Inkjet printed superparamagnetic polymer composite hemispheres with programmed magnetic anisotropy
Bibliographic record
Abstract
We present the fabrication and characterization of large arrays of inkjet-printed superparamagnetic polymer composite (SPMPC) hemispherical microstructures. SPMPCs are appealing for applications in microsystems and nanorobotics due to the added functionality of polymers and the significant magnetic attributes of embedded nanostructures. SPMPC-based microarchitectures can be used to perform different functions wirelessly in various media (e.g. water, solvents) using external magnetic fields: handling and assembling small objects, delivering drugs or biomass, or sensing specific physical or chemical changes. In this work superparamagnetic magnetite nanoparticles are dispersed in SU-8 to form magnetic hemispheres. Magnetically anisotropic hemispheres as well as standard SPMPC hemispheres are fabricated. Magnetic anisotropy is programmed by applying a magnetic field during curing. The distribution of nanoparticles inside the polymer matrix and magnetic characteristics of the SPMPC are investigated. Magnetic manipulation of hemispheres is demonstrated at liquid-liquid interfaces. Different assembly strategies to form lines or geometric shapes from hemispheres as well as their independent dynamic control are demonstrated. Finally, a two-interface assembly strategy is demonstrated to assemble hemispheres into complete spheres for advanced self-assembly tasks.
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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".