In Situ Investigations of Laser-Generated Ligand-Free Platinum Nanoparticles by X-ray Absorption Spectroscopy: How Does the Immediate Environment Influence the Particle Surface?
Bibliographic record
Abstract
Pulsed laser ablation in liquid (PLAL) has proven its usefulness as a nanoparticle (NP) synthesis method alternative to traditional chemical reduction methods, where the absence of any molecular ligands or residual reactants makes laser-generated nanoparticles ideal reference materials for charge-transfer experiments. We synthesized additive-free platinum nanoparticles by PLAL and in-situ characterized their interaction with H2O, sodium phosphate buffer, and sodium citrate as well as a TiO2 support by X-ray absorption fine structure (XAFS), i.e., X-ray absorption near-edge structure (XANES) and extended X-ray absorption fine structure (EXAFS). Differences in the white-line intensity among the colloidal particles in the three liquids indicate that the respective NP-solvent interaction varies in strength. The ions added ex situ diffuse through the particles' electric double layer and interact electrostatically with the Stern plane. Consequently, these ions weaken the interaction of the functional OH groups that are bound to the partially oxidized platinum surfaces and cause their partial reduction. Comparing XAFS spectra of laser-generated Pt NPs in citrate with wet-chemically synthesized ones (both ligand-covered) indicates different types of Pt-O bonds: a Pt(IV)O2 type in the case of wet-chemical NPs and a Pt(II)O type in the case of laser-generated NPs. A comparison of unsupported laser-generated platinum NPs in H2O with TiO2-supported ones shows no white-line intensity differences and also an identical number of Pt-O bonds in both cases. This suggests that in the deposition process at least part of the double-layer coating stays intact and that the ligand-free Pt particle properties are preserved in the TiO2-supported Pt particles, relevant for heterogeneous catalysis.
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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 | 0.001 |
| 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".