A facile, solvent-free, non-metallic approach turns waste polyethylene terephthalate into electrically conductive composites with outstanding electromagnetic interference shielding performance
Bibliographic record
Abstract
In this study, we developed a recycled polyethylene terephthalate (rPET) nanocomposite with a hierarchical filler network structure by introducing graphite nanoplatelets (GNPs) and carbon nanotubes (CNTs) as filler components. The presence of a network structure enhanced the electrical conductivity of the nanocomposites. Furthermore, the dual filler-driven hierarchical network structure enables intra-scattering of electromagnetic (EM) waves, leading to an efficient electromagnetic interference (EMI) shielding mechanism. The obtained nanocomposites exhibited a good electrical conductivity of 0.45 S/m with only 1 wt% of GNP and 1 wt% of CNT, and a very high EMI shielding effectiveness (EMI SE) of 38.6 dB (the highest value of EMI SE is 47.4 dB, obtained at 11.2 GHz) at a higher concentration of CNTs. Moreover, the nanocomposite possessed good mechanical properties, including a tensile strength of 18.6 MPa, a Young's modulus of 2932 MPa, and an impact strength of 3.6 kJ/m 2 . Thermal stability, crystallization behavior, and rheological characteristics of the nanocomposites are also investigated in this study. To evaluate their potential contribution to a circular economy, the influence of repeated processing on the EMI shielding performance of the nanocomposite is thoroughly examined. Therefore, we believe that the development of this multifunctional nanocomposite with excellent EMI shielding behavior offers a promising pathway for extending the scope of waste plastics to sustainably replace virgin plastic materials for EMI shielding of advanced electronic devices. • A high-performance rPET composite was developed using a low content of GNP and CNTs. • CNT aids in situ GNP exfoliation, forms a percolated network, and boosts electrical conductivity via a bridging mechanism. • The alteration of CNT content causes the volume exclusion effect of GNP and creates a dense percolated network. • A high EMI SE (38.6 dB) was attained by efficient absorption of EM waves through the intra-scattering mechanism. • A mechanically robust, reprocessable composite supports the circular economy for sustainable EMI shielding.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".