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Record W6920581580 · doi:10.60692/kvcs6-zj948

Characterization of Polyethylene Carrying Bags Before and After Isothermal Oxidative Aging in an Oven

2001· article· en· W6920581580 on OpenAlexaffabout

Bibliographic record

VenueGreater South Information System · 2001
Typearticle
Languageen
FieldMathematics
TopicStatistical Distribution Estimation and Applications
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsCrystallinityPolyethyleneIsothermal processThermogravimetric analysisDegradation (telecommunications)Sample (material)Activation energyAccelerated aging

Abstract

fetched live from OpenAlex

Utility of polymeric material is a major contribution to the production of waste, particularly in Pakistan. An easy escape to it is the damping in the land which is not commendable for an environmental point of view. On the other hand, the aging of polymer is analogous to its burial conditions under the soil in the absence of light. Therefore, in this research report, two different brands of polyethylene carrying bags were investigated. One sample was obtained from Pakistan abbreviated as sample `Y` while the other from Canada abbreviated as `E`. In order to accelerate the degradation process and to observe the impact of aging in a shorter span of time, these samples were heated at an elevated temperature (80�C) in an oven for the period of 20 days. The samples were characterized before and after aging with an interval of 2 days by applying different techniques like FT-IR, SEM, DSC, and thermogravimetric analysis (TGA). Carbonyl peak at 1715 cm-1 was observed only in the case of sample `E` displaying carbonyl index value as 28.45 % after 20 days of aging. The SEM images before and after aging revealed that the degradation took place at preferential sites in case of sample `Y` and at numerous sites in case of sample `E`. The results of percent crystallinity obtained by DSC showed an increasing pattern with aging for both the samples and was high in case of sample `E.` The activation energy determined by using Flynn-Wall-Ozawa showed a decreasing pattern for both the samples with aging. It concluded that the thermal aging initiates the process of degradation which was then accelerated by heating in TGA oven. The order of reaction was slightly decreased after aging for both the samples and was found to be independent of the heating rate.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.260
Threshold uncertainty score0.437

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.040
GPT teacher head0.278
Teacher spread0.238 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2001
Admission routes2
Has abstractyes

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