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Record W4417520741 · doi:10.26434/chemrxiv-2025-k0r7h

Merocyanine-linked ZwitterIonic Covalent Organic Frameworks

2025· article· W4417520741 on OpenAlexaff
Zhechang He, Chenghao Liu, Ting Yu, Junsong Xu, Pierre-Luc Thériault, Mohammad Hossein Gohari, Cory Ruchlin, Yuxuan Che, Hatem M. Titi, Stéphane Kéna‐Cohen

Bibliographic record

VenueChemRxiv · 2025
Typearticle
Language
FieldMaterials Science
TopicCovalent Organic Framework Applications
Canadian institutionsPolytechnique MontréalMcGill University
Fundersnot available
KeywordsCationic polymerizationDensity functional theoryCovalent bondExcited stateAbsorption (acoustics)NucleationDipole

Abstract

fetched live from OpenAlex

Merocyanines are classic π-conjugated systems with a significant zwitterionic character and distinct optoelectronic properties. Here, we report the synthesis of the first merocyanine-based C=C-linked COFs through the condensation of N-alkylcollidinium salts with hydroxytrimesic aldehyde. These zwitter-ionic COFs are chemically stable, with a pronounced charge-transfer absorption band to ~850 nm. Strong dipolar interactions within the COFs result in pore contrac-tion/expansion in response to the medium polarity, manifested in a macroscopic expansion and an increased surface area in high dielectric constant medium. The COFs can reversibly transition between a black neutral and a yellow cationic form upon protonation. Density Functional Theory calculations suggest an ‘omni-directional’ π-conjugation with large band dispersions, despite the trigonal lattice. Thin films of EtMER-COF prepared by heterogeneous nucleation on glass/ITO substrates reveal a vertical orientation of its 2D sheets. Time-resolved spectroscopy of thin film suggests the presence of stimulated emission as well as a long-lived excited state.

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 score0.001
Threshold uncertainty score0.005

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 communication0.0000.000
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 head0.012
GPT teacher head0.270
Teacher spread0.258 · 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
Published2025
Admission routes1
Has abstractyes

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