Effects of oxidation and silanization on the durability and tensile properties of carbon nanotube‐reinforced vinyl‐ester nanocomposites
Bibliographic record
Abstract
Abstract Carbon nanotubes (CNTs) have received significant attention due to their exceptional physical and mechanical properties. Advancements in surface modification techniques have facilitated their use in thermoset resin‐based nanocomposites, enhancing engineering properties with minimal additions (0.1 wt.%). This study investigated the effects of various functionalization methods of multi‐walled carbon nanotubes (MWCNTs) on the mechanical, physical, durability, and moisture absorption properties of vinyl‐ester resin (VE). While VE resins resist short‐term environmental degradation, prolonged exposure to moisture and alkaline chemicals can lead to deterioration. To overcome this challenge, MWCNTs were modified using wet acid oxidation, silanization (both with and without prior oxidation), and a zirconate coupling agent (SM7SN). Modifications were characterized using scanning electron microscopy (SEM) and x‐ray photoelectron spectroscopy (XPS). The tensile test and dynamic mechanical analysis (DMA) were performed on the specimens before and after conditioning in NaOH solution. Moisture absorption was measured by immersing the specimens in tap water at 50°C. The results showed that pre‐oxidation of MWCNTs before silanization significantly enhanced silane attachment and improved the durability of the nanocomposites, with a negligible reduction in mechanical properties after conditioning. Using SM7SN as a dispersing agent significantly improved matrix properties and offered a simpler modification method compared to other techniques. Highlights Covalent and non‐covalent methods improved CNT dispersion in VE matrix. Pre‐oxidation before silanization enhanced Si‐O‐C bonds. SEM and XPS confirmed successful CNT functionalization and dispersion. Functionalized CNTs reduced the moisture absorption of nanocomposites. SM7SN agent ensured uniform dispersion and simplified CNT modification.
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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 | 0.000 | 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 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".