Improving sensitivity of mercury detection using learning based smartphone colorimetry

Sajed, Samira; Arefi, Fatemeh; Kolahdouz, Mohammadreza; Sadeghi, Maryam

doi:10.1016/j.snb.2019.126942

Cited by 65 publications

(37 citation statements)

References 37 publications

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“…The analytical performance of the regression-based model was compared with previous works ( Table 1 ). 20 , 21 The practical limit of detection was improved by nearly 8 orders of magnitude in comparison to the ratiometric method using the nonlinear regression method.…”

Section: Resultsmentioning

confidence: 99%

“…A light-weight attachment housing was used based on the previous design. 20 It incorporates as the holder and basic optical element for colorimetric purposes. The schematic of the final design of the device is shown in Figure 10 .…”

Section: Methodsmentioning

confidence: 99%

“… 39 In previous studies of our group, a portable instrument for determination of mercury contamination in water samples was developed by measuring the RGB color intensity delivered to the smartphone camera. 20 …”

Section: Introductionmentioning

confidence: 99%

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High-Performance Estimation of Lead Ion Concentration Using Smartphone-Based Colorimetric Analysis and a Machine Learning Approach

et al. 2020

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Traditional methods for detection of lead ions in water samples are costly and time-consuming. In this work, an accurate smartphone-based colorimetric sensor was developed utilizing a novel machine learning algorithm. In the presence of Pb 2+ ions in the solution of specifically functionalized gold nanoparticles, the color of solution turns from red to purple. Indeed, the color variation of the solution is proportional to Pb 2+ concentration. The smartphone camera captures the corresponding color change, and the image is processed by an efficient artificial intelligence protocol. The nonlinear regression approach was used for concentration estimation, in which the parameters of the proposed model are obtained using a new feature extraction algorithm. In prediction of Pb 2+ concentration, the average absolute error and root-mean-square error were 0.094 and 0.124, respectively. The influence of pH of the medium, temperature, oligonucleotide concentration, and reaction time on the performance of the proposed sensor was carefully investigated and understood to achieve the best sensor response. This novel sensor exhibited good linearity for the detection of Pb 2+ in the concentration range of 0.5–2000 ppb with a detection limit of 0.5 ppb.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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High-Performance Estimation of Lead Ion Concentration Using Smartphone-Based Colorimetric Analysis and a Machine Learning Approach

et al. 2020

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“…7 are significantly increased with increasing synthesis temperature from 25 C to 90 C. The RGB color space has mostly been used in smartphone colorimetry (Amirjani & Fatmehsari, 2018;Firdaus, 2019;Sajed et al, 2019) and smaller error bars in Fig. 5 indicate the precision of this method.…”

Section: Effect Of Storagementioning

confidence: 92%

“…Smartphone colorimetry is therefore increasingly implemented in Ag nanostructure characterizations. The application is extended to detect ammonia (Amirjani & Fatmehsari, 2018) and mercury (Firdaus et al, 2019;Jarujamrus et al, 2018;Sajed et al, 2019) in the presence of either Ag or Au nanoparticles.…”

Section: Introductionmentioning

confidence: 99%

Effects of synthesis conditions and storage on silver nanospheres chemically reduced by mulberry leaf extracts

Pholnak¹,

Khunsare²,

Sirisathitkul³

et al. 2021

JART

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Silver (Ag) nanospheres were chemically reduced from 1 mM silver nitrate (AgNO3) solutions using mulberry (Morus alba Linn.) leaf extracts. The increase of pH in the synthesis from 9 to 13 modified the surface plasmon resonance peak in UV-Vis spectra relating to the particle diameter around 20 nm. Smaller nanoparticles were obtained from the syntheses at pH 7.4-7.7 and their density was slightly increased with the increase in synthesis temperature from 25 °C to 90 °C. The particle size was increased with increasing AgNO3 concentration from 0.1 to 0.5 M but the mixed phases were obtained from the 0.5 M AgNO3 precursor. After storing for 10 months at room temperature, the nanosuspensions remained without Ag sediment but their colors had significantly changed due to particle agglomerations. The colors were characterized by the smartphone colorimetry according to the RGB, CIE space, as well as chroma and hue angle. Based on the chroma and CIE b* values, the effect of synthesis pH in the stored nanosuspensions was comparable to that before the storage. Although the temperature had only moderate effects, nanosuspensions synthesized at 25 °C, 80 °C, and 90 °C were clearly distinguished by the hue and CIE b* values.

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Field‐Flow Fractionation Coupled to Atomic Mass Spectrometry Techniques for Inorganic Nanoparticles Characterization: Theory and Applications

Maknun,

Saenmuangchin,

Siripinyanond

2024

Encyclopedia of Analytical Chemistry

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Inorganic nanoparticles have been applied in various applications owing to their interesting properties including magnetic, thermal, optical, and catalytic performance. They can be classified as mono‐metallic nanoparticles, bimetallic nanoparticles, metal oxide nanoparticles, and quantum dots. Field‐flow fractionation (FFF) is an interesting nonchromatographic technique for size characterization of materials with nanometer range. Various subtechniques of FFF including flow, sedimentation, electrical, and thermal are described with some selected applications reviewed. Moreover, FFF can be used via off‐line and on‐line with many elemental detection techniques: GFAAS, ICP‐OES, ICP‐MS, and SP‐ICP‐MS to provide more information in term of quantification and element‐specific detection. In this article, applications of FFF with atomic spectrometric detection for various types of samples, including consumer products, food, environmental samples, biological samples, and sensors, are discussed.

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Improving sensitivity of mercury detection using learning based smartphone colorimetry

Cited by 65 publications

References 37 publications

High-Performance Estimation of Lead Ion Concentration Using Smartphone-Based Colorimetric Analysis and a Machine Learning Approach

High-Performance Estimation of Lead Ion Concentration Using Smartphone-Based Colorimetric Analysis and a Machine Learning Approach

Effects of synthesis conditions and storage on silver nanospheres chemically reduced by mulberry leaf extracts

Field‐Flow Fractionation Coupled to Atomic Mass Spectrometry Techniques for Inorganic Nanoparticles Characterization: Theory and Applications

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