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Record W4387210056 · doi:10.1002/anie.202309949

Solvent‐Free Depolymerization of Plastic Waste Enabled by Plastic‐Catalyst Interfacial Engineering

2023· article· en· W4387210056 on OpenAlexaff
Xiaoshen Bai, Divakar R. Aireddy, Amitava Roy, Kunlun Ding

Bibliographic record

VenueAngewandte Chemie International Edition · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicMicroplastics and Plastic Pollution
Canadian institutionsAdvanced Micro Devices (Canada)
Fundersnot available
KeywordsDepolymerizationCatalysisPolyethylene terephthalateMaterials sciencePolyethylenePolymerSolventChemical engineeringOrganic chemistryPolymer chemistryChemistryComposite material

Abstract

fetched live from OpenAlex

Depolymerization of condensation polymers by chemolysis often suffers from the large usage of solvents and homogeneous catalysts such as acids, bases, and metal salts. The catalytic efficiency of heterogeneous catalysts is largely constrained by the poor interfacial contact between solid catalysts and solid plastics below melting points. We report here our discovery of autogenous heterogeneous catalyst layer on polyethylene terephthalate surfaces during the generally believed homogeneous catalytic depolymerization process. Inspired by the "contact mass" concept in industrial chlorosilane production, we further demonstrate that the construction of plastic-catalyst solid-solid interfaces enables solvent-free depolymerization of polyethylene terephthalate by vapor phase methanolysis at relatively low temperatures. Trace amounts of earth-abundant element (zinc) introduced by electrostatic adsorption is sufficient for catalyzing the depolymerization. The concept of plastic-catalyst contact mass interfacial catalysis might inspire new pathways for tackling plastic waste problems.

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 categoriesnone
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 score0.186
Threshold uncertainty score0.966

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 head0.007
GPT teacher head0.197
Teacher spread0.191 · 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 designBench or experimental
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

Citations48
Published2023
Admission routes1
Has abstractyes

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