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Record W1981957804 · doi:10.1021/ac030005p

Kinetics of Metal−Fulvic Acid Complexation Using a Stopped-Flow Technique and Three-Dimensional Excitation Emission Fluorescence Spectrophotometer

2003· article· en· W1981957804 on OpenAlexaffabout
Fengchang Wu, Ryan Mills, R. Douglas Evans, Peter Dillon

Bibliographic record

VenueAnalytical Chemistry · 2003
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal ecosystems
Canadian institutionsTrent University
Fundersnot available
KeywordsChemistryFluorescenceKineticsMetalAnalytical Chemistry (journal)Kinetic energyFluorescence spectrometryFluorescence spectroscopyChromatographyOrganic chemistry

Abstract

fetched live from OpenAlex

A stopped-flow technique and three-dimensional excitation emission (Ex/Em) fluorescence spectrophotometer were used to detect the full fluorescence spectral kinetic changes that occurred during the complexation between fulvic acid (FA) and several metals [Cu(II), Ni(II), Co(II), Cd(II) and Ca(II)]. The study was carried out with a fulvic acid isolated from Cavan Bog, Canada. At pH 7, the FA reacted rapidly with all metals studied. Two major kinetically distinguishable binding sites on FA (“fast” and “slow”), having reaction half-lives of 1.3−3.9 and 34.7−69.3 s, respectively, were identified using pseudo-first-order kinetic plots. Kinetic changes of Ex and Em wavelengths of the fluorescence maximums also indicate two major binding sites. For the fast-reacting binding site, the rate constant and the site relative contribution were as follows, Cu 2+ > Ni 2+ > Co 2+ > Cd 2+ > Ca 2+, which agrees with the Irving−Williams series, indicating that complexation kinetics are affinity dependent. Within each kinetic phase, both Ex and Em wavelengths of fluorescence maximums increased with time, indicating the occurrence of structural changes during the binding process. Based on the results obtained, the use of full fluorescence spectra appears to be a promising tool for further understanding metal−FA complexation mechanisms.

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 categoriesInsufficient 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 score0.448
Threshold uncertainty score1.000

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)0.0010.000

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 head0.019
GPT teacher head0.228
Teacher spread0.209 · 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

Citations65
Published2003
Admission routes2
Has abstractyes

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