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ACS Sensors· 2016Q1

Wearable Microsensor Array for Multiplexed Heavy Metal Monitoring of Body Fluids

Wei Gao, Hnin Yin Yin Nyein, Ziba Shahpar, Hossain M. Fahad et al.

Short summary

A flexible, wearable microsensor array simultaneously detects five heavy metal ions (Zn, Cd, Pb, Cu, Hg) in human sweat and urine using electrochemical voltammetry, validated against ICP-MS.

AI-generated from the title and abstract; the full text is not read.

Key points

  • Simultaneous detection of Zn, Cd, Pb, Cu, and Hg ions in body fluids.
  • Utilizes electrochemical square wave anodic stripping voltammetry on Au and Bi microelectrodes.
  • Incorporates a skin temperature sensor for calibration and compensation.
  • Validated against ICP-MS using human sweat and urine samples.
  • Demonstrated real-time, on-body monitoring of heavy metals in sweat during exercise.

AI-generated from the title and abstract; the full text is not read.

Abstract

A flexible and wearable microsensor array is described for simultaneous multiplexed monitoring of heavy metals in human body fluids. Zn, Cd, Pb, Cu, and Hg ions are chosen as target analytes for detection via electrochemical square wave anodic stripping voltammetry (SWASV) on Au and Bi microelectrodes. The oxidation peaks of these metals are calibrated and compensated by incorporating a skin temperature sensor. High selectivity, repeatability, and flexibility of the sensor arrays are presented. Human sweat and urine samples are collected for heavy metal analysis, and measured results from the microsensors are validated through inductively coupled plasma mass spectrometry (ICP-MS). Real-time on-body evaluation of heavy metal (e.g., zinc and copper) levels in sweat of human subjects by cycling is performed to examine the change in concentrations with time. This platform is anticipated to provide insightful information about an individual's health state such as heavy metal exposure and aid the related clinical investigations.

The authors' abstract, as published at the source. ACS Sensors, 2016 · DOI ↗

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