Mechanochemical synthesis of Au, Pd, Ru and Re nanoparticles with lignin as a bio-based reducing agent and stabilizing matrix
Bibliographic record
Abstract
A versatile, low-energy and solvent-free method to access nanoparticles (NPs) of four different transition metals, based on a bottom-up mechanochemical procedure involving milling of inorganic precursors, is presented. Lignin, a biomass waste, was used effectively as a reducing agent, for the first time in a mechanochemical context, to access MNPs where M = Au, Pd, Ru, Re. A series of metal precursors was used for this reaction and their nature was shown to be integral in determining whether NPs became incorporated within the organic lignin matrix, M@lignin, or not. Specifically, organometallic precursors resulted in extensive encapsulation of the NPs, as well as improved control over their size and shape, while ionic precursors afforded matrix-free NPs. The resulting NP-containing composites were characterized through Fourier-transform infrared attenuated total reflectance (FTIR-ATR) spectroscopy, transmission electron microscopy (TEM), X-ray photoelectron spectroscopy (XPS) and powder X-ray diffraction (PXRD). This mechanochemical grinding method for accessing M@lignin (M = Au, Pd, Ru and Re) is significantly more sustainable than the traditional solvent batch syntheses of metal NPs because it relies on the use of a biomass-based polymer, it is highly atom economical, it eliminates the need for solvents and it reduces drastically the energy input.
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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".