Mechanical rolling of nickel nanowire-PVDF composites yields enhanced conductivity and electromagnetic shielding properties
Bibliographic record
Abstract
Highly flexible and lightweight PVDF-based nickel nanowires (NiNWs) composites are promising candidates for electronic devices, particularly in electromagnetic shielding. However, the alignment of NiNWs within the PVDF matrix to achieve high electrical conductivity presents a significant challenge. In this study, we present a straightforward and scalable method to prepare aligned NiNWs-PVDF composites using mechanical shear force (i.e., rolling). We firstly synthesized conductive, high aspect ratio NiNWs; we then prepared thin films of PVDF/NiNWs composites with various NiNWs concentrations (40–70 wt.%) via solution casting. These solution-cast PVDF/NiNWs composites were then mechanically rolled, and alignment of the NiNWs and PVDF lamellae was achieved, as confirmed by TEM and SAXS images. The oriented NiNWs in the PVDF matrix exhibited superior electrical conductivity compared to solution-cast samples without mechanical rolling. Notably, the percolation threshold of the rolled films was lower than that of the samples that were not rolled. Furthermore, the rolling process increased both the β-phase formation and the overall crystallinity of the PVDF matrix, as evidenced by FTIR and DSC analyses, respectively. Owing to enhancement of conductivity and dielectric permittivity, rolled composites displayed a total shielding efficiency of -42 dB over the 12 – 18 GHz frequency band (relevant to satellite and microwave communications), compared with -32 dB for non-rolled (solution cast) samples. This performance can be attributed to an absorption-dominant shielding mechanism due to the enhancement of interfacial polarization and β-phase formation. Mechanically rolled films are good candidates for use in EMI shielding applications requiring thin and lightweight materials.
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How this classification was reachedexpand
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 | 0.001 |
| 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 teacher head, 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".