Fractal Design for Advancing the Performance of Chemoresistive Sensors

Nitric oxide detection by electrochemistry selective probe: calibration in the study environment is mandatory

Nitric oxide (NO) plays a crucial role in various physiological processes, making its detection and controlled release significant for both therapeutic and environmental contexts. Electrochemical sensors are widely used for NO detection due to their high sensitivity and real-time monitoring capabilities. However, challenges such as interference from other gasotransmitters, sensor degradation, and calibration difficulties-especially in complex biological matrices-hinder accurate NO measurement. This review discusses recent advancements in electrochemical NO detection, with a focus on the impact of complex biological matrices, calibration strategies, and sensor designs. The release of NO from nanoparticles, such as S-nitrosoglutathione (GSNO)-encapsulating chitosan nanoparticles, is used as a case study for improving NO detection accuracy. Future innovations in sensor technology and nanoparticle design are expected to expand the applicability of NO detection in personalized medicine and environmental monitoring.

Biofluorometric Gas-Imaging System for Evaluating the Ripening Stages of "La France" Pear Based on Ethanol Vapor Emitted via the Epicarp

Fruits can emit ethanol, which is generated through fermentation during hypoxic storage. We imaged spatiotemporal changes in the gaseous ethanol emitted by "La France" pear via its epicarp. The gas-imaging system utilized enzymes to transduce the ethanol concentration into fluorescence intensity. Initially, the uniformity of the enzyme and coenzyme distribution was evaluated to validate the imaging capability. Subsequently, two surface-fitting methods were compared to accurately image ethanol emitted from three-dimensional (3D) objects with a double-curved surface. The imaging results of ethanol emitted from the pear indicated that the distribution of ethanol was related to lenticels, which have been reported to possess high ethanol diffusivity, on the epicarp. As quantified by the system (uniformity of coenzyme and enzymes was 93.2 and 98.8%, respectively; dynamic range was 0.01-100 ppm), ethanol concentration increased with the storage period under hypoxic conditions (0.4-5.3 ppm, from day 1 to 10). The system enables the observation of the location, quantity, and temporal pattern of ethanol release from fruit, which could be a useful technology for agricultural applications.

Graphene and metal-organic framework hybrids for high-performance sensors for lung cancer biomarker detection supported by machine learning augmentation

Conventional diagnostic methods for lung cancer, based on breath analysis using gas chromatography and mass spectrometry, have limitations for fast screening due to their limited availability, operational complexity, and high cost. As potential replacement, among several low-cost and portable methods, chemoresistive sensors for the detection of volatile organic compounds (VOCs) that represent biomarkers of lung cancer were explored as promising solutions, which unfortunately still face challenges. To address the key problems of these sensors, such as low sensitivity, high response time, and poor selectivity, this study presents the design of new chemoresistive sensors based on hybridised porous zeolitic imidazolate (ZIF-8) based metal-organic frameworks (MOFs) and laser-scribed graphene (LSG) structures, inspired by the architecture of the human lung. The sensing performance of the fabricated ZIF-8@LSG hybrid sensors was characterised using four dominant VOC biomarkers, including acetone, ethanol, methanol, and formaldehyde, which are identified as metabolomic signatures in lung cancer patients' exhaled breath. The results using simulated breath samples showed that the sensors exhibited excellent performance for a set of these biomarkers, including fast response (2-3 seconds), a wide detection range (0.8 ppm to 50 ppm), a low detection limit (0.8 ppm), and high selectivity, all obtained at room temperature. Intelligent machine learning (ML) recognition using the multilayer perceptron (MLP)-based classification algorithm was further employed to enhance the capability of these sensors, achieving an exceptional accuracy (approximately 96.5%) for the four targeted VOCs over the tested range (0.8-10 ppm). The developed hybridised nanomaterials, combined with the ML methodology, showcase robust identification of lung cancer biomarkers in simulated breath samples containing multiple biomarkers and a promising solution for their further improvements toward practical applications.

Flexible liquid metal-based microfluidic strain sensors with fractal-designed microchannels for monitoring human motion and physiological signals

With the rapid advancement of wearable electronics, there is an increasing demand for high-performance flexible strain sensors. In this work, a flexible strain sensor based on liquid metal (LM)-integrated into a microfluidic device is developed with Peano-type fractal structure design. Compared with the microfluidic sensors with straight and wavy microchannels, the sensor with Peano-shaped channels shows lower hysteresis and improved stretchability. Furthermore, the increase of the fractal order can further improve the sensing performances. The third-order Peano sensor exhibits excellent mechanical and electrical properties, including high tensile capability (490.3%), minimal hysteresis (DH = 0.86%), ultra-low detection limit (0.1%), low overshoot, rapid response time (117 ms), as well as good stability and durability. By adding two independent and perpendicular straight channels to the Peano sensing unit, the feasibility of multi-directional strain recognition is demonstrated. To further improve the sensitivity of the Peano-shaped sensor, a multi-layer Peano sensor is developed, exhibiting remarkably enhanced sensitivity while maintaining low hysteresis. Overall, the developed LM-based microfluidic strain sensors enrolling Peano fractal geometry hold high potential for various wearable electronics applications.

Handheld Crop Pest Sensor Using Binary Catalyst-Loaded Nano-SnO2 Particles for Oxidative Signal Amplification

In agriculture, pest management is a major challenge. Crop releases volatiles in response to the pest; hence, sensing these volatile signals at a very early stage will ease pest management. Here, binary catalyst-loaded SnO2 nanoparticles of <5 nm were synthesized for the repeated capture and oxidation of the signature volatile and its products to amplify the chemoresistive signal to detect concentrations as low as ≈120 ppb. The sensitivity may be due to the presence of the elements in the Sn-Fe-Pt bond evidenced by extended X-ray absorption fine-structure spectroscopy (EXAFS) that captures and oxidize the volatile without escaping. This strong catalyst may oxidize nontarget volatiles and can cause false signals; hence, a molecular sieve filter has been coupled to ensure high selectivity for the detection ofTuta absolutainfestation in tomato. Finally, with the support of a mobile power bank, the optimized sensor has been assembled into a lightweight handheld device.

Multiquanta flux jumps in superconducting fractal

We study the magnetic field response of millimeter scale fractal Sierpinski gaskets (SG) assembled of superconducting equilateral triangular patches. Directly imaged quantitative induction maps reveal hierarchical periodic filling of enclosed void areas with multiquanta magnetic flux, which jumps inside the voids in repeating bundles of individual flux quanta Φ0. The number Ns of entering flux quanta in different triangular voids of the SG is proportional to the linear size s of the void, while the field periodicity of flux jumps varies as 1/s. We explain this behavior by modeling the triangular voids in the SG with effective superconducting rings and by calculating their response following the London analysis of persistent currents, Js, induced by the applied field Ha and by the entering flux. With changing Ha, Js reaches a critical value in the vertex joints that connect the triangular superconducting patches and allows the giant flux jumps into the SG voids through phase slips or multiple Abrikosov vortex transfer across the vertices. The unique flux behavior in superconducting SG patterns, may be used to design tunable low-loss resonators with multi-line high-frequency spectrum for microwave technologies.

Metal-organic frameworks (MOFs) based luminescent and electrochemical sensors for food contaminant detection

With the increasing population, food toxicity has become a prevalent concern due to the growing contaminants of food products. Therefore, the need for new materials for toxicant detection and food quality monitoring will always be in demand. Metal-organic frameworks (MOFs) based on luminescence and electrochemical sensors with tunable porosity and active surface area are promising materials for food contaminants monitoring. This review summarizes and studies the most recent progress on MOF sensors for detecting food contaminants such as pesticides, antibiotics, toxins, biomolecules, and ionic species. First, with the introduction of MOFs, food contaminants and materials for toxicants detection are discussed. Then the insights into the MOFs as emerging materials for sensing applications with luminescent and electrochemical properties, signal changes, and sensing mechanisms are discussed. Next, recent advances in luminescent and electrochemical MOFs food sensors and their sensitivity, selectivity, and capacities for common food toxicants are summarized. Further, the challenges and outlooks are discussed for providing a new pathway for MOF food contaminant detection tools. Overall, a timely source of information on advanced MOF materials provides materials for next-generation food sensors.

Facile Fabrication of ZnO-ZnFe2O4 Hollow Nanostructure by a One-Needle Syringe Electrospinning Method for a High-Selective H2S Gas Sensor

Herein, a facile fabrication process of ZnO-ZnFe₂O₄ hollow nanofibers through one-needle syringe electrospinning and the following calcination process is presented. The various compositions of the ZnO-ZnFe₂O₄ nanofibers are simply created by controlling the metal precursor ratios of Zn and Fe. Moreover, the different diffusion rates of the metal oxides and metal precursors generate a hollow nanostructure during calcination. The hollow structure of the ZnO-ZnFe₂O₄ enables an enlarged surface area and increased gas sensing sites. In addition, the interface of ZnO and ZnFe₂O₄ forms a p-n junction to improve gas response and to lower operation temperature. The optimized ZnO-ZnFe₂O₄ has shown good H₂S gas sensing properties of 84.5 (S = R_a/R_g) at 10 ppm at 250 °C with excellent selectivity. This study shows the good potential of p-n junction ZnO-ZnFe₂O₄ on H₂S detection and affords a promising sensor design for a high-performance gas sensor.