Temperature-Dependent Infrared and Calorimetric Studies on Arsenicals Adsorption from Solution to Hematite Nanoparticles
Bibliographic record
Abstract
To address the lack of systematic and surface sensitive studies on the adsorption energetics of arsenic compounds on metal (oxyhydr)oxides, we conducted temperature-dependent ATR-FTIR studies for the adsorption of arsenate, monomethylarsonic acid, and dimethylarsinic acid on hematite nanoparticles at pH 7. Spectra were collected as a function of concentration and temperature in the range 5-50 °C (278-323 K). Adsorption isotherms were constructed from spectral features assigned to surface arsenic. Values of K(eq), adsorption enthalpy, and entropy were extracted from fitting the Langmuir model to the data and from custom-built triple-layer surface complexation models derived from our understanding of the adsorption mechanism of each arsenical. These spectroscopic and modeling results were complemented with flow-through calorimetric measurements of molar heats of adsorption. Endothermic adsorption processes were predicted from the application of mathematical models with a net positive change in adsorption entropy. However, experimentally measured heats of adsorption were exothermic for all three arsenicals studied herein, with arsenate releasing 1.6-1.9 times more heat than methylated arsenicals. These results highlight the role of hydration thermodynamics on the adsorption of arsenicals, and are consistent with the spectral interpretation of type of surface complexes each arsenical form in that arsenate is mostly dominated by bidentate, MMA by a mixture of mono- and bidentate, and DMA by mostly outer sphere.
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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 | 0.000 |
| 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 | 0.000 |
| 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".