Engineering Magnetic Biochar from Polyphenol-Functionalized Biomass for the Removal of Broad-Spectrum Water Contaminants
Bibliographic record
Abstract
Various water contaminants raise concerns about potential negative effects on aquatic ecosystems and human health, which demand breakthrough technologies for the effective removal of a wide range of water contaminants. Recently, nitrogen-doped biochar has shown promise in the removal of various contaminants due to its merits of having a high surface area, versatile surface functionality, and variable surface charge. However, obtaining nitrogen-doped biochar with a high nitrogen content and large surface area simultaneously is challenging. Herein, we developed a nitrogen-rich magnetic and porous biochar (NMPC) via facile pyrolysis of polyphenol and metal ions cofunctionalized collagen. Benefiting from a large surface area (1194.4 m 2 g –1 ) and a high nitrogen content (8.35 wt %), NMPC exhibited high adsorption performance for broad-spectrum water contaminants, including dyes, antibiotics, and heavy metal ions. Besides, NMPC could be magnetically separated for easy recycling with the embedded magnetic iron carbide (Fe 3 C) and still maintained a high removal performance even in a six-cycle test. This work provides new possibilities for the fabrication of nitrogen-rich magnetic biochar which holds great potential in efficient removal of broad-spectrum water contaminants.
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".