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Record W3047151411 · doi:10.1177/1045389x20947167

Tuning the hygro-mechanical response of paper-based systems using glycerol

2020· article· en· W3047151411 on OpenAlexaff
Isaías Cueva-Pérez, Roque A. Osornio‐Rios, Aurelio Domínguez-González, Ion Stiharu, Ángel Pérez-Cruz

Bibliographic record

VenueJournal of Intelligent Material Systems and Structures · 2020
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsConcordia University
FundersConsejo Nacional de Ciencia y Tecnología
KeywordsCantileverFlexibility (engineering)StiffnessFrequency responseMaterials scienceMechanical systemWork (physics)Microelectromechanical systemsBeam (structure)Resonance (particle physics)Mechanical engineeringEngineeringStructural engineeringNanotechnologyComposite materialElectrical engineering

Abstract

fetched live from OpenAlex

In recent years, the need for portable, low-cost, and eco-friendly devices for testing and monitoring has arisen. Paper-based devices have emerged as a response to these needs due to the properties induced by capillarity, flexibility, disposability, and biodegradability. In this work, the authors explored the possibility of tuning the hygro-mechanical response of paper-based cantilever beams using glycerol. A lumped-parameter model with non-linear stiffness is used to describe the dynamic response of the beams using three parameters. An experimental method based on resonance frequency tests is used to study the influence of glycerol on the dynamic response of four different beam configurations. The obtained results demonstrate that the resonance frequency of paper-based mechanical systems can be easily tuned by the imbibition of a glycerol–water solution. This study could lead to the development of tunable paper-based mechanical systems for specific applications such as energy harvesters and hygro-mechanical-based sensors.

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 score0.236
Threshold uncertainty score0.458

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 head0.030
GPT teacher head0.243
Teacher spread0.213 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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