Controlled Electrospray Generation of Nonspherical Alginate Microparticles
Bibliographic record
Abstract
Electrospraying is a technique used to generate microparticles in a high throughput manner. For biomedical applications, a biocompatible electrosprayed material is often desirable. Using polymers, such as alginate hydrogels, makes it possible to create biocompatible and biodegradable microparticles that can be used for cell encapsulation, to be employed as drug carriers, and for use in 3D cell culturing. Evidence in the literature suggests that the morphology of the biocompatible microparticles is relevant in controlling the dynamics of the microparticles in drug delivery and 3D cell culturing applications. Yet, most electrospray-based techniques only form spherical microparticles, and there is currently no widely adopted technique for producing nonspherical microparticles at a high throughput. Here, we demonstrate the generation of nonspherical biocompatible alginate microparticles by electrospraying, and control the shape of the microparticles by varying experimental parameters such as chemical concentration and the distance between the electrospray tip and the particle-solidification bath. Importantly, we show that these changes to the experimental setup enable the synthesis of different shaped particles, and the systematic change in parameters, such as chemical concentration, result in monotonic changes to the particle aspect ratio. We expect that these results will find utility in many biomedical applications that require biocompatible microparticles of specific shapes.
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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 | 0.001 |
| 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) | 0.000 | 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 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".