Binding Geometries of Silicate Species on Ferrihydrite Surfaces
Bibliographic record
Abstract
Silicate sorption on ferrihydrite surfaces, as monomers, oligomers, and polymers, strongly affects ferrihydrite crystallinity, thermodynamic stability, and surface reactivity. How these silicate species bind on ferrihydrite surfaces is, however, not well understood. We have determined silicate binding geometries using a combination of X-ray absorption spectroscopy (XAS), differential atomic pair distribution function (d-PDF) analysis, and density functional theory (DFT) calculations. Silicon K-edge absorption pre-edges and DFT-predicted energies indicate that silicate forms monomeric monodentate–mononuclear (MM) complexes at low silicate sorption loadings. With increasing silicate loading, the pre-edge peak shifts to higher energies, suggesting changes in the silicate binding geometry toward multidentate complexation. The d-PDF analysis determines the Si–Fe interatomic distance to be ∼3.25 Å for the high-loading samples. The DFT calculations indicate that such distance corresponds to an oligomer in the bidentate–binuclear (BB) binding geometry. The transition of the silicate sorption geometry accompanied by polymerization can affect stability of ferrihydrite and its adsorption and redox reactivity and increase the degree of Si isotopic fractionation upon silicate sorption on Fe oxides. MM monomeric complexes and BB oligomeric complexes should be used for surface complexation models predicting silicate sorption on Fe oxide surfaces.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".