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Record W3126226105 · doi:10.1021/acs.est.0c07545

Environmental Fate Modeling of Nanoplastics in a Salinity Gradient Using a Lab-on-a-Chip: Where Does the Nanoscale Fraction of Plastic Debris Accumulate?

2021· article· en· W3126226105 on OpenAlexaff
Zélie Venel, Hervé Tabuteau, Alice Pradel, Pierre‐Yves Pascal, Bruno Grassl, Hind El Hadri, Magalie Baudrimont, Julien Gigault

Bibliographic record

VenueEnvironmental Science & Technology · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicMicroplastics and Plastic Pollution
Canadian institutionsUniversité Laval
FundersAgence Nationale de la Recherche
KeywordsMicroscale chemistryMangroveSalinityEnvironmental sciencePollutionPlastic pollutionSedimentationDebrisSedimentEnvironmental chemistryChemistryEcologyMicroplasticsGeologyOceanography

Abstract

fetched live from OpenAlex

The aim of this study is to demonstrate how the flow and diffusion of nanoplastics through a salinity gradient (SG), as observed in mangrove swamps (MSPs), influence their aggregation pathways. These two parameters have never yet been used to evaluate the fate and behavior of colloids in the environment, since they cannot be incorporated into classical experimental setups. Land-sea continuums, such as estuaries and MSP systems, are known to be environmentally reactive interfaces that influence the colloidal distribution of pollutants. Using a microfluidic approach to reproduce the SG and its dynamics, the results show that nanoplastics arriving in a MSP are fractionated. First, a substantial fraction rapidly aggregates to reach the microscale, principally governed by an orthokinetic aggregation process and diffusiophoresis drift. These large nanoplastic aggregates eventually float near the water's surface or settle into the sediment at the bottom of the MSP, depending on their density. The second, smaller fraction remains stable and is transported toward the saline environment. This distribution results from the combined action of the spatial salt concentration gradient and orthokinetic aggregation, which is largely underestimated in the literature. Due to nanoplastics' reactive behavior, the present work demonstrates that mangrove and estuarine systems need to be better examined regarding plastic pollution.

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 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 score0.326
Threshold uncertainty score0.789

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 studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.011
GPT teacher head0.221
Teacher spread0.210 · 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

Citations39
Published2021
Admission routes1
Has abstractyes

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