Investigation of Mechanical, Thermal, and Surface Properties of LDPE/TPS/Cellulose Additives Composites towards Using in Packaging Application
Bibliographic record
Abstract
The widespread use of petroleum products and non-biodegradable materials in packaging has caused serious damage to the environment.The growing demand for durable packaging has encouraged researchers to explore non-toxic, compatible, and biodegradable materials.Cellulose as organic natural sustainable polymer became more usable in the fields of medical and environmental applications.In this work, the mechanical, thermal history, and water absorption characteristics of low-density polyethylene LDPE / thermoplastic starch (TPS) composite materials reinforced with different cellulose materials, including 2.5 and 5% of each of sawdust, powder cellulose, and crystalline nanocellulose CNC are examined.In order to assure well dispersion of Cellulose in composites, initially cellulose was added to TPS and then, blending in twinscrew extruder with LDPE at 190℃, and 50 rpm.Thermal history, tensile strength, Young modulus and contact angle were examined for the samples.Numerically simulation using material design-Ansys version 2021 software based on the RVE model was applied to check and validate the mechanical properties with experimental test.The experimental results show that the tensile strength, Young modulus and hardness were increased in general with the increasing of cellulose additives compared with LDPE/TPS and the 5% cellulose indicates significant increasing.The melting point, degree of crystallinity, and enthalpy increase with the increasing of cellulose additions in the DSC data, which supports the reading of tensile strength.The contact angle decreases, and water absorption increases with increasing of cellulose, but it remains within the acceptable percentage compared to LDPE/TPS.On the other hand, the electronic scanning images showed the surface characteristics and the adhesive interference of the samples, the microstructural gave a semi-homogeneous images and no significant cellulose agglomeration or cracks were observed.The numerical results proved a comfortable match with the experimental results regarding the Young's modulus and there was acceptable agreement with hardness, water absorption and melting point of the previous studies.
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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".