual NP materials comprising the DBR such as the electrical conductivity and optical transparency of ATO or the photoconductivity of SnO2, which enable new opportunities in a broad range of fields.
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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 | 0.001 |
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".