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Minimum material quality threshold for intermediate band solar cells using a multi-band device simulator with fully coupled optics

2019· article· en· W3005365732 on OpenAlexaff
Matthew M. Wilkins, Eduard C. Dumitrescu, Jacob J. Krich

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSemiconductor Quantum Structures and Devices
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsFigure of meritOptoelectronicsAbsorption (acoustics)SemiconductorSolar cellMaterials scienceDiffusionQuality (philosophy)Computer scienceOpticsPhysicsQuantum mechanics

Abstract

fetched live from OpenAlex

Intermediate band (IB) solar cells hold the promise of efficiency as high as triple-junction solar cells with much simpler cell design, containing only two semiconductor material interfaces. Though several intermediate band materials have been demonstrated, no cells have shown promising efficiencies. Many of the fundamental required properties of IB materials are well known: they should have strong subgap absorption, long carrier lifetimes, and good carrier mobilities. The tradeoffs between these properties, however, are not well understood. We present the first results using a new coupled Poisson/drift-diffusion model designed for IB materials, called Simudo. We perform a systematic study of a figure of merit for IB materials. We consider the standard p-IB-n architecture with a high density IB, in which two depletion junctions are formed. Considering fictitious materials with identical electron and hole properties (i.e., mobilities, capture cross sections), we show that this simple figure of merit provides a necessary condition for making efficient devices. Further, we show for the first time a threshold behavior, where the efficiency of the IB device exceeds that of the standard pn-junction only for sufficiently high quality material. These results both give guidance for experiments about required IB material properties and demonstrate how detailed device modeling can aid in the design of IB devices, for example by choosing layer thicknesses optimally.

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 score0.222
Threshold uncertainty score0.920

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 head0.033
GPT teacher head0.302
Teacher spread0.269 · 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

Citations2
Published2019
Admission routes1
Has abstractyes

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