Tandem electrospinning for heterogeneous nanofiber patterns
Bibliographic record
Abstract
Smart nanofibrillar constructs can be a promising technological solution for many emerging and established fields, facilitating the potential impact of nano-scale strategies to address relevant technological challenges. As a fabrication technique, electrospinning (ESP) is relatively well-known, accessible, economic, and fast, though until now has shown limitation over control and design of the fibrillar constructs which can be produced. Here, we introduce 'Tandem Electrospinning' (T-ESP), a novel technique able to create increasingly complex patterns of fibrous polymeric constructs on a micro and nano-scale. Modifying a standard ESP configuration results in a focusing electric field that is able to spatially define the deposition pattern of multiple polymer jets simultaneously. Additional spatially defined heterogeneity is achieved by tuning polymer solution properties to obtain a gradient of fiber alignment. Heterogeneous fibrous meshes are created with either random, aligned, or a divergent fiber patterns. This approach holds potential for many fields, with application examples shown for tissue engineering and separation technologies. The technique outlined here provides a rapid, versatile, and accessible method for polymeric nanofabrication of patterned heterogeneous fibrous constructs. Polymer properties are also shown to dictate fiber alignment, providing further insight into the mechanisms involved in the electrospinning fabrication process.
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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 | 0.001 |
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".