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Record W4391347552 · doi:10.3390/photonics11020130

67.5% Efficient InP-Based Laser Power Converters at 1470 nm at 77 K

2024· article· en· W4391347552 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePhotonics · 2024
Typearticle
Languageen
FieldEngineering
Topicsolar cell performance optimization
Canadian institutionsBroadcom (Canada)
Fundersnot available
KeywordsConvertersLaserPower (physics)OptoelectronicsMaterials scienceOpticsPhysics

Abstract

fetched live from OpenAlex

Recent developments in long wavelength and cryogenic laser power converters have unlocked record performances in both areas. Here, devices for an optical input at ~1470 nm are studied for cryogenic applications, combining these cryogenic and long-wavelength attributes. Multijunction laser power converters are demonstrated to have a high-efficiency operation at 77 K. The photovoltaic-power-converting III-V semiconductor devices are designed with InGaAs-absorbing layers, here with 10 thin subcells (PT10), connected by transparent tunnel junctions. Unprecedented conversion efficiencies of up to 67.5% are measured at liquid nitrogen temperatures with an output power of Pmpp = 1.35 W at an average optical input intensity of ~62 W/cm2. A remarkably low bandgap voltage offset value of Woc~50 mV is obtained at an average optical input intensity of ~31 W/cm2.

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.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.078
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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.002

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 head0.004
GPT teacher head0.185
Teacher spread0.180 · 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