Colorimetric detection for uranyl ions in water using vinylphosphonic acid functionalized gold nanoparticles based on smartphone

Advanced "turn-on" colorimetric uranium platform based on the enhanced nanozyme activity of a donor-acceptor structured covalent organic framework

BACKGROUND

The increasing uranium containing wastes generated during uranium mining and finishing pose a huge threat to the environment and human health, and thus robust strategies for on-site monitoring of uranium pollutant are of great significance for environmental protection around uranium tailings.

RESULTS

Herein, a facile "turn-on" colorimetric platform that can achieve uranium detection by spectrometry and naked eyes was developed based on the uranium-enhanced nanozyme activity of covalent organic framework (JUC-505). Thanks to the extended π-conjugated skeleton and donor-acceptor (D-A) structure, JUC-505 exhibited superior photo-activated nanozyme activity, which would be prohibited when the cyano group in JUC-505 skeleton was transformed to the amidoxime group. Further results elucidated that the coordination of uranium with amidoxime groups led to the electron transfer between uranium and the JUC-505-AO skeleton, and thus significantly restored the nanozymatic activity of JUC-505-AO with the subsequent remarkable color changes. Moreover, the uranium concentrations in uranium tailing wastewater detected by the present "turn-on" colorimetric method were well agreed with those by ICP-MS, demonstrating a high accuracy of the present method in real samples.

SIGNIFICANCE

The D-A structured JUC-505 with superior photocatalytic property and nanozymatic activity was applied to facilitate colorimetric detection of uranium, which displays the advantages of low detection limit, excellent selectivity, fast response and simple operation for uranium detection in real samples, and shows a great potential in on-site monitoring of uranium pollutant around uranium tailings as well as nuclear power plant.

Colorimetric sensing for the sensitive detection of UO22+via the phosphorylation functionalized mesoporous silica-based controlled release system

In this study, we developed a simple and sensitive colorimetric sensing method for the detection of UO22+, which was built to release MB from the molybdenum disulfide with a phosphate group (MoS2-PO4) gated mesoporous silica nanoparticles functionalized phosphate group (MSN-PO4) with UO22+ chelating. In the presence of UO22+, MoS2-PO4 can be effectively adsorbed onto the surface of MSN-PO4 based on the coordination chemistry for strong affinity between the P-O bond and UO22+. The adsorbed MoS2-PO4 was then utilized as an ideal gate material to control the release of signal molecules (MB) entrapped within the pores of MSN-PO4, resulting in a detectable decrease in the absorption peak at 663 nm. This colorimetric sensing demonstrated the advantages of simplicity and easy manipulation and exhibited a linear response to the concentration of UO22+ within the range of 0.02-0.2 μM. The detection limit of UO22+ was determined to be 0.85 nM, which was lower than the limit (130 nmol L-1) set by the US Environmental Protection Agency. Furthermore, the proposed colorimetric sensing method has been utilized to determine UO22+ in samples of Xiangjiang River and tap water, and a high recovery rate was achieved. This method shows promising potential in preventing and controlling environmental pollution.

A dual-ligand lanthanide-based metal-organic framework for highly selective and sensitive colorimetric detection of Fe2

Accumulation of heavy metals in humans and mammals causes health problems due to their abundance as transition metal ions. Iron (Fe2+) serves significantly in numerous biological processes as a heavy metal ion. In this study, we have designed and prepared a metal-organic framework (MOF) utilizing a one-step solvothermal process, incorporating a dual-ligand combination of terephthalic acid (H2BDC) and α,α',α''-tert-pyridine (TPY) with Eu3+ as the metal node. For this MOF, we termed it Eu-BDC/TPY. Eu-BDC/TPY has superior selectivity over other metal cations. It provides an accurate, sensitive, broad linear range colorimetric method for detecting Fe2+ in a concentration range of 1-50 μM with a modest limit of detection (0.33 μM). Eu-BDC/TPY detects the absence of Fe2+ quickly (within 5 seconds), which is very valuable in practical applications. In addition, the results can be used to create a digital image colorimetric card (DIC) using colorimetric software, enabling instantaneous detection of Fe2+ concentration using a smartphone.

Progress in the design of portable colorimetric chemical sensing devices

The need for precise determination of heavy metals, anions, biomolecules, pesticides, drugs, and other substances is vital across clinical, environmental, and food safety domains. Recent years have seen significant progress in portable colorimetric chemical sensing devices, revolutionizing on-the-spot analysis. This review offers a comprehensive overview of these advancements, covering handheld colorimetry, RGB-based colorimetry, paper-based colorimetry, and wearable colorimetry devices. It explores the underlying principles, functional materials (chromophoric reagents/dyes and nanoparticles), detection mechanisms, and their applications in environmental monitoring, clinical care, and food safety. Noble metal nanoparticles (NPs) have arisen as promising substitutes in the realm of sensing materials. They display notable advantages, including heightened sensitivity, the ability to fine-tune their plasmonic characteristics for improved selectivity, and the capacity to induce visible color changes, and simplifying detection. Integration of NPs fabricated paper device with smartphones and wearables facilitates reagent-free, cost-effective, and portable colorimetric sensing, enabling real-time analysis and remote monitoring.

A field-deployable water quality monitoring with machine learning-based smartphone colorimetry

Water quality monitoring is an increasing global concern as the pollution of water sources causes adverse effects on economic growth and human health. Traditional approaches to the detection of pollutants are time-consuming and labor-intensive due to the requirement of sophisticated equipment or laboratory settings. Therefore, portable devices featuring rapid response and easy operation are indispensable in water quality monitoring. Herein, smartphone-based colorimetric pollutant quantification is demonstrated in a machine learning (ML) framework. As a proof of concept, the presence of seven ions in water was analyzed using colorimetric strips. The color variation on the strip indicators was captured under eight lighting conditions with five smartphones, providing robustness against the illumination variation and camera optics for ML classifiers. Color and texture features were extracted from the images to train the classifiers. Among the twenty-three classifiers, K-Nearest Neighbors exhibits the best classification performance, leading to the integration with our custom-designed Android application called Hydro Sens. The proposed approach was also tested with real samples taken from local water sources. The results prove that incorporating color strips with ML with a smartphone application can be used for water quality monitoring, which offers promising alternatives for sophisticated equipment that is especially applicable in resource-limited settings.