Bibliographic record
Abstract
The Achilles heel of wide-band photocatalysts such as TiO2 is the insufficient photogeneration in the visible range under sunlight. This has been a longstanding impediment to large-scale, real-world deployment of titania-based photocatalysis applications. Instead of traditional band engineering through heavy-doping, we suggest enhancing photocatalytic efficiency of lightly-doped TiO2 using photonic crystal (PC) structures. This strongly increases solar photogeneration through novel wave-interference-based light trapping. Four photocatalyst structures — simple cubic woodpile (wdp), square lattice nanorod (nrPC), slanted conical-pore (scPore), and face-centered cubic inverse opal (invop) — are optimized and compared for light harvesting in the sub- and above-gap (282 to 550 nm) regions of weakly absorbing TiO2, with the imaginary part of the dielectric constant 0.01 in the visible range. The optimized lattice constants for the first three, and opal center-to-center distance for invop, are ∼300 - 350 nm. For fixed PC thickness, the ranking of visible light harvesting capability is: scPore > wdp ∼ nrPC > invop. The scPore PC deposited on highly reflective substrate is ideal for photocatalysis given its combination of enhanced light trapping and superior charge transport.
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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".