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Record W1526780104 · doi:10.1093/jaoac/85.1.154

A Review of Analytical Methods for the Determination of Sulfolane and Alkanolamines in Environmental Studies

2002· review· en· W1526780104 on OpenAlexaff
John V. Headley, Phillip M. Fedorak, Leslie C. Dickson

Bibliographic record

VenueJournal of AOAC International · 2002
Typereview
Languageen
FieldChemistry
TopicAnalytical Chemistry and Chromatography
Canadian institutionsCanadian Food Inspection AgencyUniversity of Alberta
Fundersnot available
KeywordsSulfolaneChemistryExtraction (chemistry)DerivatizationGas chromatographyMass spectrometryChromatographyGroundwaterElectrospray ionizationSample preparationEnvironmental chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Sulfolane and alkanolamines are used extensively in the processing of sour natural gases. Over many years of operation, there have been inadvertent leaks of these chemicals to groundwater and wetlands surrounding gas processing facilities, leading to uptake by vegetation. Because sulfolane and alkanolamines are extremely water-soluble, their analysis has presented challenges, particularly requirements for suitable extraction from biological matrixes and soil, along with sensitive detection using commonly available instrumentation. Analytical methods usually use gas chromatography or liquid chromatography with a variety of detector systems. Sample preparation techniques may include extraction with organic solvents, water, or a combination of these. In some cases, direct aqueous injections have been used. Derivatization of alkanolamines has been used to improve the chromatographic separations and detection. More recent procedures, using positive-ion electrospray ionization mass spectrometry (MS), have been useful for the confirmation of uptake of the alkanolamines and transformation products by wetland vegetation. Future developments will likely center on further MS analyses for identification of metabolites and transformation products in aquatic environments.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.894
Threshold uncertainty score0.458

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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 head0.074
GPT teacher head0.432
Teacher spread0.357 · 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 designOther design
Domainnot available
GenreReview

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

Citations23
Published2002
Admission routes1
Has abstractyes

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