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Record W4233277746 · doi:10.26434/chemrxiv-2021-n3c99

Cadmium-Containing Quantum Dots Used in Electronic Displays: Implications for Toxicity and Environmental Transformations

2021· preprint· en· W4233277746 on OpenAlexafffund
Aude Béchu, Jeffrey Liao, Chang Huang, Chany Ahn, Maureen McKeague, Subhasis Ghoshal, Audrey Moores

Bibliographic record

VenueChemRxiv · 2021
Typepreprint
Languageen
FieldMaterials Science
TopicQuantum Dots Synthesis And Properties
Canadian institutionsMcGill University
FundersFonds de recherche du Québec – Nature et technologiesCanada Foundation for InnovationNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsMcGill University
KeywordsQuantum dotDissolutionContext (archaeology)CadmiumNanoparticleNanotechnologyCoatingMaterials sciencePolymerCore (optical fiber)ChemistryMetallurgyOrganic chemistry

Abstract

fetched live from OpenAlex

Cadmium-containing quantum dot nanoparticles (QDs) are integrated into electronic displays because of their ability to efficiently convert colors. There are conflicting accounts as to whether these particles present a hazard to the environment, as they have been studied either as (1) embedded QDs in display screen films or (2) as model QDs with small, hydrophilic ligands. We synthesized QDs featuring the core-shell structure and the thick polymer coating present in commercial devices, to probe their fate under low pH conditions. The role of dioxygen was also explored in this context. We also used liver cells to compare the toxicity of pristine QDs to those subjected to acid dissolution.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.000
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 head0.031
GPT teacher head0.257
Teacher spread0.227 · 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 designTheoretical or conceptual
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
Published2021
Admission routes2
Has abstractyes

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