Engineering lightweight Poly(lactic acid) graphene nanoribbon nanocomposites for sustainable and stretchable electronics: Achieving exceptional electrical conductivity and electromagnetic interference shielding with enhanced thermal conductivity
Bibliographic record
Abstract
The challenge of creating flexible and sustainable electronics with properties such as lightweight, electrical and thermal conductivity, and electromagnetic interference (EMI) shielding is addressed in this study. Carbon nanotubes and graphene are commonly used in flexible electronic devices, but gaps exist in their conductivity and flexibility. A novel and scalable fabrication method is introduced, involving the creation of bio-based nanocomposites by integrating flexible and high aspect-ratio graphene nanoribbons (GNRs) into a poly(lactic acid) (PLA) matrix. The nanocomposites were drawn above the glass transition temperature (Tg) of PLA, resulting in the formation of extended shish structures, as observed in scanning electron microscope (SEM) images. These structures significantly enhanced thermal and electrical conductivities, as well as EMI shielding features. The combination of flexible GNR nanofillers with uniaxial stretching led to a substantial increase in Young's modulus, tensile strength, and toughness by approximately 550%, 440%, and 600%, respectively, most likely due to the increased network connection despite the higher flexibility of GNRs. Moreover, in-plane thermal conductivity registered a notable enhancement of approximately 110%. The EMI shielding reached 26 dB, with an absorption contribution to EMI shielding of around 45%. Additional improvement was achieved through foaming of the stretched samples, resulting in multilayer structures with alternative extended shish and foam structures. Furthermore, a significant augmentation of approximately 33 dB in total EMI shielding of the foamed samples, accompanied by an 85% increase in SEA was documented. These promising findings underscore potential applications across diverse domains, including thermal interface materials, electronic packaging, capacitors, and energy storage devices, with a specific emphasis on the realm of sustainable and stretchable electronics.
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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) | 0.000 | 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 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".