Inkjet Printhead Research Articles

Effect of high-boiling point solvents on inkjet printing of catalyst layers for proton exchange membrane fuel cells

Inkjet printing (IJP) is considered as a promising and flexible method for low cost and drop-on-demand pattern formation in proton exchange membrane (PEM) fuel cells. Although significant advancements have been made in inkjet-printed electrodes, identifying the optimal ink formulations remains challenging due to severe restrictions on the ink properties. Generally, the catalyst layer prepared by IJP using low boiling point solvents (water/alcohol) show reasonable electrochemical performance. However, low boiling point solvents lead to clogging of the printhead nozzle due to fast evaporation of the solvent in the inkjet print head. In this study, high boiling point solvents (propylene glycol (PG) or ethylene glycol (EG)) were used as additives to reduce nozzle clogging and improve printing efficiency. In this context, the relationship between catalyst ink properties, CL microstructure, and electrochemical performance of the MEA is investigated. Results show that printing time is significantly reduced with adding high boiling point solvents in the ink (66.67% reduction of time with 30 wt% PG). However, an increased amount of additive is detrimental in terms of electrochemical performance due to formation of larger agglomerates, lower porosity of the catalyst layer, and reduced ECSA. The results of this work can be used to develop a strategy to adjust the trade-off between printing time and electrochemical performance of electrodes prepared using IJP.

Design and simulation of a hybrid inkjet printhead for ejecting high viscous inks

Design and simulation of a hybrid inkjet printhead for ejecting high viscous inks

Advanced roll porous scaffold 3D bioprinting technology.

Improvements in the roll porous scaffold (RPS) 3D bioproduction technology will increase print density of 10-15µm cells by ~ 20% up to ~ 1.5 × 108 cells/mL and purity of organoid formation by > 17%. The use of 360 and 1200 dpi inkjet printheads immediately enables biomanufacturing with 10-30µm cells in a single organoid with performance > 1.8 L/h for 15µm layer thickness. The spongy bioresorbable ribbon for RPS technology is designed to solve the problems of precise placement, leakage and increasing in the number of instantly useable cell types and superior to all currently dominant 3D bioprinting methods in speed, volume, and print density without the use of expensive equipment and components. The potential of RPS for parallel testing of new substances studied was not on animals, but using generated 3D biomodels "organ on a chip". Solid organoids are more suitable for personalized medicine with simultaneous checking of several treatment methods and drugs, targeted therapy for a specific patient in vitro using the 3D composition of his personal cells, and selection of the most effective ones with the least toxicity. Overcoming the shortage of organs for implantation and personal hormone replacement therapy for everyone was achieved using printed endocrine glands based on their DNA.

A novel high-DPI and monodisperse droplet inkjet printhead with the piezoelectric cutter

High dots per inch (DPI) is the core index of inkjet printer, which is hindered by satellite ink droplet. Herein, we propose a novel high-DPI and monodisperse droplet inkjet printhead with the piezoelectric cutter. The as-established model has optimized the inkjet printhead structural parameters, actuating and cutting signal waveforms. The cutter element achieves moving the break-up point to the middle of the ink column, reducing the length of tail and generating a monodisperse droplet. Additionally, the cutter consistently reduces the droplet length with different ink properties including viscosity, density, surface tension, and contact angle, exhibiting high applicability. The research results provide an in-depth study on the design of high-DPI and monodisperse inkjet printheads, offering an efficient approach to improve inkjet printhead performance.

All inkjet-printed organic solar cells on 3D objects

Drop-on-demand inkjet printing is a promising and commercially relevant technology for producing organic electronic devices of arbitrary shape on a wide variety of different substrates. In this work we transfer the inkjet printing process of organic photovoltaic devices from 2D to 3D substrates, using a 5-axis robot system equipped with a multi-nozzle inkjet printing unit. We present a ready-to-use 3D printing system for industrial application, using a 5-axis motion system controlled by commercial 3D motion software, combined with a commonly used multi-nozzle inkjet print head controlled by the corresponding printing software. The very first time inkjet-printed solar cells on glass/ITO with power conversion efficiencies (PCEs) of up to 7% are realized on a 3D object with surfaces tilted by angles of up to 60° against the horizontal direction. Undesired ink flow during deposition of the inkjet-printed layers was avoided by proper ink formulation. In order to be able to print organic (opto-)electronic devices also on substrates without sputtered indium tin oxide bottom electrode, the bottom electrode was inkjet-printed from silver nanoparticle (AgNP) ink, resulting in the first all inkjet-printed (i.e. including bottom electrode) solar cell on a 3D object ever with a record PCE of 2.5%. This work paves the way for functionalizing even complex objects, such as cars, mobile phones, or ‘Internet of Things’ applications with inkjet-printed (opto-)electronic devices.

Binder-jet 3D printing of pea-based snacks with modulated texture

This paper demonstrates, for the very first time, 3D printing of pea-based snacks with tunable texture using the binder jetting (BJT) method, pea flour as the print powder, and an aqueous binder solution. The binding mechanism was studied by performing both Hele-Shaw cell experiments and confocal microscopy on the printed samples. As the pea flour was exposed to an aqueous binder, the starch granules swelled. These granules remained swollen but were further joined by a network of protein-rich bridges as the sample dried. Inclusion of sugar and post-printing baking tended to strengthen the as-printed samples, and the resultant mechanical properties were on par with commercial snacks that are not 3D printed. By controlling the amount of aqueous binder dispensed from the inkjet print heads, more than one order of magnitude differences in compressive strength and modulus were achieved, demonstrating the exciting potential of using 3D printing for texture modulation of plant-based snacks.

3D Inkjet Printing of Biomaterials with Solvent‐Free, Thiol‐Yne‐Based Photocurable Inks

Abstract3D inkjet printing is a fast, reliable, and non‐contact bottom–up approach to printing small and large models and is one of the fastest additive manufacturing technologies available. These attributes position inkjet printing as a promising tool for the additive manufacturing of biomaterials, for example, tissue engineering scaffolds. However, due to the stringent technical rheological requirements of current inkjet technologies, there is a lack of photopolymer resins suitable for the inkjet printing of biomaterials. Hence, a novel ink engineered for 3D piezoelectric inkjet printing of biomaterials is designed and developed. The novel resin leverages a biodegradable amino acid phosphorodiamidate matrix copolymerized with a dialkyne ether to modulate the viscosity. Copolymerization with commercially available thiols facilitates the photochemical thiol‐yne curing reaction. The ink exhibits optimal viscosity, eliminating the need for solvents, as well as reliable jetting and sufficiently swift curing kinetics. Furthermore, the formulation is successfully demonstrated in an industrial inkjet printhead. Notably, the resulting materials have low cytotoxicity and, hence, have significant promise in advancing the applications of 3D inkjet printing of biological scaffolds.

An open-source, 3D printed inkjet DNA synthesizer

Synthetic oligonucleotides have become a fundamental tool in a wide range of biological fields, including synthetic biology, biosensing, and DNA storage. Reliable access to equipment for synthesizing high-density oligonucleotides in the laboratory ensures research security and the freedom of research expansion. In this study, we introduced the Open-Source Inkjet DNA Synthesizer (OpenIDS), an open-source inkjet-based microarray synthesizer that offers ease of construction, rapid deployment, and flexible scalability. Utilizing 3D printing, Arduino, and Raspberry Pi, this newly designed synthesizer achieved robust stability with an industrial inkjet printhead. OpenIDS maintains low production costs and is therefore suitable for self-fabrication and optimization in academic laboratories. Moreover, even non-experts can create and control the synthesizer with a high degree of freedom for structural modifications. Users can easily add printheads or alter the design of the microarray substrate according to their research needs. To validate its performance, we synthesized oligonucleotides on 144 spots on a 15 × 25-mm silicon wafer filled with controlled pore glass. The synthesized oligonucleotides were analyzed using urea polyacrylamide gel electrophoresis.

Piezoelectric-based Monitoring of Pressure Variations in Inkjet Printhead

Piezoelectric-based Monitoring of Pressure Variations in Inkjet Printhead

New Designs and Manufacturing Possibilities for Inkjet Print Head Nozzle Plates

New Designs and Manufacturing Possibilities for Inkjet Print Head Nozzle Plates

Jetting and droplet formation of particle-loaded fluids

Inkjet printing is an attractive method for patterning and fabricating objects across many areas of industry. There is a growing interest in the printing of inks with high particle-loading, such as inks containing glass frit, ceramic and functional inks. However, the use of these inks is often limited due to uncertainty regarding the impact of their rheology on the printing process. Understanding of the role of complex rheology in the jetting of loaded inks is therefore needed to facilitate the wider application of inkjet printing. Here, we characterize the complex rheology and the jetting of model dispersion inks (containing 10, 15, and 23 vol. % TiO2 nanoparticles) and compared them with those without particles. The jetting of the model fluids was conducted with a commercial inkjet printhead (nozzle diameter 34 μm) and visualized with stroboscopic and ultra-high-speed imaging. For low particle concentrations, droplet formation is generally similar to those of unloaded inks, provided their Ohnesorge number and Weber number are matched, although the filament of the loaded model fluid tends to have earlier break-off, having a shorter length. The jetting reliability decreased with increase in particle-loading until reliable jetting can no longer be achieved, due to local particle–particle interactions in the ink channel and in the filament during the fast extensional thinning process. A jetting map is presented which illustrates the influence of particle-loading on the droplet formation, and indicates that the acceptable range of Ohnesorge number for jetting is reduced as the particle-loading is increased.

Machine learning approach to monitor inkjet jetting status based on the piezo self-sensing

One of the advantages of inkjet printing in digital manufacturing is the ability to use multiple nozzles simultaneously to improve the productivity of the processes. However, the use of multiple nozzles makes inkjet status monitoring more difficult. The jetting nozzles must be carefully selected to ensure the quality of printed products, which is challenging for most inkjet processes that use multi-nozzles. In this article, we improved inkjet print head monitoring based on self-sensing signals by using machine learning algorithms. Specifically, supervised machine learning models were used to classify nozzle jetting conditions. For this purpose, the self-sensing signals were acquired, and the feature information was extracted for training. A vision algorithm was developed to label the nozzle status for classification. The trained models showed that the classification accuracy is higher than 99.6% when self-sensing signals are used for monitoring. We also proposed a so-called hybrid monitoring method using trained machine learning models, which divides the feature space into three regions based on predicted jetting probability: certain jetting, certain non-jetting, and doubt regions. Then, the nozzles with uncertain status in the doubt region can be verified by jet visualization to improve the accuracy and efficiency of the monitoring process.

Taguchi's Grey Relationship Analysis (GRA) for Comparing the Performance of Various Inkjet Printheads for Tone Value Increase on Uncoated Paper Substrates

Digital technologies in printing attract more attention among the printers in recent years. All around the world, inkjet technology is used by both home and commercial printers. New applications for inkjet printing have emerged as the technology has developed, including the printing of high-quality periodicals and the packaging sector. This state-of-the-art technology, particularly Inkjet, has undergone extensive testing and refinement to ensure high quality prints High-quality printing often makes use of gloss coated sheets. Yet the high price of gloss coated paper limits its usefulness to certain types of work. Since then, it has been clear that uncoated papers are the best option for this task. The print quality would be different from that of glossy coated paper because of the roughness, porosity, and unevenness of the surface. Tone Value Increase (TVI) on uncoated paper printed with various inkjet printheads is an intriguing topic for investigation. Therefore, effective TVI is crucial. Multiple commercial inkjet printheads were tested for their TVI performance on uncoated paper and the results were compared using a novel statistical approach Taguchi's Grey Relational Analysis (GRA). The use of this statistical method yielded fruitful results in our study.

Novel and Efficient Methodology for Drop Placement Accuracy Testing of Robot-Guided Inkjet Printing onto 3D Objects

Robot-guided inkjet printing technology offers a new way for the digital and additive deposition of low-viscous inks to be made directly onto arbitrary surfaces and, thus, enables the production of individualized printed electronics on large-scale objects. When compared to conventional flatbed printing, the distance between the nozzle plate and the object’s surface varies and needs to be considered in order to match the accuracy requirements needed for the positioning of single drops. Knowledge about applicable distance limits and the influence of tunable print parameters is crucial for improving the print process and results. This study discusses the sources of errors in the inkjet printing process onto 3D objects and presents extensive results about position accuracy in relation to jetting distance for different parameter sets of functional inks, drop volumes, and piezo voltages. Additionally, an efficient novel method was applied to determine the drop position accuracy of inkjet droplets in relation to the jetting distance. The method relies on cylinder geometry for the object and an inkjet head that is guided by a six-axis robot manipulator along the cylinder’s axis. For the determination of drop placement accuracy, the position of single dots on the surface was compared to a model which considered the cylinder radii, drop velocity, and the movement speed of the guided inkjet printhead. The method and the extensive research results can be utilized for the prediction of achievable drop placement accuracy and the prior definition of distance limits.

High‐precision and high‐stability inkjet printing technology for QD color converter‐type micro‐LED display

AbstractThis paper reports on the development of a specialized inkjet printing head and equipment for high‐resolution printing of QD dispersed inks. First, to achieve a robust process, we proposed an inkjet head design compatible with higher ink viscosity and with an ink circulation system to avoid particle aggregation. Further, we introduce a multi‐restrictor inside the head to minimize ink fluid pumping pulsations on the print head and a drive‐per‐nozzle technology with individual nozzle waveform control. Results include down to 0.8 pL ± 1.8% droplet variation across all nozzles, with a three‐sigma accuracy 1.0 μm for droplet landing position. By combining optimized ink, inkjet head, and equipment technology, we successfully fabricated color converter‐type micro‐LED display panels.

Adjoint-based optimization of the actuator velocity profile in an inkjet print head

We consider the thermo-viscous acoustic flow inside the narrow channel and nozzle of an inkjet print head. We define a cost function to be the sum of the acoustic energy in the channel and the surface energy of the spherical cap of ink at the end of the nozzle. We derive the adjoint equations and obtain the sensitivity of this cost function to boundary forcing from the piezo-electric actuator opposite the nozzle. We use this forcing to eliminate residual oscillations after a droplet is ejected. We use a gradient-based optimization algorithm to find the time-varying boundary forcing that minimizes the cost function at various final times and for geometries with increasing complexity. For all geometries, the actuator first extracts fluid so that the ink/air interface becomes flat. This unavoidably sends an acoustic wave upstream, which reflects off the inlet manifold. The actuator subsequently moves to absorb this returning wave without reflection. The optimal boundary forcing and the final energy depend on the channel length, the actuator length, the forcing’s temporal resolution, and the available optimization time. The minimum time required to dampen residual oscillations is the time taken for waves to travel from the actuator to the inlet and back. For times greater than this, the total energy inside the microchannel can be reduced by a factor of 1000 compared to the uncontrolled case. This method is general and can be applied to other cost functions and initial conditions. Successful application of this method could lead more repeatable droplets at higher ejection frequencies.

Conjugate Heat Transfer in Thermal Inkjet Printheads

The mass of individual droplets ejected from a thermal inkjet printhead increases with increasing local temperature near the ejector nozzles. The amount of ink deposited on the page and so the printed image density depends on the droplet mass. Thus, printhead temperature nonuniformity results in printed image density variations that can be unacceptable to the end users of the printed output. Such temperature variations arise from a combination of the ink fluid flow and the heat transfer in both the ink and the solid components in the printhead. Conjugate heat transfer (CHT) in thermal inkjet printheads is investigated here using validated numerical simulations. A typical thermal inkjet printhead is considered here for the first time, with cold ink drawn through the solid structural components by the ejector nozzle refill. The effect of the width of the feedhole above the printhead chip on the temperature field within the chip is analyzed. Validation of the simulation model required the derivation of novel analytical solutions for the relatively simple problems of fully developed forced convection in a differentially heated planar channel and conduction against convection in plug flow. The results from numerical simulations of these two problems are found to compare well with the newly derived analytical solutions. CHT in flow over a backward-facing step with a heated downstream wall was also simulated as part of the validation process, and good agreement was observed with earlier numerical studies. For the main part of the study, it was found that increasing the width of the feedhole reduces the gradients in temperature on the surface of the printhead chip, thus reducing temperature-related printing defects.

Vorticity-induced flow-focusing leads to bubble entrainment in an inkjet printhead: Synchrotron x-ray and volume-of-fluid visualizations

The baroclinic torque, generated at the gas-liquid interface due to the misalignment of density and pressure gradients, results in a flow-focusing effect that drives bubble pinch-off from the meniscus. High-speed synchrotron X-ray imaging was used to visualize the entire gas-liquid interface. Volume-of-Fluid numerical simulations were employed to unravel the intricate mechanism.

Squeeze-Type Piezoelectric Inkjet Printhead Actuating Waveform Design Method Based on Numerical Simulation and Experiment.

The piezoelectric inkjet printing technique has been commonly used to produce conductive graphics. In this paper, a trapezoidal waveform design method for squeeze-type piezoelectric inkjet printhead is presented to provide a modified steady ejection and optimal droplet shape, in which a coupled multi-physics model of a piezoelectric inkjet printhead is developed. This research describes the effects of parameters, including rising time tr, falling time tf, and dwelling time td, of the trapezoidal waveform on the pressure at the nozzle through numerical simulations. These parameters are initially optimized based on numerical simulations and further optimized based on experimental results. When the printhead is actuated by the optimized waveform with the tr = 5 µs, td = 10 µs, and tf = 2 µs, the droplets are in optimal shape, and their size is about half the diameter of the nozzle. The experimental results validate the efficacy of this waveform design method, which combines numerical simulation and experiment, as well as demonstrating that ink droplet formation can be studied from the point of pressure variation at the nozzle.

Machine learning for predicting microfluidic droplet generation properties

Computational Fluid Dynamics (CFD) simulators are an integral tool for designing commercial microfluidic systems — allowing engineers to estimate key performance metrics prior to physical prototyping. However, CFD simulations can take from hours to weeks, depending on the simulation fidelity. This introduces a significant delay in the design iteration loop. In this work, we employ machine learning methodologies to provide estimates of performance metrics directly from microfluidic geometries in inkjet printheads — providing approximate performance metrics in seconds. Specifically, we develop a Convolutional Neural Network (CNN) based approach that operates directly on voxelized slices of printhead geometry to predict characteristics of droplet generation. This approach does not encode significant biases for the task or physics knowledge and relies on a training set of 20,000 prior simulation results to learn. Despite this, our experiments on this large dataset demonstrate that the learned model can closely approximate the CFD results at a fraction of the time cost for some performance metrics — opening the doors to real-time metric estimation as part of the microfluidic system design process. Further, we examine the learned latent representation and find it encodes a reasonable notion of geometric similarity between printhead architectures. This can allow engineers to search for existing designs with similar characteristics and help reduce duplicated effort.