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Record W2750060745 · doi:10.1002/adem.201700318

A Triboelectric Self‐Powered Sensor for Tire Condition Monitoring: Concept, Design, Fabrication, and Experiments

2017· article· en· W2750060745 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAdvanced Engineering Materials · 2017
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsUniversity of TorontoUniversity of Waterloo
Fundersnot available
KeywordsTriboelectric effectDurabilityAutomotive engineeringMaterials scienceFabricationCondition monitoringElectrical engineeringEngineeringComposite material

Abstract

fetched live from OpenAlex

This paper presents a novel type of triboelectric‐based self‐powered sensor for tire condition monitoring. The triboelectric based sensor is made of highly flexible, mechanically and thermally durable, and cost‐effective polymeric materials. The authors firstly report the location inside of a tire for attaching the sensor to monitor tire conditions. Then, the authors analyze the performance of the sensor under different frequencies and stroke displacements to show the capability of the fabricated device as a self‐powered sensor. Furthermore, the authors evaluate the durability and performance of the sensor to delineate its potential for tire condition monitoring. The results show that the fabricated self‐powered sensor has the potential of measuring the tire forces and pressure. The use of the proposed sensor for tire condition monitoring systems (TCMS) can be considered as a significant step toward developing smart tires, improving vehicles control strategy, and accordingly, enhancing passengers safety.

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.

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 score0.054
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 communication0.0000.001
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 head0.020
GPT teacher head0.268
Teacher spread0.248 · 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