Effect of carbon nanotubes on electromagnetic interference shielding of carbon fiber reinforced polymer composites
Bibliographic record
Abstract
This paper investigated the effect of carbon nanotubes (CNTs) on the electromagnetic interference shielding of carbon fiber reinforced polymer (CFRP) composites. The CNT/CFRP composites were fabricated by dry spray deposition of CNT on surfaces of carbon fiber prepregs and then out‐of‐autoclave curing process. The interlaminate shear strength of CNT/CFRP composites was first examined. It was found that the shear strength was strengthened if the CNT loading between prepregs was below 3.0 g/m 2 . Then, the electromagnetic interference shielding effectiveness (EMI SE) of CNT/CFRP composites was characterized as a function of CNT loadings over the X‐band frequency range (8.2–12.4 GHz). The tests revealed that the maximum and minimum EMI SE of CNT/CFRP composites increased by approximately 20%, from 62 to 74 dB and from 45 to 53 dB, respectively, by adding 2.5 g/m 2 CNT at the interlaminate surfaces. The results unveiled that the shielding by absorption dominates in the overall EMI SE of CNT/CFRP composites, while the shielding by reflection is almost independent of CNT loadings. It was also observed that the overall EMI SE increased linearly with CNT loading. This study shows that the spray deposition of CNT is an efficient technique to improve the EMI shielding performance of CFRP composites. POLYM. COMPOS., 39:E655–E663, 2018. © 2016 Society of Plastics Engineers
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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) | 0.001 | 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".