Precise Micropatterning of a Porous Poly(ionic liquid) via Maskless Photolithography for High-Performance Nonenzymatic H2O2 Sensing
Source abstract
Abstract
Porous poly(ionic liquid)s (PILs) recently have been serving as a multifunctional, interdisciplinary materials platform in quite a few research areas, including separation, catalysis, actuator, sensor, and energy storage, just to name a few. In this context, the capability of photopatterning PIL microstructures in a porous state on a substrate is still missing but is a crucial step for their real industrial usage. Here, we developed a method for in situ rapid patterning of porous PIL microstructures via a maskless photolithography approach coupled with a simple electrostatic complexation treatment. This breakthrough enables design of miniaturized sensors. As exemplified in this work, upon loading Pt nanoparticles into porous PIL microstructures, the hybrid sensor showed outstanding performance, bearing both a high sensitivity and a wide detection range.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Analytical Chemistry and Sensors · Gas Sensing Nanomaterials and Sensors · Advanced Chemical Sensor Technologies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Photonics Research Center, Department of Electrical Engineering, The Hong Kong Polytechnic University, Hong Kong, SAR, China
Key Laboratory of Material Chemistry for Energy Conversion and Storage, Ministry of Education, School of Chemistry and Chemical Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Max Planck Institute of Colloids and Interfaces, Department of Colloid Chemistry, D-14424 Potsdam, Germany
Department of Materials and Environmental Chemistry (MMK), Stockholm University, Svante Arrhenius väg 16 C, 10691 Stockholm, Sweden
Funding
Deutscher Akademischer Austauschdienst · Knut och Alice Wallenbergs Stiftelse · Max-Planck-Gesellschaft · Hong Kong Polytechnic University · H2020 European Research Council
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