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Record W4392597507 · doi:10.5194/egusphere-egu24-1433

Release and characterization of micro- and nano-plastic particles from different types of macro-plastics

2024· preprint· en· W4392597507 on OpenAlexaff
Tao Cheng, Somayeh Saliminasab

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicMicroplastics and Plastic Pollution
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsMacroNano-Characterization (materials science)Polymer scienceMaterials scienceNanotechnologyComposite materialComputer science

Abstract

fetched live from OpenAlex

In the environment, plastics are exposed to weathering processes such as mechanical cutting and abrasion, chemical and biological degradation, as well as UV radiation and heat. These processes breakdown larger plastics into smaller pieces and alter the physical and chemical properties of plastics. Most environment micro- and nano-plastics are generated via weathering of larger plastics. Micro- and nano-plastics are often more mobile, bioavailable, and toxic than their larger counterparts due to their smaller size. As a result, contamination of micro- and nano-plastics has become an increasing concern. Although many laboratory studies have been conducted on micro- and nano-plastics to understand their behavior in the environment, most studies were conducted using synthesized, mono-dispersed, polystyrene micro-spheres as surrogate for micro- and nano-plastics in the environment. The polystyrene micro-spheres, however, do not represent well the complex and diverse composition, size, shape, and other physiochemical properties of real-world micro- and nano-plastics. The objective of our research is to fill the gap by studying the micro- and nano-plastics released from macro-plastics including polystyrene (PS), high-density and low-density polyethylene (HDPE and LDPE), polypropylene (PP), and nylon under laboratory-controlled conditions. Plastic sheets or pellets were cut into small pieces, mixed with nano-pure water, heated, and filtered through 1 um membrane to collect fine plastics. Some macro-plastics were also “weathered” using UV radiation or high temperature. Particle concentration measurement showed that substantial quantities of fine plastics (~ 5*10^9 particles/mL) were released from PP and LDPE macro-plastics, moderate quantities were released from PS macro-plastics (~5*10^8 particles/mL), and practically no fine plastics were released from nylon or HDPE. SEM results indicated the fine plastic particles were of irregular shape and poly-dispersed with a size-range of ~100 to 400 nm, while the polystyrene micro-spheres were of spherical shape with a uniform diameter of 100 nm. Zeta-potential of LDPE fine plastics in 3 mM NaCl solution at pH 5 was ~-42 mV, more negative than those of polystyrene micro-spheres (~-25 mV). This study highlights the distinct properties of manufactured polystyrene micro-spheres and fine plastics released from macro-plastics. Results from our study suggest fine plastics released from macro-plastics may better represents the properties of micro- and nano-plastics in the environment.

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 head0.007
GPT teacher head0.189
Teacher spread0.182 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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