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Record W4226197453 · doi:10.1021/jacs.2c01884

Electrically Fueled Active Supramolecular Materials

2022· article· en· W4226197453 on OpenAlexfundno aff
Serxho Selmani, Eric L. Schwartz, Justin T. Mulvey, Wei Hong, Adam Grosvirt-Dramen, Wyeth Gibson, Allon I. Hochbaum, Joseph P. Patterson, Regina Ragan, Zhibin Guan

Bibliographic record

VenueJournal of the American Chemical Society · 2022
Typearticle
Languageen
FieldMaterials Science
TopicSupramolecular Self-Assembly in Materials
Canadian institutionsnot available
FundersBasic Energy SciencesDivision of Materials ResearchNatural Sciences and Engineering Research Council of CanadaAmerican Chemical Society Petroleum Research FundNational Science Foundation
KeywordsDissipative systemSupramolecular chemistryChemistryNanotechnologyTransient (computer programming)ElectronicsComputer scienceMoleculeMaterials sciencePhysicsOrganic chemistry

Abstract

fetched live from OpenAlex

Fuel-driven dissipative self-assemblies play essential roles in living systems, contributing both to their complex, dynamic structures and emergent functions. Several dissipative supramolecular materials have been created using chemicals or light as fuel. However, electrical energy, one of the most common energy sources, has remained unexplored for such purposes. Here, we demonstrate a new platform for creating active supramolecular materials using electrically fueled dissipative self-assembly. Through an electrochemical redox reaction network, a transient and highly active supramolecular assembly is achieved with rapid kinetics, directionality, and precise spatiotemporal control. As electronic signals are the default information carriers in modern technology, the described approach offers a potential opportunity to integrate active materials into electronic devices for bioelectronic 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 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 score0.008

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 communication0.0000.001
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 head0.006
GPT teacher head0.241
Teacher spread0.235 · 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

Citations66
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueJournal of the American Chemical SocietySame topicSupramolecular Self-Assembly in MaterialsFrench-language works237,207