Effects of adding nanodiamonds in mechanical properties of jute and ramie fiber reinforced epoxy composites
Bibliographic record
Abstract
Abstract The objective of this research is to investigate the potential impact of nanodiamond filler particles on the mechanical and morphological characteristics of epoxy composites that are fortified with ramie and jute fibers. Composed of composite laminates containing nanodiamonds at concentrations of 0.1, 0.3, and 0.5 wt. %, the laminates were produced via vacuum‐assisted resin infusion (VARI to evaluate the alterations in mechanical properties, Vickers hardness, tensile, and flexural tests were conducted on the prepared composites). The findings showed that adding 0.3 wt. % nanodiamonds to epoxy composites significantly improved the hardness of the composites about 18.56% and 34.38%, the tensile strength of the composites about 19.1% and 28.01%, and the flexural strength of the composites about 17.7% and 21.12% for ramie/epoxy and jute/epoxy, respectively. The optimal concentration of nanodiamonds for both types of fibers in order to optimize these properties was calculated to be 0.3 wt. % of nanodiamonds. A micro‐x‐ray CT scan was performed to determine the percentage of porosity in composites. The utilization of scanning electron microscopy (SEM) demonstrated that an increase in the nanodiamond content led to enhanced fiber dispersion and reduced interfacial voids. In contrast, Fourier transform infrared (FTIR) analysis unveils the hydrophobic characteristics, cellulose content, and improved interfacial bonding between the fibers and the epoxy matrix, which is attributed to the robust covalent bonding enabled by the nanodiamonds. Highlights Adding nanodiamonds (NDs) improved adhesion at fiber‐matrix interface. Mechanical properties peaked for composites with 0.3 wt. % of NDs. Beyond 0.3 wt. % of NDs, agglomerates in composites were observed through SEM. Void percent increased for composites with 0.5 wt. % of NDs.
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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) | 0.001 | 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".