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Record W3026470093 · doi:10.3390/su12104231

Biodegradation of Bioplastic Using Anaerobic Digestion at Retention Time as per Industrial Biogas Plant and International Norms

2020· article· en· W3026470093 on OpenAlexafffund
Ankita Shrestha, Mieke C. A. A. Van Eerten-Jansen, Bishnu Acharya

Bibliographic record

VenueSustainability · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicMicroplastics and Plastic Pollution
Canadian institutionsUniversity of Prince Edward Island
FundersMitacs
KeywordsBioplasticBiodegradationBiogasAnaerobic digestionWaste managementPulp and paper industryEngineeringChemistryMethaneOrganic chemistry

Abstract

fetched live from OpenAlex

Bioplastics are gaining interest as an alternative to fossil-based plastics. In addition, biodegradable bioplastics may yield biogas after their use, giving an additional benefit. However, the biodegradability time in international norms (35 days) far exceeds processing times in anaerobic digestion facilities (21 days). As the bioplastic packaging does not indicate the actual biodegradability, it is important to understand the time required to biodegrade bioplastic if it ends up in the anaerobic digestion facility along with other organic waste. For this work, cellulose bioplastic film and polylactic acid (PLA) coffee capsules were digested anaerobically at 55 ℃ for 21 days and 35 days, which are the retention times for industrial digestors and as set by international norms, respectively. Different sizes of bioplastics were examined for this work. Bioplastic film produced more biogas than bioplastic coffee capsules. The biodegradability of bioplastic was calculated based on theoretical biogas production. With an increase in retention time, biogas production, as well as biodegradability of bioplastic, increased. The biodegradability was less than 50% at the end of 35 days for both bioplastics, suggesting that complete degradation was not achieved, and thus, the bioplastic would not be suitable for use in biogas digesters currently in use.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.425
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.022
GPT teacher head0.225
Teacher spread0.203 · 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.

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

Citations77
Published2020
Admission routes2
Has abstractyes

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