Semiconductor/Carbon Quantum Dot-based Hue Recognition Strategy for Point of Need Testing: A Review

Abstract

The requirement to establish novel methods for visual detection is attracting attention in many application fields of analytical chemistry, such as, healthcare, environment, agriculture, and food. The research around subjects like "point-of-need", "hue recognition", "paper-based sensor", "fluorescent sensor", etc. has been always aimed at the opportunity to manufacture convenient and fast-response devices to be used by non-specialists. It is possible to achieve economic rationality and technical simplicity for optical sensing toward target analytes through introduction of fluorescent semiconductor/carbon quantum dot (QD) and paper-based substrates. In this Review, the mechanisms of anthropic visual recognition and fluorescent visual assays, characteristics of semiconductor/carbon QDs and ratiometric fluorescence test paper, and strategies of semiconductor/carbon QD-based hue recognition are described. We cover latest progress in the development and application of point-of-need sensors for visual detection, which is based on a semiconductor/carbon quantum dot-based hue recognition strategy generated by ratiometric fluorescence technology.

Keywords: hue; point-of-need testing; quantum dot; ratiometric fluorescence; test paper.

Figure 1

(a) Variations of color intensity (left) and color hue (right) in color wheels. (b) application of quantum dot‐based hue recognition strategy for PONT.

Figure 2

Ratiometric FL contained a response signal (a) or dual reversed response signals (b), ratiometric FL with one reference signal and two response signals (c), ratiometric FL with three signals in which one response signal has reversed change with other two signals (d).

Figure 3

(a) Mechanism and process of ratiometric fluorescent test paper for Cu²⁺ assay by a content sensitive color hue variation. Reproduced with permission from Ref. [19] Copyright 2017, American Chemical Society. (b) Visual platform for ultrasensitive monitoring of endogenous Cu²⁺ in human urine. Reproduced with permission from Ref. [24] Copyright 2017, Elsevier B.V. (c) Major modules of the “Concentration Detection” app and (d) Smartphone based platform device for uranyl ion detection. Reproduced with permission from Ref. [38] Copyright 2018, American Chemical Society. (e) Schematic illustration of the visual ratiometric detection system for CN⁻ sensing and (f) hue response of prepared test paper versus CN⁻ concentration. Reproduced with permission from Ref. [43] Copyright 2019, Elsevier B.V.

Figure 4

(a) The application of the visible identification of DPA by paper‐based sensor and mobile phone. Reproduced with permission from Ref. [60] Copyright 2020, Elsevier B.V. (b) Smartphone sensing platform based on 3D printing for thiram analysis and corresponding detection mechanism. Reproduced with permission from Ref. [69] Copyright 2020, American Chemical Society.

Figure 5

(a) Cellulose paper modified with citric acid and on‐site synthesis of CDs. (b) miRNA‐21 and circRNA‐HIAT1 activated RCA and fluorescent marking of PADs. (c) Imaging of the PADs arrays for FL color analysis of miRNA‐21 and circRNA‐HIAT1. Reproduced with permission from Ref. [82] Copyright 2021, Elsevier B.V. (d) Illustration of visual and quantitative detection of H‐FABP based on RFLFIA and traditional FLFIA. Reproduced with permission from Ref. [84] Copyright 2021, Wiley‐VCH