Colorimetric discriminatory array for detection and discrimination of antioxidants based on HAuCl4/3,3',5,5'-tetramethylbenzidine

Electrochromic Sensor Augmented with Machine Learning for Enzyme-Free Analysis of Antioxidants

Our study presents an electrochromic sensor that operates without the need for enzymes or multiple oxidant reagents. This sensor is augmented with machine learning algorithms, enabling the identification, classification, and prediction of six different antioxidants with high accuracy. We utilized polyaniline (PANI), Prussian blue (PB), and copper-Prussian blue analogues (Cu-PBA) at their respective oxidation states as electrochromic materials (ECMs). By designing three readout channels with these materials, we were able to achieve visual detection of antioxidants without relying on traditional "lock and key" specific interactions. Our sensing approach is based on the direct electrochemical reactions between oxidized electrochromic materials (ECMsox) as electron acceptors and various antioxidants, which act as electron donors. This interaction generates unique fingerprint patterns by switching the ECMsox to reduced electrochromic materials (ECMsred), causing their colors to change. Through the application of density functional theory (DFT), we demonstrated the molecular-level basis for the distinct multicolor patterns. Additionally, machine learning algorithms were employed to correlate the optical patterns with RGB data, enabling complex data analysis and the prediction of unknown samples. To demonstrate the practical applications of our design, we successfully used the EC sensor to diagnose antioxidants in serum samples, indicating its potential for the on-site monitoring of antioxidant-related diseases. This advancement holds promise for various applications, including the real-time monitoring of antioxidant levels in biological samples, the early diagnosis of antioxidant-related diseases, and personalized medicine. Furthermore, the success of our electrochromic sensor design highlights the potential for exploring similar strategies in the development of sensors for diverse analytes, showcasing the versatility and adaptability of this approach.

Optical sensor arrays for the detection and discrimination of natural products

Covering: up to the end of 2022Natural products (NPs) have found uses in medicine, food, cosmetics, materials science, environmental protection, and other fields related to our life. Their beneficial properties along with potential toxicities make the detection and discrimination of NPs crucial for their applications. Owing to the merits of low cost and simple operation, optical sensor arrays, including colorimetric and fluorometric sensor arrays, have been widely applied in the detection of small molecule NPs and discrimination of structurally similar small molecule NPs or complex mixtures of NPs. This review provides a brief introduction to the optical sensor array and focuses on its progress toward the detection and discrimination of NPs. We summarized the design principle of sensor arrays toward various NPs (i.e., saccharides and polyhydroxy compounds, organic acids, flavonoids, organic sulfur compounds, amines, amino acids, and saponins) based on their functional groups and characteristic chemical properties, along with representative examples. Moreover, the challenges and potential directions for further research of optical sensor arrays for NPs are proposed.

A template-free assembly of Cu,N-codoped hollow carbon nanospheres as low-cost and highly efficient peroxidase nanozymes

Utilizing Cu2+-coordinated poly(m-phenylenediamine) as the precursor, Cu,N-codoped hollow carbon nanospheres as low-cost and highly efficient peroxidase nanozymes were assembled through a template-free strategy for the first time.

Visual detection of multiple antioxidants based on three chloroauric acid/Au-Ag nanocubes

A colorimetric sensing method is described for discrimination of multiple antioxidants based on core-shell Au@Ag nanocubes (NCs). In order to extract data-rich colorimetric responses from the sensor array, three different concentrations of chloroaurate acid (HAuCl₄) were employed as sensing elements. Interestingly, Au³⁺ ions can be reduced to different valence states (i.e., Au(0) and Au(I)) by different antioxidants, and thus effectively inhibit the oxidation etching process of Au@Ag NCs by Au(III) ions to varying extents, generating diverse colorimetric responses (color and absorbance). This enables identification of the six antioxidants at 10 nM via linear discriminant analysis (LDA) with relative standard deviation (RSD) of 2.52% (n = 3). The discrimination ability of the sensor array was further evaluated in antioxidant binary and multicomponent mixtures. Remarkably, identification of these six antioxidants spiked in urine was realized with 100% of accuracy. Schematic presentation of colorimetric assay for antioxidants based on three chloroauric acid/Au-Ag nanocubes.

Colorimetric glucose sensing with multiple-color changes by using a MnO2 NSs-TMB nanosystem

Glucose performs many essential functions associated with metabolic processes in the living system, and is closely related to many diseases such as diabetes and hypoglycemia. Most of the existing glucose concentration detection methods require complex instruments, which undoubtedly limit its widespread use. Here, we have designed a glucose colorimetric detection system composed of glucose, glucose oxidase (GOD), manganese dioxide nanosheets (MnO₂ NSs) and 3,3',5,5'-tetramethylbenzidine (TMB) to achieve colorimetric detection with the naked eye. Compared with the single-color change of the colorimetric method in previous studies, multiple-color changes have been realized. MnO₂ NSs, as a kind of nanomaterial imitating oxidase, can directly oxidize TMB to oxTMB. Because oxTMB showed a dark yellow color when strongly oxidized and light blue when weakly oxidized, this feature can achieve multiple-color changes rather than a single-color change, which is helpful for colorimetric observation with the naked eye. Finally, we successfully used MnO₂ NSs for colorimetric detection of glucose and realized multiple-color changes, making it easier to achieve colorimetric observation with the naked eye. The linear detection range is 0-4000 μM and limit of detection is 5.0 μM. This is not only useful for glucose, but also has an important significance for other experiments considering colorimetric experiments with the naked eye.