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Record W4221034609 · doi:10.1002/solr.202200204

Ultrafast Random‐Pyramid Texturing for Efficient Monocrystalline Silicon Solar Cells

2022· article· en· W4221034609 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSolar RRL · 2022
Typearticle
Languageen
FieldEngineering
TopicSilicon and Solar Cell Technologies
Canadian institutionsMorgan Solar (Canada)
FundersNational Natural Science Foundation of China
KeywordsMonocrystalline siliconWaferMaterials scienceUltrashort pulseSiliconOptoelectronicsSolar cellEtching (microfabrication)Common emitterCrystalline siliconNanotechnologyOptics

Abstract

fetched live from OpenAlex

Herein, an ultrafast random‐pyramid texturing process is proposed for monocrystalline silicon (mono‐Si) solar cells by combining metal‐catalyzed chemical etching (MCCE) and the standard alkaline texturing process. Namely, large numbers of artificial defects are introduced on the wafer surface in 3 min by MCCE; therefore, the process duration of alkaline texturing is largely shortened from 420 s for the as‐cut wafer to 180 s for the wafer with artificial defects due to its high surface reactivity. Moreover, those tiny artificial defects are apt to form small pyramids, resulting in a better light‐trapping performance. As a demonstration, the passivated emitter rear contact solar cell with ultrafast random pyramid texture achieves a power conversion efficiency of 23.02%. Therefore, such a cost‐effective ultrafast texturing strategy can open a promising new route toward the mass production of high‐efficiency industrial mono‐Si solar cells.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.134
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.009
GPT teacher head0.199
Teacher spread0.190 · 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