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Record W1482351923 · doi:10.1002/9780470027318.a0849

Organic Acids Analysis in Environmental Samples, Ion Chromatography for Determination of

2000· other· en· W1482351923 on OpenAlex
Sigrid Peldszus

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEncyclopedia of Analytical Chemistry · 2000
Typeother
Languageen
FieldEnvironmental Science
TopicWater Treatment and Disinfection
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsIon chromatographyChemistryIon exchangeOrganic anionMatrix (chemical analysis)SnowCarboxylateInorganic ionsAdsorptionRain and snow mixedPrecipitationElutionIonChromatographyEnvironmental chemistryOrganic chemistryGeology

Abstract

fetched live from OpenAlex

Abstract The analysis of environmental samples typically focuses on aliphatic short‐chain organic acids. The origin of these organic acids and the motivation for their analyses are unique for each type of matrix. Environmental sample matrices are very diverse and can range from air and atmospheric precipitation such as fog, rain or snow to wastewater, drinking water or landfill leachates and soil pore water. This diversity in matrix leads to substantial differences in organic acid concentrations and different organic acid distributions in the various types of environmental samples. Hence, the analytical techniques employed have to be adapted to the particular matrix under investigation. Ion chromatography is one of the most commonly used techniques for organic acid analysis. Competing ion chromatographic methodologies are anion (ion)‐exchange chromatography (IC) and ion exclusion chromatography (ICE). The separation principles underlying each of these techniques are very different and may be used to their advantage. In IC, anions are separated by anion exchange processes between the cationic exchange groups on the resin and the eluent. Hence, inorganic anions and carboxylate anions may be analyzed at the same time (e.g. rain). Limitations of this technique set in when the inorganic anion concentration is much larger than the carboxylate concentration. As a consequence, inorganic anion peaks may mask or co‐elute with the analyte of interest. However, techniques have been developed to handle this problem, for example in drinking‐water analysis. The separation mechanism in ICE is much more complex than in IC involving Donnan exclusion, adsorption and steric exclusion. ICE is generally used for the separation of weak organic acids since strong organic acids elute in the system peak in the front of the chromatogram. Hence, large concentrations of strong inorganic anions, for example sulfate or chloride, will not interfere with the analysis of weak organic acids. However, organic acids with relatively low p Ka values may be difficult to separate from the system peak. In addition, weak inorganic acids such as carbonate have the potential to interfere with the analysis of certain organic acids if present in large enough concentrations. Other techniques than ion chromatography have been used for organic acid analysis. Direct injections of samples into a gas chromatograph are very common for measuring volatile fatty acids (VFAs) in wastewater. More complex gas chromatography (GC) methods, which include concentration and derivatization steps, have been employed to analyze for specific types of organic acids such as dicarboxylic acids in rain. Capillary electrophoresis (CE) is a newer technique, which has not been used widely in routine analysis. However, speciality applications such as the determination of organic acids in a single raindrop show the potential of this type of methodology.

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.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.558
Threshold uncertainty score0.980

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)0.0210.000

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 head0.005
GPT teacher head0.209
Teacher spread0.205 · 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