Direct-Ink-Writing Multifunctional Flexible Robotic Electronic Skin

Electronic skin for robotics often suffers from limited scalability, high fabrication costs, and inflexible designs, restricting its ability to support multimodal sensing and customization. To address these challenges, we propose a robotic e-skin fabricated using direct-ink-writing technology that enables rapid customization and scalable production of multimodal sensors. The e-skin can detect pressure, temperature, and shear forces through a facile sensor design modification without additional complex fabrication steps. The pyramid-structured tactile sensor demonstrates high sensitivity of 670 kPa<sup>-1</sup> in the 0-100 kPa range, maintain long-term stability over 3500 cycles, and exhibits minimal signal drift of less than 3% after 9 days of continuous testing. A 4 × 9 flexible sensor array is developed for real-time pressure mapping on both flat and curved surfaces. Integration with a robotic gripper combined with a K-nearest neighbor classifier achieves a 97.7% accuracy in object recognition. Furthermore, by replacing the sensing layer with a temperature-responsive polymer or incorporating a protrusion-patterned shear layer, the e-skin can function as a dual-mode temperature-pressure sensor or a shear-force sensor. This highly versatile and scalable e-skin platform opens new possibilities for applications in robotics, prosthetics, and human-machine interfaces.

Authors

Institutions

Publication Details

Journal
ACS Applied Materials & Interfaces
Published
2025-12-17
DOI
https://doi.org/10.1021/acsami.5c19958
Primary Topic
Advanced Sensor and Energy Harvesting Materials
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Direct-Ink-Writing Multifunctional Flexible Robotic Electronic Skin

Ka Hei Mak, Qing Liu, Mon Myat Swe, Wei Lin Leong et al.
ACS Applied Materials & Interfaces
Advanced Sensor and Energy Harvesting Materials
article

Direct-Ink-Writing Multifunctional Flexible Robotic Electronic Skin

Ka Hei Mak, Qing Liu, Mon Myat Swe, Wei Lin Leong, Yingjun Chen, Qiang He, Hailu Wang
article en

Abstract

Electronic skin for robotics often suffers from limited scalability, high fabrication costs, and inflexible designs, restricting its ability to support multimodal sensing and customization. To address these challenges, we propose a robotic e-skin fabricated using direct-ink-writing technology that enables rapid customization and scalable production of multimodal sensors. The e-skin can detect pressure, temperature, and shear forces through a facile sensor design modification without additional complex fabrication steps. The pyramid-structured tactile sensor demonstrates high sensitivity of 670 kPa<sup>-1</sup> in the 0-100 kPa range, maintain long-term stability over 3500 cycles, and exhibits minimal signal drift of less than 3% after 9 days of continuous testing. A 4 × 9 flexible sensor array is developed for real-time pressure mapping on both flat and curved surfaces. Integration with a robotic gripper combined with a K-nearest neighbor classifier achieves a 97.7% accuracy in object recognition. Furthermore, by replacing the sensing layer with a temperature-responsive polymer or incorporating a protrusion-patterned shear layer, the e-skin can function as a dual-mode temperature-pressure sensor or a shear-force sensor. This highly versatile and scalable e-skin platform opens new possibilities for applications in robotics, prosthetics, and human-machine interfaces.

ACS Applied Materials & InterfacesVol. 17(52)
Nanyang Technological University (SG)
Openalex Percentile: Top 60%
Advanced Sensor and Energy Harvesting Materials
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.