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Record W4309521299 · doi:10.1021/acsapm.2c01517

Chemically Folded Polyelectrolytes with Superior Alkaline Stability

2022· article· en· W4309521299 on OpenAlexaff
Iris Agami, Nansi Gjineci, Songlin Li, Simcha Srebnik, Dario R. Dekel, Charles E. Diesendruck

Bibliographic record

VenueACS Applied Polymer Materials · 2022
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsUniversity of British Columbia
FundersMinistry of National Infrastructure, Energy and Water ResourcesIsrael Science Foundation
KeywordsPolyelectrolyteMembraneChemical stabilityPolymerIon exchangeChemistryChemical engineeringPolymer chemistryChemical modificationMaterials scienceOrganic chemistryIon

Abstract

fetched live from OpenAlex

In recent years, the development of anion-exchange membranes (AEMs) for anion-exchange membrane fuel cell (AEMFC) applications has been rapidly growing due to their numerous advantages over mainstream proton-exchange membrane fuel cells. However, a major challenge in the development of practical AEMs is the low chemical stability of the AEM quaternary ammonium (QA) functional groups in the strongly alkaline and the relatively dry environment produced during operation of the AEMFC. Herein, we investigate the effect of polymer chain folding on the chemical stability of the QA groups. While these polymers have virtually the same chemical composition, their molecular architectures are quite different, significantly affecting the kinetics of nucleophilic attacks on the QAs embedded inside the folded chains. The stability tests reveal a remarkable improvement in the stability of the folded chains compared to the linear (unfolded) control, resulting in polyelectrolytes that are two orders of magnitude more stable. We provide here a simple method for the preparation of chemically stable AEMs with different QA groups and polymer backbones. These folded architectures present a very promising family of polyelectrolyte membranes for AEMFCs and other electrochemical applications.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.009
Threshold uncertainty score1.000

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)0.0090.000

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 head0.005
GPT teacher head0.164
Teacher spread0.159 · 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.

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

Citations4
Published2022
Admission routes1
Has abstractyes

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