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Record W7132988223

Ultra-Flexible Piezoresistive Nanocomposite Foam Sensing Systems for Force Mapping

2023· dissertation· W7132988223 on OpenAlexafffund
Pardis Ghahramani

Bibliographic record

VenueTSpace · 2023
Typedissertation
Language
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsNanocompositeFabricationCarbon nanotubePiezoresistive effectPorosityPolydimethylsiloxaneThermoplastic polyurethaneElastic modulusPorous mediumModulus
DOInot available

Abstract

fetched live from OpenAlex

The growing interest in the fabrication of flexible nanocomposite sensors, along with the limitation of current technologies, prompted us to develop new types of nanocomposite foam structures that possess high sensitivity, repeatable piezoresistive behavior, ultra-flexibility, high compressibility, and high mechanical properties. In this context, a comprehensive study that investigates the effect of various polymer matrices, conductive filler contents, foam porosity, foam morphology, and different manufacturing methods on the piezoresistivity, sensitivity, and mechanical properties of nanocomposite foams was conducted. Moreover, for a better understanding of porous and nonporous nanocomposites’ mechanical behavior, micromechanical modeling approaches were used to predict their elastic modulus.The first phase of this research work was focused on the fabrication and characterization of mechanical and electrical properties of polydimethylsiloxane (PDMS)/multi-walled carbon nanotube (MWCNT) nanocomposite foams. Eshelby-Mori-Tanaka (EMT) and Halpin-Tsai (HT) micromechanical approaches and extended Gibson and Ashby’s approach were applied to theoretically predict the elastic modulus of nonporous and porous PDMS/MWCNT. The results indicated that optimal porosity (60%) and MWCNT content (0.5 wt.%) could guarantee improvement in pressure sensitivity and Young’s modulus of PDMS/MWCNT nanocomposite foams. In the second phase, we focused on the fabrication of thermoplastic polyurethane (TPU) based closed-cell nanocomposite foams through different cost-effective scalable manufacturing methods. Porous TPU/MWCNT nanocomposites foamed by implementing a chemical blowing agent (CBA) into the nanocomposite matrix through combined compounding-compression molding methods, demonstrated high elastic modulus (up to 6.2 ± 0.6 MPa) and compressibility (up to 68.5% compressive strain). According to the theoretical approaches, different modifications of HT employed in solid TPU/MWCNT, and Gibson and Ashby’s method employed in foam TPU/MWCNT were in good agreement with experimental results in predicting elastic modulus. To improve MWCNT dispersion, TPU/MWCNT nanocomposite foams were fabricated by solvent casting method using thermo-expandable blowing agents. The cyclic mechanical testing showed a repeatable piezoresistive behavior and deformation in each cycle with constant hysteresis for these materials. Results indicated that higher MWCNT and blowing agent contents reduced the electrical resistance of nanocomposite foams; however, in response to the compression deformation and formation of new conductive pathways, an optimum level of both parameters was necessary to meet higher pressure sensitivity.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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 science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.032
GPT teacher head0.304
Teacher spread0.272 · 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 source (direct Gemma or distilled Codex), 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
Published2023
Admission routes2
Has abstractyes

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