The Effect of Cellulose Nanocrystals (CNC) on Isothermal Crystallization Kinetics of LLDPE and HDPE
Bibliographic record
Abstract
Abstract Highly porous agglomerates of spray freeze dried cellulose nanocrystals (SFD-CNC) were prepared, starting with sonicated aqueous suspensions of spray-dried cellulose nanocrystals powder (SD-CNC). Subsequently, SFD-CNC together with the SD-CNC (used as a reference) were incorporated into LLDPE and HDPE via melt compounding in a batch mixer to produce nanocomposites containing 0.5 wt.° and 2 wt.° CNC. Differential scanning calorimetry (DSC) was used to study the thermal properties and the isothermal crystallization kinetics of the polyethylenes and the nanocomposites. Polarized light microscopy (PLM) was used to evaluate the growth kinetics and spherulitic structure of polyethylene in both the filled and unfilled polymers. Avrami crystallization kinetics models were employed to analyze the DSC results. It was observed that CNC acts as a heterogeneous nucleating agent in LLDPE nanocomposites, thus yielding nucleation controlled crystallization. On the other hand, in the HDPE systems (polymer and nanocomposites) heterogeneous nucleation was followed by 3-D growth. It was observed that CNC slightly hindered the formation of chain folding for the HDPE, similar to previous studies on the polypropylene and its nanocomposites. Spray freeze drying produced twice as many nucleation sites compared to spray dried samples and it enhanced the overall crystallization rate and the crystallinity.
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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 | 0.001 |
| 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".