Improving the electrical conductivity of multi-phase polymer composites via plasticizer assisted nanoparticle dispersion
Bibliographic record
Abstract
It is well-known that electrically conductive polymer composites can be fabricated via incorporating highly conductive fillers such as carbon fibres (CFs) and carbon nanotubes (CNTs) into a polymer system through either melt blending or solvent casting method. Nevertheless, one of the greatest challenges lies in the proper particle dispersion to achieve a low percolation threshold and high conductivity performance. Recently, it was found that CNTs have phasesensitive localization property when incorporated in a composite system formed by two immersible phases, such as polylactic acid (PLA) and thermoplastic polyurethane (TPU). As a result, composites with ultra-low percolation threshold can be formed by tuning the ratio of the two polymer phases. In this study, we reported that such property can be further enhanced via the introduction of a small amount of plasticizer into the polymer system. It was observed that the incorporation of poly(ethylene glycol) (PEG) affected the immiscibility of the two polymer matrix as significant changes in morphologies and thermal behaviours were also detected. Finally, by adding 5 wt% PEG, the electrical conductivity for sample contacting 2.5 wt% CNT increased from to 6.8x10-6 to 3.6x10-4 S/cm. Such results suggest that plasticizer is an effective agent for improving particle distribution and conductivity enhancement.
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".