3D Printed Single Mold Multi-Level (SMILE) Interconnected Microfluidic Chip for Droplet Generation
Bibliographic record
Abstract
Droplet microfluidics is essential for applications such as droplet formation1, particle synthesis2, and cell and gene manipulation3. Most droplet generators use flow-focusing technology in rectangular microchannels4, typically fabricated via lithography from polydimethylsiloxane (PDMS), polymers, or glass. However, planar designs limit chip size and create alignment challenges for inner and outer capillaries4. To address these limitations, multilevel soft lithography can be used to create non-planar channel configurations, such as 3D mixers with multi-layered microchannels for improved diffusion5. However, its reliance on multiple photolithography steps makes it complex and time-consuming. Moreover, 2D patterning in soft lithography restricts the development of advanced 3D designs, like microfluidic networks and non-planar devices6. Alternatively, 3D printing provides an efficient solution for creating molds, enabling the fabrication of detailed and complex microfluidic structures with greater accuracy and simplicity. In this study, we developed one-step SMILE technology to fabricate a microfluidic device for generating suspended droplets using a T-junction configuration. While SMILE supports multi-level designs, we demonstrate it here with two levels for simplicity. The chip features top and bottom microchannels, Fig. 1A, enabling higher throughput in a compact design. Before mold fabrication, numerical flow analysis within the channels was performed using a 3D simulation of the device in COMSOL Multiphysics 6.0. Although droplet size can be controlled by adjusting the velocity ratio of the two phases, the size of the vertical channels provides an additional parameter for fine-tuning droplet size. In this study, the channels for transferring oil and water are rectangular, with dimensions of 400 × 400 µm and 600 × 600 µm, located at the top and bottom levels, respectively, Fig.1A. To complete the design, a collection chamber was incorporated to gather the generated droplets. A time-domain study employed a two-phase level-set method to predict the flow behavior. The velocity fields and droplet generation were investigated for continuous phase flow rate ratios of 1, 5, 10, 15, and 20. This analysis was conducted to study the chip's performance with a constant design. The initial flow rates for both water and oil at their respective inlets were 6.66 ml/min. Fig.1B presents the velocity field for a flow rate ratio of 10, showing average velocities of 0.694 mm/s at the oil inlet and 3.08 mm/s at the water inlet. The droplet generation process is illustrated in Figs.1C and 1D. At a flow rate ratio of 10, droplet breakup occurs in 2.12 seconds. However, increasing the flow rate ratio leads to faster droplet formation. The computed capillary numbers corresponding to each flow rate ratio are shown in Fig.1E. The capillary number increases linearly with the flow rate ratio, indicating that higher flow rates of the continuous phase (water) lead to greater shear forces acting on the dispersed phase (oil). This increase in the capillary number corresponds to faster droplet formation, as the higher shear force promotes quicker droplet breakup. Using the optimized design based on the simulation results, the mold for the microfluidic chip was 3D printed with an SLA 3D printer (Form 3, Formlabs, US) using high-temperature resin. After post-processing, the 3D-printed parts were successfully used to fabricate a microfluidic chip from PDMS. The mold used for fabrication consists of two separable parts, Fig.1F. Four pillars extend from the bottom mold, Fig.1F, creating vertical channels connecting the final device's top and bottom planar microfluidic channels. Two inlets and one outlet are designed and positioned at the same level to simplify integration with microfluidic tubing. When the mold parts are assembled, a gap forms between them, shaping the cavity where the PDMS is poured, Fig.1Gi. Once the PDMS cures, Fig.1Gii, the molded microfluidic layer is peeled off from the mold, Fig.1H. This PDMS layer is then bonded between two plain PDMS layers, closing the channels on the top and bottom to complete the chip, Fig.1H. Before bonding, three holes are punched into the top plain PDMS layer, aligned with the locations of the two inlets and the outlet. The performance of the chip was tested by injecting water and oil into the inlets. The experimental results closely matched the simulation outcomes, validating the functionality of the proposed compact microfluidic design for simultaneous droplet generation. This design has potential applications in bio-related fields, such as cell encapsulation for 3D bioprinting, single-cell analysis, and high-throughput assays. Reference: 1.Shang, L. et al., Chem. Rev., 2017. 2.Niculescu, A. G. et al., Nanomaterials, 2021. 3.Ryckelynck, M. et al., Rna, 2015. 4.Dewandre, A. et al., Sci. Rep., 2020. 5.Bathini, S. et al., Biosens. Bioelectron. 2021. 6.Su, R. et al., Lab Chip, 2023. Figure 1
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".