Programming photonic crystal growth: linking top-down templating with bottom-up self-organization
Bibliographic record
Abstract
Pure, defect-free bulk semiconductor is the foundation of electronics. On its own, however, perfect periodicity does not give rise to useful function. Selective doping and, better yet, heteroepitaxy, are needed for diodes, transistors, resonant tunneling devices, and lasers. Analogously, perfect photonic crystals are a necessary building block, but not an end in themselves, in the implementation of novel, integrable photonic function. Photonic crystal heterostructures, and interfaces between finite photonic crystals and nonperiodic media, are needed to enable in- and out-coupling, guiding, and wavelength selection, to name a few examples. We summarize herein advances in the realization and design of photonic crystal heterostructures and heterointerfaces. In Section 2 we show how bottom-up self-assembly of colloidal crystals can be merged with top-down pattern definition to determine the orientation and placement of finite-sized photonic crystal regions on a planar substrate. In Section 3 we describe the development of a conceptually- and analytically-tractable theory to enable convenient design using combinations of photonic crystals and homogeneous media.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".