Page 38 - IJB-10-4

International Journal of Bioprinting 3D bioprinting in otorhinolaryngology

Figure 1. 3D bioprinting techniques (adapted with permission from ref. ).
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membrane rupture and cell death. In addition, with the 2.2. Droplet-based 3D bioprinting
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extrusion injection method, bioinks can block the nozzle; Droplet-based 3D bioprinting can be divided into inkjet,
however, this is not a concern when using nozzle-free laser- acoustic droplet, and microvalve droplet bioprinting.
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based 3D bioprinting. In comparison with the other two Inkjet bioprinting uses the gravitational, thermal, and
commonly used bioprinting techniques (i.e., droplet-based electrostatic mechanics of bioinks to eject droplets onto
bioprinting and laser-based bioprinting), increasing the a receiving substrate. Acoustic droplet bioprinting
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speed and throughput of extrusion-based 3D bioprinting uses gentle sound waves to eject droplets from an open
results in low-resolution printed models. 26,29 Several pool, whereas microvalve droplet bioprinting uses an
methods have been proposed to correct these shortcomings electromechanical valve to pressurize the bioink, creating
and enhance the bioprinting accuracy and efficiency. Wang enough pressure to overcome the surface pressure and
et al. monitored the bioprinting process using optical produce droplets. 34,35 Owing to the non-contact nature
coherence tomography (OCT) and constructed a multi- of the droplet-based 3D bioprinting techniques, harmful
material static model and time-related control model for stress is avoided, making these methods convenient for in
extrusion-based multi-material bioprinting. This model situ bioprinting. Further advantages of droplet-based 3D
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effectively reduced the time required for bioprinting and bioprinting include a low cost, faster bioprinting speed, high
identified a relationship between fiber size and other repeatability, resolution, and cell viability. 36-39 However,
parameters. The establishment of this model created droplet-based 3D bioprinting has many disadvantages.
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an effective and fast error identification and correction Bioink fragments can cause blockages in the orifices,
process, by which the disadvantages of conventional and consequently, the selected bioink must have a low
extrusion-based 3D bioprinting can be minimized and the viscosity (<10 mPa·s) and cell density (<10 cells/mL). 40,41
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advantages exploited accordingly. Brion et al. developed The acoustic droplet jet bioprinting process may introduce
a multi-head neural network that could automatically interfering factors that disrupt process control, and sound
establish a bioprinting database using data acquisition waves could fail to spray droplets of viscous bioinks with
and annotation. The trained neural network identified high cell concentrations. Controlled by the valve opening
the most recent bioprinting parameters and monitored time of pressure, the microvalve bioprinter is more suitable
and corrected various bioprinting errors to optimize the for high viscous materials, but the bioprinting of high-
bioprinting process. Extrusion-based 3D bioprinting resolution bioinks is relatively poor.
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can also be combined with multi-materials, coaxial,
and micro-bioprinting techniques. These combinations However, several methods could be used to address these
simultaneously improve bioink printability, bioprinting disadvantages and improve the bioprinting process. Zhang
speed, and model resolution. Bagnol et al. explored the et al. proposed a novel hybrid machine-learning method
combination of coaxial bioprinting and micro-extrusion- for exploring the bioprinting space using Latin hypercube
based 3D bioprinting. Coaxial printed calcium phosphate sampling. Their results revealed a causal relationship
(CaP) cement with water and ethanol mixtures was between the morphology of the deposited droplets and the
used to refine the coaxial bioprinting and increase shape characteristics of printed lines using K-means clustering.
stabilities. With the integration of multidisciplinary These data ensured bioprinting quality in the design
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technologies, errors have been effectively reduced. space using support vector machines. Gaussian process
Nonetheless, future developments are expected to enhance regression was adopted to establish the geometrical
the application value of extrusion-based 3D bioprinting. characteristics of the droplets in the process model and

Volume 10 Issue 4 (2024) 30 doi: 10.36922/ijb.3006