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
Why this work is in the frame
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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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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 | 0.001 | 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 | 0.001 | 0.001 |
| 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 it