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Record W2018829532 · doi:10.1021/es803178f

ATR-FTIR Studies on the Nature of Surface Complexes and Desorption Efficiency of <i>p</i>-Arsanilic Acid on Iron (Oxyhydr)oxides

2009· article· en· W2018829532 on OpenAlexaff
Matthew Chabot, Tuan K.A. Hoang, Hind A. Al‐Abadleh

Bibliographic record

VenueEnvironmental Science & Technology · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicArsenic contamination and mitigation
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsChemistryArsenicDesorptionFourier transform infrared spectroscopyAdsorptionAttenuated total reflectionAqueous solutionPhosphateProtonationInorganic chemistryBioavailabilityDenticityNuclear chemistryInfrared spectroscopyIonMetalOrganic chemistryChemical engineering

Abstract

fetched live from OpenAlex

The fate of organoarsenicals introduced to the environment through the application of arsenic-contaminated manure has attracted considerable attention after the recent implementation of the latest maximum contaminant level (MCL) of total arsenic in drinking water by the U.S. Environmental Protection Agency (EPA). We report herein detailed spectroscopic analysis of the surface structure of p-arsanilic acid (p-AsA) adsorbed on Fe-(oxyhydr)oxides using attenuated total internal reflectance Fourier transform infrared spectroscopy (ATR-FTIR). Spectra of p-AsA(ads) were collected in situ as a function of pH and ionic strength and using D20 at 298 K in flow mode. Results indicate the formation of inner-sphere complexes, which are likely monodentate and become protonated under acidic pH(D). We also examined the desorption efficiency of p-AsA(ads) due to flowing electrolyte and phosphate solutions as low as 0.1 mol/m3 (3 ppm P) by collecting ATR-FTIR spectra as a function of time. Our results suggest that aqueous phosphate is an efficient desorbing anion of p-AsA(ads), which has implications on its bioavailability and mobility in geochemical 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
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 score0.096
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.004
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 head0.007
GPT teacher head0.237
Teacher spread0.229 · 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

Citations66
Published2009
Admission routes1
Has abstractyes

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