Stereolithographic printing of ionically-crosslinked alginate hydrogels for degradable biomaterials and microfluidics

Abstract

3D printed biomaterials with spatial and temporal functionality could enable interfacial manipulation of fluid flows and motile cells. However, such dynamic biomaterials are challenging to implement since they must be responsive to multiple, biocompatible stimuli. Here, we show stereolithographic printing of hydrogels using noncovalent (ionic) crosslinking, which enables reversible patterning with controlled degradation. We demonstrate this approach using sodium alginate, photoacid generators and various combinations of divalent cation salts, which can be used to tune the hydrogel degradation kinetics, pattern fidelity, and mechanical properties. This approach is first utilized to template perfusable microfluidic channels within a second encapsulating hydrogel for T-junction and gradient devices. The presence and degradation of printed alginate microstructures were further verified to have minimal toxicity on epithelial cells. Degradable alginate barriers were used to direct collective cell migration from different initial geometries, revealing differences in front speed and leader cell formation. Overall, this demonstration of light-based 3D printing using non-covalent crosslinking may enable adaptive and stimuli-responsive biomaterials, which could be utilized for bio-inspired sensing, actuation, drug delivery, and tissue engineering.

Figure 1

(A) Approach for 3D printing and degradation of alginate microstructures. (i) Precursor solutions consisting of alginate, cation salts and photoacid generators were selectively illuminated with 355 nm UV light, resulting in (ii) direct writing due to ionic crosslinking, which could undergo (iii) triggered degradation by using EDTA to chelate cations. (B) Schematic of stereolithography apparatus. (C) Photopolymerization mechanism based on UV excitation of photoacid generators to dissociate cationic salts. (D) Degradation mechanism using EDTA to chelate cations.

Figure 2

(A) Approach to measure degradation kinetics of printed alginate disks (10 mm diameter, 1 mm height) in EDTA, and representative images of alginate degradation over 80 min. Scale bar = 1 cm. (B) Degradation kinetics as a function of ionic composition can be classified into three regimes based on “no printing,” “fast degradation,” (Fast) and “slow degradation” (Slow).

Figure 3

(A) Initial CAD design for a flat spiral structure. (B) Schematic showing 3D printing of spiral structure. (C) Representative brightfield image of a printed spiral. (D) Corresponding feature detection with overcure (red) and undercure (green). (E) Pattern fidelities for various precursor solutions with varying ion composition and concentration (n = 6 per condition). Error bars represent standard deviation. (F) Experimental setup for the modified “windowpane” test used to calculate the working curve. (G) The working curve for 3% alginate with 1:3 Ba²⁺: Mg²⁺ used to empirically determine the critical exposure (E_C) and the penetration depth (D_P). Trendline R² = 0.9984. Error bars represent standard deviation.

Figure 4

(A) Mechanical measurements using spherical indentation with representative force relaxation trace. Shear modulus was calculated using the instantaneous load response (red circle). (B) Box and whisker plots of shear moduli for various precursor formulations, with mean shear modulus listed for each formulation. Data sets were compared using a Student’s t-test and statistically significant differences between conditions are indicated by * (p < 0.05) and ** (p < 0.01).

Figure 5

(A) CAD representation of the stepped ramp geometry starting with a height of 175 μm, increasing by 175 μm to a final height of 1.6 mm. (B) Printed alginate stepped ramp after post-curing with 1X CaCl₂. (C) Cross-sectional view of the stepped ramp showing incremental height increases of ~250 μm (indicated by red lines). (D) CAD representation of a microfluidic mixer with 1.4 mm-high reservoirs and 350 μm-high mixing channel. (E) Printed microfluidic mixer showing raised reservoirs. (F) Microfluidic gravity-driven mixing of green food dye with water. Black dashed line marks the outline of the printed part.

Figure 6

Comparison of gradients generated between source and sink channels of even and uneven height, which are expected to establish spatially nonuniform concentration profiles both laterally and vertically. (A) Even 100 μm source and sink channels encapsulated in 1.5% agarose. (i) CAD design, (ii) Alginate printed template. (B) Evans Blue intensity (arbitrary units) as a function of time for a gradient between source and sink channels. Note that the intensity profiles at location 1 and 2 are comparable over time. (C) COMSOL simulations of Evans Blue diffusion in 1.5% agarose between even channels. (D) Simulated concentration profiles as a function of time at a height of z = 500 μm. (E) Uneven 100–500 μm source and 100 μm sink channels encapsulated in 1.5% agarose (i) CAD design, (ii) Alginate printed template. (F) Evans Blue intensity (arbitrary units) as a function of time for a gradient between source and sink channels. Note that the intensity profile is diffusing more rapidly at location 1 relative to location 2, due to the taller source channel at location 1. (G) COMSOL simulations of Evans Blue diffusion in 1.5% agarose between even channels. (H) Simulated concentration profiles as a function of time at a height of z = 500 μm.

Figure 7

(A) Schematic of the experimental design for collective cell migration from initially straight or convex geometries. Alginate structures were printed, cells seeded around the printed structures, and allowed to grow to 90% confluency. Alginate structures were then degraded using 10mM EDTA, and cells allowed to migrate into the empty space over time. Red boxes indicate an example of the field of view imaged. Black arrows indicate direction of cell migration. (B–E) Representative fluorescence microscopy images of MCF-10A cell front migration at 12 hr timepoints. (B) DMSO control from initially straight geometries, (C) OHT treatment from initially straight geometries. (D) DMSO control from initially convex geometries, (E) OHT treatment from initially convex geometries.