Surface Modification of Gadolinium Oxide Thin Films and Nanoparticles using Poly(ethylene glycol)-Phosphate
Bibliographic record
Abstract
The performance of nanomaterials for biomedical applications is highly dependent on the nature and the quality of surface coatings. In particular, the development of functionalized nanoparticles for magnetic resonance imaging (MRI) requires the grafting of hydrophilic, nonimmunogenic, and biocompatible polymers such as poly(ethylene glycol) (PEG). Attached at the surface of nanoparticles, this polymer enhances the steric repulsion and therefore the stability of the colloids. In this study, phosphate molecules were used as an alternative to silanes or carboxylic acids, to graft PEG at the surface of ultrasmall gadolinium oxide nanoparticles (US-Gd<sub>2</sub>O<sub>3</sub>, 2–3 nm diameter). This emerging, high-sensitivity “positive” contrast agent is used for signal enhancement in <i>T</i><sub>1</sub>-weighted molecular and cellular MRI. Comparative grafting assays were performed on Gd<sub>2</sub>O<sub>3</sub> thin films, which demonstrated the strong reaction of phosphate with Gd<sub>2</sub>O<sub>3</sub> compared to silane and carboxyl groups. Therefore, PEG-phosphate was preferentially used to coat US-Gd<sub>2</sub>O<sub>3</sub> nanoparticles. The grafting of this polymer on the particles was confirmed by XPS and FTIR. These analyses also demonstrated the strong attachment of PEG-phosphate at the surface of Gd<sub>2</sub>O<sub>3</sub>, forming a protective layer on the nanoparticles. The stability in aqueous solution, the relaxometric properties, and the MRI signal of PEG-phosphate-covered Gd<sub>2</sub>O<sub>3</sub> particles were also better than those from non-PEGylated nanoparticles. As a result, reacting PEG-phosphate with Gd<sub>2</sub>O<sub>3</sub> particles is a promising, rapid, one-step procedure to PEGylate US-Gd<sub>2</sub>O<sub>3</sub> nanoparticles, an emerging “positive” contrast agent for preclinical molecular and cellular applications.
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 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 | 0.001 |
| 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) | 0.023 | 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 teacher head, 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".