Chromogenic Anticounterfeit and Security Papers: An Easy and Effective Approach

Sensory Polymers: Trends, Challenges, and Prospects Ahead

In recent years, sensory polymers have evolved significantly, emerging as versatile and cost-effective materials valued for their flexibility and lightweight nature. These polymers have transformed into sophisticated, active systems capable of precise detection and interaction, driving innovation across various domains, including smart materials, biomedical diagnostics, environmental monitoring, and industrial safety. Their unique responsiveness to specific stimuli has sparked considerable interest and exploration in numerous applications. However, along with these advancements, notable challenges need to be addressed. Issues such as wearable technology integration, biocompatibility, selectivity and sensitivity enhancement, stability and reliability improvement, signal processing optimization, IoT integration, and data analysis pose significant hurdles. When considered collectively, these challenges present formidable barriers to the commercial viability of sensory polymer-based technologies. Addressing these challenges requires a multifaceted approach encompassing technological innovation, regulatory compliance, market analysis, and commercialization strategies. Successfully navigating these complexities is essential for unlocking the full potential of sensory polymers and ensuring their widespread adoption and impact across industries, while also providing guidance to the scientific community to focus their research on the challenges of polymeric sensors and to understand the future prospects where research efforts need to be directed.

Durable Nanocellulose-Stabilized Emulsions of Dithizone/Chloroform in Water for Hg2+ Detection: A Novel Approach for a Classical Problem

The use of dithizone (DTZ) for colorimetric heavy-metal detection is approximately one century old. However, its pending stability issues and the need for simple indicators justify further research. Using cellulose nanofibers, we attained DTZ-containing emulsions with high stability. These emulsions had water (at least 95 wt %) and acetic acid (1-8 mL/L) conforming the continuous phase, while dispersed droplets of diameter <1 μm contained chloroform-solvated DTZ (3 wt %). The solvation cluster was computed by molecular dynamics simulations, suggesting that chloroform slightly reduces the dihedral angle between the two sides of the thiocarbazone chain. Nanocellulose concentrations over 0.2 wt % sufficed to obtain macroscopically homogeneous mixtures with no phase separation. Furthermore, the rate of degradation of DTZ in the nanocellulose-stabilized emulsion did not differ significantly from a DTZ/chloroform solution, outperforming DTZ/toluene and DTZ/acetonitrile. Not only is the emulsion readily and immediately responsive to mercury(II), but it also decreases interferences from other ions and from natural samples. Unexpectedly, neither lead(II) nor cadmium(II) triggered a visual response at trace concentrations. The limit of detection of these emulsions is 15 μM or 3 mg/L, exceeding WHO limits for mercury(II) in drinking water, but they could be effective at raising alarms.

Easy Nitrite Analysis of Processed Meat with Colorimetric Polymer Sensors and a Smartphone App

We have developed an in situ methodology for determining nitrite concentration in processed meats that can also be used by unskilled personnel. It is based on a colorimetric film-shaped sensory polymer that changes its color upon contacting the meat and a mobile app that automatically calculates the manufacturing and residual nitrite concentration by only taking digital photographs of sensory films and analyzing digital color parameters. The film-shaped polymer sensor detects nitrite anions by an azo-coupling reaction, since they activate this reaction between two of the four monomers that the copolymer is based on. The sensory polymer is complemented with an app, which analyzes the color in two different digital color spaces (RGB and HSV) and performs a set of 32 data fittings representing the concentration of nitrite versus eight different variables, finally providing the nitrite concentration of the test samples using the best fitting curve. The calculated concentration of nitrite correlates with a validated method (ISO 2918: 1975) usually used to determine nitrite, and no statistically significant difference between these methods and our proposed one has been found in our study (26 meat samples, 8 prepared, and 18 commercial). Our method represents a great advance in terms of analysis time, simplicity, and orientation to use by average citizens.