A Facile Controlled Preparation Method of Multifunctional Core-Shell Magnetic Nanoparticles, and Their Potential Use in Microfluidic Separations
Bibliographic record
Abstract
Organotriethoxysilanes bearing carboxylic acid, ester, and amine functional groups were synthesized via a facile photoinitiated catalyst-free thiol-ene click reaction.We demonstrate these systems to be excellent candidates to make core-shell magnetic nanoparticles composed of a single-core structure and a variety of surface functionalities via a simple synthesis in a reverse microemulsion system.Compared to the most commonly used surface modification strategies, this method avoids complicated multi-step procedures and tedious removal of metal catalysts from the nanoparticle surface.Also, the density of surface functionality is more predictable and tunable.The results show that the morphology, size, and colloidal stability can be well predicted and amply controlled.The organotriethoxysilanes were confirmed by 1 H-NMR and 13 C-NMR spectra.The surface functionalized nanoparticles were characterized by a variety of methods, including TEM, FTIR, UV-Vis.The resulting nanoparticles have been shown to be monodisperse core-shell structures with a chemically active silica coating onto a single magnetic core, a system with great potential for a broad range of applications, such as biomedicine, catalysis, purification and separation.Notably, we have demonstrated the potential applicability of these systems for use in a closed-cycle magnetic purification (CCMP) microfluidic device by studying the reversible and quantitative affinity of resulting IONPs towards ionic compounds.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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".