Bibliographic record
Abstract
There has been a recent drive in many industries to replace legacy metallic components with lightweight fiber reinforced polymer composites. Thermoplastic matrix composites are becoming increasingly more prominent due to their inherent benefits such as the ability to be re-melted and re-shaped or recycled, fatigue and impact resistance, and corrosion resistance. Macromechanical properties of fiber reinforced polymers depend on a variety of factors from the mechanical properties of the fibers and matrix, the geometrical properties of the reinforcing fibers, voids and cracks in the composite, and the strength of the fiber-matrix adhesion. The fiber-matrix adhesion governs the effectiveness of the transfer of stress from the typically weak polymer matrix to the reinforcing fibers. Poor fiber-matrix interfacial shear strengths (IFSS) can lead to premature debonding of the fiber from the matrix which allows for the propagation of micro-cracks throughout the material, ultimately leading to failure. Various micromechanical tests, such as single-fiber pullout, have been developed to directly measure the adhesive strength between fibers and polymers. In this research, a novel single-fiber pullout test methodology has been utilized to measure the fiber-matrix adhesion for a variety of industrially relevant fiber reinforced thermoplastic composites. Carbon fiber proved to have greater adhesion to thermoplastic polyester elastomers than poly(p-phenylene-2,6-benzobisoxazole) (PBO) fibers due to their increased roughness and chemical functionality. Carbon content at higher binding energies of glass fiber sizings correlated to increased IFSS with polyketone matrices. Modification of high-density polyethylene (HDPE) with maleic anhydride is effective to improve adhesion to both carbon and glass fibers, and the effect of carbon fiber sizing to the IFSS was weaker than matrix modification. Surface texturing of carbon fiber with graphene nanoplatelets led to increased adhesion to HDPE matrices through mechanical interlocking effects due to increased fiber surface roughness. Fiber-matrix adhesion was found to decrease with increasing temperature due to the reduction of the compressive stresses at the fiber-matrix interface formed from polymer shrinkage during cooling. In general, the chemical properties of the matrix were the most critical factor to adhesion. The results of this research can be used to further the development of fiber reinforced thermoplastics.
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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 | 0.001 | 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 | 0.001 |
| 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".