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Record W3171008847 · doi:10.1364/oe.427218

Photonic crystal light trapping for photocatalysis

2021· article· en· W3171008847 on OpenAlexafffund
Xiwen Zhang, Sajeev John

Bibliographic record

VenueOptics Express · 2021
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPhotonic Crystals and Applications
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaOntario Research Foundation
KeywordsPhotocatalysisMaterials sciencePhotonic crystalVisible spectrumOptoelectronicsOpticsNanorodNanotechnologyPhysicsChemistry

Abstract

fetched live from OpenAlex

The Achilles heel of wide-band photocatalysts such as TiO 2 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 TiO 2 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 TiO 2 , 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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.519
Threshold uncertainty score0.664

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.014
GPT teacher head0.258
Teacher spread0.244 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2021
Admission routes2
Has abstractyes

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