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Record W4310364551 · doi:10.3389/fmars.2022.1046179

Photodegradation of biobased polymer blends in seawater: A major source of microplastics in the marine environment

2022· article· en· W4310364551 on OpenAlexaff
Shasha Zhao, Liuqingqing Liu, Chenguang Li, Hao Zheng, Yadan Luo, Lihua Pang, Qianhui Lin, Hongyu Zhang, Cuizhu Sun, Lingyun Chen, Fengmin Li

Bibliographic record

VenueFrontiers in Marine Science · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicMicroplastics and Plastic Pollution
Canadian institutionsUniversity of Alberta
FundersNational Natural Science Foundation of China
KeywordsPhotodegradationPolylactic acidPolypropyleneMaterials sciencePolymer blendPolymerThermoplasticMicroplasticsStarchPolyethyleneChemical engineeringPolymer chemistryPolycarbonateComposite materialOrganic chemistryChemistryPhotocatalysisCopolymerEnvironmental chemistry

Abstract

fetched live from OpenAlex

Introduction Biobased polymer blends have been recommended as an eco-friendly solution to abate plastic pollution in the environment. However, the formation of microplastics (MPs) by photodegradation of biobased polymer blends in the marine environment is still not well understood. In this study, we investigated the formation of MPs and the changes in the physicochemical properties of three types of biobased polymer blends after photodegradation in seawater. Methods The investigated materials included non-biodegradable polyethylene/ thermoplastic starch blends (PE/TPS) and polypropylene/thermoplastic starch blends (PP/TPS), as well as biodegradable polylactic acid/poly (butylene adipate-co-terephthalate)/thermoplastic starch blends (PLA/PBAT/TPS). The control groups were the corresponding neat polymers, including polyethylene (PE), polypropylene (PP), and polylactic acid (PLA). Results The size distribution of the pristine and aged MPs indicated that the polymer blends were more likely to produce small-sized particles after photodegradation due to their poorer mechanical properties and lower resistance to UV irradiation than the neat polymers. Noticeable surface morphology alterations, including cracks, holes, and pits, were observed for polymer blends after photodegradation, while neat polymers were relatively resistant. After photodegradation, the attenuated total reflection Fourier transformed infrared spectroscopy (ATR-FTIR) spectrum of the polymer blends showed a significant decrease in the characteristic bands of thermoplastic starch (TPS), indicating depletion of their starch fractions. The C1s spectra of the polymer blends demonstrated that the aged MPs contained fewer -OH groups than the pristine MPs, further confirming the photodegradation of TPS. The molecular weight distribution curve of the polymer blends shifted significantly towards low molecular weight, suggesting the occurrence of chain scission during photodegradation. These results indicate that the polymer blends have a higher degree of photodegradation than neat polymers, and thereby generate more small-sized MPs than neat polymers. Photodegradation caused changes in the contact angle and surface charge of MPs derived from biobased polymer blends, which may affect the vector effects of MPs on any coexisting pollutants. Discussion In summary, polymer blends may pose a higher risk to the marine environment than neat polymers, and caution should be taken in promoting biobased polymer blends.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.004
GPT teacher head0.177
Teacher spread0.173 · 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 source (direct Gemma or distilled Codex), 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

Citations13
Published2022
Admission routes1
Has abstractyes

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