Printing Three-Dimensional Heterogeneities in the Elastic Modulus of an Elastomeric Matrix
Bibliographic record
Abstract
We present a rapid and controllable method to create microscale heterogeneities in the 3D stiffness of a soft material by printing patterns with a ferrofluid ink. An ink droplet moved through a liquid polydimethylsiloxane (PDMS) volume using an externally applied magnetic field sheds clusters of magnetic nanoparticles (MNPs) in its wake. By varying the field spatiotemporally, a well-defined three-dimensional curvilinear feature is printed that contains MNP clusters. Subsequent cross-linking of the PDMS preserves the feature in place after the magnetic field is removed. Since the ferrofluid ink interferes with the cross-linking of PDMS, a 3D print containing ink density variations leads to corresponding spatial deviations in the elastic modulus of the matrix. The modulus is mapped in the experiments with atomic force microscopy. This rapid method to print 3D heterogeneities in soft matter promises the ability to mimic mechanical variations that occur in natural biomaterials.
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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".