A core-shell FeNiP@ SrFe-MOF magnetic powder with rapid and efficient degradation of dye and Cr(VI) wastewater
Bibliographic record
Abstract
In this paper, a kind of zero valent FeNi alloy nano powder was coated on the surface of SrFe-MOF powder by a simple, efficient and pollution-free method, forming a core-shell type FeNi p @SrFe-MOF Magnetic powder (FSM). The FSM powder has a porous structure and a specific surface area of 9.16 m 2 /g. Under normal temperature and without changing the pH value of the wastewater, the FSM magnetic powder has an immediate and efficient removal effect on Congo Red (CR) dye wastewater. Furthermore, the powder has good cycling performance, with the CR removal rate remaining above 90% after five cycles. Also at room temperature and without changing the pH value of the wastewater, the FSM magnetic powder provides some immediate, efficient, and repeated use degradation of Cr(VI) wastewater. The FSM magnetic powder has good magnetic properties so that can be easily separated and recovered by magnetic field treatment, which solves the problem of efficient recovery of raw materials in wastewater treatment . FSM magnetic powder has broad application prospects for treating wastewater.
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) | 0.001 | 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".