Remolding of Cross-Linked Polyethylene Cable Waste: Thermal and Mechanical Property Assessment
Bibliographic record
Abstract
Plastic has entered in all our manufacturing commodities, i.e. household, medical, automotive, and aerospace. It is offsetting metal, glass and cellulosic containers and is becoming the material choice Formos disposable items. On the average 12% ofour MSWareplastics(LDPE, Polypropylene, PET, Polystyrene).As itscharacterizedwithahigh value waste streamand slowlydegradable, effortsin reprocessing and reducing itsnegative environmental impactare increasing.Aspolymerization of methane, it draws aparamount amount of fossil fueland whenitburnsits reactin enthalpyisequivalent to dieselcombustion(43 MJ/kg).In attempt torecycle LDPE that mildly cross-linkedfor cable manufacturing, this work explores thechanges of material properties following remolding, re-extruding/calendaring and injection.This waste can mount over120 tons annually from onecable industryof single production line.Thermal analysis of the plastic using the Differential Scanning Calorimetry (DSC) to inferthemelting and molding conditionsis carried first.Second, tensileand dynamicsamplespreparationisconductedfollowingshredding, sieving, and infusion/mixinginmini extruder and theHAAKE MiniJet IIinjected mold.Third,uniaxial staticand dynamictestsare carriedutilizing Instron tensileandthe dynamicDMA 8000machine. It was observed as the amount of waste infusion is increased the sample ductility and strength ismildlyreduced. Dynamic testsshowed that the molded XLPEhas ahigher viscosity than LDPE at phase shift of10.75°for XLPE compared to9.88°for LDPE.Finally, in the view of these results a Visco-elastic material model is inferred for the reproduction of experimental result sin static and dynamic loading conditions
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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 | 0.001 | 0.001 |
| 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".