Direct Creation of Highly Conductive Laser‐Induced Graphene Nanocomposites from Polymer Blends
Bibliographic record
Abstract
The current state‐of‐the‐art mixing strategies of nanoparticles with insulating polymeric components have only partially utilized the unique electrical conductivity of graphene in nanocomposite systems. Herein, this paper reports a nonmixing method of direct creation of polymer/graphene nanocomposites from polymer blends via laser irradiation. Polycarbonate‐laser‐induced graphene (PC‐LIG) nanocomposite is produced from a PC/polyetherimide (PC/PEI) blend after exposure to commercially available laser scribing with a power of ≈6 W and a speed of ≈2 cm s−1. Extremely high electrical conductivities are obtained for the PC‐LIG nanocomposites, ranging from 26 to 400 S m−1, depending on the vol% of the starting PEI phase in the blend. To the authors' knowledge, these conductivity values are at least one order of magnitude higher than the values that are previously reported for conductive polymer/graphene nanocomposites prepared via mixing strategies. The comprehensive microscopy and spectroscopy characterizations reveal a complete graphitization of the PEI phase with columnar microstructure embedded in the PC phase.
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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".