Perturbation of Taranakite Formation by Ferrous and Ferric Iron under Acidic Conditions
Bibliographic record
Abstract
Taranakite is an important reaction product of monoammonium phosphate fertilizer with soils. Its formation affects the transformation of nutrients in soils. The effect of different molar ratios of Fe(II)/Al and Fe(III)/Al on the formation of taranakite at pH 4.0 was investigated in this study. The results show that Fe(II) ion significantly perturbed the formation of taranakite at the Fe/Al molar ratio of 1.2 When the Fe/Al molar ratio was increased to 2.5, the formation of taranakite was completely inhibited by Fe(II), whereas, under the same condition, some crystalline taranakite was still observed in the Fe(III) system. Although Fe(III) had less effect on the crystallization of taranakite than Fe(II) at lower Fe/Al molar ratios, it also completely inhibited the formation of taranakite at the molar ratio of Fe(III)/Al ≥ 5. The solid products formed in the Fe(III) or Fe(II) system contained a substantial amount of Fe(III) and a much higher proportion of phosphate than that was required for the formation of NH 4 –taranakite. As indicated by the solution phase analysis at the end of the experiment, more Fe ions were present in the solution in the Fe(II) system, compared with the Fe(III) system, to perturb the nucleation and crystallization of taranakite. Since iron is a very common element in soil, taranakite formation may be perturbed in soils with high Fe content, especially under reduced and acidic conditions.
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 | 0.000 |
| 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".