Characterization of Au and Pd nanoparticles by high-temperature TGA–MS
Bibliographic record
Abstract
Noble metal nanoparticles (NPs) prepared by a surfactant-free single-phase solution method have been proposed to contain fewer ionic contaminants than similar NPs prepared by a two-phase method. Reported herein is the possible contamination of Au and Pd NPs, prepared by a surfactant-free single-phase method, with Li2CO3 and other Li salts. High-temperature thermal gravimetric analysis measurements coupled with mass spectrometry (TGA–MS) up to 1100 °C were employed to determine the relative amounts of ionic contaminants since protecting thiolate groups and inorganic contaminants were removed in separate weight loss events. Assignment of the different weight loss events was supported by MS analysis of the evolved gases. TGA–MS also revealed the presence of larger amounts of oxidized sulfur species in the Pd NPs. High-resolution transmission electron microscopy (HRTEM), UV–vis, IR, elemental analysis (EA), and X-ray photoelectron spectroscopy (XPS) measurements complemented the characterization of the NPs. The amount of ionic contaminants crucially depended on the workup conditions, and quenching of the reaction mixture with ethanol was found to be essential for the formation of Li2CO3. Workup procedures that avoid the formation of TGA–MS detectable ionic contaminants are proposed along with purification steps for contaminated NPs.Key words: gold, palladium, nanoparticles, TGA, mass spectrometry, ionic contamination.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
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".