Biomedical fibres produced by electrospinning are ultrathin scaffolds for the body: they help regenerate tissue, guide cell growth, and release drugs in a controlled way.
Yet producing high-quality, aligned nanofibers typically requires specialized equipment, complex setups, and high costs. Their performance depends on one critical factor: how well ordered they are. Achieving that level of control, however, has long been a challenge.
What if producing highly aligned biomedical fibres didn’t require an expensive kit or elaborate modifications, but merely a simple fold of aluminium guided by smart data analysis?
The solution is remarkably simple: an aluminum sheet that can be easily attached and removed, requiring no modifications to the electrospinning apparatus.
It turns out that a folded aluminium sheet, given an origami-like geometry and mounted on a conventional electrospinning drum, achieves something unusual in the field: it yields highly aligned, bead-free polymer fibres without modifying existing lab equipment.
The study “An origami-based technique for simple, effective and inexpensive fabrication of highly aligned far-field electrospun fibers” combines materials science and machine learning to pinpoint the parameters that truly determine fibre quality. Its clear advantage: it replaces complex arrangements with a practical, scalable and low-cost solution.
The research shows that improving outcomes often requires better design, not more technology.
Change the process, not the machine
When biomedical fibres are aligned in a single direction, their mechanical properties improve, and they can guide cell growth more effectively. Techniques using parallel electrodes, magnetic fields, specialised collectors, or post-stretching treatments are expensive, often need extra components, limit the collection area, or introduce multi-step processes.
In shared labs, where equipment must remain versatile, modifying a standard electrospinning system is seldom viable. Rather than changing the machine, the team proposed folding a thin aluminium sheet to create a series of regular creases, which could be placed on a conventional rotating drum collector.
These folds—inspired by origami—guide fibre deposition and encourage directional alignment without altering the original system.
This design enables evaluation of when fibres align best and which manufacturing conditions maximise the advantages of an origami-style collector.
The fibres were made from poly(ε-caprolactone) (PCL), a biodegradable polymer widely used in biomedical applications. The team ran 243 experiments, systematically varying polymer concentration, flow rate, needle-to-collector distance, drum rotation speed, and needle diameter.
Each sample was analysed by scanning electron microscopy (SEM), fibre diameter measurements, water contact-angle analysis, fast Fourier transform (FFT), and surface intensity mapping.
One variable stood out clearly
The Main Driver
After demonstrating the origami design’s ability to guide alignment, the next step was to identify which process variables most influenced final material quality.
Polymer concentration—the material used to form the fibres, here PCL, proved to be the key factor. At 5% solution, no continuous fibres formed, only scattered structures. At 10% and 15%, uniform, bead-free, and markedly better-aligned fibres were obtained.
Other variables fine-tune the result. Diameters ranged from 1.2 to 1.6 micrometres. Higher flow rates produced thicker fibres, while increasing needle-to-collector distance reduced diameter. Drum speed had little effect on dimension.
Surface properties changed, too. Fibres remained hydrophobic (115°–129°), but higher flow produced denser, more water-repellent mats. A greater distance created more porous structures, allowing better water penetration.
To assess alignment, the team used fast Fourier transform (FFT), a mathematical tool that reveals orientation patterns in microscopic structures. Disordered fibres produce diffuse signals; aligned fibres yield defined, consistent patterns.
Surface analyses confirmed that, as conditions were optimised, fibre orientation became more uniform.
One more step
The study’s most innovative aspect is the integration of machine learning.
Fibres were classified as high quality if they met four criteria: strong alignment, absence of beads, homogenous deposition, and uniform thickness. Only 14.8% of the 243 experiments met all criteria.
Two interpretable machine-learning models—logistic regression and decision trees—were applied to identify which parameters most strongly predicted high-quality fibres.
Both models confirmed polymer concentration as the dominant factor, followed by drum rotation speed.
The logistic regression model correctly predicted high-quality fibres 88% of the time, far above chance.
Those patterns clarify the mechanism: at low concentration, fibres fail to align; at intermediate concentrations, results improve, but only if the drum spins fast enough. In other words, success requires the right combination of settings, not a single tweak.
Another key finding concerns the collector itself. The folded aluminium sheet with regular creases prevented bead formation, a defect that degrades fibre quality. The creases act as guides that order deposition without adding anything to the equipment.
Taken together, the results show that a simple folded-aluminium collector can produce highly aligned, defect-free fibres using a conventional kit. Coupled with machine-learning tools to optimise manufacturing conditions, this approach offers an accessible, low-cost option for labs seeking to develop high-quality biomedical materials
Main reference
Hosseinian, H., Jimenez-Moreno, M., Sher, M. et al.An origami-based technique for simple, effective, and inexpensive fabrication of highly aligned far-field electrospun fibers. Sci Rep 13, 7083 (2023)
Other references
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Author
Hamed Hosseinian. Researcher in nanotechnology and bioengineering, specializing in synthetic biology, gene editing, and three-dimensional models for biomedical applications. His work focuses on developing experimental platforms to study complex cellular systems, evaluate therapeutic strategies, and advance scientific and technological innovation. He is a research professor at the Tecnológico de Monterrey School of Engineering and Sciences.
