doi: 10.1038/ncomms8959.
Towards outperforming conventional sensor arrays with fabricated individual photonic vapour sensors inspired by Morpho butterflies
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- PMID:26324320
- PMCID: PMC4569698
- DOI: 10.1038/ncomms8959
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Towards outperforming conventional sensor arrays with fabricated individual photonic vapour sensors inspired by Morpho butterflies
Radislav A Potyrailo et al. Nat Commun..
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Abstract
Combining vapour sensors into arrays is an accepted compromise to mitigate poor selectivity of conventional sensors. Here we show individual nanofabricated sensors that not only selectively detect separate vapours in pristine conditions but also quantify these vapours in mixtures, and when blended with a variable moisture background. Our sensor design is inspired by the iridescent nanostructure and gradient surface chemistry of Morpho butterflies and involves physical and chemical design criteria. The physical design involves optical interference and diffraction on the fabricated periodic nanostructures and uses optical loss in the nanostructure to enhance the spectral diversity of reflectance. The chemical design uses spatially controlled nanostructure functionalization. Thus, while quantitation of analytes in the presence of variable backgrounds is challenging for most sensor arrays, we achieve this goal using individual multivariable sensors. These colorimetric sensors can be tuned for numerous vapour sensing scenarios in confined areas or as individual nodes for distributed monitoring.
Figures
(a) Schematic of the tree-like tapered structure of natural butterfly scales with its chemical gradient of surface polarity. (b) Schematic of the fabricated nanostructure and design criteria for vapour-selectivity control. (c,d) Scanning electron microscopy (SEM) images ofMorpho sulkowskyi scales and fabricated six-lamellae nanostructures. (e,f) Iridescent colouration ofM. sulkowskyi scales and fabricated nanostructures. Shown in (f) are six regions of nanostructures that were fabricated with and without lamella; each region was 2 × 2 mm. On illumination with a white light, three replicate regions of nanostructures with lamella reflected blue light while three other replicate regions of nanostructures without lamella (with only ridges) reflected red light.
Employed in simulations were three vapours withn=1.3, 1.4 and 1.5 and their three vapour concentrations related to condensed liquid layers of thicknessd=5, 10 and 15 nm. Adsorption of vapours on its (a) horizontal, H, and (b) vertical, V, regions of top, middle and bottom segments (sections 1–3, respectively) of the nanostructure. Reflectance spectra of adsorption of vapours on (c) horizontal and (d) vertical regions of the nanostructure. Shown are examples of adsorbed vapour withn=1.5 andd=15 nm. Dotted lines, spectra without adsorbed vapours (a blank withn=1,d=0 nm). (e) PCA scores plot of combined horizontal and vertical coverage of the nanostructure with three different vapours on top, middle and bottom segments. Contributions of PCs: 49.8%, 27.1% and 17.3%, for PC1–PC3, respectively, capturing 94.2% of the total variance.
(a) Nanostructure design with a distribution of adsorbed vapours (n=1.3, 1.4 and 1.5;d=5, 10 and 15 nm) over individual lamella 1–6. (b,d) Examples of reflectance spectra withk=0 and 0.05, respectively. (c,e) PCA scores plots withk=0 and 0.05, respectively. Numbers of each arm in (c,e) correspond to the lamella (L) of the six-lamellae stack. Vapour discrimination in (c) was limited to confined effects of symmetrical lamella and resulted only in three resolved arms; contributions of PCs: 53.1%, 27.1% and 17.0%, for PC1–PC3, respectively, capturing 97.2% of the total variance. A complete vapour discrimination in (e) was due to resolved effects of non-zero lamella absorption, resulting in all six resolved arms; contributions of PCs: 47.4%, 22.0% and 13.5%, for PC1–PC3, respectively, capturing 82.9% of the total variance.
(a–d) Schematics and SEMs of nanostructures with 2, 3, 4 and 6 lamellae, respectively. (e) Nanostructure with six lamellae and microribs. (f) Histogram of automated image analysis of lamellae thickness in six-lamellae nanostructures. Thickness measurements of lamella were taken 400 nm from the centre of the ridge. Resulting thickness of 86±6 nm (mean±1 σ,n=70) was a convolution of real thickness and image analysis uncertainty of±2 nm of edge determination.
(a–e) ΔR spectra on exposure to benzene (Ben), acetonitrile (ACN), methyl ethyl ketone (MEK), methanol (MeOH) and water (H2O) vapours. Concentrations of vapours: 0.05, 0.07, 0.09 and 0.11 P/P0 (labelled as red, green, blue and black lines, respectively), whereP is vapour partial pressure andP0 is the saturated vapour pressure. (f) Discrimination of vapours using PCA. High contributions of the first three PCs (55.7%, 30.3% and 12.1%, for PC1–PC3, respectively, capturing 98.1% of the total variance) illustrate the high degree of data dimensionality even on exposure to only several vapours. (g) HCA dendrogram classification of ΔR spectra from five vapours at their four concentrations (1–4) using Ward minimum variance method.
(a) Designed map of concentrations of two individual model vapours and their binary mixtures mixed with water vapour for studies of responses of sensors, (b–g) PCA scores plots of responses of tested sensor systems to vapours and their mixtures. Sensors: (b) FS-functionalized naturalMorpho scales and (c) FS-functionalized fabricated nanostructure—response to methanol and propanol in the presence of water. (d) bare and (e) FS-functionalized fabricated nanostructures—response to methanol and ethanol in the presence of water. (f) QCM and (g) MOX sensor arrays—response to methanol and ethanol in the presence of water. Concentrations of methanol, ethanol and propanol, 0.05, 0.07, 0.09 and 0.11 P/P0. Concentration of water vapour, 0.4 P/P0. Vapour labels: methanol (CH3OH), ethanol (CH3CH2OH), propanol (CH3CH2CH2OH) and water (H2O). Contributions of PCs of different sensors are summarized in Supplementary Table 2 and Supplementary Note 4.
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