Effects of pH and Dissolved Silicate on Phosphate Mineral-Water Partitioning with Goethite
Bibliographic record
Abstract
Release of sorbed phosphate from ferric iron oxyhydroxides can contribute to excessive algal growth in surface water bodies. Dissolved silicate has been hypothesized to facilitate phosphate desorption by competing for mineral surface sites. Here, we conducted phosphate and silicate adsorption experiments with goethite under a wide pH range (3–11), both individually (P or Si) and simultaneously (P plus Si). The entire experimental data set was successfully reproduced by the charge distribution multisite surface complexation (CD-MUSIC) model. Phosphate adsorption was highest under acidic conditions and gradually decreased from near-neutral to alkaline pH conditions. Maximum silicate adsorption, in contrast, occurred under alkaline conditions, peaking around pH 10. The competitive effect of silicate on phosphate adsorption was negligible under acidic conditions, becoming more pronounced under alkaline conditions and elevated molar Si:P ratios (>4). In a subsequent experiment, desorption of phosphate with increasing pH was monitored, in the presence or absence of dissolved silicate. While, as expected, desorption of phosphate was observed during the transition from acidic to alkaline conditions, a fraction of phosphate remained irreversibly bound to goethite. Even at high Si:P ratios and alkaline pH, dissolved silicate did not affect phosphate desorption, implying that kinetic factors prevented silicate from displacing phosphate from goethite binding sites.
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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 | 0.000 |
| 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".