Reactivity of Fe-amended biochar for phosphorus removal and recycling from wastewater
Bibliographic record
Abstract
Using biochar to remove phosphorus (P) from wastewater has the potential to improve surface water quality and recycle recovered P as a fertilizer. In this research, effects of iron modification on P sorption behavior and molecular characterization on two different biochars and an activated carbon were studied. A biochar produced from cow manure anaerobic digest fibers (AD) pyrolyzed under NH3 gas had the greatest phosphate sorption capacity (2300 mg/kg), followed by the activated carbon (AC) (1500 mg/kg), and then the biochar produced from coniferous forest biomass (BN) (300 mg/kg). Modifying the biochars and AC with 2% iron by mass increased sorption capacities of the BN biochar to 2000 mg/kg and the AC to 2300 mg/kg, but decreased sorption capacity of the AD biochar to 1700 mg/kg. Molecular analysis of the biochars using P K-edge X-ray absorption near edge structure (XANES) spectroscopy indicated that calcium phosphate minerals were the predominant species in the unmodified biochar. However, in the Fe-modified biochars, XANES data suggest that P was sorbed as P-Fe-biochar ternary complexes. Phosphorus sorbed on unmodified BN biochar was more available for release (greater than 35% of total P released) than the AD biochar (less than 1%). Iron modification of the BN biochar decreased P release to 3% of its total P content, but in the AD biochar, P release increased from 1% of total P in the unmodified biochar to 3% after Fe modification. Results provide fundamental information needed to advance the use of biochar in wastewater treatment processes and recover it for recycling as a slow-release soil fertilizer.
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) | 0.000 | 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 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".