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Record W2156753932 · doi:10.1897/03-217

Noncovalent interactions between aromatic compounds and dissolved humic acid examined by nuclear magnetic resonance spectroscopy

2004· article· en· W2156753932 on OpenAlexaff
Myrna J. Simpson, André J. Simpson, Patrick G. Hatcher

Bibliographic record

VenueEnvironmental Toxicology and Chemistry · 2004
Typearticle
Languageen
FieldEnvironmental Science
TopicIsotope Analysis in Ecology
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
FundersDivision of Chemistry
KeywordsNuclear magnetic resonance spectroscopyChemistrySpectroscopyHumic acidNuclear magnetic resonanceEnvironmental chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

We examined the molecular-level interactions of aromatic compounds with a humic acid that was extensively characterized with one- and two-dimensional nuclear magnetic resonance (NMR) spectroscopy. Interactions of target compounds were evaluated by monitoring their NMR relaxation properties. Solid-state NMR revealed that the humic sample contains paraffinic carbon (31%), substituted aliphatic carbon (30%), and aromatic carbon (28%). The liquid-state experiments further identified amino acids, a range of carbohydrates, methoxylated aromatics (likely derived from lignin), and a series of aliphatic chains. The 13C spin-lattice relaxation time (T1) of site-specifically labeled naphthalene, 1-naphthol, and quinoline was found to decrease with increasing additions of humic acid. The 1H T1 values also were measured and demonstrated that the association with the humic acid was not specific because all the protons acquired the T1 value of the humic acid at the same rate. The lack of a chemical shift change and an increase in signal line broadening indicates that the interaction between these compounds and humic acid is noncovalent. These interactions were detected at low humic concentrations (5 mg C/L) and suggest that low concentrations of humic material, which are prevalent in both aquatic and terrestrial systems, will significantly affect the fate and transport of contaminants in the environment.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.528
Threshold uncertainty score0.997

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.004
GPT teacher head0.205
Teacher spread0.201 · 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

Citations46
Published2004
Admission routes1
Has abstractyes

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