Hierarchical Nanoparticle Bragg Mirrors: Tandem and Gradient Architectures
Bibliographic record
Abstract
To manipulate electrons in semiconductor electronic and optical devices, the usual approach is through materials composition, electronic bandgap, doping, and interface engineering. More advanced strategies for handling electrons in semiconductor devices include composition-controlled heterostructures and gradient structures. By analogy to the manipulation of electrons in semiconductor crystals by electronic bandgaps, photons in photonic crystals can be managed using photonic bandgaps. In this context, the simplest photonic crystal is the Bragg mirror, a periodic dielectric construct whose photonic bandgap is engineered through variations of the optical thickness of its constituent layers. Traditionally the materials comprising these periodic dielectric layers are nonporous, and they have mainly been used in the field of optical and photonic devices. More recently these Bragg mirrors have been made porous by building the layers from nanoparticles with functionality and utility that exploit their internal voids. These structures are emerging in the area of photonic color-coded chemical sensing and controlled chemical release. Herein, a strategy for enhancing the functionality and potential utility of nanoparticle Bragg mirrors by making the constituent dielectric layers aperiodic and porous is described. It is exemplified by prototypical tandem and gradient structures that are fully characterized with regards to their structure, porosity, and optical and photonic properties.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".