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Record W2549765291 · doi:10.1016/j.carbon.2016.11.011

NanoRelease: Pilot interlaboratory comparison of a weathering protocol applied to resilient and labile polymers with and without embedded carbon nanotubes

2016· article· en· W2549765291 on OpenAlexaff
Wendel Wohlleben, Christopher T. Kingston, Janet Carter, Endalkachew Sahle‐Demessie, Socorro Vázquez‐Campos, Brad Acrey, Chia-Ying Chen, Ernest Walton, Heiko Egenolf, Philipp Müller, Richard G. Zepp

Bibliographic record

VenueCarbon · 2016
Typearticle
Languageen
FieldMaterials Science
TopicNanoparticles: synthesis and applications
Canadian institutionsNational Research Council Canada
FundersNational Chung-Hsing University
KeywordsMaterials sciencePolymerCarbon nanotubeWeatheringMatrix (chemical analysis)TransferabilityInductively coupled plasmaComposite materialChemical engineeringNanotechnologyComputer scienceGeology

Abstract

fetched live from OpenAlex

A major use of multi-walled carbon nanotubes (MWCNTs) is as functional fillers embedded in a solid matrix, such as plastics or coatings. Weathering and abrasion of the solid matrix during use can lead to environmental releases of the MWCNTs. Here we focus on a protocol to identify and quantify the primary release induced by weathering, and assess reproducibility, transferability, and sensitivity towards different materials and uses. We prepared 132 specimens of two polymer-MWCNT composites containing the same grade of MWCNTs used in earlier OECD hazard assessments but without UV stabilizer. We report on a pilot inter-laboratory comparison (ILC) with four labs (two US and two EU) aging by UV and rain, then shipping for analysis. Two labs (one US and one EU) conducted the release sampling and analysis by Transmission Electron Microscopy (TEM), Inductively Coupled Plasma- Mass Spectrometry (ICP-MS), UltravioleteVisible Spectroscopy (UVeVis), Analytical Ultracentrifugation (AUC), and Asymmetric Flow Field Flow Fractionation (AF4). We compare results between aging labs, between analysis labs and between materials. Surprisingly, we found quantitative agreement between analysis labs for TEM, ICP-MS, UVeVis; low variation between aging labs by all methods; and consistent rankings of release between TEM, ICP-MS, UVeVis, AUC. Significant disagreement was related primarily to differences in aging, but even these cases remained within a factor of two.

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 score0.015
Threshold uncertainty score0.375

Codex and Gemma teacher scores by category

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

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 head0.015
GPT teacher head0.273
Teacher spread0.259 · 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

Citations66
Published2016
Admission routes1
Has abstractyes

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