Evidence map›Paper›PMID 38569038›Full record

ArticleScience advances2024

Magnetic resonance insights into the heterogeneous, fractal-like kinetics of chemically recyclable polymers.

Sophia N Fricke, Shira Haber, Mutian Hua, Mia Salgado, Brett A Helms, Jeffrey A Reimer

Abstract read
In one paragraph

Article in Science advances, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Sophia N FrickeDepartment of Chemical and Biomolecular Engineering, University of California, Berkeley, Berkeley, CA 94720, USA.ORCID 0000-0002-0183-466X
Shira HaberMaterials Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.ORCID 0009-0003-6916-4871
Mutian HuaMaterials Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.ORCID 0000-0002-4079-316X
Mia SalgadoDepartment of Chemical and Biomolecular Engineering, University of California, Berkeley, Berkeley, CA 94720, USA.
Brett A HelmsMaterials Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.ORCID 0000-0003-3925-4174
Jeffrey A ReimerDepartment of Chemical and Biomolecular Engineering, University of California, Berkeley, Berkeley, CA 94720, USA.ORCID 0000-0002-4191-3725

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Moving toward a circular plastics economy is a vital aspect of global resource management. Chemical recycling of plastics ensures that high-value monomers can be recovered from depolymerized plastic waste, thus enabling circular manufacturing. However, to increase chemical recycling throughput in materials recovery facilities, the present understanding of polymer transport, diffusion, swelling, and heterogeneous deconstruction kinetics must be systematized to allow industrial-scale process design, spanning molecular to macroscopic regimes. To develop a framework for designing depolymerization processes, we examined acidolysis of circular polydiketoenamine elastomers. We used magnetic resonance to monitor spatially resolved observables in situ and then evaluated these data with a fractal method that treats nonlinear depolymerization kinetics. This approach delineated the roles played by network architecture and reaction medium on depolymerization outcomes, yielding parameters that facilitate comparisons between bulk processes. These streamlined methods to investigate polymer hydrolysis kinetics portend a general strategy for implementing chemical recycling on an industrial scale.

Identifiers

PMID38569038
PMCPMC10990270

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.