MétaCan
Menu
Back to cohort
Record W310472651

Analysis of organic matter at the soil-water interface by NMR spectroscopy: Implications for contaminant sorption processes

2009· article· en· W310472651 on OpenAlexaff
Myrna J. Simpson, André J. Simpson

Bibliographic record

VenueAGU Spring Meeting Abstracts · 2009
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNMR spectroscopy and applications
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSorptionMagic angle spinningChemistryOrganic matterSoil waterEnvironmental chemistrySoil organic matterCarbon-13 NMRNuclear magnetic resonance spectroscopyNMR spectra databaseMagic angleAdsorptionOrganic chemistrySoil scienceGeologySpectral line
DOInot available

Abstract

fetched live from OpenAlex

Contaminant sorption to soil organic matter (OM) is the main fate of nonionic, hydrophobic organic contaminants in terrestrial environments and a number of studies have suggested that both soil OM structure and physical conformation (as regulated by the clay mineral phase) govern contaminant sorption processes. A great deal of this evidence has come from macroscopic observations with contaminants and soil fractions as well as a recent mass balance approach where the sum of the parts exceeded the whole suggesting that the physical arrangement of OM in organo-mineral complexes may be more important than OM structure in sorption processes (1). In addition, recent studies with constructed organo-mineral complexes have suggested that aliphatic OM is preferred over aromatic moieties and suggests that clay minerals play an indirect role by governing the sorption of organic contaminants by controlling the surface accessibility of OM at the soil-water interface (2,3). To investigate this further, a number of soil samples were characterized by both solid-state 13C Cross Polarization Magic Angle Spinning (CPMAS) NMR and 1H High Resolution Magic Angle Spinning (HR-MAS) NMR. HR-MAS NMR is an innovative NMR method that allows one to examine samples that are semi-solid using liquid state NMR methods (ie: observe 1H which is more sensitive than 13C). With HR-MAS NMR, only those structures that are in contact with the solvent are NMR visible thus one can probe different components within a mixture using different solvents. The 1H HR-MAS NMR spectrum of a grassland soil swollen in water (D2O) is dominated by signals from alkyl and O-alkyl structures but signals from aromatic protons are negligible (the peak at 8.2ppm is attributed to formic acid). When the soil is swollen in DMSO-d6, a solvent which is more penetrating and capable of breaking hydrogen bonds, aromatic signals are visible suggesting that the aromatic structures are buried within the soil matrix and do not exist at the soil-water interface. The 13C solid-state NMR data confirms that aromatic carbon is present in substantial amounts (estimated at 40% of the total 13C signal) therefore, the lack of 1H aromatic signals in the HR-MAS NMR spectrum indicates that aromatic structures are buried and that the soil-water interface is dominated by aliphatic chains, carbohydrates, and peptides. The NMR data indicates that the mineral component of soils governs the physical conformation of OM at the soil-water interface. Consequently, one must consider that one-dimensional NMR methods provide an excellent overview of all the structures that are present but they do not provide direct information regarding which structures are available or accessible at the soil-water interface for contaminant interactions.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.000
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 head0.008
GPT teacher head0.293
Teacher spread0.285 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueAGU Spring Meeting AbstractsSame topicNMR spectroscopy and applicationsFrench-language works237,207