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Record W4408940237 · doi:10.3390/mi16040382

PEDOT:PSS-MWCNT Nanocomposite Wire for Routing in Energy Harvesting Devices

2025· article· en· W4408940237 on OpenAlexaff
S. Haghgooye Shafagh, Imran A. Deen, Dhilippan M. Panneerselvam, Muthukumaran Packirisamy

Bibliographic record

VenueMicromachines · 2025
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsConcordia University
Fundersnot available
KeywordsPEDOT:PSSMaterials scienceVoltageComposite materialPiezoelectricityBattery (electricity)PolydimethylsiloxaneOptoelectronicsPolymerElectrical engineeringPower (physics)

Abstract

fetched live from OpenAlex

Polydimethylsiloxane (PDMS) and poly(3,4-ethylene dioxythiophene):poly(4-styrene-sulfonate) (PEDOT:PSS) composites were tested to determine their suitability for charging small-scale batteries in conjunction with a piezoelectric actuator as an energy harvester. Two different PEDOT:PSS patterns (zigzag and serpentine) were tested, and the maximum DC voltage of a system incorporating PEDOT:PSS was determined. The aim of this work is to study the effect of soft corners in the electrical routing of aircraft and IoT sensors. The zigzag and serpentine patterns were considered for this study because of their simplicity in design. Without the polymer, 2.3 V was produced by the actuator, while adding PEDOT:PSS resulted in the voltage being reduced to 1.7 V. The piezoelectric actuator was connected to a 3.6 V rechargeable Li-ion battery, and the battery's voltage was recorded over 1 h. The voltage from the piezoelectric actuator was 3.8 V. Without PEDOT:PSS, the battery was charged to a maximum of 3 V. Adding the PEDOT:PSS to the circuit reduced the maximum charge to a voltage of 2 V. The results indicate that while PEDOT:PSS composites can be used in conjunction with piezoelectric energy harvesters, more work is still needed to optimize the system to increase efficiency and charging rates.

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 score0.148
Threshold uncertainty score0.945

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 head0.008
GPT teacher head0.237
Teacher spread0.230 · 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

Citations3
Published2025
Admission routes1
Has abstractyes

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