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Record W4392578050 · doi:10.1117/12.3001145

High-efficiency multi-junction photovoltaic laser power converters for various power and spectral range applications

2024· article· en· W4392578050 on OpenAlexaff
Simon Fafard

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
Topicsolar cell performance optimization
Canadian institutionsBroadcom (Canada)
Fundersnot available
KeywordsConvertersPhotovoltaic systemPower (physics)Range (aeronautics)OptoelectronicsMaterials scienceElectrical engineeringElectronic engineeringEngineeringPhysics

Abstract

fetched live from OpenAlex

Stable and reliable optical power converting devices have been obtained for various Optical Wireless Power Transmission (OWPT) applications, including power-over-fiber (PoF) or power beaming uses. They are obtained using vertical multi-junction laser power converters (LPCs) based on the GaAs and InP material systems. The LPCs are high-performance photovoltaic (PV) devices typically optimized for a narrow wavelength range. Such Optical Power Converters (OPCs) enable several isolated electrical power or remoter power applications using high-power lasers for their input power. Broadcom’s vertical multijunction PV devices (VEHSA design) permit optical-to-electrical conversion with record efficiencies and output power capabilities. This presentation will review the recent developments in both, the GaAs-based and InP-based systems. For example, high-efficiency and high-power capabilities have recently been demonstrated at ~1480nm for the InP-based PT10-InGaAs/InP, which are designed with 10 InGaAs subcells lattice-matched to InP and connected with transparent tunnel junctions. These long-wavelength OPCs are capable of directly producing electrical output voltages in the 5 V range.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.922
Threshold uncertainty score0.495

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)0.0000.000

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 head0.006
GPT teacher head0.203
Teacher spread0.197 · 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 designSimulation or modeling
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

Citations0
Published2024
Admission routes1
Has abstractyes

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