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Record W2145529988 · doi:10.1139/cjp-2013-0526

The mean crystallite size within a hydrogenated nanocrystalline silicon based photovoltaic solar cell and its role in determining the corresponding crystalline volume fraction

2014· article· en· W2145529988 on OpenAlexafffundvenue
Katharina Schmidt, Yue Lin, M. Beaudoin, Guangrui Xia, Stephen K. O’Leary, Guangyang Yue, Baojie Yan

Bibliographic record

VenueCanadian Journal of Physics · 2014
Typearticle
Languageen
FieldEngineering
TopicThin-Film Transistor Technologies
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCrystalliteNanocrystalline materialVolume fractionScherrer equationRaman spectroscopySolar cellDiffractionNanocrystalline siliconVolume (thermodynamics)Materials scienceAnalytical Chemistry (journal)Crystalline siliconPhysicsOpticsNanotechnologyThermodynamicsComposite materialChemistryOptoelectronicsOrganic chemistry

Abstract

fetched live from OpenAlex

We examine the dependence of the crystalline volume fraction on the mean crystallite size for hydrogenated nanocrystalline silicon based photovoltaic solar cells; this work builds upon an earlier study by Schmidt et al. (Mater. Res. Soc. Symp. Proc. 1536 (2013)). For each photovoltaic solar cell considered, the X-ray diffraction and Raman spectra are measured. Through the application of Scherrer’s equation, the X-ray diffraction results are used to determine the corresponding mean crystallite sizes. Through peak decomposition, the Raman results are used to estimate the corresponding crystalline volume fraction. Plotting the crystalline volume fraction as a function of the mean crystallite size, it is found that larger mean crystallite sizes tend to favor reduced crystalline volume fractions. The ability to randomly pack smaller crystallites with a greater packing fraction than their larger counterparts was suggested as a possible explanation for this observation.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.655
Threshold uncertainty score0.784

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.173
Teacher spread0.168 · 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

Citations6
Published2014
Admission routes3
Has abstractyes

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