Thermosensitive polymer-grafted iron oxide nanoparticles studied by<i>in situ</i>dynamic light backscattering under magnetic hyperthermia
Bibliographic record
Abstract
Thermometry at the nanoscale is an emerging area fostered by intensive research on nanoparticles (NPs) that are capable of converting electromagnetic waves into heat. Recent results suggest that stationary gradients can be maintained between the surface of NPs and the bulk solvent, a phenomenon sometimes referred to as 'cold hyperthermia'. However, the measurement of such highly localized temperatures is particularly challenging. We describe here a new approach to probing the temperature at the surface of iron oxide NPs and enhancing the understanding of this phenomenon. This approach involves the grafting of thermosensitive polymer chains to the NP surface followed by the measurement of macroscopic properties of the resulting NP suspension and comparison to a calibration curve built up by macroscopic heating. Superparamagnetic iron oxide NPs were prepared by the coprecipitation of ferrous and ferric salts and functionalized with amines, then azides using a sol-gel route followed by a dehydrative coupling reaction. Thermosensitive poly[2-(dimethylamino)ethyl methacrylate] (PDMAEMA) with an alkyne end-group was synthesized by controlled radical polymerization and was grafted using a copper assisted azide-alkyne cycloaddition reaction. Measurement of the colloidal properties by dynamic light scattering (DLS) indicated that the thermosensitive NPs exhibited changes in their Zeta potential and hydrodynamic diameter as a function of pH and temperature due to the grafted PDMAEMA chains. These changes were accompanied by changes in the relaxivities of the NPs, suggesting application as thermosensitive contrast agents for magnetic resonance imaging (MRI). In addition, a new fibre-based backscattering setup enabled positioning of the DLS remote-head as close as possible to the coil of a magnetic heating inductor to afford in situ probing of the backscattered light intensity, hydrodynamic diameter, and temperature. This approach provides a promising platform for estimating the response of magnetic NPs to application of a radiofrequency magnetic field or for understanding the behaviour of other thermogenic NPs.
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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 | 0.000 |
| 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".