Facile Synthesis of Well-Dispersed Superparamagnetic γ-Fe<sub>2</sub>O<sub>3</sub> Nanoparticles Encapsulated in Three-Dimensional Architectures of Cellulose Aerogels and Their Applications for Cr(VI) Removal from Contaminated Water
Bibliographic record
Abstract
With the increasing emphasis on green chemistry, cellulose aerogels that consist of abundant three-dimensional (3D) architectures have been considered as a class of idea green matrix materials to encapsulate various nanoparticles for synthesis of miscellaneous functional materials. Herein, a facile template synthesis combined with chemical coprecipitation was implemented to prepare hybrid γ-Fe 2 O 3 @cellulose aerogels (γ-Fe 2 O 3 @CA). The γ-Fe 2 O 3 nanoparticles are well dispersed and immobilized in the micro/nanoscale pore structure of the aerogels, and exhibit superparamagnetic behavior. The particle sizes, pore characteristic parameters, magnetic property, and mechanical strength of the synthetic γ-Fe 2 O 3 @CA could be flexibly tailored by adjusting the concentrations of the initial reactants. In addition, γ-Fe 2 O 3 @CA exhibits rapid adsorption rate and excellent adsorption ability to remove Cr(VI) heavy metal ions. Moreover, combined with the advantages of environmental benefits, facile convenient preparation method, high specific surface area and strong mechanical strength, and strong magnetic responsiveness, this class of green γ-Fe 2 O 3 @CA is more favorable and suitable for Cr(VI) removal from contaminated water, and also useful in many other applications.
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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".