MétaCan
Menu
Back to cohort
Record W3135835848 · doi:10.1117/12.2584689

Ultrathin monochromatic photonic power converters with nanostructured back mirror for light trapping of 1310-nm laser illumination

2021· article· en· W3135835848 on OpenAlexaff
Neda Nouri, Christopher E. Valdivia, Meghan N. Beattie, Marziyeh Zamiri, Jacob J. Krich, Karin Hinzer

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
Topicsolar cell performance optimization
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsMaterials scienceMonochromatic colorOptoelectronicsOpticsPhotonicsLaserOptical powerAbsorption (acoustics)WavelengthConvertersPhotonic crystalPhotovoltaic systemPower (physics)Photonic integrated circuitTransmission (telecommunications)TrappingTelecommunicationsElectrical engineeringPhysics

Abstract

fetched live from OpenAlex

Photonic power converters (PPCs) are one of the main components of optical power transmission systems, generating electrical power via the photovoltaic effect. We simulate ultrathin PPCs designed for operating at the telecommunication wavelength of 1310 nm with 9 and 12 times thinner absorbing layers using cubic and pyramidal nanostructured back reflectors (BRs), respectively. While increasing efficiency by 13% (rel.) over conventional PPCs, results also show superior light trapping for pyramidal BR with twice the absorption of a simple double pass absorber layer of the same thickness and higher short-circuit current for pillar BR reaching 94% of an ideal Lambertian surface.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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 head0.005
GPT teacher head0.182
Teacher spread0.176 · 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 source (direct Gemma or distilled Codex), 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

Citations10
Published2021
Admission routes1
Has abstractyes

Explore more

Same topicsolar cell performance optimizationFrench-language works237,207