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Record W2089692139 · doi:10.1063/1.4901167

Cost-effective nanostructured thin-film solar cell with enhanced absorption

2014· article· en· W2089692139 on OpenAlexaff
Peng Hui Wang, R.-E. Nowak, Stefan Geißendörfer, Martin Vehse, Nies Reininghaus, В. П. Сергеев, Karsten von Maydell, Alexandre G. Brolo, Carsten Agert

Bibliographic record

VenueApplied Physics Letters · 2014
Typearticle
Languageen
FieldEngineering
TopicThin-Film Transistor Technologies
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsMaterials scienceSolar cellFabricationPlasmonic solar cellThin filmNanotechnologyOptoelectronicsAmorphous siliconQuantum dot solar cellPhotovoltaicsHybrid solar cellElectrodePolymer solar cellNanostructurePhotovoltaic systemCrystalline silicon

Abstract

fetched live from OpenAlex

Nanostructured transparent conductive electrodes are highly interesting for efficient light management in thin-film solar cells, but they are often costly to manufacture and limited to small scales. This work reports on a low-cost and scalable bottom-up approach to fabricate nanostructured thin-film solar cells. A folded solar cell with increased optical absorber volume was deposited on honeycomb patterned zinc oxide nanostructures, fabricated in a combined process of nanosphere lithography and electrochemical deposition. The periodicity of the honeycomb pattern can be easily varied in the fabrication process, which allows structural optimization for different absorber materials. The implementation of this concept in amorphous silicon thin-film solar cells with only 100 nm absorber layer was demonstrated. The nanostructured solar cell showed approximately 10% increase in the short circuit current density compared to a cell on an optimized commercial textured reference electrode. The concept presented here is highly promising for low-cost industrial fabrication of nanostructured thin-film solar cells, since no sophisticated layer stacks or expensive techniques are required.

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)0.000
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 score0.328
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 head0.005
GPT teacher head0.177
Teacher spread0.172 · 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.

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

Citations21
Published2014
Admission routes1
Has abstractyes

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