Synthesis and Applications of Biopolymer /FeO Nanocomposites: A Review
Bibliographic record
Abstract
Magnetic oxide nanoparticles have engaged most consideration due to their rare character, such as easy separation, surface-to- volume ratio, paramagnetic and high surface area. Natural biopolymers, namely, (Chitosan, Guar-Gum, Tamarind, Alginate, Dextran, Pectin) have posed as an incredible host for the preparation of magnetic nanoparticles. Biopolymer based magnetic nanocomposites have been fabricated from long time using method like co-precipitations, green synthesis, in-situ, hydrothermal and wet chemical method. Properties of biopolymer magnetic nanocomposites draw attention to the researchers towards fabricating at the nano level for various applications like as adsorptions inorganic metal, organic impurity, targeted drug-delivery, bio-sensing, catalysis activity, antimicrobial activity, antifungal activity, antioxidant activity, anti-cancer activity, energy, environmental remediation, waste water treatment and textiles. This review is designed to report very firstly reported biopolymer magnetic nanoparticles (BMNPs) in last ten years and attractive approach in various 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".