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Record W2412502437 · doi:10.1063/1.4953336

Light-trapping in perovskite solar cells

2016· article· en· W2412502437 on OpenAlexaff
Qingguo Du, Guansheng Shen, Sajeev John

Bibliographic record

VenueAIP Advances · 2016
Typearticle
Languageen
FieldEngineering
TopicPerovskite Materials and Applications
Canadian institutionsUniversity of Toronto
FundersU.S. Department of Energy
KeywordsPhotocurrentMaterials sciencePerovskite (structure)Energy conversion efficiencyAbsorption (acoustics)Solar cellOptoelectronicsPerovskite solar cellSolar cell efficiencyPhotonic crystalTrappingOpticsChemistryPhysicsCrystallography

Abstract

fetched live from OpenAlex

We numerically demonstrate enhanced light harvesting efficiency in both CH3NH3PbI3 and CH(NH2)2PbI3-based perovskite solar cells using inverted vertical-cone photonic-crystal nanostructures. For CH3NH3PbI3 perovskite solar cells, the maximum achievable photocurrent density (MAPD) reaches 25.1 mA/cm2, corresponding to 92% of the total available photocurrent in the absorption range of 300 nm to 800 nm. Our cell shows 6% absorption enhancement compared to the Lambertian limit (23.7 mA/cm2) and has a projected power conversion efficiency of 12.9%. Excellent solar absorption is numerically demonstrated over a broad angular range from 0 to 60 degree for both S- and P- polarizations. For the corresponding CH(NH2)2PbI3 based perovskite solar cell, with absorption range of 300 nm to 850 nm, we find a MAPD of 29.1 mA/cm2, corresponding to 95.4% of the total available photocurrent. The projected power conversion efficiency of the CH(NH2)2PbI3 based photonic crystal solar cell is 23.4%, well above the current world record efficiency of 20.1%.

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 score0.005

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.203
Teacher spread0.198 · 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

Citations59
Published2016
Admission routes1
Has abstractyes

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