Sustainable, 3D-Printed Liquid Metal Reprogrammable Resonator Toward Fully Circular RF Sensing and Identification

Microwave resonators are widely used for spectral RF sensing and identification, but their sustainability and circularity are hindered by a lack of reconfigurability. Herein, we present the first sustainable, fully recyclable, and reprogrammable microwave sensing resonator, based on liquid metal and additively manufactured, modular multilayered fluidic channels. The proposed structure leverages a capacitively coupled 3D-printed liquid resonator layer over a microstrip transmission line layer, with additional sensing layers integrated vertically above the recodable identification liquid Galinstan resonators. The resonators’ layer and its subsequent sensing layers are printed using semi-transparent stereolithography (SLA) resin. Five identification bits are implemented using refillable resonators, allowing the resonator to be recoded by filling or emptying liquid metal within different channels. Circularity is demonstrated over 12 cycles, with the liquid metal recovered with under 1.5% waste at the end of life. The encoding and sensing notch filters are implemented using five bandstop resonators at 2–7 GHz. The resonator is demonstrated in two exemplary sensing applications, including real-permittivity-based liquid detection and touch sensing, showing the potential uses in human–RFID interaction.

Authors

Institutions

Publication Details

Journal
IEEE Transactions on Microwave Theory and Techniques
Published
2025-12-09
DOI
https://doi.org/10.1109/tmtt.2025.3638629
Primary Topic
Nanomaterials and Printing Technologies
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Sustainable, 3D-Printed Liquid Metal Reprogrammable Resonator Toward Fully Circular RF Sensing and Identification

Xiaochuan Fang, Mahmoud Wagih
IEEE Transactions on Microwave Theory and Techniques
Nanomaterials and Printing Technologies
article

Sustainable, 3D-Printed Liquid Metal Reprogrammable Resonator Toward Fully Circular RF Sensing and Identification

Xiaochuan Fang, Mahmoud Wagih
article en

Abstract

Microwave resonators are widely used for spectral RF sensing and identification, but their sustainability and circularity are hindered by a lack of reconfigurability. Herein, we present the first sustainable, fully recyclable, and reprogrammable microwave sensing resonator, based on liquid metal and additively manufactured, modular multilayered fluidic channels. The proposed structure leverages a capacitively coupled 3D-printed liquid resonator layer over a microstrip transmission line layer, with additional sensing layers integrated vertically above the recodable identification liquid Galinstan resonators. The resonators’ layer and its subsequent sensing layers are printed using semi-transparent stereolithography (SLA) resin. Five identification bits are implemented using refillable resonators, allowing the resonator to be recoded by filling or emptying liquid metal within different channels. Circularity is demonstrated over 12 cycles, with the liquid metal recovered with under 1.5% waste at the end of life. The encoding and sensing notch filters are implemented using five bandstop resonators at 2–7 GHz. The resonator is demonstrated in two exemplary sensing applications, including real-permittivity-based liquid detection and touch sensing, showing the potential uses in human–RFID interaction.

IEEE Transactions on Microwave Theory and TechniquesVol. 74(3)
University of Glasgow (GB)
Openalex Percentile: Top 60%
Nanomaterials and Printing Technologies
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.