Enhanced carbon fiber interface with thermoplastics via nanostructure surface modification: Failure, morphology and wettability analysis
Bibliographic record
Abstract
Improving the fiber-matrix adhesion in thermoplastic composites remains a significant challenge due to the lack of chemical bonding between thermoplastics and common reinforcing fibers. This study investigates the effectiveness of carbon fibers enhanced with nanostructure surface modification for strengthening the interfacial adhesion to thermoplastic matrices. The fiber surface was modified with graphene nanoplatelets (GNP) through a facile coating method, and the apparent interfacial shear strength (IFSS) was determined by single-fiber pullout tests. GNP-coated fiber improved IFSS by 74 % with neat high-density polyethylene (HDPE-Neat) and 28 % with maleic anhydride-grafted HDPE (HDPE-8MA), while IFSS reduced by 27 % with polyamide 6 (PA6) due to different failure mechanisms. Morphology, chemical, and wettability analysis were conducted on the nano-enhanced carbon fibers to quantitatively elucidate these findings on micro/nanoscale, combining machine learning-based image segmentation, X-ray photoelectron spectroscopy (XPS), and contact-angle measurements of intermittent beading on fibers. • Evaluated interfacial shear strength of nano-enhanced carbon fiber within thermoplastic using single fiber pullout test. • Revealed failure mechanisms and effects of nano-coating on carbon fiber in various thermoplastic matrices. • Applied convolutional neural network technique to quantify the nano-enhanced surface morphology. • Quantified the wettability of thermoplastic to fibers using fiber beading method.
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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".