ATR-FTIR and Flow Microcalorimetry Studies on the Initial Binding Kinetics of Arsenicals at the Organic–Hematite Interface
Bibliographic record
Abstract
The environmental fate of arsenic compounds depends on their surface interactions with geosorbents that include clays, metal oxides, and natural organic matter (NOM). While a number of batch studies reported that NOM inhibits the uptake of arsenicals, it remains unclear how different classes of organic functional groups affect their binding mechanisms. We report herein the adsorption kinetics of arsenate and dimethylarsinic acid (DMA) with hematite nanoparticles pre-exposed to three types of low molecular weight organics: citrate, oxalate, and pyrocatechol as representatives to the majority of reactive organic functional groups in NOM. These studies were conducted using attenuated total internal reflection Fourier transform infrared spectroscopy (ATR-FTIR) and flow microcalorimetry at pH 7 with an emphasis on the role that electrolytes (KCl, NaCl, and KBr) play in the adsorption process. Results show that (1) negatively charged carboxylate versus hydrophobic phenyl groups influence amounts and initial rates of arsenicals adsorption on hematite nanoparticles to varying degrees depending on the type of complexes they form, (2) the type of electrolytes affects initial adsorption rate of DMA to a greater extent than arsenate when oxalate is present on the surface, and (3) the extent of organics retention by hematite nanoparticles is influenced by the type of the desorbing agent.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".