Monitoring microbial growth on a microfluidic lab-on-chip with electrochemical impedance spectroscopic technique

Flexible-fabricated sensor module with programmable magnetic actuators coupled to L-cysteine functionalized Ag@Fe3O4 complexes for Cu2+ detection in fish tissues

Heavy metal contamination for seafood, particularly fish, is arising great concerns, and consequentially it is necessary to develop a simple and direct detection method. In this work, Ag@Fe₃O₄ is successfully prepared by simple solvothermal method, and we present a flexible-fabricated sensor module with assembled programmable magnetic actuators. The resulting sensor integrates a three-electrode system with two programmable magnetic actuators at the bottom of the device, which regulates the amount of current by adjusting the brake to control the adsorption force and vibration. The L-Cysteine functionalized Ag@Fe₃O₄ is coated on the surface of the electrode, then the Cu²⁺ is dropped into the reaction tank. Its performance is studied by cyclic voltammetry and electrochemical impedance spectroscopy, and the key experimental conditions such as deposition potential, deposition time, and electrolyte pH are gradually optimized. Under optimal conditions, Cu²⁺ can be detected over a wide linear range (0.01 ~ 4 μM) and at a low LOD (0.34 nM). The results show that the proposed method has a good application prospect in the detection of Cu²⁺. This method is successfully applied to Cu²⁺ analysis in fish samples with an acceptable recovery of 93 ~ 102%.

Analytical methods for assessing antimicrobial activity of nanomaterials in complex media: advances, challenges, and perspectives

Assessing the antimicrobial activity of engineered nanomaterials (ENMs), especially in realistic scenarios, is of great significance for both basic research and applications. Multiple analytical methods are available for analysis via off-line or on-line measurements. Real-world samples are often complex with inorganic and organic components, which complicates the measurements of microbial viability and/or metabolic activity. This article highlights the recent advances achieved in analytical methods including typical applications and specifics regarding their accuracy, cost, efficiency, and user-friendliness. Methodological drawbacks, technique gaps, and future perspectives are also discussed. This review aims to help researchers select suitable methods for gaining insight into antimicrobial activities of targeted ENMs in artificial and natural complex matrices.

A Detection Method for Crop Fungal Spores Based on Microfluidic Separation Enrichment and AC Impedance Characteristics

The timely monitoring of airborne crop fungal spores is important for maintaining food security. In this study, a method based on microfluidic separation and enrichment and AC impedance characteristics was proposed to detect spores of fungal pathogens that cause diseases on crops. Firstly, a microfluidic chip with tertiary structure was designed for the direct separation and enrichment of Ustilaginoidea virens spores, Magnaporthe grisea spores, and Aspergillus niger spores from the air. Then, the impedance characteristics of fungal spores were measured by impedance analyzer in the enrichment area of a microfluidic chip. The impedance characteristics of fungal spores were analyzed, and four impedance characteristics were extracted: absolute value of impedance (abs), real part of impedance (real), imaginary part of impedance (imag), and impedance phase (phase). Finally, based on the impedance characteristics of extracted fungal spores, K-proximity (KNN), random forest (RF), and support vector machine (SVM) classification models were established to classify the three fungal spores. The results showed that the microfluidic chip designed in this study could well collect the spores of three fungal diseases, and the collection rate was up to 97. The average accuracy of KNN model, RF model, and SVM model for the detection of three disease spores was 93.33, 96.44 and 97.78, respectively. The F1-Score of KNN model, RF model, and SVM model was 90, 94.65, and 96.18, respectively. The accuracy, precision, recall, and F1-Score of the SVM model were all the highest, at 97.78, 96.67, 96.69, and 96.18, respectively. Therefore, the detection method of crop fungal spores based on microfluidic separation, enrichment, and impedance characteristics proposed in this study can be used for the detection of airborne crop fungal spores, providing a basis for the subsequent detection of crop fungal spores.