Smartphone-Based Rapid Quantitative Detection Platform with Imprinted Polymer for Pb (II) Detection in Real Samples.

This paper reports the successful development and application of an efficient method for quantifying Pb2+ in aqueous samples using a smartphone-based colorimetric device with an imprinted polymer (IIP). The IIP was synthesized by modifying the previous study; using rhodizonate, 2-acrylamido-2-methylpropane sulfonic acid (AMPS), N,N'-methylenebisacrylamide (MBA), and potassium persulfate (KPS). The polymers were then characterized. An absorption study was performed to determine the optimal conditions for the smartphone-based colorimetric device processing. The device consists of a black box (10 × 10 × 10 cm), which was designed to ensure repeatability of the image acquisition. The methodology involved the use of a smartphone camera to capture images of IIP previously exposed at Pb2+ solutions with various concentrations, and color channel values were calculated (RGB, YMK HSVI). PLS multivariate regression was performed, and the optimum working range (0-10 mg L-1) was determined using seven principal components with a detection limit (LOD) of 0.215 mg L-1 and R2 = 0.998. The applicability of a colorimetric sensor in real samples showed a coefficient of variation (% RSD) of less than 9%, and inductively coupled plasma mass spectrometry (ICP-MS) was applied as the reference method. These results confirmed that the quantitation smartphone-based colorimetric sensor is a suitable analytical tool for reliable on-site Pb2+ monitoring.

Transparent, flexible, and eco-friendly starch-based films for reversible optoelectronic noses for food spoilage monitoring in smart packaging.

A reversible optoelectronic nose is presented consisting of ten acid-base indicators incorporated into a starch-based film, covering a wide pH range. The starch substrate is odorless, biocompatible, flexible, and exhibits high tensile resistance. This optical artificial olfaction system was used to detect the early stages of food decomposition by exposing it to the volatile compounds produced during the spoialge process of three food products (beef, chicken, and pork). A smartphone was used to capture the color changes caused by intermolecular interactions between each dye and the emitted volatiles over time. Digital images were processed to generate a differential color map, which uses the observed color shifts to create a unique signature for each food product. To effectively discriminate among different samples and exposure times, we employed chemometric tools, including hierarchical cluster analysis (HCA) and principal component analysis (PCA). This approach detects food deterioration in a practical, cost-effective, and user-friendly manner, making it suitable for smart packaging. Additionally, the use of starch-based films in the food industry is preferable due to their biocompatibility and biodegradability characteristics.

Digital microfluidic platform assembled into a home-made studio for sample preparation and colorimetric sensing of S-nitrosocysteine.

Digital microfluidics (DMF) is a versatile lab-on-a-chip platform that allows integration with several types of sensors and detection techniques, including colorimetric sensors. Here, we propose, for the first time, the integration of DMF chips into a mini studio containing a 3D-printed holder with previously fixed UV-LEDs to promote sample degradation on the chip surface before a complete analytical procedure involving reagent mixture, colorimetric reaction, and detection through a webcam integrated on the equipment. As a proof-of-concept, the feasibility of the integrated system was successfully through the indirect analysis of S-nitrosocysteine (CySNO) in biological samples. For this purpose, UV-LEDs were explored to perform the photolytic cleavage of CySNO, thus generating nitrite and subproducts directly on DMF chip. Nitrite was then colorimetrically detected based on a modified Griess reaction, in which reagents were prepared through a programable movement of droplets on DMF devices. The assembling and the experimental parameters were optimized, and the proposed integration exhibited a satisfactory correlation with the results acquired using a desktop scanner. Under the optimal experimental conditions, the obtained CySNO degradation to nitrite was 96%. Considering the analytical parameters, the proposed approach revealed linear behavior in the CySNO concentration range between 12.5 and 400 µmol L-1 and a limit of detection equal to 2.8 µmol L-1. Synthetic serum and human plasma samples were successfully analyzed, and the achieved results did not statistically differ from the data recorded by spectrophotometry at the confidence level of 95%, thus indicating the huge potential of the integration between DMF and mini studio to promote complete analysis of lowmolecular weight compounds.

Comparative Colorimetric Sensor Based on Bi-Phase γ-/α-Fe2O3 and γ-/α-Fe2O3/ZnO Nanoparticles for Lactate Detection.

This work reports on Fe2O3 and ZnO materials for lactate quantification. In the synthesis, the bi-phase γ-/α-Fe2O3 and γ-/α-Fe2O3/ZnO nanoparticles (NPs) were obtained for their application in a lactate colorimetric sensor. The crystalline phases of the NPs were analyzed by XRD and XPS techniques. S/TEM images showed spheres with an 18 nm average and a needle length from 125 to 330 nm and 18 nm in diameter. The γ-/α-Fe2O3 and γ-/α-Fe2O3/ZnO were used to evaluate the catalytic activity of peroxidase with the substrate 3,3,5,5-tetramethylbenzidine (TMB), obtaining a linear range of 50 to 1000 µM for both NPs, and a 4.3 µM and 9.4 µM limit of detection (LOD), respectively. Moreover, γ-/α-Fe2O3 and γ-/α-Fe2O3/ZnO/lactate oxidase with TMB assays in the presence of lactate showed a linear range of 50 to 1000 µM, and both NPs proved to be highly selective in the presence of interferents. Finally, a sample of human serum was also tested, and the results were compared with a commercial lactometer. The use of ZnO with Fe2O3 achieved a greater response toward lactate oxidation reaction, and has implementation in a lactate colorimetric sensor using materials that are economically accessible and easy to synthesize.

µOPTO: A microfluidic paper-based optoelectronic tongue as presumptive tests for the discrimination of alkaloid drugs for forensic purposes.

Natural and synthetic alkaloids are widely used for several applications, ranging from clinical purposes to criminal activities. Presumptive color tests are considered a leading tool to reveal on-scene substance identification via rapid chemical reactions that result in visual color changes. Colorimetric tests are popular due to their inherent simplicity, low cost, promptitude and portability; however, in many cases the results of such tests may not be predictable, partly because of the interference from similar species. In this proof-of-concept study, we present a paper-based microfluidic optoelectronic tongue - the so-called µOPTO - comprised of 6 indicators in lieu of one specific test and capable of discriminating 8 different alkaloid drugs (i.e. scopolamine, atropine, cocaine, morphine, ephedrine, caffeine, dipyrone and alprazolam) used for recreational, criminal and medical purposes. The wax printing method was employed to fabricate the microfluidic analytical device with six circular spots for reagent accommodation connected to a centered spot to enable simultaneous reactions with one sample injection. Digital images were obtained using an ordinary flatbed scanner, and the RGB information from before and after sample exposure was extracted using appropriate software. The color changes related to each spot were used to build differential maps with a unique fingerprint for each drug. The chemometric tools (i.e. PCA and HCA) showed suitable discrimination of all studied alkaloids in different quantities. To demonstrate a practical application, different alcoholic beverages spiked with scopolamine - a famous substance that causes drug abuse - were analyzed using the optoelectronic tongue. The results showed that small quantities of the drug were identified in different beverages, demonstrating that our device has the potential to be used in situ to prevent ingestion of contaminated samples.

Paper-based optoelectronic nose for identification of indoor air pollution caused by 3D printing thermoplastic filaments.

Commercial printers based on fused deposition modeling (FDM) are widely adopted for 3D printing applications. This method consists of the heating of polymeric filaments over the melting point followed by their deposition onto a solid base to create the desirable 3D structure. Prior investigation using chromatographic techniques has shown that chemical compounds (e.g. VOCs), which can be harmful to users, are emitted during the printing process, producing adverse effects to human health and contributing to indoor air pollution. In this study, we present a simple, inexpensive and disposable paper-based optoelectronic nose (i.e. colorimetric sensor array) to identify the gaseous emission fingerprint of five different types of thermoplastic filaments (ABS, TPU, PETG, TRITAN and PLA) in the indoor environment. The optoelectronic nose is comprised of selected 15 dyes with different chemical properties deposited onto a microfluidic paper-based device with spots of 5 mm in diameter each. Digital images were obtained from an ordinary flatbed scanner, and the RGB information collected before and after air exposure was extracted by using an automated routine designed in MATLAB, in which the color changes provide a unique fingerprint for each filament in 5 min of printing. Reproducibility was obtained in the range of 2.5-10% (RSD). Hierarchical clustering analysis (HCA) and principal component analysis (PCA) were successfully employed, showing suitable discrimination of all studied filaments and the non-polluted air. Besides, air spiked with vapors of the most representative VOCs were analyzed by the optoelectronic nose and visually compared to each filament. The described study shows the potential of the paper-based optoelectronic nose to monitor possible hazard emissions from 3D printers.

Anthocyanin immobilization in carboxymethylcellulose/starch films: A sustainable sensor for the detection of Al(III) ions in aqueous matrices.

A robust and sustainable sensor for the detection of Al(III) ions in water was developed by immobilization of anthocyanin (AN) from black rice in a film formed by carboxymethylcellulose (CMC) and starch. Characterization of the films was performed using solubility, thickness, FTIR, and mechanical analysis. The film exhibited an irreversible color change from red to purple in response to the presence of Al(III). The best colorimetric response of the sensor was observed at pH 4.5 and a time of 60 min, achieving the detection of 3 mg L-1 of Al(III). For concentrations higher than 5 mg L-1, the sensor response time decreased to 20 min. The minimum Al(III) concentration detected with the naked eye was lower than the maximum permissible concentrations in aqueous effluents according to different legislations, indicating the potential of this study to develop sensors for the detection of Al(III).

Detection of hydrogen peroxide (H2O2) using a colorimetric sensor based on cellulose nanowhiskers and silver nanoparticles.

Hydrogen peroxide (H2O2) is an important compound for several industrial sectors, but it becomes harmful to human health under high concentrations. Thus, the development of simple, low cost and fast analytical methods capable to detect and monitor H2O2 is fundamentally important. In the present study, we report a simple route for synthesizing silver nanoparticles (AgNPs) in the presence of a nanostructured polysaccharide (cellulose nanowhiskers) to produce a hybrid material, which was employed as a colorimetric probe for H2O2 detection. Our results revealed that AgNPs tend to experience catalytic decomposition when exposed to H2O2, causing a decrease of AgNPs absorption band at 410 nm in accordance with H2O2 concentration. This decrease was linearly dependent on H2O2 concentration (in the ranges 0.01-30 µM and 60-600 µM), yielding limits of detection of 0.014 µM and 112 µM, respectively. The easy-to-interpret H2O2 sensor also proved to be suitable for real samples analysis even in the presence of other interfering substances.

A colorimetric microwell method using a desktop scanner for biochemical assays.

A method for rapid, inexpensive and sensitive simultaneous analysis of glucose, creatinine, triglycerides, total cholesterol and total protein is needed to analyze blood. The proposed method is based on the production of a specific color after reaction. The method was adapted to a 64-microwell plate format, and it uses the transparency scanner feature of a commercially available desktop scanner. Each microwell plate had an 8×8 array of flat-bottomed 250µL microwells, and these microwells were used to simultaneously house the solutions for clinical assay. The scanned image was saved in TIFF format in a portable computer and then processed using a Graphic User Interface (GUI) designed in our laboratory to obtain analytical curves and to automate the mathematics and statistics calculations. This automation improved the analytical frequency of the method. The results showed that it is possible to measure a few microliters of solution with exactitude and precision better than 5.30%. The measured concentration ranges of glucose, triglycerides, creatinine, total cholesterol and total protein were 0.781 to 100, 1.56 to 200, 0.031 to 4.0, 1.56 to 200mg dL(-1) and 0.031 to 4.0g dL(-1), respectively. The limits of detection were 16.2, 51.7, 0.12, 41.5mg dL(-1) and 0.62g dL(-1) for glucose, triglycerides, creatinine, total cholesterol and total protein, respectively. The recoveries were from 98.7% to 101.3% for total cholesterol, 98.7% to 124.9% for triglycerides, 54.2% to 98.3% for total protein, 89.6% to 101% for glucose and 65.7% to 115.4% for creatinine. The results provided by the scanner were compared with those obtained with a commercial photometer and did not show significant differences at a confidence level of 95%. Good results were obtained for the correlation coefficient and Root Mean Square Error of Prediction (RMSEP) values for the five parameters, especially the total cholesterol and creatinine. The RMSEP values for glucose, creatinine, triglyceride, total cholesterol and total protein were 8.05, 0.28, 7.69, 1.41mg dL(-1) and 2.2g dL(-1), respectively.