Environmental formaldehyde sensing at room temperature by smartphone-assisted and wearable plasmonic nanohybrids.
Andreas T GüntnerFlorian M SchenkPublished in: Nanoscale (2023)
Formaldehyde is a toxic and carcinogenic indoor air pollutant. Promising for its routine detection are gas sensors based on localized surface plasmon resonance (LSPR). Such sensors trace analytes by converting tiny changes in the local dielectric environment into easily readable, optical signals. Yet, this mechanism is inherently non-selective to volatile organic compounds (like formaldehyde) and yields rarely detection limits below parts-per-million concentrations. Here, we reveal that chemical reaction-mediated LSPR with nanohybrids of Ag/AgO x core-shell clusters on TiO 2 enables highly selective formaldehyde sensing down to 5 parts-per-billion (ppb). Therein, AgO x is reduced by the formaldehyde to metallic Ag resulting in strong plasmonic signal changes, as measured by UV/Vis spectroscopy and confirmed by X-ray diffraction. This interaction is highly selective to formaldehyde over other aldehydes, alcohols, ketones, aromatic compounds (as confirmed by high-resolution mass spectrometry), inorganics, and quite robust to relative humidity changes. Since this sensor works at room temperature, such LSPR nanohybrids are directly deposited onto flexible wristbands to quantify formaldehyde between 40-500 ppb at 50% RH, even with a widely available smartphone camera (Pearson correlation coefficient r = 0.998). Such chemoresponsive coatings open new avenues for wearable devices in environmental, food, health and occupational safety applications, as demonstrated by an early field test in the pathology of a local hospital.
Keyphrases
- room temperature
- ionic liquid
- high resolution mass spectrometry
- high resolution
- single molecule
- healthcare
- quantum dots
- visible light
- label free
- liquid chromatography
- heart rate
- minimally invasive
- air pollution
- emergency department
- high speed
- computed tomography
- loop mediated isothermal amplification
- social media
- health information
- machine learning
- highly efficient
- real time pcr
- particulate matter
- single cell
- acute care
- polycyclic aromatic hydrocarbons
- life cycle
- gas chromatography