Electro spun nanomats strengthened glass fiber hybrid composites: Improved mechanical properties using continuous nanofibers
Bibliographic record
Abstract
Hybrid multiscale fiber reinforced epoxy composite laminates were developed and characterized from woven glass fiber mats reinforced with nonwoven PAN nanofibers (diameters of 200 nm) produced via electrospinning. An electrospinning setup was designed and developed to produce continuous E‐spun nonwoven nanofiber (NWNF) mats, which were then used as interlaminar reinforcements for producing multiscale hybrid composites. A detailed study for producing E‐spun nanomats considering both solution and operation parameters was undertaken discussed and optimum spinning parameters were obtained. These parameters were used to produce the E‐spun NWNF mats, which were inserted into the interlaminar region of glass fiber reinforced epoxy laminates. The impact absorption energy, tensile strength, and flexural strength of the hybrid multiscale composites were found to increase with an increase in E‐spun NWNF mat weight fraction. These properties were compared to those of a conventional glass fiber composite laminates prepared from the neat epoxy resin with 32% glass fiber volume fraction. The effectiveness of the strengthening/toughening strategy formulated in this study indicates that the feasibility of using the E‐spun NWNF mat reinforcements for improving the mechanical properties of structured high‐performance composites. In addition, the present study provides motivation for the long‐term development of high‐strength high toughness bulk structural nanocomposites for broader engineering applications.
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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".