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Record W1512931248 · doi:10.1109/pvsc.1997.653917

18.5% efficient first-generation MIS inversion-layer silicon solar cells

2002· article· en· W1512931248 on OpenAlexfundno aff
A. Metz, Renate Meyer, Burkhard Kuhlmann, M. Grauvogl, Rudolf Hezel

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSilicon and Solar Cell Technologies
Canadian institutionsnot available
FundersInstitute of Gender and Health
KeywordsSiliconOptoelectronicsMaterials scienceSilicon on insulatorEnergy conversion efficiencyInversion (geology)DopingSilicon solar cell

Abstract

fetched live from OpenAlex

In this paper, progress in the development of high-efficiency metal-insulator-semiconductor inversion-layer (MIS-IL) silicon solar cells at ISFH is presented. We fabricated MIS-IL solar cells showing independently confirmed energy conversion efficiencies of up to 18.5%. This represents the highest value reported to date for MIS-IL silicon cells. The increase in cell efficiency has been possible by improvements along several lines: (i) reduced perimeter recombination losses, (ii) a reduced contact resistance of the MIS front grid, and (iii) reduced rear surface recombination losses. The cells are characterised in detail and design modifications for further improvements towards 20% efficiency are presented.

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 score0.003
Threshold uncertainty score0.011

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 communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.002

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 head0.197
Teacher spread0.162 · 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

Citations11
Published2002
Admission routes1
Has abstractyes

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