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 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".