Fill, Fold, Photo: Preconcentration and Multiplex Detection of Trace Level Heavy Metals in Water

多路复用 重金属 折叠(高阶函数) 化学 色谱法 跟踪(心理语言学) 环境化学 放射化学 计算机科学 生物 语言学 生物信息学 哲学 程序设计语言
作者
Prakash Aryal,Jason Boes,Eric Brack,Todd E. Alexander,Charles S. Henry
出处
期刊:ACS Sensors [American Chemical Society]
卷期号:9 (10): 5479-5488 被引量:7
标识
DOI:10.1021/acssensors.4c01708
摘要

Heavy metal contamination is an increasing global threat to human and environmental health, particularly in resource-limited areas. Traditional platforms for heavy metal detection are labor intensive and expensive and require lab facilities. While paper-based colorimetric sensors offer a simpler approach, their sensitivity limitations prevent them from meeting legislative requirements for many metals. Existing preconcentration systems, on the other hand, can achieve lower detection limits but typically focus on analyzing only one metal, making comprehensive monitoring difficult. We address these limitations by introducing a low-cost preconcentration system coupled with colorimetric analysis for the simultaneous detection of seven metal ions at low ppb levels without the need for external equipment outside a smartphone. The system achieved detection limits of 15 ppb (Ni(II)), 7 ppb (Cu(II)), 2 ppb (Fe(III)), 20 ppb (Cr(VI)), 13 ppb (Pb(II)), 26 ppb (Hg(II)), and 15 ppb (Mn(II)) with six out of seven limits of detection values falling well below EPA regulatory guidelines for drinking water. The user-friendly Fill, Fold, Photo approach eliminates complex pretreatment steps. Smartphone-based detection offers portable quantification within seconds. Employing masking strategies ensured higher selectivity for each assay on the card, while our packaging protocols enable system stability for over 4 weeks of study, facilitating mass production and deployment within a realistic time frame. To validate the sensor's performance in real-world scenarios, the sensor was tested with environmental water samples. The sensor demonstrated good recovery, ranging from 77% to 94% compared to the standard ICP-MS method. Furthermore, spike recovery analysis confirmed the sensor's accuracy, with a relative standard deviation (RSD) of less than 15%. This technology holds significant promise for future development as a convenient, portable solution for field-based monitoring of a broad spectrum of water contaminants, including pesticides, PFAS, fertilizers, and beyond.
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