3D Printed Single Mold Multi-Level (SMILE) Interconnected Microfluidic Chip for Droplet Generation
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».