Effect of environmental temperature and semi‐crystalline order on the toughening of polyamide 1010 by <scp>2D</scp> nanomaterials
Bibliographic record
Abstract
Abstract By incorporating nanomaterials into polymer matrices, nanocomposites can be produced with enhanced properties, combining the ease of processing thermoplastics with the superior physical characteristics of nanoparticles. In this study, fully bio‐based polyamide 1010 was used as the polymer matrix, with graphene oxide (GO), hexagonal‐boron nitride (h‐BN), and molybdenum disulfide (MoS 2 ), both individually and in hybrids, serving as fillers. The tensile behavior of these nanocomposites was evaluated at room temperature and −40 °C, along with their morphology and microstructure. Results showed that the nanomaterials slightly shifted the polymer's crystallization temperature upward, indicating a small nucleating effect, but also hindered the development of crystalline domains, reducing the crystallization kinetics. Despite no change in the final crystalline form, nanocomposites with h‐BN and MoS 2 showed lower microstructural order as evidenced by XRD. Regarding tensile behavior, GO provided the greatest toughening at room temperature due to its larger lateral dimensions and good chemical affinity with the matrix. However, at low temperatures, h‐BN‐based nanocomposites maintained the toughening effect better than GO‐based ones. This can be attributed to the lower order of the polymer's semi‐crystalline structure promoted by h‐BN, allowing greater energy dissipation. Surprisingly, hybrid fillers did not exhibit synergistic effects, with one nanomaterial hampering the effect of the other. However, SEM analysis indicated that the fracture mechanisms of the nanocomposites remained unchanged from the neat polymer, which makes them interesting options for applications that require desirable mechanical properties at a wide temperature range. Highlights GO showed the best toughening of polyamide 1010 at room temperature. Toughening at room temperature is mainly due to nanomaterials physical traits. Most nanofillers lowered polyamide's overall microstructural order. Toughening at −40 °C is mainly due to lower microstructural order.
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 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 | 0.001 | 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) | 0.000 | 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".