Enhancing Droplet Quality of Edible Ink in Single and Multi-Drop Methods by Optimization the Waveform Design of DoD Inkjet Printer

The multi-drop method with a good droplet quality is a big challenge in inkjet technology. In this study, optimization of Drop on Demand (DoD) inkjet printer waveform design was conducted. The effectiveness of the waveform design, so-called W waveform, from previous study as a preliminary vibration for the multi-drop ejection method was investigated. The unmodified W waveform was proven not to be an effective waveform for lower viscosity of liquid, especially when compared by the standard waveform obtained from a print-head manufacturer. Edible ink with a viscosity below the optimum range for print-head specifications was employed as the operating liquid. The preliminary vibration W waveform was modified to improve the droplet quality of the edible ink. It was proven that a 40% adjusted voltage of the rear wave of the W waveform was effective as the optimum waveform design for edible ink. The droplet quality of the multi-drop ejection method for grey-scale technology was improved by optimizing the W waveform design.

Assessing the Relationships between Interdigital Geometry Quality and Inkjet Printing Parameters

Drop on demand (DoD) inkjet printing is a high precision, non-contact, and maskless additive manufacturing technique employed in producing high-precision micrometer-scaled geometries allowing free design manufacturing for flexible devices and printed electronics. A lot of studies exist regarding the ink droplet delivery from the nozzle to the substrate and the jet fluid dynamics, but the literature lacks systematic approaches dealing with the relationship between process parameters and geometrical outcome. This study investigates the influence of the main printing parameters (namely, the spacing between subsequent drops deposited on the substrate, the printing speed, and the nozzle temperature) on the accuracy of a representative geometry consisting of two interdigitated comb-shape electrodes. The study objective was achieved thanks to a proper experimental campaign developed according to Design of Experiments (DoE) methodology. The printing process performance was evaluated by suitable geometrical quantities extracted from the acquired images of the printed samples using a MATLAB algorithm. A drop spacing of 140 µm and 170 µm on the two main directions of the printing plane, with a nozzle temperature of 35 °C, resulted as the most appropriate parameter combination for printing the target geometry. No significant influence of the printing speed on the process outcomes was found, thus choosing the highest speed value within the investigated range can increase productivity.

A high-throughput 3D bioprinted cancer cell migration and invasion model with versatile and broad biological applicability

Understanding the underlying mechanisms of migration and metastasis is a key focus of cancer research. There is an urgent need to develop in vitro 3D tumor models that can mimic physiological cell-cell and cell-extracellular matrix interactions, with high reproducibility and that are suitable for high throughput (HTP) drug screening. Here, we developed a HTP 3D bioprinted migration model using a bespoke drop-on-demand bioprinting platform. This HTP platform coupled with tunable hydrogel systems enables (i) the rapid encapsulation of cancer cells within in vivo tumor mimicking matrices, (ii) in situ and real-time measurement of cell movement, (iii) detailed molecular analysis for the study of mechanisms underlying cell migration and invasion, and (iv) the identification of novel therapeutic options. This work demonstrates that this HTP 3D bioprinted cell migration platform has broad applications across quantitative cell and cancer biology as well as drug screening.

On-demand Ferrofluid Droplet Formation With Non-linear Magnetic Permeability in the Presence of a Non-uniform Magnetic Field

The magnetic actuation of ferrofluid droplets offers an inspiring tool in widespread engineering and biological applications. In this study, the dynamics of ferrofluid droplet generation with a Drop-on-Demand feature under a non-uniform magnetic field is investigated by multiscale numerical modeling. Langevin equation is assumed for ferrofluid magnetic susceptibility due to the strong applied magnetic field. Large and small computational domains are considered. In the larger domain, the magnetic field is obtained by solving Maxwell equations. In the smaller domain, a coupling of continuity, Navier Stokes, two-phase flow, and Maxwell equations are solved by utilizing the magnetic field achieved by the larger domain for the boundary condition. The Finite volume method and coupling of level-set and Volume of Fluid methods are used for solving equations. The droplet formation is simulated in a two-dimensional axisymmetric domain. The method of solving fluid and magnetic equations is validated using a benchmark. Then, ferrofluid droplet formation is investigated experimentally and the numerical results are in good agreement with the experimental data. The effect of 12 dimensionless parameters including the ratio of magnetic, gravitational, and surface tension forces, the ratio of the nozzle and magnetic coil dimensions, and ferrofluid to continuous-phase properties ratios are studied. The results showed that by increasing the magnetic Bond number, gravitational Bond number, Ohnesorge number, dimensionless saturation magnetization, initial magnetic susceptibility of ferrofluid, the generated droplet diameter reduces, whereas the formation frequency increases. The same results were observed when decreasing the ferrite core diameter to outer nozzle diameter, density, and viscosity ratios.

Multi-Material 3D Printing of a Customized Sports Mouth Guard: Proof-of-Concept Clinical Case

A multilayer mouth guard is known to have the best protective performance. However, its manufacturing in a digital workflow may be challenging with regards to virtual design and materialization. The present case demonstrates a pathway to fabricate a multilayer individualized mouth guard in a fully digital workflow, which starts with intraoral scanning. A free-form CAD software was used for the virtual design. Two various CAM techniques were used, including Polyjet 3D printing of rubber-like soft material and silicone printing using Drop-on-Demand technique. For both methods the outer layer was manufactured from more rigid materials to facilitate its protective function; the inner layer was printed from a softer material to aid a better adaptation to mucosa and teeth. Both 3D printed multilayer mouth guards showed a clinically acceptable fit and were met with patient appraisal. Their protective capacities must be evaluated in further clinical studies.

Experimental Methods to Enable High-Throughput Characterization of New Structural Materials

Data-driven methods for developing new structural materials require large databases to identify new materials from known process routes, the resulting microstructures, and their properties. Due to the high number of parameters for such process chains, this can only be achieved with methods that allow high sample throughputs. This paper presents the experimental approach of the "Farbige Zustände" method through a case study. Our approach features a high-temperature drop-on-demand droplet generator to produce spherical micro-samples, which are then heat-treated and subjected to various short-time characterizations, which yield a large number of physical, mechanical, technological, and electrochemical descriptors. In this work, we evaluate achievable throughput rates of this method resulting in material property descriptions per time unit. More than 6000 individual samples could be generated from different steels, heat-treated and characterized within 1 week. More than 90,000 descriptors were determined to specify the material profiles of the different alloys during this time. These descriptors are used to determine the material properties at macro-scale.