Miniaturization of an enclosed electrospinning process to enhance reproducibility in the fabrication of rapidly dissolving cell‐based biosensors
Bibliographic record
Abstract
There is broad interest in producing electrospun films embedded with biological materials. It is well known that electrospinning requires careful control of the process conditions, especially the environmental conditions such as relative humidity (RH). Given that commercial electrospinning systems are expensive (> $10,000) and are typically too large to be used in standard biological safety cabinets (BSC), we designed and built a miniaturized electrospinning box (E-Box) that will fit inside a BSC, and the RH can be easily controlled using simple instrumentation (gas cylinder, regulator, needle valve, rotameter). It uses an inexpensive computerized numerical control machine to control the spinneret positioning and collector rotational speed-all the parts for the device (except the syringe pump and voltage supply) can be purchased for approximately $1000. We demonstrate the usefulness of our design in optimizing the production of Escherichia coli-embedded pullulan-trehalose films to be used as rapidly dissolving biosensors for environmental monitoring. At a fixed electrospinning recipe, we showed that decreasing the RH from approximately 48% to 22% resulted in the average fiber diameter increasing from 240 (± 11) nm to 314 (± 8) nm. We also demonstrate the usefulness of our design in performing sequential electrospinning experiments to evaluate process performance reproducibility. For example, from just 1 mL of a polymer solution, we produced 16 electrospun films (approximately 3 cm by 8 cm each)-from those films we hole-punched approximately 80 biosensor discs which were then used in subsequent experiments to determine the amount of two different biocides (Grotan BK and triclosan) in aqueous samples. The technique developed in this study is ideal for creating electrospun materials in high quantities that are highly reproducible through the precise control of RH.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".