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Record W2027560023 · doi:10.1063/1.3495799

Lateral, high-quality, metal-catalyzed semiconductor growth on amorphous and lattice-mismatched substrates for photovoltaics

2010· article· en· W2027560023 on OpenAlexaff
Nathaniel J. Quitoriano

Bibliographic record

VenueApplied Physics Letters · 2010
Typearticle
Languageen
FieldEngineering
TopicNanowire Synthesis and Applications
Canadian institutionsMcGill University
Fundersnot available
KeywordsPhotovoltaicsMaterials scienceNucleationSemiconductorAmorphous solidCrystal growthAmorphous semiconductorsOptoelectronicsNanotechnologyEngineering physicsThin filmPhotovoltaic systemCrystallographyChemistryPhysicsElectrical engineeringEngineering

Abstract

fetched live from OpenAlex

Solar-derived energy is universally available but is not yet cost-competitive. Next generation solar cells are expected to have high efficiencies, associated with single-crystalline semiconductors, at reduced costs, associated with amorphous substrates. Here we report the growth of high-quality semiconductors (Ge and Si) on amorphous and lattice-mismatched substrates using metal-catalyzed, lateral growth. Using this technique, we engineer the location of crystal nucleation, by controlling the catalyst location, and can thus prevent the formation of grain boundaries, typical when crystals grow together. The results presented here provide a foundation upon which next generation photovoltaics may be built.

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 categoriesMeta-epidemiology (narrow)
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 score0.028
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)0.0000.000

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 head0.016
GPT teacher head0.229
Teacher spread0.213 · 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.

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

Citations11
Published2010
Admission routes1
Has abstractyes

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