Mechanochemical synthesis of Au, Pd, Ru and Re nanoparticles with lignin as a bio-based reducing agent and stabilizing matrix
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it