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Record W4409362337 · doi:10.1021/acs.jpclett.5c00248

Noncovalent Interactions Promoted Kinetics in Perylene Diimide-Based Aqueous Zn-Ion Batteries: An <i>Operando</i> Attenuated Total Reflection Infrared Study

2025· article· en· W4409362337 on OpenAlexafffund
Sneha Melath, Xiang You, Lingzi Sang

Bibliographic record

VenueThe Journal of Physical Chemistry Letters · 2025
Typearticle
Languageen
FieldEngineering
TopicAdvanced battery technologies research
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaCanada Foundation for InnovationGovernment of Alberta
KeywordsPeryleneAttenuated total reflectionDiimideKineticsInfraredAqueous solutionIonPhotochemistryChemistryMaterials sciencePhysical chemistryOpticsMoleculePhysicsOrganic chemistry

Abstract

fetched live from OpenAlex

Perylene-based organic anodes, as an alternative to metallic Zn for aqueous Zn-ion batteries, store Zn 2+ through a Zn enolate coordination mechanism, which potentially bypasses challenges such as dendrite and hydroxide formation associated with a Zn anode. However, organic anodes exhibit low electrical conductivity and show a low rate performance. Molecular aggregation of conjugated aromatics plays a key role in the electrical conductivity of this class of material, and it is important to understand their impact on the battery rate performance. In this work, we combined electrochemistry and in situ attenuated total reflection infrared characterization to demonstrate the dominating role of aggregates in perylene-based electrodes in the enhancement of the electrode kinetics. We demonstrated the use of noncovalent interaction to form a supermolecular network that exhibits more than 4 orders of magnitude increase in the electron transfer rate, and provides nearly doubled charge storage capacity. The aggregation of perylene units was driven by π–π stacking and hydrogen bonding between the active material and a mediator, ethylene diamine. We showed that, in practice, this additive-mediated aggregation can occur during solution process at a moderate temperature.

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 score0.095
Threshold uncertainty score0.684

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.013
GPT teacher head0.291
Teacher spread0.278 · 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

Citations2
Published2025
Admission routes2
Has abstractyes

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