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Record W4321375159 · doi:10.1002/adfm.202213677

Tough Transient Ionic Junctions Printed with Ionic Microgels

2023· article· en· W4321375159 on OpenAlexafffund
Ran Huo, Guangyu Bao, Zixin He, Xuan Li, Zhenwei Ma, Zhen Yang, Roozbeh Siavash Moakhar, Shuaibing Jiang, Christopher Chung‐Tze‐Cheong, Alexander Nottegar, Changhong Cao, Sara Mahshid, Jianyu Li

Bibliographic record

VenueAdvanced Functional Materials · 2023
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsUniversity of British ColumbiaMcGill University
FundersFonds de recherche du Québec – Nature et technologiesKillam TrustsNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsMcGill University
KeywordsIonic bondingMaterials scienceNanotechnologyFabricationTransient (computer programming)Ionic liquidOptoelectronicsIonComputer scienceChemistry

Abstract

fetched live from OpenAlex

Abstract Emerging soft ionotronics better match the human body mechanically and electrically compared to conventional rigid electronics. They hold great potential for human‐machine interfaces, wearable and implantable devices, and soft machines. Among various ionotronic devices, ionic junctions play critical roles in rectifying currents as electrical p–n junctions. Existing ionic junctions, however, are limited in electrical and mechanical performance, and are difficult to fabricate and degrade. Herein, the design, fabrication, and characterization of tough transient ionic junctions fabricated via 3D ionic microgel printing is reported. The 3D printing method demonstrates excellent printability and allows one to fabricate ionic junctions of various configurations with high fidelity. By combining ionic microgels, degradable networks, and highly charged biopolymers, the ionic junctions feature high stretchability (stretch limit 27), high fracture energy (>1000 Jm −2 ), excellent electrical performance (current rectification ratio >100), and transient stability (degrade in 1 week). A variety of ionotronic devices, including ionic diodes, ionic bipolar junction transistors, ionic full‐wave rectifiers, and ionic touchpads are further demonstrated. This study merges ionotronics, 3D printing, and degradable hydrogels, and will motivate the future development of high‐performance transient ionotronics.

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)
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 score0.359
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)0.0010.001

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 head0.014
GPT teacher head0.206
Teacher spread0.192 · 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

Citations29
Published2023
Admission routes2
Has abstractyes

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