Printing spongy all-in-liquid materials
Bibliographic record
Abstract
Abstract Printing structured networks of functionalized droplets in a liquid medium enables engineering collectives of living cells for functional purposes [1, 2], bacterial ecology [3], and promises enormous applications in processes ranging from energy storage [4, 5] to drug delivery [6, 7]and tissue engineering [8]. Current approaches are limited to drop-by-drop printing [1, 2] or face limitations in reproducing the sophisticated internal features of a structured material and its interactions with the surrounding media [6, 9–11]. Here, we report on a simple approach for creating stable liquid filaments of silica nanoparticle dispersions and use them as inks to print all-in-liquid materials that consist of a network of droplets. Silica nanoparticles stabilize liquid filaments at Weber numbers two orders of magnitude smaller than previously reported in liquid-liquid systems by rapidly producing a concentrated microemulsion zone at the oil-water interface. We experimentally demonstrate that the printed aqueous phase is emulsified in-situ; consequently, a 3D structure is achieved with flexible walls consisting of layered microemulsions. The tube-like printed features have a spongy texture resembling miniaturized versions of “tube sponges” found in the oceans. A scaling analysis based on the interplay between hydro-dynamics and emulsification kinetics reveals that liquid filaments are formed when emulsions are generated and remain at the interface during the printing period. We demonstrate the utilization of filaments of the nanoparticle dispersions for printing fluidic channels and propose to use them as lab-on-a-chip devices.
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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".