Evolving Beyond Nature: AI-Driven Rapid Advancements of Bio-Inspired Electronic Skin

Electronic skin (e-skin) is a rapidly developing technology at the intersection of humans and machines with tremendous applications in robotics, healthcare, sports performance, and other fields of wearable electronics. With the recent popularization of artificial intelligence (AI), data-driven approaches to materials design and geometric functional optimization have accelerated e-skin development, enabling optimized microstructures, novel functionalities, and improved usability. Recent progress in bioinspired materials design and functionality enhancements for e-skin is paving the way for future-facing machine learning-powered tools to further accelerate their development. In this review, we discuss the traditional properties of e-skins, their sensing mechanisms, and suitable applications. Then, we investigate and categorize bioinspired structures from material, structural, and functional perspectives. Next, we outline the fundamentals of AI for data-driven optimization and how it can be utilized toward bioinspired e-skin design. This blended e-skin design framework culminates in novel applications, from customizable layouts for personalized therapeutics to dynamic environment recognition for enhanced robotics. Finally, we will explore the challenges of e-skin human-machine interfaces, including their hardware limitations, sensitivity, and longevity issues, and offer insights into future-proof research.

Publication Details

Journal
ACS Applied Materials & Interfaces
Published
2026-03-24
DOI
https://doi.org/10.1021/acsami.5c24192
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

Evolving Beyond Nature: AI-Driven Rapid Advancements of Bio-Inspired Electronic Skin

ACS Applied Materials & Interfaces
Advanced Sensor and Energy Harvesting Materials
article

Evolving Beyond Nature: AI-Driven Rapid Advancements of Bio-Inspired Electronic Skin

article en

Abstract

Electronic skin (e-skin) is a rapidly developing technology at the intersection of humans and machines with tremendous applications in robotics, healthcare, sports performance, and other fields of wearable electronics. With the recent popularization of artificial intelligence (AI), data-driven approaches to materials design and geometric functional optimization have accelerated e-skin development, enabling optimized microstructures, novel functionalities, and improved usability. Recent progress in bioinspired materials design and functionality enhancements for e-skin is paving the way for future-facing machine learning-powered tools to further accelerate their development. In this review, we discuss the traditional properties of e-skins, their sensing mechanisms, and suitable applications. Then, we investigate and categorize bioinspired structures from material, structural, and functional perspectives. Next, we outline the fundamentals of AI for data-driven optimization and how it can be utilized toward bioinspired e-skin design. This blended e-skin design framework culminates in novel applications, from customizable layouts for personalized therapeutics to dynamic environment recognition for enhanced robotics. Finally, we will explore the challenges of e-skin human-machine interfaces, including their hardware limitations, sensitivity, and longevity issues, and offer insights into future-proof research.

ACS Applied Materials & Interfaces
Industry, innovation and infrastructure
Openalex Percentile: Top 11%
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.