Modeling Phosphate Adsorption by Agricultural and Natural Soils
Bibliographic record
Abstract
The nonideal competitive adsorption model (NICA) was developed from the Langmuir adsorption theory and the Freundlich empirical equation. It is well suited to describing ion adsorption in complex systems like soils, which have multiple ions, highly heterogeneous surfaces, and a variety of adsorption sites on the particle surfaces. It has been successfully used to model the binding of protons and metal ions to humic substances, but its application for the adsorption of anions to heterogeneous surfaces has not been documented. The purpose of this study was to adapt the NICA model to describe hydroxyl and phosphate adsorption. Results show that by considering two types of surface sites, the NICA model can provide an excellent fit ( R 2 > 0.99) of the hydroxyl adsorption data obtained from −11 to −4 of log[OH], which corresponds to soil pH from 3 to 10. By using the parameters generated from hydroxyl adsorption, including adsorption maxima ( Q max,OH ), binding strength ( K OH ), and nonideality ( m ), the NICA model gave a remarkable goodness of fit ( R 2 > 0.98) for the phosphate adsorption data obtained at different pH values. The model sensitivity test showed that the Type 1 surface ( S OH 2 + ) has up to 100 times greater contribution to phosphate adsorption than the Type 2 surface ( S OH). Thus, the model may be simplified into a three‐parameter model by only considering the Type 1 surface for phosphate adsorption in acidic soils. The adapted NICA model can thus describe phosphate adsorption combined with hydroxyl adsorption and the parameters ( n PO4,1 / n OH,1 < 1, where n PO4,1 accounts for nonideality of PO 4 3− and n OH,1 accounts for nonideality of OH − , both on a Type 1 surface) reveal the multidentate binding of phosphate. It provides a promising tool for analyzing competitive anion adsorption processes in soils.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| 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".