Ultralow-Threshold and Lightweight Biodegradable Porous PLA/MWCNT with Segregated Conductive Networks for High-Performance Thermal Insulation and Electromagnetic Interference Shielding Applications
Bibliographic record
Abstract
Lightweight, biodegradable, thermally insulating, and electrically conductive materials play a vital role in achieving the sustainable development of our society. The fabrication of such multifunctional materials is currently very challenging. Here, we report a general, facile, and eco-friendly way for the large-scale fabrication of ultralow-threshold and biodegradable porous polylactic acid (PLA)/multiwalled carbon nanotube (MWCNT) for high-performance thermal insulation and electromagnetic interference (EMI) shielding applications. Thanks to the unique structure of the microporous PLA matrix embedded by conductive 3D MWCNT networks, the lightweight porous PLA/MWCNT with a density of 0.045 g/cm 3 possesses a percolation threshold of 0.00094 vol %, which, to our knowledge, is the minimum value reported so far. Furthermore, the material exhibits excellent thermal insulation performance with a thermal conductivity of 27.5 mW·m –1 ·K –1, which is much lower than the best value of common thermal insulation materials. Moreover, it also shows outstanding EMI shielding performance characterized by its high shielding effectiveness (SE) values and absorption-dominated shielding feature. More importantly, its specific EMI SE is as high as 1010 dB·cm 3 ·g –1, which is superior to those of other shielding materials reported so far. Thus, this novel multifunctional material and its general fabrication methodology provide a promising way to meet the growing demand for high-performance multifunctional materials in sustainable development.
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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) | 0.001 | 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".