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Frontiers of Environmental Science & Engineering

ISSN 2095-2201

ISSN 2095-221X(Online)

CN 10-1013/X

Postal Subscription Code 80-973

2018 Impact Factor: 3.883

Front. Environ. Sci. Eng.    2022, Vol. 16 Issue (12) : 155    https://doi.org/10.1007/s11783-022-1590-z
RESEARCH ARTICLE
A cellphone-based colorimetric multi-channel sensor for water environmental monitoring
Yunpeng Xing1,2, Boyuan Xue2, Yongshu Lin2, Xueqi Wu2, Fang Fang3, Peishi Qi1(), Jinsong Guo3(), Xiaohong Zhou2()
1. State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150090, China
2. State Key Joint Laboratory of ESPC, Center for Sensor Technology of Environment and Health, School of Environment, Tsinghua University, Beijing 100084, China
3. Key Laboratory of the Three Gorges Reservoir Region’s Eco-Environments of MOE, Chongqing University, Chongqing 400030, China
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Abstract

● A cellphone-based colorimetric multi-channel sensor for in-field detection.

● A universal colorimetric detection platform in the absorbance range of 400–700 nm.

● Six-fold improvement of sensitivity by introducing a transmission grating.

● Quantifying multiple water quality indexes simultaneously with high stability.

The development of colorimetric analysis technologies for the commercial cellphone platform has attracted great attention in environmental monitoring due to the low cost, high versatility, easy miniaturization, and widespread ownership of cellphones. This work demonstrates a cellphone-based colorimetric multi-channel sensor for quantifying multiple environmental contaminants simultaneously with high sensitivity and stability. To improve the sensitivity of the sensor, a delicate optical path system was created by using a diffraction grating to split six white beams transmitting through the multiple colored samples, which allows the cellphone CMOS camera to capture the diffracted light for image analysis. The proposed sensor is a universal colorimetric detection platform for a variety of environmental contaminants with the colorimetry assay in the range of 400–700 nm. By introducing the diffraction grating for splitting light, the sensitivity was improved by over six folds compared with a system that directly photographed transmitted light. As a successful proof-of-concept, the sensor was used to detect turbidity, orthophosphate, ammonia nitrogen and three heavy metals simultaneously with high sensitivity (turbidity: detection limit of 1.3 NTU, linear range of 5–400 NTU; ammonia nitrogen: 0.014 mg/L, 0.05–5 mg/L; orthophosphate: 0.028 mg/L, 0.1–10 mg/L; Cr (VI): 0.0069 mg/L, 0.01–0.5 mg/L; Fe: 0.025 mg/L, 0.1–2 mg/L; Zn: 0.032 mg/L, 0.05–2 mg/L) and reliability (relative standard deviations of six parallel measurements of 0.37%–1.60% and recoveries of 95.5%–106.0% in surface water). The miniature sensor demonstrated in-field sensing ability in environmental monitoring, which can be extended to point-of-care diagnosis and food safety control.

Keywords Colorimetric analysis      Multi-channel sensor      Cellphone      Water quality indexes      Environmental monitoring     
Corresponding Author(s): Peishi Qi,Jinsong Guo,Xiaohong Zhou   
Issue Date: 23 June 2022
 Cite this article:   
Yunpeng Xing,Boyuan Xue,Yongshu Lin, et al. A cellphone-based colorimetric multi-channel sensor for water environmental monitoring[J]. Front. Environ. Sci. Eng., 2022, 16(12): 155.
 URL:  
https://academic.hep.com.cn/fese/EN/10.1007/s11783-022-1590-z
https://academic.hep.com.cn/fese/EN/Y2022/V16/I12/155
Fig.1  Schematic diagram of the cellphone-based multi-channel sensor. (A) The light path design and the corresponding components of experimental setup, and (B) the structural design.
Fig.2  Wavelength calibration for the cellphone-based multi-channel sensor. (A) Spectra of white LED and monochromatic light through the light dispersion of transmission grating; (B) Spectral curves of white LED and monochromatic light using the gray analysis model; (C) Linear fitting curve of wavelength and pixel.
Substances Factors Model types
R G B H S V Gray
Turbidity Slope 0.0013 0.0015 0.0014 ? ?0.0009 0.0013 0.0016
Intercept ?0.003 ?0.003 -0.002 ? 0.089 ?0.002 0.0014
Correlation coefficient 0.997 0.995 0.991 ? 0.964 0.995 0.999
Ammonia nitrogen Slope 0.007 0.016 0.040 ? ?0.028 0.036 0.0070
Intercept 0.003 0.003 0.006 ? 0.053 0.007 1.4E?4
Correlation coefficient 0.88 0.965 0.97 ? 0.965 0.979 0.999
Orthophosphate Slope 0.161 0.093 0.101 ? -0.051 0.165 0.288
Intercept ?5.3E?5 0.002 ?0.004 ? 0.029 3.4E?4 ?4.0E?4
Correlation coefficient 0.985 0.934 0.930 ? 0.919 0.985 0.999
Tab.1  Slope, intercept, and correlation coefficient of calibration curves of turbidity, ammonia nitrogen, and orthophosphate based on different models
Fig.3  Comparison of image analysis models for the cellphone-based multi-channel sensor. Calibration curves of (A) turbidity, (C) ammonia nitrogen, and (E) orthophosphate based on the R, G, B, S, and V models; and calibration curves of (B) turbidity, (D) ammonia nitrogen, and (F) orthophosphate based on the gray model.
Fig.4  Screen shots of the android-based cellphone software for water quality indexes analysis. (A) Main menu of the software; (B) Interface of creating a new test; (C) Interface of shooting or uploading an image; (D) Preview of the intercepted regions and signal value curves of different channels before generating the absorbance; (E) Interface of exhibiting historical data (including sample name, concentration, time, and GPS coordinates); (F) Interface of temporal and spatial analysis on the AutoNavi Map.
Fig.5  Calibration curves of the multi-channel sensor (n = 3). Calibration curves for (A) turbidity, (B) ammonia nitrogen, (C) orthophosphate, and three heavy metal ions of (D) Cr (VI), (E) Fe, and (F) Zn. Blue words and dotted lines represent the LODs for different water quality indexes.
Fig.6  Repeatability tests of our cellphone-based multi-channel sensor (n = 3). Concentrations of water quality indexes were set as follows: turbidity of 200 NTU, ammonia nitrogen of 2.5 mg/L, orthophosphate of 5 mg/L, Cr (VI) of 0.25 mg/L, Fe of 1 mg/L, and Zn of 1 mg/L.
Water quality index Added (NTU for turbidity, mg/L for others) Measured by spectrophotometer (NTU for turbidity, mg/L for others) Measured by our sensor (NTU for turbidity, mg/L for others) Recovery by our sensor (%)
Turbidity 0 3.3 4.3±0.8 ?
50 51.5 55.5±2.0 102.3±3.1
100 106.5 108.4±2.0 104.1±2.1
200 210.8 196.5±1.7 96.1±1.1
Ammonia nitrogen 0 0.055 0.048±0.008 ?
0.5 0.547 0.548±0.014 100.0±1.5
1 1.067 1.094±0.023 104.6±2.2
2 2.090 2.101±0.053 102.7±2.3
Orthophosphate 0 0.062 0.059±0.004 ?
1 1.054 1.061±0.013 100.2±1.6
2 2.030 2.122±0.070 103.1±3.3
4 4.011 3.881±0.046 95.5±1.1
Cr (VI) 0 0 < LOD ?
0.05 0.048 0.052±0.001 105.6±1.8
0.1 0.102 0.101±0.004 100.6±4.1
0.2 0.201 0.196±0.004 98.1±1.9
Fe 0 0.053 0.061±0.012 ?
0.25 0.304 0.326±0.012 106.0±5.3
0.5 0.564 0.561±0.010 99.9±2.0
1 1.067 1.037±0.028 97.6±1.8
Zn 0 0.103 0.111±0.016
0.25 0.353 0.344±0.070 104.6±3.1
0.5 0.604 0.609±0.071 105.2±5.5
1 1.124 1.093±0.047 101.0±2.0
Tab.2  Recovery of six water quality indexes analysis in real water sample (n = 3 for our sensor)
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