Probing the multi-step crystallization dynamics of micelle templated nanoparticles: structural evolution of single crystalline γ-Fe<sub>2</sub>O<sub>3</sub>
Bibliographic record
Abstract
Iron oxide nanoparticles synthesized with narrow size distribution were characterized using Raman spectroscopy, transmission electron microscopy and a superconducting quantum interference device magnetometer to investigate their composition, crystal structure and magnetic properties. Raman allowed us to explore the polymorphous transition of the iron oxide from the beginning of the synthesis process, as Raman can be used to monitor the precursors, the diblock-copolymer micelles and the resultant particles simultaneously under various processing steps. As different polymorphs possess distinct Raman active phonon modes, it also allows the identification of the exact phases of the resultant nanoparticles. Consequently, we show that the reverse micelle process results in pure phase nanoparticles only under certain conditions. Using insights obtained from examining the entire synthesis process, we can adjust the structure of small nanoparticles (∼6 nm) to achieve coercivity and saturation magnetization values that are usually only obtainable from larger particles (25 nm or larger). In this way, we show a route to tunable magnetic response based on the purity of the crystal phase rather than the particle size. By understanding the evolution of the entire synthesis process, it is possible to adjust the processing conditions to yield monodisperse single crystal phase nanoparticles for widespread use in a variety of applications.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".