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Record W4294993074 · doi:10.1016/j.mtsust.2022.100233

Wearable and flexible electrodes in nanogenerators for energy harvesting, tactile sensors, and electronic textiles: novel materials, recent advances, and future perspectives

2022· article· en· W4294993074 on OpenAlexaff
Roohollah Bagherzadeh, Saeid Abrishami, Armineh Shirali, Amin Reza Rajabzadeh

Bibliographic record

VenueMaterials Today Sustainability · 2022
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsMcMaster University
Fundersnot available
KeywordsWearable computerEnergy harvestingWearable technologyTactile sensorNanotechnologyElectronicsMaterials scienceElectrodeComputer scienceEnergy (signal processing)Electrical engineeringEngineeringEmbedded systemPhysicsArtificial intelligence

Abstract

fetched live from OpenAlex

There are numerous drivers in the context of sustainable energy production from ambient mechanical energy sources, such as body motions, due to the increasing world demand for alternative energy. Recent progress has been made in the energy harvesting technologies based on piezoelectric nanogenerators (PENGs) and triboelectric nanogenerators (TENGs) to convert such ambient biomechanical energy into electricity. The PENGs and TENGs technologies have been successfully utilized to provide sufficient energy for low-power electronic devices, such as biomedical sensors for health monitoring. However, the successful implementations of such technologies, including their electrodes as the critical component of the nanogenerators, require unique properties such as flexibility, wearability, and stretchability. As a result, this review summarizes recent progress on PENGs and TENGs technologies and applications with a focus on new electrode materials that could provide flexibility, wearability, and stretchability capabilities to these types of nanogenerators. This review shed light on the role of wearable electrodes in different applications such as devices with smart tactile sensing mechanisms and electronic textiles . This review also outlines the future prospect and potential direction toward the advancement of such technologies and their performance.

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)0.001
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 score0.216
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.005
GPT teacher head0.217
Teacher spread0.212 · 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

Citations58
Published2022
Admission routes1
Has abstractyes

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