Deposition Of Liquid Metal Research Articles

Hierarchical Structure by Self‐sedimentation of Liquid Metal for Flexible Sensor Integrating Pressure Detection and Triboelectric Nanogenerator

AbstractMultifunctional integration is a pivotal aspect in the development of flexible sensors that facilitate augmented interaction with the physical environment. However, contemporary multifunctional flexible sensors face challenges arising from their complex structures and high costs. Therefore, polydimethylsiloxane (PDMS) /liquid metal (LM) films with a hierarchical structure prepared via the gravity‐induced deposition of LM are applied to multifunctional flexible sensors. PDMS/LM films exhibit remarkable flexibility and function as flexible electrodes for pressure sensors as well as a single‐electrode mode triboelectric nanogenerator (TENG). The porous PDMS/carbon nanotube (CNT) dielectric with an array structure is developed for pressure sensing, showing commendable sensitivity (0.671 kPa−1) even under low pressures (0–17 kPa). Compared with solid and porous dielectrics, its sensitivity is improved over the entire pressure range. The sensor achieves a response time of ≈80 ms and demonstrates exceptional stability. Furthermore, when the PDMS/LM film with a hierarchical structure functions as a TENG, it can serve as a self‐powered sensor for measuring dynamic forces and aid in material recognition with the assistance of capacitive pressure sensing. This study provides a new perspective on the simplified design and integration of multifunctional flexible sensors.

3D printing of liquid metal EGaIn through laser induced forward transfer: A proof-of-concept study

In this work, we introduce Laser Induced Forward Transfer (LIFT) of Liquid Metal (LM) alloys, specifically Eutectic Gallium-Indium (EGaIn). LIFT for LM provides a fairly simple manufacturing method with better control and modularity for patterning LMs compared to existing methods such as imprinting, and extrusion. LIFT is a direct-write printing method which is used for a wide range of applications, such as, laser printing of electronic materials, biomolecules, and cells. Our preliminary results demonstrate great potential of LIFT process for LM deposition and patterning, and can help achieve finer and scalable features with high throughput.

Patterned Liquid Metal Contacts for Printed Carbon Nanotube Transistors.

Flexible and stretchable electronics are poised to enable many applications that cannot be realized with traditional, rigid devices. One of the most promising options for low-cost stretchable transistors are printed carbon nanotubes (CNTs). However, a major limiting factor in stretchable CNT devices is the lack of a stable and versatile contact material that forms both the interconnects and contact electrodes. In this work, we introduce the use of eutectic gallium-indium (EGaIn) liquid metal for electrical contacts to printed CNT channels. We analyze thin-film transistors (TFTs) fabricated using two different liquid metal deposition techniques-vacuum-filling polydimethylsiloxane (PDMS) microchannel structures and direct-writing liquid metals on the CNTs. The highest performing CNT-TFT was realized using vacuum-filled microchannel deposition with an in situ annealing temperature of 150 °C. This device exhibited an on/off ratio of more than 104 and on-currents as high as 150 μA/mm-metrics that are on par with other printed CNT-TFTs. Additionally, we observed that at room temperature the contact resistances of the vacuum-filled microchannel structures were 50% lower than those of the direct-write structures, likely due to the poor adhesion between the materials observed during the direct-writing process. The insights gained in this study show that stretchable electronics can be realized using low-cost and solely solution processing techniques. Furthermore, we demonstrate methods that can be used to electrically characterize semiconducting materials as transistors without requiring elevated temperatures or cleanroom processes.

Efficient additive manufacturing production of oxide- and nitride-dispersion-strengthened materials through atmospheric reactions in liquid metal deposition

Despite being extremely attractive compounds for strengthening, oxides and nitride particles have found only limited use in metallic materials design, as obtaining appropriate size and dispersion up to now necessitates production by time- and cost-intensive powder metallurgy processes. Here we present an alternative production method, based on the oxide and nitride formation during liquid-metal-deposition procedures in oxygen and/or nitrogen containing atmospheres. Rapid solidification of the small liquid zone suppresses floatation and agglomeration of particles, while subsequent thermo-mechanical treatments densify the material and aids particle dispersion. The in-situ particle formation coupled to the high deposition rates ensures a drastically shortened production chain. The feasibility of the method is exemplarily demonstrated on austenitic stainless steel and commercially available deposition techniques as used in additive manufacturing, performed without shielding gas but instead at air. Even without substantial optimisation of processes and material, >2vol.% of hard and stable Cr2N particles with sizes down to 80nm could be evenly dispersed, resulting in pronounced strengthening at both room temperature and 700°C without significant loss in ductility. Future possibilities for creating novel generations of cost effective and lean high strength materials, especially for high temperature applications, are outlined and discussed.

Overview of Inverse Thermal Analysis of Drop-by-Drop Liquid-Metal Deposition Using Green’s Functions

An overview is presented of general aspects of a methodology for inverse thermal analysis of drop-by-drop liquid-metal deposition based on Green’s functions. This methodology is constructed according to the general physical characteristics of rapid prototyping processes employing drop-by-drop liquid-metal deposition. This methodology represents a specific extension of a methodology using basis functions that was introduced previously for inverse analysis of welding processes, and of energy deposition in general. The formal structure of the methodology follows from a specific definition of the inverse heat transfer problem, which is well posed for inverse analysis of heat deposition processes. This definition is based on the assumption of the availability of information concerning spatially distributed boundary and constraint values. This information would be obtained in principle from both experimental measurements obtained in the laboratory, as well as numerical simulations performed using models having been constructed using basic theory.

A Physically Consistent Path-Weighted Diffusivity Function for Modeling of Drop-by-Drop Liquid Metal Deposition

A physically consistent path-weighted diffusivity function is derived for parametric representation of heat diffusion patterns occurring within a volume of material where in there exists inhomogeneous or anisotropic thermal diffusivity. A physically consistent parametric representation of energy deposition processes, where there exists spatially dependent thermal diffusivity, provides for more optimal inverse analysis of such processes. The path-weighted diffusivity function presented here further extends an inverse analysis approach presented previously. The general functional characteristics of the path-weighted diffusivity function derived are examined via a prototype simulation of drop-by-drop liquid metal deposition upon a material characterized by anisotropic thermal diffusivity. The results of this simulation are compared with previous simulations concerning path-weighted thermal diffusivity. The parametric representations presented, which are constructed using path-weighted sums of analytic basis functions, are examined from the perspective of discrete numerical methods. This perspective provides a foundation for the development of control algorithms for process optimization with respect to achieving specific microstructures within a fabricated structure.

A General Algorithm for Inverse Modeling of Layer-By-Layer Liquid-Metal Deposition

In order that freeform fabrication techniques, which entail layer-by-layer liquid-metal deposition, transition from prototyping to manufacturing, these techniques must be made reliable and consistent. Accordingly, detailed microstructural and thermal characterizations of the structures produced are needed in order to advance these fabrication techniques. The inherent complexity of layer-by-layer liquid-metal deposition, which is characteristic of energy and mass deposition processes in general, is such that process modeling based on basic theory alone, which represents the direct-problem approach, is extremely difficult. A general approach to overcoming the difficulties associated with this inherent complexity is the inverse problem approach. Presented here is a general algorithmic structure for inverse modeling of heat transfer that occurs during layer-by-layer fabrication. This general algorithmic structure represents an extension and refinement of an algorithmic structure presented previously and is potentially adaptable for prediction of temperature histories within parts having complex geometries and for the construction of process-control algorithms.

An Algorithm for Inverse Modeling of Layer-by-Layer Deposition Processes

Metallic parts can be made by deposition of liquid metal in a layer-by-layer fashion. By this means, layered structures can be produced that are made up of overlapping reinforced droplets. In particular, prototypes, i.e., customized parts and tooling, can be produced in this way. In order that layer-by-layer fabrication techniques transition from prototyping to manufacturing, however, the processes must be made reliable and consistent. Accordingly, detailed microstructural and thermal characterizations of the product are needed to advance manufacturing goals based on layer-by-layer deposition processes. The inherent complexity of layer-by-layer deposition processes, characteristic of energy and mass deposition processes in general, is such that process modeling based on theory, or the direct-problem approach, is extremely difficult. A general approach to overcoming difficulties associated with this inherent complexity is the inverse-problem approach. Presented here is an algorithm for inverse modeling of heat transfer occurring during layer-by-layer deposition, which is potentially adaptable for prediction of temperature histories in samples that are made by layer-by-layer deposition processes.