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Record W4237073139 · doi:10.32920/ryerson.14651637.v1

Mixed Metal-Organic Dyes With Dual Electrophores: Designing More Robust Dyes for Light-Harvesting Applications

2021· preprint· en· W4237073139 on OpenAlexaff
Jennifer Huynh

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldMaterials Science
TopicLuminescence and Fluorescent Materials
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsBODIPYRedoxChemistryCombinatorial chemistryFerroceneNanotechnologyPhotochemistryMaterials scienceOrganic chemistryFluorescenceElectrochemistryElectrode

Abstract

fetched live from OpenAlex

Donor-π-spacer-acceptor architectures are a favourable motif in the design of dyes for light harvesting applications. Organic compounds offer cost-effectiveness and synthetic design versatility, while inorganic compounds possess long term redox stability and wide range for absorption. Uniting both types of molecules allows utilization of these properties. Several projects were undertaken with the theme of a hybrid dye system and study of their redox stability. Chapter 1 gives a brief overview of the inorganic and organic compounds that paved the research in DSSC dyes. Chapter 2 details a review on copper(I) dyes in the literature and preliminary synthesis towards a D-π-A templated copper(I) dye. Chapter 3 looks into the robust potential of novel BODIPY dyes that utilize ferrocene as an electron rich donor. Chapter 4 represents a series of BODIPY-redox active donor dyads, the study of their redox stability provide insight on the decomposition pathway of these conjugates.

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 score0.002
Threshold uncertainty score0.007

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 communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.034
GPT teacher head0.251
Teacher spread0.216 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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