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Record W1853832826 · doi:10.1139/cjc-2015-0350

Surface interactions of monomethylarsonic acid with hematite nanoparticles studied using ATR-FTIR: adsorption and desorption kinetics

2015· article· en· W1853832826 on OpenAlexaffvenue
Md Abdus Sabur, Hind A. Al‐Abadleh

Bibliographic record

VenueCanadian Journal of Chemistry · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicArsenic contamination and mitigation
Canadian institutionsWilfrid Laurier UniversityUniversity of Waterloo
Fundersnot available
KeywordsHematiteArsenateChemistryAdsorptionDesorptionArsenicInorganic chemistryAqueous solutionFourier transform infrared spectroscopyArseniteKineticsAttenuated total reflectionInfrared spectroscopyChemical engineeringOrganic chemistryMineralogy

Abstract

fetched live from OpenAlex

Monomethylarsonic acid (MMA) is an organoarsenical compound which, along with dimethylarsinic acid (DMA), poses health and environmental concerns. Little is known about the surface chemistry of MMA at the molecular level with materials relevant to geochemical environments and industrial sectors. We report the structure of MMA surface complexes and the adsorption/desorption kinetics of MMA to and from hematite as a model for reactive iron-containing materials commonly found in geosorbents and arsenic-removal technologies. Attenuated total internal reflectance Fourier transform infrared (ATR-FTIR) spectroscopy was used to study the surface interactions at the molecular level. Spectra of adsorbed MMA (MMA(ads)) were collected as a function of time and aqueous-phase concentration. Values for the apparent rates of adsorption and desorption were extracted from experimental data at pH 7 as a function of spectral components during the initial times of surface interactions (0–5 min). Results showed that MMA adsorbs on hematite nanoparticles with rates 1.3 to 1.6 times slower than arsenate. The desorption of MMA(ads) by hydrogen phosphate from hematite surfaces is 2× faster than arsenate, and proceeds with an overall nonunity order, suggesting the existence of more than one type of surface complex at equilibrium. Also, hydrogen phosphate leads to the desorption of about 67% of MMA(ads) compared with 26% of surface arsenate. Adsorption kinetics for aqueous hydrogen phosphate were also investigated in the absence and presence of surface arsenic and followed this order: fresh hematite > MMA/hematite ≥ iAs(V)/hematite. From this study, it can be inferred that, on average, the presence of the methyl group in MMA results in weaker surface interactions with hematite relative to arsenate under neutral pH because of the simultaneous formation of mono- and bidentate MMA complexes compared with predominantly bidentate complexes for arsenate.

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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.026
GPT teacher head0.234
Teacher spread0.208 · 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

Citations11
Published2015
Admission routes2
Has abstractyes

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