Mechanical, thermal, rheological, and morphological characterization of polyolefin/activated attapulgite nanocomposites
Bibliographic record
Abstract
Abstract Linear low‐density polyethylene (LLDPE) and high‐density polyethylene (HDPE) are polyolefins widely used in the packaging sector. Seeking to improve the mechanical properties with good cost‐effectiveness, attapulgite (ATP) was chosen as a reinforcing filler for the polyolefins. ATP is a hydrated magnesium and aluminum clay mineral with a microfibrous morphology, and the purity of this filler depends on the deposit. ATP is associated with the presence of accessory minerals that need to be removed so as not to interfere with its final application. Thus, an ATP purification process was carried out through physical separation and chemical treatment with hydrogen peroxide (H2O2) and sulfuric acid (H2SO4). This purification process despite having a low yield and is very effective in reducing impurities and organic matter. This ATP was named ATPa. LLDPE/ATPa and HDPE/ATPa nanocomposites with the addition of 1, 3, and 5 wt% of ATPa were prepared by extrusion and hot pressing. The mechanical properties (Shore D hardness, tensile tests, and Izod impact strength), thermal properties (differential scanning calorimetry—DSC and thermogravimetric analysis—TGA), X‐ray diffraction, rheological, and transmission scanning microscopy (TEM) were determined for these nanocomposites. The mechanical properties of the nanocomposites increased with the addition of ATPa. HDPE/ATPa nanocomposites showed more promise than LLDPE/ATPa nanocomposites. The addition of 5 wt% ATPa increased the tensile strength by 14% for the HDPE matrix and 5% for the LLDPE matrix and increased the elastic modulus by 46% for HDPE and 26% for LLDPE.
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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".