Correlation between physicochemical properties of superparamagnetic iron oxide nanoparticles and their reactivity with hydrogen peroxide
Bibliographic record
Abstract
The present study focuses on the effects of the physicochemical properties of superparamagnetic PEG-modified, positively charged, and negatively charged iron oxide nanoparticles (SPIONs) on their reactivity with hydrogen peroxide. Our hypothesis was that the reactivity of SPIONs in this reaction would depend on their surface properties. The comparative study of the nanoparticles with DLS and TEM revealed the average sizes of PEG-modified, positively charged, and negatively charged SPIONs. We observed that the reactivity of negatively charged SPIONs with hydrogen peroxide was less than that of positively charged SPIONs and that of these second nanoparticles was less than that of PEG-modified SPIONs. This difference in the reactivity of these SPIONs with hydrogen peroxide was attributed to the presence of carboxyl or amine groups on their surface. However, the values of the rate constants of the reactions of PEG-modified, positively charged, and negatively charged SPIONs with hydrogen peroxide showed that the reaction of negatively charged SPIONs with hydrogen peroxide was more rapid than that of PEG-modified SPIONs and the reaction of these second SPIONs with hydrogen peroxide was more rapid than that of positively charged SPIONs. The surface study of the SPIONs using XPS showed that the high-resolution spectra of these nanoparticles changed after reaction with hydrogen peroxide, which indicates their surface modifications. These investigations can help develop more appropriate nanoparticles with controlled physicochemical properties.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".