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Record W2892541424 · doi:10.1002/anie.201810809

Precise Control of Thermal and Redox Properties of Organic Hole‐Transport Materials

2018· article· en· W2892541424 on OpenAlexafffund
Valerie A. Chiykowski, Yang Cao, Hairen Tan, Daniel P. Tabor, Edward H. Sargent, Alán Aspuru‐Guzik, Curtis P. Berlinguette

Bibliographic record

VenueAngewandte Chemie International Edition · 2018
Typearticle
Languageen
FieldMaterials Science
TopicConducting polymers and applications
Canadian institutionsVector InstituteUniversity of TorontoUniversity of British Columbia
FundersCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of CanadaNederlandse Organisatie voor Wetenschappelijk OnderzoekCanadian Institute for Advanced ResearchNational Science Foundation
KeywordsRedoxThermalMaterials scienceChemistryInorganic chemistryThermodynamicsPhysics

Abstract

fetched live from OpenAlex

Abstract We report design principles of the thermal and redox properties of synthetically accessible spiro‐based hole transport materials (HTMs) and show the relevance of these findings to high‐performance perovskite solar cells (PSCs). The chemical modification of an asymmetric spiro[fluorene‐9,9′‐xanthene] core is amenable to selective placement of redox active triphenylamine (TPA) units. We therefore leveraged computational techniques to investigate five HTMs bearing TPA groups judiciously positioned about this asymmetric spiro core. It was determined that TPA groups positioned about the conjugated fluorene moiety increase the free energy change for hole‐extraction from the perovskite layer, while TPAs about the xanthene unit govern the T g values. The synergistic effects of these characteristics resulted in an HTM characterized by both a low reduction potential (≈0.7 V vs. NHE) and a high T g value (>125 °C) to yield a device power conversion efficiency (PCE) of 20.8 % in a PSC.

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 categoriesInsufficient payload (model declined to judge)
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 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)0.0010.000

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 head0.017
GPT teacher head0.241
Teacher spread0.224 · 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

Citations56
Published2018
Admission routes2
Has abstractyes

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