Highly electrically conductive and high performance EMI shielding nanowire/polymer nanocomposites by miscible mixing and precipitation
Bibliographic record
Abstract
Metal nanowire/polymer nanocomposites are advanced materials for electrically conductive applications. Metal nanowires have high surface area, high aspect ratios, and high electrical conductivity, which are critical for the synthesis of conductive polymer nanocomposites using extremely low amounts of conductive filler. In this work, lightweight, thin, and highly conductive copper nanowire/polystyrene nanocomposites were prepared using a novel method of nanocomposite preparation termed miscible solvent mixing and precipitation (MSMP). Suspensions of high aspect ratio copper nanowires were mixed with polystyrene solutions to produce polymer nanocomposites with segregated nanowire networks resembling cell-like structures. Highly electrically conductive networks of nanowires were obtained beyond a percolation threshold of ϕc = 0.67 vol% and percolated nanocomposites showed electrical conductivities up to 104 S m−1, which exceeds the conductivity range generally reported for carbon nanofiller-based nanocomposites. The significant potential of these nanocomposites for electrical applications like electromagnetic interference (EMI) shielding was further demonstrated. Metal nanowire/polymer nanocomposites sheets of 0.21 mm in thickness exhibited EMI SE of more than 20 dB for copper nanowire concentrations of only 1.3 vol%.
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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".