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Record W2158359806 · doi:10.1002/jcc.23070

Relationship between dye–iodine binding and cell voltage in dye‐sensitized solar cells: A quantum‐mechanical look

2012· article· en· W2158359806 on OpenAlexaff
Abu Asaduzzaman, Guy A. G. Chappellaz, Georg Schreckenbach

Bibliographic record

VenueJournal of Computational Chemistry · 2012
Typearticle
Languageen
FieldEnergy
TopicTiO2 Photocatalysis and Solar Cells
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsBromineIodineChemistryChalcogenPhotochemistrySteric effectsSolar cellRedoxOxygenBinding energyInorganic chemistryStereochemistryOrganic chemistryMaterials science

Abstract

fetched live from OpenAlex

It has been proposed that iodine binding to dyes may actually decrease the cell efficiency of a dye-sensitized solar cell. A previous experimental study showed that a two-atom change from oxygen to sulfur increased recombination of iodine with injected electrons by a factor of approximately 2. Here, it is shown that iodine binding is a plausible explanation for this effect. The steric and conjugation effects are quantified separately using a set of model compounds. Quantum-chemical calculations show that elongation of the hydrocarbon chain has only an insignificant effect on the iodine and bromine binding to the chalcogen atoms (O, S, Se). The conjugation, however, significantly disfavors the iodine and bromine interaction. Iodine and bromine binding to the dye and model compounds containing sulfur is significantly more favorable than to their oxygen containing counterparts. Bromine binding to dyes is shown to be stronger than that of iodine. Accordingly, bromine binding to dyes may contribute significantly to the observed lower efficiencies in cells using Br(3)(-)/Br(-) as the redox couple.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 integrity0.0010.000
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 head0.024
GPT teacher head0.253
Teacher spread0.229 · 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 designSimulation or modeling
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

Citations18
Published2012
Admission routes1
Has abstractyes

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