MétaCan
Menu
Back to cohort
Record W2782914499 · doi:10.1002/adfm.201705079

Reinforcing the Built‐In Field for Efficient Charge Collection in Polymer Solar Cells

2018· article· en· W2782914499 on OpenAlexaff
Jong‐Hoon Lee, Song Yi Jeong, Geunjin Kim, Byoungwook Park, Junghwan Kim, Seyoung Kee, Bongseong Kim, Kwanghee Lee

Bibliographic record

VenueAdvanced Functional Materials · 2018
Typearticle
Languageen
FieldEngineering
TopicOrganic Electronics and Photovoltaics
Canadian institutionsUniversity of Toronto
FundersKorea Institute of Energy Technology Evaluation and PlanningNational Research Foundation of KoreaMinistry of Science, ICT and Future PlanningMinistry of Trade, Industry and EnergyGwangju Institute of Science and Technology
KeywordsMaterials sciencePolymerPhotoactive layerElectric fieldDipoleSemiconductorPolymer solar cellOptoelectronicsPhotovoltaic systemCharge (physics)MetalNanotechnologyHybrid solar cellEnergy conversion efficiencyComposite materialElectrical engineering

Abstract

fetched live from OpenAlex

Abstract The collection efficiency of photogenerated charges in polymer solar cells (PSCs) is strongly influenced by the built‐in field (Ein) that develops across the photoactive materials. Here, by investigating the Ein‐development regimes in PSCs by introducing two types of interlayers, electric dipole layers (EDLs) and charge transport layers (CTLs), the device architecture is optimized to result in a larger Ein. By incorporating a pair of EDLs on both sides of the photoactive layer, the Ein is modulated by shifting the vacuum energy at each metal–semiconductor interface, providing a larger Ein than that in conventional PSCs using typical CTLs, such as metal oxides and/or conducting polymers. These devices using paired EDLs exhibit an average PCE of 9.8%, which far surpasses the average PCE of ≈8.5% for paired CTLs.

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 score0.001
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)0.0010.000

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 head0.007
GPT teacher head0.207
Teacher spread0.200 · 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

Citations33
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueAdvanced Functional MaterialsSame topicOrganic Electronics and PhotovoltaicsFrench-language works237,207