Grantee Research Project Results

A Smartphone-based Colorimetric Lead Sensor for Household Lead Detection

EPA Grant Number: SV840830
Title: A Smartphone-based Colorimetric Lead Sensor for Household Lead Detection
Investigators: Wei, Haoran
Institution: University of Wisconsin - Madison
EPA Project Officer: Callan, Richard
Phase: II
Project Period: September 1, 2024 through April 23, 2025
Project Amount: $100,000
RFA: 18th Annual P3 Awards: A National Student Design Competition Focusing on People, Prosperity and the Planet Phase 2 (2024) Recipients Lists
Research Category: Clean Water , Drinking Water , Children's Health , Endocrine Disruptors , Heavy Metal Contamination of Soil/Water , P3 Awards , P3 Challenge Area - Safe and Sustainable Water Resources , Urban Air Toxics , Water , Water Quality

Description:

We will employ a novel structure of AuNPs, namely, Janus gold nanorods (AuJNRs), for lead sensor development. Compared with gold nanospheres (AuNSs) that possess only one LSPR band at ⁓520 nm, AuJNRs exhibit a transverse and a longitudinal LSPR band at ⁓520 and ⁓810 nm, respectively. This dual-band nature provides an additional dimension that can be harnessed for effective lead sensing. We will use the tap water collected in Madison, WI to test the stability of AuNSs and AuJNRs because of its high ionic strength (conductivity: ⁓750 μS/cm). Our preliminary data showed that AuNSs exhibited severe and continuous aggregation, which was induced by high ionic strength. In contrast, AuJNRs did not aggregate in high ionic strength environments, which underscored the remarkable stability of AuJNRs, making them an ideal candidate for the development of colorimetric sensors that are effective in tap water. Furthermore, we will engineer an innovative etching-based approach. When the surface of the AuJNRs is coated with thiosulfate, the addition of 2-mercaptoethanol and Pb2+ results in a selective etching at the tips of the AuJNRs and a substantial reduction of the longitudinal LSPR band. This unique optical response will be harnessed for accurate Pb2+ quantification.

To test the sensor’s sensitivity, different volumes of a Pb2+ standard solution will be spiked into tap water samples to create a concentration gradient (2-10,000 ppb). Our preliminary results showed that light extinction around the longitudinal LSPR band at 765 and 810 nm exhibited a linear decrease as the concentration of Pb2+ increased from 0.01-50 μM (2 to 10,000 ppb) at 0 min. At 15 min, this linear decrease was markedly accentuated, providing a distinct dynamic range for lead quantification. Our data demonstrated the exceptional sensitivity (an LOQ < 2 ppb) and quantitative performance (R2 > 0.98) of the AuJNR-based colorimetric sensors for lead detection in Madison tap water. To learn about lead pollution in disadvantaged communities, we will collect tap water samples from affected communities, including households and public places, such as water fountains in libraries and parks and analyzed lead using both the AuJNR-based colorimetric sensor and inductively coupled plasma mass spectrometry (ICP-MS). The preliminary results revealed a significant discrepancy between the average lead levels in the first-draw samples (2.44 ppb) and samples after 3-min flushing (0.60 ppb). This disparity suggests that water stagnation increases the lead concentration in tap water. Furthermore, we will train the households severely affected by lead pollution to collect data using the developed sensors and educate children from these households via well-designed outreach and educational activities.

Objective:

The overall goal of this research is to develop an affordable and easy-to-use sensor for highly selective and sensitive detection of lead in tap water. Lead poisoning has severe implications for brain function and the nervous system, particularly affecting the growth and development of children. One of the major sources of lead exposure stems from the corrosion of lead service lines (LSLs) in aging drinking water distribution infrastructures. The Flint Water Crisis serves as a stark reminder that lead poisoning often disproportionately affects underserved communities, who have limited economic and political influence to identify and address such issues. The initial crucial step in determining the magnitude and scope of lead pollution in tap water is rapid and cost-effective testing, which, unfortunately, is not currently available. Hence, there is a significant need for affordable household sensors that can offer regular monitoring of lead levels in drinking water.

To achieve this overall goal, we have developed a detailed research plan that consists of four interconnected objectives: 1) optimize the selectivity and sensitivity of the Janus gold nanorod (AuJNR)-based colorimetric sensors for lead analysis, 2) mitigate the influence of water matrices on sensor performance using machine learning models, 3) integrate the AuJNR-based colorimetric sensors with smartphone technologies, and 4) educate children from low-income families in affected communities about the use of our lead sensor and health hazards of lead. This project aligns with the P3 approach in that it emphasizes the protection of the environment and public health from lead contamination.

Approach:

The study aims to utilize smartphone cameras for lead analysis, bringing a new level of convenience and accessibility. This approach will enable households within the most affected communities to operate these sensors, providing unprecedented insights into the spatiotemporal distribution of lead pollution and potentially evidence linking water quality to health outcomes. In combination with citizen science and a wealth of data collected nationwide, user-uploaded test results will amass a comprehensive dataset on the cloud platform. This rich data repository can then serve as a valuable resource for drinking water management, especially lead pollution prevention.

Expected Results:

Approximately 40% of households in Milwaukee County still receive drinking water through lead service lines. According to a 2023 report from the Wisconsin Department of Health, about 5% of children tested in Milwaukee County have blood lead levels higher than 5 μg/dL, which is 43% higher than the standard reference value (i.e., 3.5 μg/dL) established by the Centers for Disease Control and Prevention to identify children with elevated lead blood levels. We propose to use smartphone-based colorimetric sensors to quantify lead in tap water within affected communities in Milwaukee. We developed a comprehensive research and education plan comprising four interrelated objectives, including 1) optimizing the sensitivity of the AuJNR-based colorimetric sensors for lead quantification in tap water, 2) quantifying the influence of different tap water matrices on lead sensor performance via machine learning, 3) gaining insights into lead contamination patterns within affected communities via smartphone-enabled colorimetric sensors, and 4) educate children from low-income families in affected communities about the use of our lead sensor and health hazards of lead.

Our efforts will initially focus on enhancing the stability, sensitivity, and selectivity of the AuJNR-based sensors within the extremely hard tap water in Madison, WI. After optimizing the sensors in Madison tap water, we will proceed to collect tap water from diverse locations across the United States to quantify the influence of water matrices on sensor performance. Calibration curves for lead spiked into these water samples will be obtained. By employing machine learning techniques, we will establish models for predicting lead calibration curves using basic water quality parameters. We will then harness the ubiquity, exceptional light detection capabilities, computing power, and cloud storage of smartphones, thereby transforming our lab-based colorimetric sensors into user-friendly household tools. To enhance K12 educational outreach, we will engage children from lead-affected communities in Milwaukee by orchestrating hands-on experiments centered around lead analysis as part of the Wisconsin Science Festival. We will identify interested households and provide them with comprehensive training on the proper use and data collection techniques with our newly developed sensors. By integrating educational outreach with research via citizen science, we aim to illuminate spatiotemporal variations in lead levels within the affected communities in Milwaukee.

Supplemental Keywords:

Drinking water monitoring, Analytical method, Plasmonic colorimetric sensor, Midwest, Environmental Chemistry, Lead pollution, Citizen science, Big data

P3 Phase I:

A smartphone-based colorimetric sensor for household lead detection  | Final Report