Phosphorus recovery and reuse by pyrolysis: Applications for agriculture and environment
Bibliographic record
Abstract
Phosphorus ore extraction for soil fertilization supports the demand of modern agriculture, but extractable resource limitations, due to scarcity, impose a P reuse and recycling research agenda. Here we propose to integrate biochar production (pyrogenic carbon) with municipal and agricultural waste management systems, to recover and reuse phosphorous that would otherwise be lost from the ecological food web. A meta-analysis and available data on total P in biochar indicated that P-enriched feedstocks include animal manure, human excreta, and plant-biomass collected from P-polluted sites. Phosphorus in biochar could participate in P equilibriums in soils and is expected to supply P. The release, sorption and desorption of P by biochar will codetermine the potential of P replenishment by biochar and P loss from biochar-amended soils. Abiotic and biotic factors are expected to affect sorption/desorption of P between biochar and soil aggregates, and P acquisition by plants. Chemical extraction, using acid or alkaline solutions, is considered as a means for P retrieval from high P biochar, especially for biochar with high heavy metal contents. To bridge the gap between academia and practice, this paper proposes future development for phosphorus acclamation by pyrolysis: 1) identification of high-P bio-waste for pyrolysis; 2) retrieval of P by using biochar as soil amendment or by chemical leaching; 3) biochar modification by inorganic nutrients, P solubilizing microorganisms and other organic matter; and 4) compatible pyrolysis equipment fit to the current waste management context, such as households, and waste water treatment plants.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".