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Record W4387184073 · doi:10.1515/pac-2022-0401

Analytical chemistry of engineered nanomaterials: Part 2. analysis in complex samples (IUPAC Technical Report)

2023· article· en· W4387184073 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

VenuePure and Applied Chemistry · 2023
Typearticle
Languageen
FieldChemistry
TopicAnalytical chemistry methods development
Canadian institutionsUniversité de MontréalNational Research Council Canada
FundersInternational Union of Pure and Applied Chemistry
KeywordsChemistryChemical nomenclatureNanomaterialsComplex matrixNanotechnologyCharacterization (materials science)Sample preparationBiochemical engineeringEnvironmental chemistryChromatographyOrganic chemistryEngineeringMaterials science

Abstract

fetched live from OpenAlex

Abstract 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)0.001
metaresearch head score (Gemma)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.047
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.038
GPT teacher head0.302
Teacher spread0.265 · 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 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

Citations15
Published2023
Admission routes1
Has abstractyes

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