ATPSpin: A Single Microfluidic Platform that Produces Diversified ATPS-Alginate Microfibers
Bibliographic record
Abstract
Abstract Microfluidic spinning is emerging as a useful technique in the fabrication of alginate fibers, enabling applications in drug screening, disease modeling, and disease diagnostics. In this paper, by capitalizing on the benefits of aqueous two-phase systems (ATPS) to produce diverse alginate fiber forms, we introduce an ATPS-Spinning platform (ATPSpin). This ATPS-enabled method efficiently circumvents the rapid clogging challenges inherent to traditional fiber production techniques by regulating the interaction between alginate and cross-linking agents like Ba2+ ions. By varying system parameters under the guidance of a regime map, our system produces several fiber formssolid, hollow, and droplet-filledconsistently and reproducibly from a single device. We demonstrate that the resulting alginate fibers possess distinct features, including biocompatibility. We also encapsulate HEK293 cells in the microfibers as a proof-of-concept that this versatile microfluidic fiber generation platform may have utility in tissue engineering and regenerative medicine applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".