Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/88832
Title: Numerical characterization of nano-droplet formation in 3D nano-inkjet printing
Authors: Aphinyan, Suphanat
Keywords: DRNTU::Engineering::Mechanical engineering
Issue Date: 12-Sep-2018
Source: Aphinyan, S. (2018). Numerical characterization of nano-droplet formation in 3D nano-inkjet printing. Doctoral thesis, Nanyang Technological University, Singapore.
Abstract: The process of nano-droplet formation is crucial to the development of 3D nano-inkjet printing. In this thesis, nano-droplet formation is numerically characterized using dissipative particle dynamics (DPD). Due to size constraints and challenges in the development and optimization of 3D nano-inkjet printer nozzles, experimental means to characterize the nano-droplet formation process is currently impractical. Hence, numerical method becomes a valuable tool to characterize this process effectively. Dissipative particle dynamics (DPD) and many-body DPD (MDPD) are employed in this work to study nano-droplet formation process. Such mesoscale particle-based Molecular Dynamics (MD) simulation techniques provide cost effective means to study such systems. This project starts by investigating agglomeration, which is a process that leads to nozzle clogging in 3D nano-inkjet printing. Nozzle clogging is one of the major hurdle in the development of 3D nano-inkjet printing today. Using DPD, a UV ink system, which comprises of oligomers and monomers of polyethylene glycol (PEG) and polystyrene (PS) with benzophenone (BZP) as the photo-initiator, is modelled. The results show that a 3:1 ratio of PEG to PS provides the best morphology in terms of particle uniformity, agglomerate size, and particulate dispersion. A surfactant, sodium dodecyl sulphate (SDS), is added to the model to improve these agglomeration behaviors. It is found that SDS can help to prevent further agglomeration while reducing the average size of the agglomerates from 520 Å to 440 Å and improving their distribution from 7 clusters to 14 smaller clusters. Next, a nano-nozzle together with UV ink system is modelled using MDPD to simulate the process of nano-droplet formation through a 3D nano-inkjet nozzle. Comparison with microscale experiment confirms that the model gives good agreement and consistency within 10% range of error. The validated model is subjected to varying temperature and pressure in order to predict the influence of these external parameters on nano-droplet formation. It is found that higher temperatures and applied pressures increase droplet velocity and reduce droplet break-up time. In addition, higher temperatures increase the droplets’ diameter while higher effective pressures reduce it. The MDPD simulation also revealed that apart from ink agglomeration, ink deposition on the nozzle’s wall is another potential source of nozzle clogging. Simulation results show the addition of surfactant can effectively reduce such ink deposition. Only a small amount of surfactant between the interval of 0.2-3.0 wt% is sufficient to reduce ink deposition by 60%. Increasing the amount of surfactant beyond 3.0 wt% does not give better improvements. To further reduce ink deposition, other de-agglomeration techniques are suggested to modify the interaction between ink and wall of nozzle. This can be achieved via physical or chemical techniques such as electric field or application of a non-wetting coat. By combining these de-agglomeration techniques, it is shown that ink deposition can be reduced by nearly 92%. Thus, nano-droplet formation in 3D nano-inkjet printing and challenges in its breakthrough such as agglomeration, which can lead to nozzle clogging, may be numerically studied by MDPD, as a cheaper alternative approach, and providing approximate guidelines for further experimental research and development.
URI: https://hdl.handle.net/10356/88832
http://hdl.handle.net/10220/45973
DOI: https://doi.org/10.32657/10220/45973
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:MAE Theses

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