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Record W3011928758 · doi:10.1002/pip.3271

Forming submicron in micron texture on the diamond‐wire‐sawn mc‐Si wafer by introducing artificial defects

2020· article· en· W3011928758 on OpenAlexaff
Chengkun Wu, Shuai Zou, Jingyan Zhu, Xiaoya Ye, Jianming Ding, Hua Sun, Xusheng Wang, Guoqiang Xing, Xiaohong Zhang, Xiaodong Su

Bibliographic record

VenueProgress in Photovoltaics Research and Applications · 2020
Typearticle
Languageen
FieldEngineering
TopicNanowire Synthesis and Applications
Canadian institutionsMorgan Solar (Canada)
FundersPriority Academic Program Development of Jiangsu Higher Education InstitutionsNational Natural Science Foundation of China
KeywordsWaferMaterials scienceTexture (cosmology)Microscale chemistryPassivationReflection (computer programming)DiamondOptoelectronicsEtching (microfabrication)SiliconSurface finishComposite materialOpticsLayer (electronics)

Abstract

fetched live from OpenAlex

Abstract Based on a traditional acid etch system (i.e., HNO 3 /HF), a complex texture comprising microscale and submicroscale structures was produced on the surface of a diamond‐wire‐sawn (DWS) multicrystalline Si (mc‐Si) wafer, upon whose surface it is typically difficult to form an effective texture for suppressing the reflection of incident light. Immersing the as‐cut wafer into an HF/HNO 3 /AgNO 3 solution introduced a large number of artificial defects onto the wafer surface. A subsequent HNO 3 /HF etch induced a micron texture expanded from the original DWS‐induced damage as well as a submicron texture converted from the artificial defects. The multiscale textured DWS exhibited a reflectivity of ~19%, which is much lower than the reflectivity after only an HNO 3 /HF etch (~28%). Therefore, the solar cell performance was improved owing primarily to improved optical antireflection and surface passivation. The method is simple and can be easily scaled up into the in‐line texture process.

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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.266
Threshold uncertainty score0.630

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.034
GPT teacher head0.295
Teacher spread0.260 · 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 teacher head, 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

Citations16
Published2020
Admission routes1
Has abstractyes

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