journal article·2020-03

Electrochemically driven multi-material 3D-printing

Adriano Ambrosi, Richard D. Webster, Martin Pumera

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Abstract

Abstract Major efforts for the advancement of additive manufacturing are lately focused on the development of multi-material 3D-printing (mMat-3DP) methods which can enable the fabrication of complete devices in a single printing process combining materials with different properties (structural, functional, conductive, etc.). Printing conductive (metal and non-metal) materials with low-energy-consuming and economical methods is of particular interest since it would facilitate the production of electrodes, catalytic surfaces and electronic circuitry in general for countless applications. In order to contribute to the future vision of mMat-3DP, we wish to show here an economical method to selectively deposit different conductive materials (metal and conductive polymer) by means of electrochemical driving forces. A custom-made electrochemical liquid dispenser with embedded electrodes is used to electrodeposit selectively a metal (Cu), a conductive polymer (polyaniline), or a combination of the two, with both precursors present simultaneously and conveniently in the common electrolytic bath. Combining the 3D-patterning ability of a desktop 3D-printer with a concurrent control of the electrochemical process, selective deposition is demonstrated over a conductive graphite foil used as the cathode. Printing and electrochemical parameters have been optimized using scanning electron microscopy and energy dispersive X-ray spectroscopy to characterize the printed structures. The electrochemical 3D-printing method, being inherently low-cost, scalable and compatible with electrode fabrication methods shall find a broad scope of applications.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Additive Manufacturing and 3D Printing Technologies · Advanced Sensor and Energy Harvesting Materials · Nanomaterials and Printing Technologies

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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