Synthesis, characterization, and application of Fe<sub>3</sub>O<sub>4</sub>/Ag magnetic composites for mercury removal from water
Bibliographic record
Abstract
Engineered nanocomposites (NCs) have recently emerged as materials of great scientific and technological interest. In these materials, different components are combined to yield a nanoentity with desired properties not afforded by the constituent materials. Designing novel NCs and synthetic routes that enable controlling the size and functionalities remains an active area of research. Here, we present a two-step method of synthesizing Ag–Fe 3 O 4 NCs with tunable sizes. Unlike previously reported structures, the prepared NCs do not have a familiar core–shell architecture. Instead, small Fe 3 O 4 nanoparticles (NPs) are embedded in a larger silver matrix. The superparamagnetic Fe 3 O 4 NPs endow the NC with magnetic properties, enabling easy separation from solution. The degree of the NC response to an external magnetic field can be controlled by varying the concentration of Fe 3 O 4 NPs during the synthesis. The Ag matrix serves to protect the embedded Fe 3 O 4 NPs from degradation and can be used for further functionalization of the NCs with different sulfhydryl containing linkers. To demonstrate utility, we show how decorating the outer layer of the Ag NC with diphenyl-4,4'-dithiol transforms the NCs into a water purifying system capable of sequestering highly toxic Hg 2+ ions from solution magnetically.
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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 | 0.000 |
| 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".