Microfluidic-assisted fiber production: Potentials, limitations, and prospects
Bibliographic record
Abstract
Besides the conventional fiber production methods, microfluidics has emerged as a promising approach for the engineered spinning of fibrous materials and offers excellent potential for fiber manufacturing in a controlled and straightforward manner. This method facilitates low-speed prototype synthesis of fibers for diverse applications while providing superior control over reaction conditions, efficient use of precursor solutions, reagent mixing, and process parameters. This article reviews recent advances in microfluidic technology for the fabrication of fibrous materials with different morphologies and a variety of properties aimed at various applications. First, the basic principles, as well as the latest developments and achievements of microfluidic-based techniques for fiber production, are introduced. Specifically, microfluidic platforms made of glass, polymers, and/or metals, including but not limited to microfluidic chips, capillary-based devices, and three-dimensional printed devices are summarized. Then, fiber production from various materials, such as alginate, gelatin, silk, collagen, and chitosan, using different microfluidic platforms with a broad range of cross-linking agents and mechanisms is described. Therefore, microfluidic spun fibers with diverse diameters ranging from submicrometer scales to hundreds of micrometers and structures, such as cylindrical, hollow, grooved, flat, core-shell, heterogeneous, helical, and peapod-like morphologies, with tunable sizes and mechanical properties are discussed in detail. Subsequently, the practical applications of microfluidic spun fibers are highlighted in sensors for biomedical or optical purposes, scaffolds for culture or encapsulation of cells in tissue engineering, and drug delivery. Finally, different limitations and challenges of the current microfluidic technologies, as well as the future perspectives and concluding remarks, are presented.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".