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Record W2059548502 · doi:10.1116/1.2155532

Near-unity ideality factor diodes using nanocrystalline Si/multicrystalline Si heterojunctions for photovoltaic application

2006· article· en· W2059548502 on OpenAlexaff
Mahdi Farrokh-Baroughi, Czang-Ho Lee, Siva Sivoththaman

Bibliographic record

VenueJournal of Vacuum Science & Technology A Vacuum Surfaces and Films · 2006
Typearticle
Languageen
FieldEngineering
TopicThin-Film Transistor Technologies
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMaterials scienceNanocrystalline materialPassivationHeterojunctionOptoelectronicsDiodePhotovoltaic systemQuantum efficiencySiliconNanotechnologyElectrical engineeringLayer (electronics)

Abstract

fetched live from OpenAlex

High-quality heterojunction diodes with near-unity ideality factor were fabricated by direct deposition of a (n+) nanocrystalline silicon film on top of fine-grained (p) multicrystalline silicon substrates. A very good ideality factor of 1.08 was achieved using a single-diode model in the medium forward-bias regime. Current-voltage (IV) characteristics of the diodes measured in the dark show that the recombination at the heterointerface is much less than the recombination at the space-charge region in the low forward-bias regime confirming the fact that the junction quality is good and is suitable for photovoltaic applications. Internal quantum efficiency measurements performed on these cells show a high (>70%) blue response partly due to a high transparency of the n+ nanocrystalline material. Illuminated IV of the heterojunction solar cells show a high fill factor of 78%–79% and an acceptable open circuit voltage of 550mV for a simple structure without a rear-surface passivation or transparent conductive oxide.

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 score0.001
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 head0.014
GPT teacher head0.246
Teacher spread0.232 · 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

Citations4
Published2006
Admission routes1
Has abstractyes

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