Bibliographic record
Abstract
Piezoresistivity in conductive polymer nanocomposites occurs because of the disturbance of particle networks in the polymer matrix. The piezoresistance effect becomes more prominent if the matrix material is compliant making these materials attractive for applications that require flexible force and displacement sensors such as e-textiles and biomechanical measurement devices. However, the exact mechanisms of piezoresistivity including the relationship between the matrix polymer, conductive particle, internal structure and the composite’s piezoresistance need to be better understood before it can be applied for such applications. The objective of this thesis is to report on the development of conductive polymer nanocomposites for use as flexible sensors and electrodes. Electrically conductive and piezoresistive nanocomposites were fabricated by a scalable melt compounding process. Particular attention was given to elucidating the role of matrix and filler materials, plastic deformation and porosity on the electrical conduction and piezoresistance. These effects were parametrically investigated through characterizing the morphology, electrical properties, rheological properties, and piezoresistivity of the polymer nanocomposites. The electrical and rheological behavior of the nanocomposites was modeled by the percolation-power law. Furthermore, a model was developed to describe the piezoresistance behavior during plastic deformation in relation to the stress and filler concentration.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".