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Record W2883441222 · doi:10.1088/2058-8585/aad5a4

Rotate-to-bend setup for fatigue bending tests on inkjet-printed silver lines

2018· article· en· W2883441222 on OpenAlexafffund
Bernhard Huber, Jakob Schober, Michael Kaiser, Andreas Ruëdiger, Christina Schindler

Bibliographic record

VenueFlexible and Printed Electronics · 2018
Typearticle
Languageen
FieldEngineering
TopicNanomaterials and Printing Technologies
Canadian institutionsHydro-QuébecInstitut National de la Recherche Scientifique
FundersNatural Sciences and Engineering Research Council of CanadaBayerische Forschungsallianz
KeywordsBendingMaterials scienceComposite material3d printedInkjet printingStructural engineeringMechanical engineeringNanotechnologyEngineeringManufacturing engineeringInkwell

Abstract

fetched live from OpenAlex

Abstract For the growing field of flexible electronics the performance of fabricated flexible devices during bending deformation has to be investigated in order to guarantee their resilience against small bending radii and bending fatigue. We report on a rotate-to-bend apparatus, which allows for arbitrary sequences of compressive as well as tensile bending and a wide range of selectable bending radii without applying additional strain. The electrical characterization can be conducted simultaneously with high speed measurement. We test the rotate-to-bend device on inkjet-printed conducting paths of Ag nanoparticles and Ag nanowires on various foil substrates. Fatigue bending cycles show that tensile deformation leads to a higher increase in resistance of the printed lines compared to compressive strain. This is caused by the higher tendency of microcracks to form during tensile bending. We also show the high negative impact of the substrate thickness on bending fatigue during full bending cycles. Here, Ag nanowires show superior fatigue behaviour compared to the nanoparticle lines due to their flexible, mesh-like network. The rotate-to-bend apparatus could become an efficient and inexpensive device for the testing of flexible devices.

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 score0.124
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)0.0000.000

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 head0.027
GPT teacher head0.284
Teacher spread0.256 · 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

Citations3
Published2018
Admission routes2
Has abstractyes

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