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Record W2944798949 · doi:10.1021/acs.jpclett.9b01222

Effect of H- and J-Aggregation on the Photophysical and Voltage Loss of Boron Dipyrromethene Small Molecules in Vacuum-Deposited Organic Solar Cells

2019· article· en· W2944798949 on OpenAlexfundno aff
Tian‐Yi Li, Johannes Benduhn, Zhi Qiao, Yuan Liu, Yue Li, Rishi Shivhare, Frank Jaiser, Pei Wang, Jie Ma, Olaf Zeika, Dieter Neher, Stefan C. B. Mannsfeld, Zaifei Ma, Koen Vandewal, Karl Leo

Bibliographic record

VenueThe Journal of Physical Chemistry Letters · 2019
Typearticle
Languageen
FieldEngineering
TopicOrganic Electronics and Photovoltaics
Canadian institutionsnot available
FundersChina Scholarship CouncilBundesministerium für Bildung und ForschungDonghua UniversityCanadian Institute for Advanced Research
KeywordsOrganic solar cellBODIPYEnergy conversion efficiencyBoronSmall moleculeOpen-circuit voltageMaterials scienceOptoelectronicsMoleculeQuantum efficiencyPhotochemistryQuantum yieldChemistryVoltageFluorescenceOpticsOrganic chemistryPhysics

Abstract

fetched live from OpenAlex

) and related photon energy loss mechanisms is critical to increase the power conversion efficiency (PCE) of small-molecule organic solar cells (OSCs), especially those with near-infrared (NIR) absorbers. In this work, two NIR boron dipyrromethene (BODIPY) molecules are characterized for application in planar (PHJ) and bulk (BHJ) heterojunction OSCs. When two H atoms are substituted by F atoms on the peripheral phenyl rings of the molecules, the molecular aggregation type in the thin film changes from the H-type to J-type. For PHJ devices, the nonradiative voltage loss of 0.35 V in the J-aggregated BODIPY is lower than that of 0.49 V in the H-aggregated device. In BHJ devices with a nonradiative voltage loss of 0.35 V, a PCE of 5.5% is achieved with an external quantum efficiency (EQE) maximum of 68% at 700 nm.

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 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 score0.007
Threshold uncertainty score0.318

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 head0.002
GPT teacher head0.172
Teacher spread0.170 · 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

Citations49
Published2019
Admission routes1
Has abstractyes

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