Lignin-Induced Click Synthesis of Au, Ag, Pd, and Iron Oxide Nanoparticles and Their Nanocomposites in Aqueous Bulk and at the Solid–Liquid Interface
Bibliographic record
Abstract
Application of lignin in green click synthesis of Au, Ag, Pd, Au–Ag bimetallic, Au–Ag–Pd trimetallic, and Fe3O4–Au–Ag–Pd nanocomposite nanoparticles (NPs) in aqueous bulk as well as at the solid–liquid interface was developed. Crossed-linked lignols of aqueous solubilized lignin or adsorbed at the solid–liquid interface acted as potential electron donors in click reduction of the metal center that depended on the oxidation state, molecular geometry, and oxidizing ability of electron-deficient species. Click reduction occurred in aqueous bulk and at the solid–liquid interface, which was a rapid and thermodynamically driven reaction at room temperature and did not depend on reaction conditions such as pH and temperature. Quantitative information was drawn by using UV–visible, DLS, and IR studies, whereas the elemental composition of different kinds of lignin-stabilized nanomaterials was determined by using TEM, FESEM, and XPS analyses. Au–Ag–Pd trimetallic NPs produced in aqueous bulk or at the solid–liquid interface because of the click reduction were homogenously mixed at the solid–liquid interface but retained the core–shell in aqueous bulk. Lignin-stabilized Fe3O4–Au–Ag–Pd nanocomposite NPs were highly responsive to the external magnetic field. They proved to be excellent vehicles for quick and rapid extraction of KMnO4 from contaminated water, which was highly specific and sustainable.
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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) | 0.000 | 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".