XANES Determination of Adsorbed Phosphate Distribution between Ferrihydrite and Boehmite in Mixtures
Bibliographic record
Abstract
Iron‐ and Al‐(hydr)oxide minerals are important sorbents for retaining PO 4 in soils. Our objective was to determine the distribution of adsorbed PO 4 between ferrihydrite and boehmite in aqueous mixtures of these minerals. Phosphate was adsorbed in aqueous suspensions up to maximum concentrations of 1860, 850, and 1420 mmol kg −1 for ferrihydrite, boehmite, and 1:1 (by mass) mixtures of these minerals at pH 6. The solids were analyzed as moist pastes using P K‐XANES (X‐ray absorption near edge structure) spectroscopy. The adsorption isotherm for the mixed‐mineral suspensions could essentially be described as a linear combination of Freundlich isotherm models for each single‐mineral system, indicating negligible mineral interactive effects on PO 4 adsorption in the mixtures. X‐ray absorption near edge structure spectra for PO 4 adsorbed on ferrihydrite or in ferrihydrite/boehmite mixtures showed a pre‐edge feature at approximately 2146 eV that was absent in boehmite systems. Linear combination fitting of the pre‐edge region of XANES spectra for mixtures with average spectra for PO 4 adsorbed on boehmite or ferrihydrite alone, indicated that 59 to 97% of the PO 4 was adsorbed on ferrihydrite in the mixtures. With increasing concentration of adsorbed PO 4 in the mineral mixtures, the concentration adsorbed on the ferrihydrite component increased linearly. Phosphate distribution trends in the mixtures suggested an affinity preference for ferrihydrite at the lowest adsorbed PO 4 concentration (100 mmol kg −1 minerals), no affinity preference for either mineral at intermediate concentrations (200 to 600 mmol PO 4 kg −1 ), and the possibility of a surface precipitate involving Al at the highest concentration (1300 mmol PO 4 kg −1 ).
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".