Controlled Deposition of Single-Walled Carbon Nanotubes Doped Nanofibers Mats for Improving the Interlaminar Properties of Glass Fiber Hybrid Composites
Bibliographic record
Abstract
The properties of glass fiber composites were improved by strengthening the interlaminar regions using electrospun nanofibers mats. However, the chaotic nature of the electrospinning process at the collector restricts the controlled deposition and alignment of nanofibers and limits the use of electrospun nanofibers as secondary reinforcements. Hence, auxiliary vertical electrodes were used, which drastically reduced the diameter of the nanofibers from 450 nm to 150 nm and also improved the alignment of nanofibers. The aligned nanofibers were then used for doping the functionalized single-walled carbon nanotubes (f-SWCNTs) with nanofibers, which controlled the inherent issues associated with SWCNTs such as agglomeration, poor dispersion, and alignment. This process produced f-SWCNTs doped nanofiber mats. A series of tensile, three-point flexural, and Charpy impact tests showed that 30 vol% glass fiber composites reinforced with 0.5 wt% of randomly oriented nanofiber (RONFs) mats improved the properties of the hybrid composites compared to 0.1 wt%, 0.2 wt%, and 1 wt% RONFs mats reinforced glass fiber hybrid composites. The increase in properties for 0.5 wt% composites was attributed to the higher specific surface area and resistance to the relative slip within the interlaminar regions. The 0.5 wt% RONFs were then used to produce 0.5 wt% of continuous-aligned nanofiber (CANFs) mats, which showed improved mechanical properties compared to 0.5 wt% randomly oriented nanofiber (RONFs) mats reinforced hybrid composites. The CANFs mats with reduced diameter increased the tensile strength, flexural strength, and impact resistance by 4.71%, 17.19%, and 20.53%, respectively, as compared to the random nanofiber mats. The increase in properties could be attributed to the reduced diameter, controlled deposition, and alignment of the nanofibers. Further, the highest increase in mechanical properties was achieved by the addition of f-SWCNTs doped CANFs mats strengthened hybrid composites, and the increase was 30.34% for tensile strength, 30.18% for flexural strength, and 132.29% for impact resistance, respectively. This improvement in properties was made possible by orderly alignments of f-SWCNTs within the nanofibers. The SEM images further confirmed that auxiliary vertical electrodes (AVEs) reduced the diameter, improved the alignment and molecular orientation of the nanofibers, and thus helped to reinforce the f-SWCNTs within the nanomats, which improved the properties of the glass hybrid composites.
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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 | 0.001 |
| 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".