Hammerhead Shark‐Inspired Microvillus‐Structured Ionic Elastomers for Wet Gas Sensing Based on Solvated Ion Transport

Advanced Functional Materials, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 13, 2025

Abstract Water molecules are ubiquitous disruptors of conventional gas sensing materials, often leading to diminished performance in materials that reliant on electronic signal transmission. This creates the pressing need for efficient with anti‐humidity interference properties. Here, a hammerhead shark‐inspired microvillus‐structured ionic elastomer based transmission nanoconfined space is constructed by incorporating liquids into polymer matrix. The elastomers optimized microvillus structure demonstrated 1.68‐fold higher response than flat ones, short time (9 s) toward 30 ppm triethylamine (TEA), excellent selectivity and low limit detection (LOD) (104.56 ppb). Such serves as proof‐of‐concept effectively combining solvated ion transport design develop advanced systems. With such an evident (23.52%), similar (12 s), LOD (498.05 ppb), long‐term stability (at least days) achieved at relative humidity 70%. Mechanistic investigations revealed effective ions facilitated after sequential water TEA surroundings while significantly enhanced transport. Furthermore, utility system shrimp decay monitoring under wet conditions.

Language: Английский

Tailored Fluorescent Metal–Organic Frameworks Hybrid Membrane Sensor Arrays: Simultaneous and Selective Quantification of Multiple Antibiotics

Advanced Science, Journal Year: 2025, Volume and Issue: unknown

Published: April 26, 2025

Abstract Sensor array offers significant potential for rapid, high‐throughput antibiotic detection. However, cross‐reactivity‐based sensor arrays often lack accuracy, despite comprehensive data analysis; while traditional high‐affinity‐based sensors based on antibodies/aptamers frequently suffer from complicated design and poor robustness. Here, a filterable paper‐based fluorescent metal–organic frameworks (MOFs) is developed one‐to‐one recognition quantification of multiple antibiotics. Three representative MOFs are designed to exceptional affinity specificity the target antibiotic. A filtration‐assisted detection enhances sensitivity, achieving parts‐per‐billion (ppb)‐level in mixed solutions. The proposed approach integrates signal generation, streamlined 10‐min process. robustness also enables direct raw samples containing organic solvents, which not achievable by conventional methods. Notably, can be easily incorporated into smartphone‐based portable device, coupled with user‐friendly image analysis applet one‐step extraction quantitative chicken samples. Leveraging MOFs’ versatility, this method extended simultaneously detect broad range antibiotics, offering universal, accurate various chemical targets.

Language: Английский

Discrimination of Respiratory Tract Infections by a Reduced Graphene Oxide Array Modified with Metal−Organic Frameworks and Metal Phthalocyanines

ACS Nano, Journal Year: 2025, Volume and Issue: unknown

Published: May 16, 2025

As a prevalent clinical condition, it is critical to distinguish between bacterial and viral respiratory tract infections given their pivotal role in guiding appropriate pharmaceutical interventions preventing antibiotic misuse. Exhaled breath (EB) contains spectrum of disease-specific biomarkers, enabling precise diagnostic analysis. Thus, EB analysis using an electronic nose (e-nose) record electrical response fingerprints discriminate pathogens via machine learning algorithms has emerged as promising noninvasive technology. In this study, graphene-based e-nose sensor array modified with metal-organic frameworks (MOFs) metal phthalocyanines (MPcs) was developed by multiple reduction methods. The demonstrated excellent capability distinguishing two types samples collected from healthy individuals spiked acetone isoprene, which are closely associated infections. Furthermore, model constructed 145 comprising 89 infection cases 56 cases. A weighted fusion classification model, integrating the support vector machine, random forest, Lasso regression (Lasso), achieved accuracy 83.7% validation group, area under curve (AUC) 0.87. An independent external trial involving 43 patients (including 6 unidentified cases) yielded 75.7% AUC 0.81 for Additionally, 75% rate discriminating mycoplasma linear discriminant These results suggest that MOFs MPcs tool diagnosing infections, aiding optimized treatment decisions potentially improving therapeutic efficiency.

Language: Английский