Zero-waste valorization of K-rich igneous rocks for cleaner potassic fertilizers
Bibliographic record
Abstract
Potassium (K) deficiency in agricultural soils is a growing global concern, particularly in Africa, where soil nutrient depletion outpaces replenishment. This study investigates the potential of high-K igneous rocks with ultrapotassic syenite affinity as a sustainable source of K for agriculture through alkaline hydrothermal treatment. The objective was to optimize K release from these silicate rocks using a systematic experimental approach. A composite sample of ultrapotassic syenite rocks (∼15 wt% K 2 O) underwent alkaline hydrothermal treatment in an autoclave with varying time, temperature, particle size, CaO addition, and liquid (water) to solid ratio. A Fractional Factorial Design (FFD) was used to screen key variables, followed by a Central Composite Design (CCD) for K release optimization. Mineralogical characterization was performed using XRD, SEM-EDS, and XANES, while leaching tests assessed elemental release. Results showed that temperature, Solid (CaO)/Solid (syenite) ratio, and Liquid (water)/Solid (feed) ratio were the most significant factors affecting K release. The maximum K release of ∼70.6 % was achieved under conditions of 200 °C, 7 h, Solid (CaO)/Solid (syenite) of 0.9, and Liquid (water)/Solid (feed) of 0.43. XRD and SEM analyses revealed the formation of secondary phases such as portlandite and calcium-aluminum-silicate-hydrate (CASH). XANES analysis indicated the formation of K 2 SiO 3 under high treatment conditions. Leaching tests demonstrated rapid initial K release (up to 2096 mg/l within 15 min) followed by sustained slower release. This optimization study explores treated syenite ore as an innovative K fertilizer, demonstrating its potential to release essential nutrients like Ca and Si. Unlike conventional KCl fertilizers, this approach may improve soil properties and effective plant growth.
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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 | 0.001 | 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".