Preparation and Properties of GFRP with Carbon Nanotube Interface Modification and Tension Control
Bibliographic record
Abstract
In response to the problem of interlayer performance degradation caused by insufficient fiber/resin interface bonding and fiber relaxation during the molding process of Glass Fiber Reinforced Polymer (GFRP) composites, a preparation strategy of "nanoscale interface modification molding tension synergy" is proposed. Firstly, a chemical grafting method promoted by silane coupling and coupling agents was used to stably graft aminated carbon nanotubes (CNTs) onto the surface of glass fibers to enhance interfacial bonding; Secondly, design and manufacture a hot press mold that integrates a dual-mode tensioning mechanism (double-sided synchronous displacement tensioning and center guided anti deviation) and a uniform heating scheme to achieve stable tensioning and uniform temperature field control of short fibers during the curing process. Prepare GFRP laminates using the same raw materials and curing system, and conduct short beam shear tests in accordance with JC/T 773-2010. The results showed that the interlaminar shear strength (ILSS) of CNT modified laminates increased from (53.46 ± 2.36) MPa to (68.89 ± 2.90) MPa, an increase of 28.9%. Research has shown that the multi-level interface structure formed by CNT grafting can enhance mechanical interlocking and chemical bonding, while tension forming and uniform heating provide stable process guarantees for interface infiltration and curing. This study provides an engineering implementation path for the controllable molding and interface enhancement of GFRP components for automotive lightweighting.
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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 | 0.000 |
| 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 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".