The long-term vision of the Intelligent Textiles for Health Research, Engineering, Augmentation, and Design Lab (I-THREAD Lab) is to revolutionise and pioneer advanced textiles for Health, Sustainability, and Beyond through transdisciplinary strategies.
What we do
The I-THREAD Laboratory focuses on developing and applying flexible devices for human-oriented applications through multidisciplinary, multiscale, and AI-assisted engineering approaches. Our research includes intelligent wearable devices for monitoring applications, closed-loop diagnostic and therapeutic biomedical systems, functional and smart textiles (including electronic fibers, intelligent yarns and fabrics, silk protein materials, and biomedical textiles), augmented sensing technologies powered by AI for human-robot interaction, renewable energy harvesting devices, and large-scale fabrication and manufacturing technologies for producing textile devices.
Why it is important?
As global challenges in healthcare and sustainability continue to intensify, the integration of intelligent textiles with multidisciplinary technologies offers transformative opportunities to address these critical issues. Our research focuses on the development of advanced wearable devices and closed-loop biomedical systems, designed to provide accessible and cost-effective personalized healthcare solutions. Beyond advancing healthcare innovation, our work also contributes to the design of renewable energy harvesting systems, aligning with the urgent need for environmental sustainability. By leveraging scalable manufacturing techniques, we aim to deliver impactful, bench-to-bedside solutions that benefit both society and technological progress.
How can it benefit patients?
Our work aims to benefit patients by enabling continuous health monitoring, accurate diagnostics, and personalized therapies through intelligent wearable devices and close-loop biomedical systems. These innovations improve treatment outcomes, enhance patient comfort, and reduce the burden on traditional healthcare systems. By integrating renewable energy solutions, we also ensure sustainable and cost-effective medical devices. Additionally, our augmented sensing technologies support advanced rehabilitation and assistive systems, enhancing interactions among surgeons, patients, and robotic systems to improve healthcare outcomes.
Meet the team
Dr Liyun Ma
Dr Liyun Ma
Assistant Professor
Masters and undergraduate students
- Jinan Kang, MEng, Department of Mechanical Engineering (2025–2026)
- Junkai Wang, MSc, Department of Bioengineering (2025–2026)
- Zewei Yan, MRes, Department of Surgery and Cancer / Hamlyn Centre (2025–2026)
- Chen Zhe, MRes, Department of Surgery and Cancer / Hamlyn Centre (2025–2026)
- Lifan Xuan, MRes, Department of Surgery and Cancer / Hamlyn Centre (2025–2026)
Results
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Journal articleShen S, Li Y, Ma L, 2026,
Review: novel strategies for electric field-assisted high-efficient photocatalysis
, Journal of Materials Science, Vol: 61, Pages: 9113-9145, ISSN: 0022-2461Water pollution exerts profound and far-reaching effects on human health and the global ecosystem, posing a critical challenge to sustainable development. Various methods have been developed to treat wastewater. However, traditional wastewater treatment methods fail to achieve advanced purification for organic pollutants. Therefore, photocatalysis, which represents a clean and sustainable technology with the potential to replace traditional methods, has been developed as a promising approach to treat wastewater. Nevertheless, its photocatalytic efficiency is impeded by the rapid recombination of photoinduced charges, which restricts its overall performance. Therefore, enhancing photocatalytic performance through construction of sustainable electric fields has become a key research focus. Recently, novel electric fields include triboelectric, piezoelectric, and pyroelectric fields, compensate for the drawbacks of traditional electric fields that are rely on electrodes and electrolytes, offering new pathways to improve photocatalytic efficiency. In this review, the interrelations between theoretical principles and photocatalytic activities within the framework of the three electric fields are systematically discussed, encompassing their influence factors, design strategies, and improving approaches. A comprehensive and in-depth analysis of high-efficiency photocatalytic systems enhanced by the electric fields is highlighted, especially nanogenerator (TENG) and contact-electro-catalysis (CEC), concentrating on the design of sustainable energy conversion devices, including structural inventions, material selections, mechanical forces, and other influencing factors. Finally, the challenges and perspectives for enhancing photocatalysis are summarized, which provide valuable theoretical supports and experimental guidance for researchers specializing in photocatalysis, triboelectrics, piezoelectrics, pyroelectrics, and relevant fields.
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Journal articleNie D, Zhang S, Zheng Y, et al., 2026,
Surface functionalization of water transportation management yarns for bioinspired humidity sensors
, Chemical Engineering Journal, Vol: 530, ISSN: 1385-8947Humidity sensors play crucial roles in various industries and daily life by measuring the amount of water vapor in the air or other gases, and their continued miniaturization and flexibilization are expanding their applications to wearable devices. However, stability defects such as interface charge transfer fluctuations induced by deformation and baseline drift caused by cross-interference of temperature and humidity need to be overcome urgently. In this study, inspired by the water collection ability and transportation ability of spider silk structures, a flexible and sensitive fiber humidity sensor is constructed by surface functionalization of profiled fibers using poly(3,4-ethylenedioxythiophene)-poly(styrenesulfonate) (PEDOT:PSS). Notably, the fiber humidity sensor exhibits fast response (4 s) and recovery time (29 s), a broad operation range (6–85% relative humidity), outstanding linearity, repeatability, and stability. This is attributed to the moisture absorption advantages of the bionic groove structure and weak hydrophilicity of the profiled fibers, as well as the rapid moisture adsorption and desorption capacity and structural performance stability under bending deformation of the continuous lamellar PEDOT:PSS. The yarn can be used not only for diagnosing breathing patterns and monitoring the breathing rate and depth, but also shows potential application value in non-contact human-computer interaction and other fields.
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Journal articleZhang J, Shao J, Monluc H, et al., 2025,
Dynamic Modeling of a Stream-Current-Based Microfluidic Nanogenerator
, ACS NANO, Vol: 19, Pages: 41330-41341, ISSN: 1936-0851 -
Journal articleImani IM, Ma L, Hwang J-H, et al., 2025,
Ultrasound-Driven Triboelectric Technology for Functional Wireless Power Transfer
, ADVANCED MATERIALS TECHNOLOGIES, ISSN: 2365-709X -
Journal articleChen K, Loynachan CN, Thanapongpibul C, et al., 2025,
Photo-PISA Driven <i>In Situ</i> Encapsulation of Nanocluster-Based Sensors within Stimuli-Responsive Polymersomes for AND Logic Gate Sensing
, ACS SENSORS, ISSN: 2379-3694 -
Journal articleSanli A, Hu T, Li L, et al., 2025,
Autonomous devices for drug delivery
, NATURE BIOMEDICAL ENGINEERING, Vol: 9, Pages: 1182-1183, ISSN: 2157-846X -
Journal articleChen K, Li L, Peng Q, et al., 2025,
A Smart Textile-Based Tactile Sensing System for Multi-Channel Sign Language Recognition
, SENSORS, Vol: 25 -
Conference paperWen S, Middleton M, Ping S, et al., 2025,
AdaptiveCoPilot: Design and Testing of a NeuroAdaptive LLM Cockpit Guidance System in both Novice and Expert Pilots
, 2025 IEEE Conference Virtual Reality and 3D User Interfaces (VR), Publisher: IEEE, Pages: 656-666 -
Conference paperWen S, Ping S, Wang J, et al., 2024,
AdaptiveVoice: Cognitively Adaptive Voice Interface for Driving Assistance
, CHI '24: CHI Conference on Human Factors in Computing Systems, Publisher: ACM, Pages: 1-18 -
Journal articleWu R, Ma L, Chen Z, et al., 2024,
Stretchable spring-sheathed yarn sensor for 3D dynamic body reconstruction assisted by transfer learning
, INFOMAT, Vol: 6- Cite
- Citations: 9
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Bessemer Building
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Imperial College
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