MétaCan
Menu
Back to cohort
Record W4224083011 · doi:10.1002/sus2.55

Sustainable chemical upcycling of waste polyolefins by heterogeneous catalysis

2022· article· en· W4224083011 on OpenAlex
Mingyu Chu, Wei-Lin Tu, Shuangqiao Yang, Congyang Zhang, Qingye Li, Qiao Zhang, Jinxing Chen

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSusMat · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicMicroplastics and Plastic Pollution
Canadian institutionsWestern University
FundersNational Natural Science Foundation of China
KeywordsPolyolefinCatalysisChemical industryBiochemical engineeringStatus quoMaterials scienceWaste managementNanotechnologyChemistryOrganic chemistryEngineeringEconomics

Abstract

fetched live from OpenAlex

Abstract The mass production of disposable polyolefin products has led to serious plastic pollution and an imbalance between manufacturing and recycling. Given these challenges, the chemical upcycling of waste polyolefins has attracted extensive attention due to its high efficiency and economic benefits. Herein, we review the development of polyolefin chemical upcycling in heterogeneous catalysis. The status quo of polyolefin recycling is first discussed. We then introduce the advanced strategies for chemical upcycling in the view of different value‐added products and discuss their challenges and prospects. Our in‐depth analysis centers on the catalytic mechanism and the design principle of heterogeneous catalysts. Finally, we outlook the promising directions to facilitate the degradation process via polymer and catalyst design and optimized catalytic engineering. Innovative strategies are expected to promote the chemical upcycling of polyolefins, bringing great promise for the sustainable development of society.

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.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.048
Threshold uncertainty score0.998

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.003
GPT teacher head0.179
Teacher spread0.175 · 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