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Record W1967656253 · doi:10.1109/pvsc.2005.1488154

Measurements on monocrystalline CuInSe/sub 2/ cells

2005· article· en· W1967656253 on OpenAlexaff
C.H. Champness, Hongxia Du, I. Shih

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicChalcogenide Semiconductor Thin Films
Canadian institutionsMcGill University
Fundersnot available
KeywordsMonocrystalline siliconIngotEquivalent series resistanceWaferMaterials scienceCapacitanceCurrent densityEnergy conversion efficiencySolar cellSaturation currentOptoelectronicsSchottky diodeAnalytical Chemistry (journal)SiliconVoltageElectrical engineeringPhysicsChemistryMetallurgyElectrode

Abstract

fetched live from OpenAlex

A series of measurements has been made on a number of photovoltaic cells fabricated with the layer structure Au-CuInSe/sub 2/-CdS-ZnO-In, where the CuInSe/sub 2/ consisted of a wafer of p-type monocrystalline material cut from an ingot grown by a vertical Bridgman method. A plot of conversion efficiency (/spl eta/) and short circuit current density (j/sub SC/) against cell series resistance (R/sub AS/), obtained from dark current-voltage characteristics, indicated a general increase of /spl eta/ and j/sub SC/ with decrease of R/sub AS/. The measurements also showed a general increase of j/sub SC/ with increase of the apparent hole concentration, p/sub MS/, obtained from Mott-Schottky plot slopes. Minority diffusion length estimates, obtained by the photo-current-capacitance method, were found to decrease with increase of p/sub MS/. As a result, surprisingly, diffusion lengths were smaller in the better performance cells. The best monocrystalline CuInSe/sub 2/ cell had an original total area efficiency of 11.4% (or 12.5 %, active area), without an A.R. coating.

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)0.001
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 score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.035
GPT teacher head0.215
Teacher spread0.180 · 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

Citations0
Published2005
Admission routes1
Has abstractyes

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Same topicChalcogenide Semiconductor Thin FilmsFrench-language works237,207