Electron and X-ray Focused Beam-Induced Cross-Linking in Liquids: Toward Rapid Continuous 3D Nanoprinting and Interfacing using Soft Materials.
Tanya GuptaEvgheni StrelcovGlenn HollandJoshua SchumacherYang YangMandy B EschVladimir A AksyukPatrick ZellerMatteo AmatiLuca GregorattiAndrei KolmakovPublished in: ACS nano (2020)
Multiphoton polymer cross-linking evolves as the core process behind high-resolution additive microfabrication with soft materials for implantable/wearable electronics, tissue engineering, microrobotics, biosensing, drug delivery, etc. Electrons and soft X-rays, in principle, can offer even higher resolution and printing rates. However, these powerful lithographic tools are difficult to apply to vacuum incompatible liquid precursor solutions used in continuous additive fabrication. In this work, using biocompatible hydrogel as a model soft material, we demonstrate high-resolution in-liquid polymer cross-linking using scanning electron and X-ray microscopes. The approach augments the existing solid-state electron/X-ray lithography and beam-induced deposition techniques with a wider class of possible chemical reactions, precursors, and functionalities. We discuss the focused beam cross-linking mechanism, the factors affecting the ultimate feature size, and layer-by-layer printing possibilities. The potential of this technology is demonstrated on a few practically important applications such as in-liquid encapsulation of nanoparticles for plasmonic sensing and interfacing of viable cells with hydrogel electrodes.
Keyphrases
- electron microscopy
- high resolution
- tissue engineering
- drug delivery
- solid state
- ionic liquid
- high glucose
- diabetic rats
- mass spectrometry
- induced apoptosis
- drug induced
- single molecule
- machine learning
- cancer therapy
- endothelial cells
- drug release
- oxidative stress
- cell cycle arrest
- risk assessment
- computed tomography
- blood pressure
- signaling pathway
- endoplasmic reticulum stress
- quantum dots
- hyaluronic acid
- low cost
- deep learning
- carbon nanotubes
- neural network