J. Semicond. > 2025, Volume 46 > Issue 10 > 102601
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A miniaturized wireless electrical impedance myography platform for the long-term adaptive muscle fatigue monitoring

Shanshan Yu1, 2, §, Yichao Gan1, §, Feifan Song1, Qiongzhang Wang1, Hao Tang1, and Zhao Li1, 2,

+ Author Affiliations

 Corresponding author: Hao Tang, haotang@semi.ac.cn; Zhao Li, zhaoli@semi.ac.cn

DOI: 10.1088/1674-4926/25020029CSTR: 32376.14.1674-4926.25020029

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Abstract: Accurate quantification of exercise interventions and changes in muscle function is essential for personalized health management. Electrical impedance myography (EIM) technology offers an innovative, noninvasive, painless, and easy-to-perform solution for muscle health monitoring. However, current EIM platforms face a number of limitations, including large device size, wired connections, and instability of the electrode-skin interface, which limit their applicability for monitoring muscle movement. In this study, a miniature wireless EIM platform with a user-friendly smartphone app is proposed and developed. The miniature, wireless, multi-frequency (20 kHz−1 MHz) EIM platform is equipped with flexible microneedle array electrodes (MAE). The advantages of MAEs over conventional electrodes were demonstrated by physical field modeling simulations and skin-electrode contact impedance comparison tests. The smartphone APP was developed to wirelessly operate the EIM platform, and to transmit and process real-time muscle impedance data. To validate its effectiveness, a seven-day adaptive fatigue training study was conducted, which demonstrated that the EIM platform was able to detect muscle adaptations and serve as a reliable indicator of fatigue. This study presents an innovative approach to applying EIM technology to muscle health monitoring and exercise testing, thereby advancing the development of personalized health management and athletic performance assessment.

Key words: electrical impedance myography, flexible microneedle array electrodes, miniaturized wireless platform, smartphone APP, muscle fatigue monitoring



[1]
Enoka R M, Duchateau J. Muscle fatigue: What, why and how it influences muscle function. J Physiol, 2008, 586(1), 11 doi: 10.1113/jphysiol.2007.139477
[2]
Li N, Zhou R, Krishna B, et al. Non-invasive techniques for muscle fatigue monitoring: A comprehensive survey. ACM Comput Surv, 2024, 56(9), 1 doi: 10.1145/3648679
[3]
Qin X K, Zhong B W, Lv S X, et al. A zero-voltage-writing artificial nervous system based on biosensor integrated on ferroelectric tunnel junction. Adv Mater, 2024, 36(32), e2404026 doi: 10.1002/adma.202404026
[4]
Guven A E, Chiapparelli E, Camino−Willhuber G, et al. Assessing paraspinal muscle atrophy with electrical impedance myography: Limitations and insights. J Orthop Res, 2024, 42(9), 2072 doi: 10.1002/jor.25848
[5]
Schrunder A F, Rodriguez S, Rusu A. A finite element analysis and circuit modelling methodology for studying electrical impedance myography of human limbs. IEEE T-BME, 2022, 69(1), 244 doi: 10.1109/TBME.2021.3091884
[6]
Kapravchuk V, Briko A, Kobelev A, et al. An approach to using electrical impedance myography signal sensors to assess morphofunctional changes in tissue during muscle contraction. Biosensors, 2024, 14(2), 76 doi: 10.3390/bios14020076
[7]
Chrzanowski S M, Nagy J A, Pandeya S, et al. Electrical impedance myography correlates with functional measures of disease progression in D2-mdx mice and boys with Duchenne muscular dystrophy. J Neuromuscul Dis, 2023, 10(1), 81 doi: 10.3233/JND-210787
[8]
Rutkove S B. Electrical impedance myography: Background, current state, and future directions. Muscle Nerve, 2009, 40(6), 936 doi: 10.1002/mus.21362
[9]
Critcher S, Freeborn T J. Short-term evaluation of dry electrodes fabricated using flexible printed circuits processes for bioimpedance measurements. 2020 SoutheastCon, 2020, 1 doi: 10.1109/SoutheastCon44009.2020.9249690
[10]
Li Z, Li Y, Liu M S, et al. Microneedle electrode array for electrical impedance myography to characterize neurogenic myopathy. Ann Biomed Eng, 2016, 44(5), 1566 doi: 10.1007/s10439-015-1466-5
[11]
Li Z, Chen L F, Zhu Y, et al. Handheld electrical impedance myography probe for assessing carpal tunnel syndrome. Ann Biomed Eng, 2017, 45(6), 1572 doi: 10.1007/s10439-017-1819-3
[12]
Huang L K, Huang L N, Gao Y M, et al. Electrical impedance myography applied to monitoring of muscle fatigue during dynamic contractions. IEEE Access, 2020, 8, 13056 doi: 10.1109/ACCESS.2020.2965982
[13]
Kusche R, Oltmann A, Rostalski P. A wearable dual-channel bioimpedance spectrometer for real-time muscle contraction detection. IEEE Sens J, 2024, 24(7), 11316 doi: 10.1109/JSEN.2024.3359284
[14]
Qin X K, Zhong B W, Xu H, et al. Manufacturing high−performance flexible sensors via advanced patterning techniques. Int J Extrem Manuf, 2025, 7, 032003 doi: 10.1088/2631-7990/ada857
[15]
Ren L, Liu B, Zhou W, et al. A mini review of microneedle array electrode for bio-signal recording: A review. IEEE Sens J, 2020, 20(2), 577 doi: 10.1109/JSEN.2019.2944847
[16]
Wang Z Y, Huang D, Li J S, et al. Ag/AgCl-covered flexible microneedle array dry electrode for electrocardiography recording. 2023 IEEE 18th International Conference on Nano/Micro Engineered and Molecular Systems (NEMS), 2023, 74 doi: 10.1109/NEMS57332.2023.10190967
[17]
Wolf M P, Salieb-Beugelaar G B, Hunziker P. PDMS with designer functionalities: Properties, modifications strategies, and applications. Prog Polym Sci, 2018, 83, 97 doi: 10.1016/j.progpolymsci.2018.06.001
[18]
Wang R X, Bai J X, Zhu X H, et al. A PDMS-based microneedle array electrode for long-term ECG recording. Biomed Microdevices, 2022, 24(3), 27 doi: 10.1007/s10544-022-00626-y
[19]
Reddy J N. An introduction to the finite element method. J Press Vessel Technol, 1989, 111(3), 348 doi: 10.1115/1.3265687
[20]
Tang X, Dong Y Z, Li Q G, et al. Using microneedle array electrodes for non-invasive electrophysiological signal acquisition and sensory feedback evoking. Front Bioeng Biotechnol, 2023, 11, 1238210 doi: 10.3389/fbioe.2023.1238210
[21]
Li J S, Ma Y D, Huang D, et al. High-performance flexible microneedle array as a low-impedance surface biopotential dry electrode for wearable electrophysiological recording and polysomnography. Nanomicro Lett, 2022, 14(1), 132 doi: 10.1007/s40820-022-00870-0
[22]
Singh O P, El-Badawy I M, Sundaram S, et al. Microneedle electrodes: Materials, fabrication methods, and electrophysiological signal monitoring-narrative review. Biomed Microdevices, 2025, 27(1), 9 doi: 10.1007/s10544-024-00732-z
[23]
Liu Z J, Xu X Y, Huang S, et al. Multichannel microneedle dry electrode patches for minimally invasive transdermal recording of electrophysiological signals. Microsyst Nanoeng, 2024, 10, 72 doi: 10.1038/s41378-024-00702-8
[24]
Xu P, Yang X D, Ma W, et al. A study of handgrip force prediction scheme based on electrical impedance myography. IEEE J−ERM, 2023, 7(1), 90 doi: 10.1109/JERM.2023.3241769
[25]
Cebrián-Ponce Á, Irurtia A, Carrasco-Marginet M, et al. Electrical impedance myography in health and physical exercise: A systematic review and future perspectives. Front Physiol, 2021, 12, 740877 doi: 10.3389/fphys.2021.740877
[26]
Hülkenberg A, Strotmann N, Mokni A, et al. The influence of muscle contraction intensity on electrical impedance myography. 2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2024, 1 doi: 10.1109/EMBC53108.2024.10782074
[27]
Byström S. Estimation of aerobic and anaerobic metabolism in isometric forearm exercise. Ups J Med Sci, 1994, 99(1), 51 doi: 10.3109/03009739409179350
[28]
IEEE Standard on Piezoelectricity. ANSI/IEEE Std 176−1987. New York: IEEE, 1988, 1
[29]
Ji H W, Wang M Y, Wang Y T, et al. Skin-integrated, biocompatible, and stretchable silicon microneedle electrode for long-term EMG monitoring in motion scenario. NPJ Flex Electron, 2023, 7, 46 doi: 10.1038/s41528-023-00279-8
[30]
Gao K P, Yang H J, Wang X L, et al. Soft pin-shaped dry electrode with bristles for EEG signal measurements. Sensor Actuat A−Phys, 2018, 283, 348 doi: 10.1016/j.sna.2018.09.045
[31]
Shiffman C A, Aaron R, Rutkove S B. Electrical impedance of muscle during isometric contraction. Physiol Meas, 2003, 24(1), 213 doi: 10.1088/0967-3334/24/1/316
Fig. 1.  (Color online) Schematic of the miniaturized, wireless EIM platform. (a) EIM measurement schematic; (b) miniaturized EIM platform; (c) EIM smartphone APP; (d) principle of EIM−MAE acquisition.

DownLoad: Full-Size Img PowerPoint
Fig. 2.  (Color online) Simulation design and results, (a) arm tissue layer modeling; (b) reference line location map; (c) current density distribution at MAE; (d) current density distribution at Convention C. Frequency characteristics of EIM simulation results, (e) Z; (f) θ; (g) R; (h) X. Potential along (i) reference line 1; (j) reference line 2; (k) reference line 3; (l) current density on reference line 3. Potential distribution of current source electrodes for different models, (m) MAEN; (n) Convention N; (o) MAEC; (p) Convention C.

DownLoad: Full-Size Img PowerPoint
Fig. 3.  (Color online) MAE preparation and physical diagram. Schematics of the MAE fabrication steps, (a) rigid master mold; (b) PDMS casting and curing; (c) PDMS demolding; (d) soft negative mold; (e) PDMS casting and curing; (f) PDMS microneedle array; (g) Ti/Au sputtering; (h) circuit connection. Photograph of (i) master mold; (j) PDMS negative mold; (k) PDMS substrate; (l) PDMS−MAE; (m) distortion characterization of MAE. Electron micrograph of (n) microneedle array; (o) enlarged view of microneedle; (p) surface comparison of untreated (left) and treated (right) microneedles.

DownLoad: Full-Size Img PowerPoint
Fig. 4.  (Color online) MAE performance testing, (a) surface condition after MAE electrode penetration: immediately (left), after 10 min (middle), after 50 min (right). (b) Electrode configuration diagram showing the dry electrode (top), MAEs (middle), and gel wet electrode (bottom). (c) Contact impedance of θ (n = 3, mean ± standard error). (d) Contact impedance of Z (n = 3, mean ± standard error). (e) Frequency-dependent Z. (f) R of muscle relaxation and contraction states. (g) X of muscle relaxation and contraction states.

DownLoad: Full-Size Img PowerPoint
Fig. 5.  (Color online) Equipment performance testing, (a) software interface; (b) virtual model; (c) measurement module; (d) one-handed measurement photo. Comparison measurements, (e) Z comparison (solid line: device, dotted line: E4980AL); (f) θ comparison; (g) repeated experiments results for R (n = 10, mean ± standard error); (h) repeated experiments results for X (n = 10, mean ± standard error).

DownLoad: Full-Size Img PowerPoint
Fig. 6.  (Color online) Fatigue cycle test, (a) protocol of muscle fatigue test. Experiment procedure, (b) muscle relaxation, (c) muscle contraction. EIM measurement results of muscle fatigue experiment, (d) heatmap of R, (e) 3D graph of R, (f) heatmap of X, (g) 3D graph of X, (h) inter-group data of R, (i) box plot of R, (j) inter-group data of X, (k) box plot of X.

DownLoad: Full-Size Img PowerPoint
Fig. 7.  (Color online) EIM measurement results of muscle adaptability experiment, left arm: (a) heatmap of R, (b) 3D graph of R, (c) heatmap of X, (d) 3D graph of X; right arm: (e) heatmap of R, (f) 3D graph of R, (g) heatmap of X, (h) 3D graph of X. R-value box plot, (i) left arm, (j) right arm; X−value box plot, (k) left arm, (l) right arm.

DownLoad: Full-Size Img PowerPoint

Table 1.   Electrode properties and electrode skin interface considerations.

ParametersMAENMAECConvention NConvention C
Type of electrodeMAEMAEConventional electrodesConventional electrodes
Electrode−skin interface consideredNoYesNoYes
DownLoad: CSV

Table 2.   The impedance values of four models at 100 kHz.

Parameters MAEN MAEC $ \dfrac{\text{MAEC}\text{−}\text{MAEN}}{\text{MAEN}} $ Convention N Convention C $ \dfrac{\text{Conventio C}\text{−}\text{Convention N}}{\text{Convention N}} $
Z 124.534 127.973 0.028 127.493 144.717 0.135
θ 0.147 0.145 −0.014 0.150 0.139 −0.073
R 123.188 126.633 0.028 126.070 143.329 0.137
X 18.261 18.473 0.012 18.998 19.995 0.052
DownLoad: CSV

Table 3.   fθ of four models at 100 kHz.

Parameters MAEN MAEC Convention N Convention C
fθ 88687 80259 99578 60153
DownLoad: CSV

Table 4.   Frequency corresponding to the maximum phase angle under different electrode width considering the electrode skin interface.

Electrode width (mm) 2 3 4 5
fθmN (Hz) 133200 126750 88051 88687
fθmC (Hz) 132260 124520 75541 80259
fθmN− fθmC (Hz) 940 2230 12510 8428
fθcN (Hz) 137560 127650 107460 99578
fθcC (Hz) 118870 96030 58435 60153
fθcN− fθcC (Hz) 18690 31620 49025 39425
DownLoad: CSV

Table 5.   Frequency corresponding to the maximum phase angle under different electrode length considering the electrode skin interface.

Electrode length (mm) 5 10 15
fθmN (Hz) 125850 88687 87107
fθmC (Hz) 115090 80259 66104
fθmN− fθmC (Hz) 10760 8428 21003
fθcN (Hz) 143630 99578 78287
fθcC (Hz) 122760 63748 40369
fθcN− fθcC (Hz) 20870 35830 37918
DownLoad: CSV

Table 6.   Upper arm dimension change.

Date 1 2 3 4 5 6 7
Left arm (mm) 1.46 1.32 1.44 1.57 1.46 1.43 1.63
Right arm (mm) 1.52 1.57 1.62 1.45 1.48 1.53 1.38
DownLoad: CSV
[1]
Enoka R M, Duchateau J. Muscle fatigue: What, why and how it influences muscle function. J Physiol, 2008, 586(1), 11 doi: 10.1113/jphysiol.2007.139477
[2]
Li N, Zhou R, Krishna B, et al. Non-invasive techniques for muscle fatigue monitoring: A comprehensive survey. ACM Comput Surv, 2024, 56(9), 1 doi: 10.1145/3648679
[3]
Qin X K, Zhong B W, Lv S X, et al. A zero-voltage-writing artificial nervous system based on biosensor integrated on ferroelectric tunnel junction. Adv Mater, 2024, 36(32), e2404026 doi: 10.1002/adma.202404026
[4]
Guven A E, Chiapparelli E, Camino−Willhuber G, et al. Assessing paraspinal muscle atrophy with electrical impedance myography: Limitations and insights. J Orthop Res, 2024, 42(9), 2072 doi: 10.1002/jor.25848
[5]
Schrunder A F, Rodriguez S, Rusu A. A finite element analysis and circuit modelling methodology for studying electrical impedance myography of human limbs. IEEE T-BME, 2022, 69(1), 244 doi: 10.1109/TBME.2021.3091884
[6]
Kapravchuk V, Briko A, Kobelev A, et al. An approach to using electrical impedance myography signal sensors to assess morphofunctional changes in tissue during muscle contraction. Biosensors, 2024, 14(2), 76 doi: 10.3390/bios14020076
[7]
Chrzanowski S M, Nagy J A, Pandeya S, et al. Electrical impedance myography correlates with functional measures of disease progression in D2-mdx mice and boys with Duchenne muscular dystrophy. J Neuromuscul Dis, 2023, 10(1), 81 doi: 10.3233/JND-210787
[8]
Rutkove S B. Electrical impedance myography: Background, current state, and future directions. Muscle Nerve, 2009, 40(6), 936 doi: 10.1002/mus.21362
[9]
Critcher S, Freeborn T J. Short-term evaluation of dry electrodes fabricated using flexible printed circuits processes for bioimpedance measurements. 2020 SoutheastCon, 2020, 1 doi: 10.1109/SoutheastCon44009.2020.9249690
[10]
Li Z, Li Y, Liu M S, et al. Microneedle electrode array for electrical impedance myography to characterize neurogenic myopathy. Ann Biomed Eng, 2016, 44(5), 1566 doi: 10.1007/s10439-015-1466-5
[11]
Li Z, Chen L F, Zhu Y, et al. Handheld electrical impedance myography probe for assessing carpal tunnel syndrome. Ann Biomed Eng, 2017, 45(6), 1572 doi: 10.1007/s10439-017-1819-3
[12]
Huang L K, Huang L N, Gao Y M, et al. Electrical impedance myography applied to monitoring of muscle fatigue during dynamic contractions. IEEE Access, 2020, 8, 13056 doi: 10.1109/ACCESS.2020.2965982
[13]
Kusche R, Oltmann A, Rostalski P. A wearable dual-channel bioimpedance spectrometer for real-time muscle contraction detection. IEEE Sens J, 2024, 24(7), 11316 doi: 10.1109/JSEN.2024.3359284
[14]
Qin X K, Zhong B W, Xu H, et al. Manufacturing high−performance flexible sensors via advanced patterning techniques. Int J Extrem Manuf, 2025, 7, 032003 doi: 10.1088/2631-7990/ada857
[15]
Ren L, Liu B, Zhou W, et al. A mini review of microneedle array electrode for bio-signal recording: A review. IEEE Sens J, 2020, 20(2), 577 doi: 10.1109/JSEN.2019.2944847
[16]
Wang Z Y, Huang D, Li J S, et al. Ag/AgCl-covered flexible microneedle array dry electrode for electrocardiography recording. 2023 IEEE 18th International Conference on Nano/Micro Engineered and Molecular Systems (NEMS), 2023, 74 doi: 10.1109/NEMS57332.2023.10190967
[17]
Wolf M P, Salieb-Beugelaar G B, Hunziker P. PDMS with designer functionalities: Properties, modifications strategies, and applications. Prog Polym Sci, 2018, 83, 97 doi: 10.1016/j.progpolymsci.2018.06.001
[18]
Wang R X, Bai J X, Zhu X H, et al. A PDMS-based microneedle array electrode for long-term ECG recording. Biomed Microdevices, 2022, 24(3), 27 doi: 10.1007/s10544-022-00626-y
[19]
Reddy J N. An introduction to the finite element method. J Press Vessel Technol, 1989, 111(3), 348 doi: 10.1115/1.3265687
[20]
Tang X, Dong Y Z, Li Q G, et al. Using microneedle array electrodes for non-invasive electrophysiological signal acquisition and sensory feedback evoking. Front Bioeng Biotechnol, 2023, 11, 1238210 doi: 10.3389/fbioe.2023.1238210
[21]
Li J S, Ma Y D, Huang D, et al. High-performance flexible microneedle array as a low-impedance surface biopotential dry electrode for wearable electrophysiological recording and polysomnography. Nanomicro Lett, 2022, 14(1), 132 doi: 10.1007/s40820-022-00870-0
[22]
Singh O P, El-Badawy I M, Sundaram S, et al. Microneedle electrodes: Materials, fabrication methods, and electrophysiological signal monitoring-narrative review. Biomed Microdevices, 2025, 27(1), 9 doi: 10.1007/s10544-024-00732-z
[23]
Liu Z J, Xu X Y, Huang S, et al. Multichannel microneedle dry electrode patches for minimally invasive transdermal recording of electrophysiological signals. Microsyst Nanoeng, 2024, 10, 72 doi: 10.1038/s41378-024-00702-8
[24]
Xu P, Yang X D, Ma W, et al. A study of handgrip force prediction scheme based on electrical impedance myography. IEEE J−ERM, 2023, 7(1), 90 doi: 10.1109/JERM.2023.3241769
[25]
Cebrián-Ponce Á, Irurtia A, Carrasco-Marginet M, et al. Electrical impedance myography in health and physical exercise: A systematic review and future perspectives. Front Physiol, 2021, 12, 740877 doi: 10.3389/fphys.2021.740877
[26]
Hülkenberg A, Strotmann N, Mokni A, et al. The influence of muscle contraction intensity on electrical impedance myography. 2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2024, 1 doi: 10.1109/EMBC53108.2024.10782074
[27]
Byström S. Estimation of aerobic and anaerobic metabolism in isometric forearm exercise. Ups J Med Sci, 1994, 99(1), 51 doi: 10.3109/03009739409179350
[28]
IEEE Standard on Piezoelectricity. ANSI/IEEE Std 176−1987. New York: IEEE, 1988, 1
[29]
Ji H W, Wang M Y, Wang Y T, et al. Skin-integrated, biocompatible, and stretchable silicon microneedle electrode for long-term EMG monitoring in motion scenario. NPJ Flex Electron, 2023, 7, 46 doi: 10.1038/s41528-023-00279-8
[30]
Gao K P, Yang H J, Wang X L, et al. Soft pin-shaped dry electrode with bristles for EEG signal measurements. Sensor Actuat A−Phys, 2018, 283, 348 doi: 10.1016/j.sna.2018.09.045
[31]
Shiffman C A, Aaron R, Rutkove S B. Electrical impedance of muscle during isometric contraction. Physiol Meas, 2003, 24(1), 213 doi: 10.1088/0967-3334/24/1/316

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    Received: 24 February 2025 Revised: 03 April 2025 Online: Accepted Manuscript: 28 April 2025Uncorrected proof: 19 May 2025Published: 09 October 2025

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      Article Navigation >  Journal of Semiconductors  > 2025  >  46(10): 102601
      Shanshan Yu, Yichao Gan, Feifan Song, Qiongzhang Wang, Hao Tang, Zhao Li. A miniaturized wireless electrical impedance myography platform for the long-term adaptive muscle fatigue monitoring[J]. Journal of Semiconductors, 2025, 46(10): 102601. doi: 10.1088/1674-4926/25020029 ****S S Yu, Y C Gan, F F Song, Q Z Wang, H Tang, and Z Li, A miniaturized wireless electrical impedance myography platform for the long-term adaptive muscle fatigue monitoring[J]. J. Semicond., 2025, 46(10), 102601 doi: 10.1088/1674-4926/25020029
      Citation:
      Shanshan Yu, Yichao Gan, Feifan Song, Qiongzhang Wang, Hao Tang, Zhao Li. A miniaturized wireless electrical impedance myography platform for the long-term adaptive muscle fatigue monitoring[J]. Journal of Semiconductors, 2025, 46(10): 102601. doi: 10.1088/1674-4926/25020029 ****
      S S Yu, Y C Gan, F F Song, Q Z Wang, H Tang, and Z Li, A miniaturized wireless electrical impedance myography platform for the long-term adaptive muscle fatigue monitoring[J]. J. Semicond., 2025, 46(10), 102601 doi: 10.1088/1674-4926/25020029
      shu

      A miniaturized wireless electrical impedance myography platform for the long-term adaptive muscle fatigue monitoring

      DOI: 10.1088/1674-4926/25020029
      CSTR: 32376.14.1674-4926.25020029
      • 1.

        State Key Laboratory of Optoelectronic Materials and Devices, Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China

      • 2.

        College of Materials Science and Opto-Electronic Technology, University of Chinese Academy of Sciences, Beijing 100049, China

      More Information
      • Shanshan Yu got her B.S. from University of Science and Technology Beijing in 2022. Now she is a M.D student at University of Chinese Academy of Sciences under the supervision of Prof. Zhao Li. Her research focuses on development of semiconductor biochemical sensing and detection equipment
      • Yichao Gan got his B.S. from Minzu University of China in 2025. Now he is a Ph.D. student at Institute of Semiconductors, Chinese Academy of Sciences under the supervision of Prof. Zhao Li. His research focuses on development of semiconductor biochemical sensing, detection equipment, robotics and embodied AI
      • Hao Tang received the B.Eng. degree from Beijing University of Posts and Telecommunications and M.Eng. degree from Tsinghua University. He did his Ph.D. studies at Princeton University from 2016 to 2023. His research interests include developing new architectures for point-of-care diagnostics and chip-scale platforms for cellular assays. In particular, he has been interested in high throughput, pneumatic-free, and cytometry with high specificity and sensitivity. He joined the Institute of Semiconductors, Chinese Academy of Sciences as a research engineer since 2024
      • Zhao Li received his B.Eng. degree in Communication Engineering from Wuhan University of Technology in 2012, and the Ph.D. Degree in Physical Electronics from the University of Chinese Academy of Sciences in 2017. From 2017 to 2021, he worked as a postdoctoral researcher in Ping Wang's lab at the Perelman School of Medicine, University of Pennsylvania. Since 2021, he has been with the Institute of Semiconductors, Chinese Academy of Sciences, as a full professor working on the novel biochemical sensors and medical testing equipment development
      • Corresponding author: haotang@semi.ac.cn; zhaoli@semi.ac.cn
      • Received Date: 2025-02-24
      • Revised Date: 2025-04-03
        • Available Online: 2025-04-28

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