Reinforcement of nylon 6 with functionalized silica nanoparticles for enhanced tensile strength and modulus
Bibliographic record
Abstract
Pristine and functionalized silica (SiO(2)) nanoparticles were dispersed into nylon 6 and drawn into filaments through melt extrusion. The loading fraction of particles in both cases was 1.0 wt%. Fourier transform infrared (FTIR) studies revealed that reinforcement of pristine silica nanoparticles enhances the bond strength of each of the three basic bonds of nylon 6 namely, hydroxyl, amide, and carbonyl. As a result, the improvement over neat nylon in strength and modulus was 36% and 28% respectively, without any loss of fracture strain (80%). A silane coupling agent was then used through wet chemical treatment to functionalize silica nanoparticles. Functionalization induced an additional covalent Si-O-Si (siloxane) bond between silica particles and nylon backbone polymer while the enhancement in the basic bonds was retained. FTIR and x-ray photoelectron spectroscopy (XPS) studies confirmed the formation of the siloxane bond. This added chemical bond resulted in 76% and 55% improvement in tensile strength and modulus, and still retained 30% fracture strain. Calculation of the upper bound on Young's modulus indicates that one can reach within 5% of the bound with pristine silica particles, but it is exceeded by 15% when particles are functionalized.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".