Quantification of doping state of redox sensitive nanoparticles for probing the invasiveness of cancer cells using surface enhanced Raman scattering

Chemically renewable SERS sensor for the inspection of H2O2 residue in food stuff

Hydrogen peroxide (H2O2) residue in foodstuffs will bring great harm to human health. We immobilize the composite of the reduced polyaniline (PANIR) modified gold nanoparticles on the surface of ITO (ITO/AuNPs/PANIR) to develop surface-enhanced Raman scattering (SERS) sensor for H2O2.detection. The principle is that PANIR is oxidized by H2O2 to generate a new SERS peak at 1460 cm-1 for realizing quantitative analysis of H2O2. Fe2+-Fenton reaction is introduced to catalytically react with H2O2 to hydroxyl radical, which speeds up the oxidation of PANIR. Before SERS detection, acidic treatment could guarantee the reduced state of PANIR in composite. Limit of detection of ITO/AuNPs/PANIR-based SERS assay for H2O2 is down to 1.78 × 10-12 mol/L and a good linear relationship from 1 × 10-10 to 3.16 × 10-7 mol/L is achieved. Furthermore, the SERS sensor could be regenerated by acidic treatment. As a scenario, the renewable SERS sensor is utilized to monitor H2O2 residues in food and environmental samples.

A regression-based machine learning approach for pH and glucose detection with redox-sensitive colorimetric paper sensors

Colorimetric paper sensors are used in various fields due to their convenience and intuitive manner. However, these sensors present low accuracy in practical use because it is difficult to distinguish color changes for a minute amount of analyte with the naked eye. Herein, we demonstrate that a machine learning (ML)-based paper sensor platform accurately determines the color changes. We fabricated a colorimetric paper sensor by adsorbing polyaniline nanoparticles (PAni-NPs), whose color changes from blue to green when the ambient pH decreases. Adding glucose oxidase (GOx) to the paper sensor enables colorimetric glucose detection. Target analytes (10 μL) were aliquoted onto the paper sensors, and their images were taken with a smartphone under the same conditions in a darkroom. The red-green-blue (RGB) data from the images were extracted and used to train and test three regression models: support vector regression (SVR), decision tree regression (DTR), and random forest regression (RFR). Of the three regression models, RFR performed the best at estimating pH levels (R² = 0.957) ranging from pH 2 to 10 and glucose concentrations (R² = 0.922) ranging from 0 to 10 mg mL^-1.

Colorimetric Sensing of Lactate in Human Sweat Using Polyaniline Nanoparticles-Based Sensor Platform and Colorimeter

In emergency medicine, the lactate level is commonly used as an indicator of the severity and response to the treatment of hypoperfusion-related diseases. Clinical lactate measurements generally require 3 h for clinical determination. To improve the current gold standard methods, the development of sensor devices that can reduce detection time while maintaining sensitivity and providing portability is gaining great attention. This study aimed to develop a polyaniline (PAni)-based single-sensor platform for sensing lactate in human sweat using a CIELAB color system-based colorimetric device. To establish a lactate sensing platform, PAni nanoparticles were synthesized and adsorbed on the filter paper surface using solvent shift and dip-coating methods, respectively. PAni is characterized by a chemical change accompanied by a color change according to the surrounding environment. To quantify the color change of PAni, a CIELAB color system-based colorimetric device was fabricated. The color change of PAni was measured according to the chemical state using a combination of a PAni-based filter paper sensor platform and a colorimetric device, based on the lactate concentration in deionized water. Finally, human sweat was spiked with lactate to measure the color change of the PAni-based filter paper sensor platform. Under these conditions, the combination of polyaniline-based sensor platforms and colorimetric systems has a limit of detection (LOD) and limit of quantitation (LOQ) of 1 mM, linearity of 0.9684, and stability of 14%. Tbe confirmed that the color of the substrate changes after about 30 s, and through this, the physical fatigue of the individual can be determined. In conclusion, it was confirmed through this study that a combination of the PAni paper sensor platform and colorimeter can detect clinically meaningful lactate concentration.