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Record W1188249945 · doi:10.1016/j.egypro.2015.07.025

Experimental Determination of the Uncertainty of the Absorption Coefficient of Crystalline Silicon

2015· article· en· W1188249945 on OpenAlexfundno aff
Carsten Schinke, Christian Peest, Karsten Bothe, Jan Schmidt, Rolf Brendel, M. Vogt, Ingo Kröger, S. Winter, A. Schirmacher, Siew Yee Lim, Hieu T. Nguyen, Daniel Macdonald

Bibliographic record

VenueEnergy Procedia · 2015
Typearticle
Languageen
FieldEngineering
TopicSilicon and Solar Cell Technologies
Canadian institutionsnot available
FundersInstitute of Gender and HealthDeutsche Bundesstiftung Umwelt
KeywordsAttenuation coefficientMaterials scienceTransmittanceCrystalline siliconAbsorption (acoustics)SiliconOpticsMolar absorptivityRange (aeronautics)Measurement uncertaintyWavelengthLuminescenceAnalytical Chemistry (journal)OptoelectronicsChemistryPhysicsMathematicsStatistics

Abstract

fetched live from OpenAlex

Based on a combined analysis of spectroscopic ellipsometry, reflectance and transmittance measurements as well as spectrally resolved luminescence measurements and spectral responsivity measurements, we present data of the coefficient of band-to-band absorption of crystalline silicon at 295 K in the wavelength range 250 – 1450 nm. A systematic measurement uncertainty analysis according to the “Guide to the Expression of Uncertainty in Measurements” (GUM) is carried out for each method, showing that the relative uncertainty of the absorption coefficient data so determined is of the order of 0.3% at 300 nm, 1% at 900 nm, 10% at 1200 nm and 180% at 1450 nm. The data are consolidated by comparison of measurements carried out independently at different institutions. The uncertainty of solar cell energy conversion predictions by means of simulations due to the uncertainty of the absorption coefficient data is shown to be of the order of 0.1% relative.

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)0.003
metaresearch head score (Gemma)0.008
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 score0.003
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.215
Teacher spread0.200 · 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

Citations6
Published2015
Admission routes1
Has abstractyes

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