Synthesizing Conductive Metal–Organic Framework Nanosheets for High-Performing Chemiresistive Sensors

作者：Chuanhui Huang, Shirong Huang, Wei Wang, Xing Huang, Arezoo Dianat, Rashid Iqbal, Geping Zhang, Naisa Chandrasekhar, Luis Antonio Panes‐Ruiz, Yang Lu, Zhongquan Liao, Bergoi Ibarlucea, Chenchen Wang, Xinliang Feng, Gianaurelio Cuniberti, Renhao Dong⧫ · 发表于：ACS Applied Materials & Interfaces · 年份：2025 · DOI：10.1021/acsami.5c00064 · 被引用次数：15 · 研究领域：Gas Sensing Nanomaterials and Sensors、Advanced Nanomaterials in Catalysis、Metal-Organic Frameworks: Synthesis and Applications

High Resolution Image Download MS PowerPoint Slide Two-dimensional conjugated metal–organic frameworks (2D c -MOFs) are emerging as unique electrode materials with great potential for electronic applications. However, traditional devices based on c -MOFs often utilize them directly in the powder or nanoparticle form, leading to weak adhesion to the device substrate and resulting in low stability and high noise levels in the final device. In this study, we present a novel approach utilizing thin c -MOFs synthesized via a general MOF nanosheet sacrifice approach, enhancing their aspect ratio and flexibility for high-performance electronic applications. The resultant benzene-based Cu-BHT nanosheets feature a thin thickness (around 5 nm) and a high aspect ratio (>100), affording Cu-BHT exceptional flexibility with a 10-fold decrease in Young’s modulus (0.98 GPa) and hardness (0.09 GPa) compared to bulk Cu-BHT nanoparticles (10.79 and 0.75 GPa, respectively). This heightened flexibility enables the Cu-BHT nanosheets to conform to the channels of the electrodes, ensuring robust adhesion to the electrode substrate and improving device stability. As a proof-of-concept, the chemiresistive nanosensor based on Cu-BHT nanosheets demonstrates an 8.0-fold decrease in the coefficient of variation of the response intensity and a 47.1-fold increase in the signal-to-noise ratio compared to sensors based on bulk Cu-BHT nanoparticles. Combined with the machine learning algorithms, the Cu-BHT nan...