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Record W2055375233 · doi:10.1109/wcpec.2006.279333

Solar Cell Efficiency Improvement by New Metallization Techniques - the Day4 Electrode Concept

2006· article· en· W2055375233 on OpenAlexaff
Andreas Schneider, Lewis P. Rubin, Geoffrey D. Rubin

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSilicon and Solar Cell Technologies
Canadian institutionsBurnaby Hospital
Fundersnot available
KeywordsBusbarMaterials scienceInterconnectionOptoelectronicsScreen printingElectrodeSolar cellVoltageElectrical engineeringComputer scienceComposite materialChemistryEngineeringTelecommunications

Abstract

fetched live from OpenAlex

The standard module cell interconnection technique requires printed Ag front side busbars and rear side Ag/Al pads. From the standpoint of cell performance these contacts reduce cell efficiency strongly as a result of large cell shading and reduced surface passivations. Cells without busbars and pads would show a cell current and voltage increase and simplify the printing procedure. In this paper we introduce a new cell metallization technique which could be directly applied to standard industrial cells eliminating the need for rear pads and front busbars. Experiments were carried out to study the current and voltage gain of modified cells with the proviso that the cell fill factor will maintain at high level. Optimization experiments showed up to 0.8% higher cell efficiency for modified cells if compared to reference cells. Technique was furthermore successfully applied to 8 inch cells which showed fill factors of 77.9%

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 communication0.0010.001
Open science0.0010.000
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 head0.003
GPT teacher head0.170
Teacher spread0.167 · 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

Citations37
Published2006
Admission routes1
Has abstractyes

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