MétaCan
Menu
Back to cohort
Record W4392709572 · doi:10.1515/iupac.95.0006

Analytical Chemistry of Engineered Nanomaterials: Part 2. Analysis in Complex Samples

2024· dataset· en· W4392709572 on OpenAlexaff
Ján Labuda, Jiřı́ Barek, Zuzana Gajdosechova, Silvana do Couto Jacob, Linda J. Johnston, Petra Krystek, Zoltán Mester, Josino Costa Moreira, Veronika Svitková, Kevin J. Wilkinson

Bibliographic record

VenueIUPAC Standards Online · 2024
Typedataset
Languageen
FieldChemistry
TopicInorganic and Organometallic Chemistry
Canadian institutionsUniversité de MontréalNational Research Council Canada
Fundersnot available
KeywordsNanomaterialsNanotechnologyChemistryMaterials science

Abstract

fetched live from OpenAlex

Recently, the scope, regulation, legislation, and metrology of the analytical chemistry of engineered nanomaterials (ENMs) have been reviewed in the Part 1 of the IUPAC Technical Report. Chemical analysis of nanomaterials in complex sample matrices presents a substantial challenge for analytical science and regulatory agencies. The purpose of the present Part 2 is to discuss the detection, characterization, and quantification of nanomaterials in samples of complex matrices including methods for sample preparation and fitness for purpose. Analytical methods applied to analysis in matrices of environmental samples, food, cosmetics, and biological samples as well as those used to monitor the fate of ENMs in the environment and biological systems are reported. Tables of numerous recently published works on analyses of typical ENMs with detailed protocols and conclusive comments are presented. There is a rapid development in the field mostly in the stage of accumulation of factual material. The single-particle inductively coupled plasma mass spectrometry is already widely used at the chemical analysis of metal-containing nanoparticles.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.105
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.1050.000

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 head0.020
GPT teacher head0.353
Teacher spread0.333 · 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 designNot applicable
Domainnot available
GenreDataset

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

Explore more

Same venueIUPAC Standards OnlineSame topicInorganic and Organometallic ChemistryFrench-language works237,207