A Microfluidic Contact Lens With Optimized Channel Placement for Enhanced Sensitivity for Continuous Intraocular Pressure Monitoring

IF 4.5 2区 综合性期刊 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
IEEE Sensors Journal Pub Date : 2026-10-15 Epub Date: 2026-09-10 DOI:10.1109/JSEN.2026.3730668
Elham Alaei;Syed Ali Raza Bukhari;Tanzila Afrin;Laura Wells;Robert Campbell;Yongjun Lai
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引用次数: 0

Abstract

Monitoring continuous intraocular pressure (IOP) is essential for the diagnosis and management of glaucoma; however, current microfluidic sensing techniques lack design optimization and leave substantial room for improvement. In this study, we present a highly sensitive microfluidic contact lens designed to transform intraocular deformation into measurable fluid displacement. The device incorporates a wide reservoir channel, coupled to a narrow indicator channel, enabling amplified displacement, while an integrated air reservoir improves the overall sensitivity. The lens is fabricated using Ecoflex-0030, a soft and biocompatible elastomer with a low Young’s modulus whose strain responsiveness surpasses conventional polydimethylsiloxane (PDMS)-based microfluidic contact lenses. Experimental evaluations using a synthetic eye model demonstrate the highest displacement sensitivity reported to date for microfluidic contact lens sensors, with consistent performance validated through repeated trials and cyclic pressure testing. To further improve device performance, a comprehensive microchannel-location study was conducted that revealed strongly nonuniform strain distributions across the corneal surface. Four lens designs with varying microchannel locations were fabricated and extensively tested to obtain the ideal location for microchannel placement. The sensitivity characterization revealed substantial variation across the different channel locations, with values of 0.5057,1.032, 0.775, and 0.614 mm/mmHg observed for the designs D6, D7, D8, and D9 (at 6, 7, 8, and 9 mm diameter), respectively. The final optimized layout of D7 produced a superior response, reaching 1.032 mm/mmHg, which surpasses previously reported performance in microfluidic contact lenses. These findings demonstrate the importance of mechanical coupling and structural optimization to obtain a highly sensitive and reliable sensor.
一种优化通道放置的微流控隐形眼镜,用于提高连续眼压监测的灵敏度
持续监测眼压(IOP)对青光眼的诊断和治疗至关重要;然而,目前的微流控传感技术缺乏设计优化,还有很大的改进空间。在这项研究中,我们提出了一种高灵敏度的微流体隐形眼镜,旨在将眼内变形转化为可测量的流体位移。该装置包括一个宽储层通道,与一个窄指示通道耦合,可以放大位移,而一个集成的空气储层提高了整体灵敏度。这种隐形眼镜是用Ecoflex-0030制成的,这是一种柔软的生物相容性弹性体,具有低杨氏模量,其应变响应性优于传统的聚二甲基硅氧烷(PDMS)微流控隐形眼镜。使用合成眼模型的实验评估表明,微流控隐形眼镜传感器具有迄今为止报道的最高位移灵敏度,并通过反复试验和循环压力测试验证了其一致的性能。为了进一步提高器件性能,进行了一项全面的微通道定位研究,揭示了角膜表面强烈不均匀的应变分布。制作了四种不同微通道位置的透镜设计,并进行了广泛的测试,以获得微通道放置的理想位置。灵敏度表征显示不同通道位置的差异很大,D6、D7、D8和D9(直径为6、7、8和9 mm)的值分别为0.5057、1.032、0.775和0.614 mm/mmHg。最终优化的D7布局产生了优异的响应,达到1.032 mm/mmHg,超过了先前报道的微流控隐形眼镜的性能。这些发现证明了机械耦合和结构优化对于获得高灵敏度和可靠的传感器的重要性。
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来源期刊
IEEE Sensors Journal
IEEE Sensors Journal 工程技术-工程:电子与电气
CiteScore
7.70
自引率
14.00%
发文量
2058
审稿时长
5.2 months
期刊介绍: The fields of interest of the IEEE Sensors Journal are the theory, design , fabrication, manufacturing and applications of devices for sensing and transducing physical, chemical and biological phenomena, with emphasis on the electronics and physics aspect of sensors and integrated sensors-actuators. IEEE Sensors Journal deals with the following: -Sensor Phenomenology, Modelling, and Evaluation -Sensor Materials, Processing, and Fabrication -Chemical and Gas Sensors -Microfluidics and Biosensors -Optical Sensors -Physical Sensors: Temperature, Mechanical, Magnetic, and others -Acoustic and Ultrasonic Sensors -Sensor Packaging -Sensor Networks -Sensor Applications -Sensor Systems: Signals, Processing, and Interfaces -Actuators and Sensor Power Systems -Sensor Signal Processing for high precision and stability (amplification, filtering, linearization, modulation/demodulation) and under harsh conditions (EMC, radiation, humidity, temperature); energy consumption/harvesting -Sensor Data Processing (soft computing with sensor data, e.g., pattern recognition, machine learning, evolutionary computation; sensor data fusion, processing of wave e.g., electromagnetic and acoustic; and non-wave, e.g., chemical, gravity, particle, thermal, radiative and non-radiative sensor data, detection, estimation and classification based on sensor data) -Sensors in Industrial Practice
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