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Record W4299884524 · doi:10.17615/xt80-7f04

Spectroscopic detection of halogen bonding resolves dye regeneration in the dye-sensitized solar cell

2020· article· en· W4299884524 on OpenAlexfundno aff
Curtis P. Berlinguette, Fraser G. L. Parlane, Cameron W. Kellett, Pierre Kennepohl, Sarah J. C. Simon, Gerald J. Meyer, Chantal L. Mustoe, Wesley B. Swords

Bibliographic record

VenueUNC Libraries · 2020
Typearticle
Languageen
FieldEnergy
TopicTiO2 Photocatalysis and Solar Cells
Canadian institutionsnot available
FundersBasic Energy SciencesStewart Blusson Quantum Matter Institute, University of British ColumbiaCanada Research ChairsU.S. Department of EnergyNational Science Foundation
KeywordsDye-sensitized solar cellHalogenSolar cellMaterials scienceHalogen lampPhotochemistryHalogen bondChemistryOptoelectronicsPhysical chemistryOrganic chemistryOpticsElectrode

Abstract

fetched live from OpenAlex

The interactions between a surface-adsorbed dye and a soluble redox-active electrolyte species in the dye-sensitized solar cell has a significant impact on the rate of regeneration of photo-oxidized dye molecules and open-circuit voltage of the device. Dyes must therefore be designed to encourage these interfacial interactions, but experimentally resolving how such weak interactions affect electron transfer is challenging. Herein, we use X-ray absorption spectroscopy to confirm halogen bonding can exist at the dye-electrolyte interface. Using a known series of triphenylamine-based dyes bearing halogen substituents geometrically positioned for reaction with halides in solution, halogen bonding was detected only in cases where brominated and iodinated dyes were photo-oxidized. This result implies that weak intermolecular interactions between photo-oxidized dyes and the electrolyte can impact device photovoltages. This result was unexpected considering the low concentration of oxidized dyes (less than 1 in 100,000) under full solar illumination.

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 score0.052
Threshold uncertainty score0.414

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 head0.018
GPT teacher head0.200
Teacher spread0.182 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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