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Record W2329636685 · doi:10.1021/jf501638k

Analytical Method for the Determination of Trace Toxic Elements in Milk Based on Combining Fe<sub>3</sub>O<sub>4</sub> Nanoparticles Accelerated UV Fenton-like Digestion and Solid Phase Extraction

2014· article· en· W2329636685 on OpenAlexaff
Xi Ai, Li Wu, Mengni Zhang, Xiandeng Hou, Lu Yang, Chengbin Zheng

Bibliographic record

VenueJournal of Agricultural and Food Chemistry · 2014
Typearticle
Languageen
FieldChemistry
TopicAnalytical chemistry methods development
Canadian institutionsNational Research Council Canada
FundersProgram for New Century Excellent Talents in UniversityMinistry of Education of the People's Republic of ChinaNational Natural Science Foundation of China
KeywordsNitric acidExtraction (chemistry)Certified reference materialsChemistryDigestion (alchemy)Solid phase extractionDetection limitHydrochloric acidNanoparticleMagnetic nanoparticlesSample preparationChromatographyAnalytical Chemistry (journal)Materials scienceInorganic chemistryNanotechnology

Abstract

fetched live from OpenAlex

A UV Fenton-like digestion method was developed first time for a complete digestion of milk samples by using 1.6 g L(-1) Fe3O4 magnetic nanoparticles, 0.2% (v/v) nitric acid, and 6% (w/w) H2O2. During the digestion, the liberated As-, Sb-, and Bi-containing species were preconcentrated onto the surface of Fe3O4 magnetic nanoparticles, which were conveniently separated with a hand-held magnet and subsequently dissolved in hydrochloric acid prior to hydride generation atomic fluorescence spectrometric detection. Owing to the integration of UV Fenton-like digestion, solid phase extraction, and magnetic separation into a single step, the developed method significantly simplifies sample preparation steps and reduces chemical consumption and hazardous waste. Limits of detection of 0.0015, 0.0022, and 0.0025 μg L(-1) were obtained for As, Sb, and Bi, respectively, using a 50 mL milk sample. The method was applied to the determination of these elements in a Certified Reference Material and milk samples.

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 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 score0.038
Threshold uncertainty score0.733

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.030
GPT teacher head0.317
Teacher spread0.287 · 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

Citations28
Published2014
Admission routes1
Has abstractyes

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