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Record W4413221985 · doi:10.1021/acs.estlett.5c00630

Biocalcification Induces the Preferential Settling of Small Buoyant Microplastics in Freshwater

2025· article· en· W4413221985 on OpenAlexaff
Yang Li, Dongyu Xu, Ke Sun, Baoshan Xing, Bo Gao

Bibliographic record

VenueEnvironmental Science & Technology Letters · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicMicroplastics and Plastic Pollution
Canadian institutionsWater Security Agency
FundersNational Science Fund for Distinguished Young ScholarsChina Institute of Water Resources and Hydropower ResearchNational Natural Science Foundation of China
KeywordsMicroplasticsSettlingEnvironmental scienceOceanographyFisheryBiologyGeologyEnvironmental engineering

Abstract

fetched live from OpenAlex

The vertical transport of microplastics (MPs) in freshwater is poorly understood. In this study, the effects of biocalcification on the settling behavior of buoyant MPs in the presence of Microcystis aeruginosa and Ca 2+ were investigated via incubation experiments. Here, we show that the formation of biogenic calcite on the surface of MPs resulted in an increase in density and, subsequently, the settling of MPs, with its effect varying significantly with the size and aging of MPs. Specifically, biogenic calcite preferentially facilitated the sinking of smaller MPs compared to larger ones, mainly due to their different adsorption affinities for extracellular polymeric substances (EPS), which provide nucleation sites for calcite. Notably, aging further enhanced this size-dependent settling of MPs, which was primarily attributable to differences in the selective binding of macromolecules in EPS. Compared to the pristine MPs, the aged ones tend to acquire more polar macromolecules from EPS, which resulted in greater complexation interactions between the MPs and Ca 2+ and consequently increased biogenic calcite precipitation and enhanced settling of MPs. The findings of this study highlight the importance of biogenic calcite-mediated settling of MPs in freshwater environments.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
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 score0.143
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.003
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.006
GPT teacher head0.193
Teacher spread0.187 · 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 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

Citations2
Published2025
Admission routes1
Has abstractyes

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