MOF/Polymer-Integrated Multi-Hotspot Mid-Infrared Nanoantennas for Sensitive Detection of CO2 Gas

Abstract

Metal–organic frameworks (MOFs) have been extensively used for gas sorption, storage and separation owing to ultrahigh porosity, exceptional thermal stability, and wide structural diversity. However, when it comes to ultra-low concentration gas detection, technical bottlenecks of MOFs appear due to the poor adsorption capacity at ppm-/ppb-level concentration and the limited sensitivity for signal transduction. Here, we present hybrid MOF-polymer physi-chemisorption mechanisms integrated with infrared (IR) nanoantennas for highly selective and ultrasensitive CO2 detection. To improve the adsorption capacity for trace amounts of gas molecules, MOFs are decorated with amino groups to introduce the chemisorption while maintaining the structural integrity for physisorption. Additionally, leveraging all major optimization methods, a multi-hotspot strategy is proposed to improve the sensitivity of nanoantennas by enhancing the near field and engineering the radiative and absorptive loss. As a benefit, we demonstrate the competitive advantages of our strategy against the state-of-the-art miniaturized IR CO2 sensors, including low detection limit, high sensitivity (0.18%/ppm), excellent reversibility (variation within 2%), and high selectivity (against C2H5OH, CH3OH, N2). This work provides valuable insights into the integration of advanced porous materials and nanophotonic devices, which can be further adopted in ultra-low concentration gas monitoring in industry and environmental applications.

Triboelectric-induced ion mobility for artificial intelligence-enhanced mid-infrared gas spectroscopy

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Machine learning-augmented surface-enhanced spectroscopy toward next-generation molecular diagnostics

“…73–85 The nanofabrication technologies for preparing SEIRA/SERS substrates include chemical preparation methods, photolithography, electron beam lithography, magnetron sputtering, electron beam evaporation, and more. 86–107 The widely used theory for modeling SERS/SEIRA includes perturbation theory, 108 temporal coupled-mode theory, 109,110 coupled harmonic oscillator theory, 111 and so on. As mentioned earlier, machine learning is complementary to SERS/SEIRA and offers unparalleled possibilities for solving pressing challenges related to spectral artifacts, overlapping, and huge volumes of spectral data (Fig.…”

Ultrasensitive Molecular Fingerprint Retrieval Using Strongly Detuned Overcoupled Plasmonic Nanoantennas

“…According to the ratio of system radiative loss (𝛾 r ) to absorptive loss (𝛾 a ), TCMT divides PNAs into three cases: undercoupled (UC, 𝛾 r /𝛾 a <1), critically coupled (CC, 𝛾 r /𝛾 a = 1), and overcou-pled (OC, 𝛾 r /𝛾 a >1). [24] Current research on loss engineering mainly focuses on the improvement of the enhancement factor of PNAs [21,[25][26][27][28][29][30] (Note S1 and Figure S1, Supporting Information). In fact, the Q factor of PNAs with various losses is different, which inspires us to use loss engineering to improve the bandwidth of PNAs and maintain high light-matter coupling under spectral detuning.…”