3D Printing of Molds for Soft Lithography
Bibliographic record
Abstract
Microfabrication of PDMS based microfluidic devices involves fabrication of micromolds which need expensive infrastructure, such as clean room, photolithography equipment, photoresists, substrates, masks etc. In this paper, we present fabrication of micromolds using three dimension (3D) printing for soft lithography [1, 2]. By employing 3D printing technology overcomes the challenges posed by time consuming and difficult to manufacture, with state of the art 2-D MEMS fabrication technology [3]. Compared to the conventional method of microfabrication, 3D printed mold can be processed more rapidly, augment use of materials, intricate design which is challenging by photolithography [4,5]. Here, we designed and fabricated micro features using binder jetting printer (Objet30 Pro) and photopolymer jetting printer (Formlabs Form 2). Objet30 Pro uses the technique of depositing adhesive liquid onto the powder photopolymer on the build tray. Binding process between the powdered particles make a solid structure. During the printing of master no support structures where used. The master was also printed using the deposition of photopolymer using the Formlabs Form 2 printer. Concept behind the fabrication is curing the photopolymer by UV light which is deposited pointwise layer-by layer. Since, the fabrication of the master is done in slices the surface of the master was rutted which gave bumpy replication but, were able to print the feature (Round trench, Hexagonal trench) size of 200μm (Fig 1 and 2). Mold fabricated using the Objet30 pro printer gave fine structures with resolution of 300μm in depth and feature size of 500μm were printed flawless. This technique forms a 3D printed structure by holding the particles and does not require support structure. References: Khosla, Ajit, and Bonnie L. Gray. "(Invited) Micropatternable Multifunctional Nanocomposite Polymers for Flexible Soft NEMS and MEMS Applications." ECS Transactions 45.3 (2012): 477-494. doi: 10.1149/1.3700913 Khosla, A. (2011). Micropatternable multifunctional nanocomposite polymers for flexible soft MEMS applications (Doctoral dissertation, Applied Science: School of Engineering Science). http://summit.sfu.ca/item/12017 Khosla, Ajit. "Nanoparticle-doped electrically-conducting polymers for flexible nano-micro Systems." The Electrochemical Society Interface 21.3-4 (2012): 67-70.doi: 10.1149/2.F04123-4if Takamatsu, K., Basher, S., He, S., Sato, K., Yoshida, K., Sakai, K., ... & Khosla, A. (2017, September). 3D Printing of Micromolds and Microfluidic Devices. In Meeting Abstracts(No. 49, pp. 2113-2113). The Electrochemical Society. Takamatsu, K., Yamada, N., Wada, M., Ahmed, K., Kawakami, M., Kassegne, S., ... & Khosla, A. (2016, September). 3-D Printed Polymer MEMS. In Meeting Abstracts (No. 51, pp. 3861-3861). The Electrochemical Society. Figure 1
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".