A High‐Stability Pressure‐Sensitive Implantable Memristor for Pulmonary Hypertension Monitoring

Pulmonary hypertension (PH) significantly affects the quality of life and lifespan of humans and has promoted the development of flexible implantable electronic devices for PH diagnosis and prevention. Traditional implantable devices based on the von Neumann architecture face insurmountable challenges in processing large amounts of biological data due to computational bottlenecks. Memristors, with integrated in-memory sensing and computing capabilities, can effectively eliminate computational bottlenecks and become one of the most promising products in implantable devices for health monitoring. Here, a memristor with the Ag/MnO<sub>2</sub>/BaTiO<sub>3</sub>/FTO structure is implemented and implanted into Sprague‒Dawley (SD) rats. With polydimethylsiloxane (PDMS) packaging, the device can be continuously worked in vivo for up to four weeks, demonstrating excellent stability and biocompatibility. Furthermore, a memristive sensor array is designed for pulmonary artery blood pressure monitoring based on the pressure-responsive characteristics of the as-prepared memristive device. The front-end memristive sensor array can collect and feedback pressure signal, while noise reduction is achieved through memristive logic circuits, and ultimately the memristor neural network processes and classifies the information. Therefore, this work demonstrates the potential of implantable memristors for pulmonary artery pressure monitoring and provides new inspiration for the design of efficient, real-time, and reliable implantable pressure monitoring devices in medical health monitoring.

Authors

Institutions

Publication Details

Journal
Advanced Materials
Published
2024-11-12
DOI
https://doi.org/10.1002/adma.202411659
Citations
45
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
9.39

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A High‐Stability Pressure‐Sensitive Implantable Memristor for Pulmonary Hypertension Monitoring

Zelin Cao, Xianxia Yan, Mengna Wang, Bai Tao Sun et al.
45 citations
Advanced Materials
Advanced Memory and Neural Computing
9.39
article

A High‐Stability Pressure‐Sensitive Implantable Memristor for Pulmonary Hypertension Monitoring

Zelin Cao, Xianxia Yan, Mengna Wang, Bai Tao Sun, Tianfu Zhao, Sida Qin, Yiwei Liu, Xiaoliang Chen, Yu Cui, Guangdong Zhou, Siyu Sun, Jinyou Shao, Kaikai Gao
article en
45 citations

Abstract

Pulmonary hypertension (PH) significantly affects the quality of life and lifespan of humans and has promoted the development of flexible implantable electronic devices for PH diagnosis and prevention. Traditional implantable devices based on the von Neumann architecture face insurmountable challenges in processing large amounts of biological data due to computational bottlenecks. Memristors, with integrated in-memory sensing and computing capabilities, can effectively eliminate computational bottlenecks and become one of the most promising products in implantable devices for health monitoring. Here, a memristor with the Ag/MnO<sub>2</sub>/BaTiO<sub>3</sub>/FTO structure is implemented and implanted into Sprague‒Dawley (SD) rats. With polydimethylsiloxane (PDMS) packaging, the device can be continuously worked in vivo for up to four weeks, demonstrating excellent stability and biocompatibility. Furthermore, a memristive sensor array is designed for pulmonary artery blood pressure monitoring based on the pressure-responsive characteristics of the as-prepared memristive device. The front-end memristive sensor array can collect and feedback pressure signal, while noise reduction is achieved through memristive logic circuits, and ultimately the memristor neural network processes and classifies the information. Therefore, this work demonstrates the potential of implantable memristors for pulmonary artery pressure monitoring and provides new inspiration for the design of efficient, real-time, and reliable implantable pressure monitoring devices in medical health monitoring.

Advanced MaterialsVol. 37(3)
Southwest University (CN), First Affiliated Hospital of Xi'an Jiaotong University (CN), Second Affiliated Hospital of Xi'an Jiaotong University (CN), Xi'an Jiaotong University (CN)
National Natural Science Foundation of China
Responsible consumption and production
Openalex Percentile: Top 1%
Advanced Memory and Neural Computing
9.39
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.