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Record W4229075662 · doi:10.1364/oe.455919

Large area stimulated emission luminescent solar concentrators modelled using detailed balance consistent rate equations

2022· article· en· W4229075662 on OpenAlexfundno aff
Andrew G. Flood, Nazir P. Kherani

Bibliographic record

VenueOptics Express · 2022
Typearticle
Languageen
FieldChemistry
TopicPhotochemistry and Electron Transfer Studies
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaOntario Research Foundation
KeywordsDetailed balanceLuminescenceOpticsRate equationWork (physics)PhysicsEnergy balanceComputational physicsSolar energyMechanicsSpontaneous emissionPhotonMaterials scienceClassical mechanicsThermodynamicsLaserKinetics

Abstract

fetched live from OpenAlex

Stimulated emission luminescent solar concentrators (SELSCs) have the potential to reduce escape cone losses in luminescent solar concentrators (LSCs). However, a functional SELSC is yet to be demonstrated. Previous numerical studies and detailed balance limits provide guidance, but they also contradict and likely overestimate performance and underestimate requirements. In this work, we introduce a rate-equation model with inversion requirements compatible with detailed balance limits and apply this model to the numerical modelling of window-sized SELSCs. We find that the optimal pump photon energy for both LSCs and SELSCs is 1.35 eV and the potential improvement of SELSCs over LSCs is found to be 19.3%. The efficiencies found are much lower than those specified in previous work due to the increase in Stokes shift required for a highly luminescent material. We also find that SELSCs are more attractive at higher matrix losses, that emission linewidths <0.05 eV are desirable, and that SELSC devices can potentially achieve performance equal to LSCs at low illumination levels and simultaneously exceed it by up to 16.5% at 1-sun illumination.

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 categoriesMeta-epidemiology (narrow)
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 score0.431
Threshold uncertainty score1.000

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 studies0.0010.000
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 head0.027
GPT teacher head0.254
Teacher spread0.226 · 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.

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

Citations2
Published2022
Admission routes1
Has abstractyes

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