Surface chemistry of metal oxide nanoparticles: NMR and TGA quantification
Bibliographic record
Abstract
Surface functionalization is widely used to control the behavior of nanomaterials for a range of applications. However, methods to accurately quantify surface functional groups and coatings are not yet routinely applied to nanomaterial characterization. We have employed a combination of quantitative NMR (qNMR) and thermogravimetric analysis (TGA) to address this problem for commercial cerium, nickel, and iron oxide nanoparticles (NPs) that have been modified to add functional coatings with (3-aminopropyl)triethoxysilane (APTES), stearic acid, and polyvinylpyrrolidone (PVP). The qNMR method involves quantification of material that is released from the NPs and quantified in the supernatant after removal of NPs. Removal of aminopropylsilanes was accomplished by basic hydrolysis whereas PVP and stearic acid were removed by ligand exchange using sodium hexametaphosphate and pentadecafluorooctanoic acid, respectively. The method accuracy was confirmed by analysis of NPs with a known content of surface groups. Complementary TGA studies were carried out in both air and argon atmosphere with FT-IR of evolved gases in argon to confirm the identity of the functional groups. TGA measurements for some unfunctionalized samples show mass loss due to unidentified components which makes quantification of functional groups in surface-modified samples less reliable. XPS provides information on the presence of surface contaminants and the level of surface hydroxylation for selected samples. Despite the issues associated with accurate quantification using TGA, the TGA estimates agree reasonably well with the qNMR data for samples with high surface loading. This study highlights the issues in analysis of commercial nanomaterials and is an advance towards the development of generally applicable methods for quantifying surface functional groups.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".