Water Self-Purification with Zero External Consumption by Livestock Manure Resource Utilization
Bibliographic record
Abstract
Improper disposal of waste biomass and an increasing number of emerging contaminants (ECs) in water environment are universal threats to the global environment. Here, we creatively propose a sustainable strategy for the direct resource transformation of livestock manure (LM) into an innovative catalyst (Fe-CCM) for water self-purification with zero external consumption. ECs can be rapidly degraded in this self-purification system at ambient temperature and atmospheric pressure, without any external oxidants or energy input, accompanied by H 2 O and dissolved oxygen (DO) activation. The performance of the self-purification system is not affected by various types of salinity in the wastewater, and the corresponding second-order kinetic constant is improved 7 times. The enhanced water self-purification mechanism reveales that intermolecular forces between anions and pollutants reinforce electron exchange between pollutants and metal sites on the catalyst, further inducing the utilization of the intrinsic energy of contaminants, H 2 O, and DO through the interfacial reaction. This work provides new insights into the rapid removal of ECs in complicated water systems with zero external consumption and is expected to advance the resource utilization of livestock waste.
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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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".