Highly sensitive pH sensor based on flexible polyaniline matrix for synchronal sweat monitoring

Zhao, Yue; Yu, Yaoguang; Zhao, Shifan; Zhu, Rui; Zhao, Jie; Cui, Guofeng

doi:10.1016/j.microc.2022.108092

Cited by 29 publications

(20 citation statements)

References 65 publications

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“…The limit of detection (S/N = 3) for the developed sweat volume sensor was also found to be 4 μL. The results of our developed sensor are comparable with those of other wearable sensors reported for measuring pH and volume of sweat. ,− …”

Section: Resultssupporting

confidence: 77%

Smart Wearable Nanopaper Patch for Continuous Multiplexed Optical Monitoring of Sweat Parameters

Sharifi,

Ardalan,

Tabatabaee

et al. 2023

Anal. Chem.

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Notwithstanding the substantial progress in optical wearable sensing devices, developing wearable optical sensors for simultaneous, real-time, and continuous monitoring of multiple biomarkers is still an important, yet unmet, demand. Aiming to address this need, we introduced for the first time a smart wearable optical sensor (SWOS) platform combining a multiplexed sweat sensor sticker with its IoT-enabled readout module. We employed our SWOS system for on-body continuous, real-time, and simultaneous fluorimetric monitoring of sweat volume (physical parameter) and pH (chemical marker). Herein, a variation in moisture (5−45 μL) or pH (4.0−7.0) causes a color/fluorescence change in the copper chloride/fluorescein immobilized within a transparent chitin nanopaper (ChNP) in a selective and reversible manner. Human experiments conducted on athletic volunteers during exercise confirm that our developed SWOS platform can be efficiently exploited for smart perspiration analysis toward personalized health monitoring. Moreover, our system can be further extended for the continuous and real-time multiplexed monitoring of various biomarkers (metabolites, proteins, or drugs) of sweat or other biofluids (for example, analyzing exhaled breath by integrating onto a facemask).

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Section: Resultssupporting

confidence: 77%

Smart Wearable Nanopaper Patch for Continuous Multiplexed Optical Monitoring of Sweat Parameters

Sharifi,

Ardalan,

Tabatabaee

et al. 2023

Anal. Chem.

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“…The results demonstrate that an average sensitivity of −64.41 mV pH −1 is obtained from six separate trials, and the relative standard deviation (RSD) is 1.69%, which is attributed to the good exibility and response performance of the 3D PANI pH sensor. 24 Fig. 7(c) shows that when the pH of the buffer solution changes from 4.10 to 5.07, 90% of the equilibrium potential value of the 3D PANI pH sensor is achieved in just 8 s in the normal state, which means a faster response time of our exible pH sensor than those of PANI exible pH sensors listed in Table 1.…”

Section: Ph Sensing Performances Of the 3d Pani Ph Sensormentioning

confidence: 92%

“…Aer two cycles, the hysteresis width is only 1.20 mV, which is lower than the same type of exible pH sensor listed in Table 1. 6,24,27 Conversely, in the bent state, the hysteresis width of the 3D PANI pH sensor is only 5.60 mV aer two cycles. Therefore, it is suggested that the 3D PANI pH sensor has good reversibility.…”

Section: Ph Sensing Performances Of the 3d Pani Ph Sensormentioning

confidence: 99%

“…Due to its higher surface-to-volume ratio and capacity of accommodating a greater number of H + binding sites, PANI possesses the enhanced pH-responsive properties. [24][25][26] The reference electrodes can provide a stable reference potential for accurate pH measurements. Ag/AgCl is oen used as the reference electrode in view of its potential stability and environmental friendliness.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A flexible pH sensor based on polyaniline@oily polyurethane/polypropylene spunbonded nonwoven fabric

Zhu,

Sun,

et al. 2024

RSC Adv.

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“…Measurements can still be trusted with a pH value susceptibility limit of 4 to 9. However, the sensor can present data that is not the same as the actual value or experience an error [7].…”

Section: Introductionmentioning

confidence: 99%

Reliability Analysis of pH Measurement on TLC4502 with E201C Electrodes based on ATmega328P Microcontroller: Approach to Analysis of Variation with ANOVA

Julian

Wahyuni

Ulhaq

2023

ELKHA

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The development of the water management system has so far reached the stage of utilizing IoT technology in the monitoring and operation process. An essential factor in water that affects the quality of a substance is pH. The research aims to analyze and ensure that the devices have a small pH measurement error rate with TLC4502 & E201C. The calibration process was carried out using linear regression, and value of 0.99 was obtained. Analysis was carried out using one-way ANOVA and Tukey HSD methods, and it was found that all data pairs rejected the null hypothesis (H0) and accepted the alternate hypothesis (H1). This hypothesis indicated a significant difference in the relative measurement error of pH in each condition. The standard error value of each measurement after filtration was 0.00, with an uncertainty value ranging from 0.07 to 0.02. If the sensor can provide measurement results with low error and high accuracy, then the sensor can be widely circulated and used. Through this research, the feasibility of a measuring instrument was developed based on the perspective of errors and high accuracy. A quality measuring instrument certainly helpful in various fields from the fisheries, hydroponics, and environmental sectors.

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Highly sensitive pH sensor based on flexible polyaniline matrix for synchronal sweat monitoring

Cited by 29 publications

References 65 publications

Smart Wearable Nanopaper Patch for Continuous Multiplexed Optical Monitoring of Sweat Parameters

Smart Wearable Nanopaper Patch for Continuous Multiplexed Optical Monitoring of Sweat Parameters

A flexible pH sensor based on polyaniline@oily polyurethane/polypropylene spunbonded nonwoven fabric

Reliability Analysis of pH Measurement on TLC4502 with E201C Electrodes based on ATmega328P Microcontroller: Approach to Analysis of Variation with ANOVA

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