A Step-by-Step Strategy for Controlled Preparations of Complex Heterostructured Colloids
Bibliographic record
Abstract
Interest in the preparation of colloidal heterostructures with complex shapes, structures, and spatial compositions is driven by their unique optical, electrical, magnetic, or rheological properties. Despite recent advances, it is highly desired but challenging to further extend the library of heterostructured particles with increased degrees of complexity. Here we report a general step-by-step strategy for controlled preparations of complex heterostructured colloids with various structures and compositions. A local-curvature-controlled emulsion polymerization method is first employed for site-selective growth of secondary polystyrene nanostructures on different nonspherical colloidal seeds. The following functionalization and selective removal steps further increase the degree of particle complexity. We also demonstrate a new type of chemically powered nanomotors based on heterostructured α-Fe 2 O 3 @SiO 2 /Pt particles. This growth strategy is a versatile, general method suitable for the preparation of complex heterostructured particles with tailored structures, compositions, and functionalities, paving the way for their applications for various purposes.
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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) | 0.001 | 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 | 0.001 |
| 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".