LETTER pubs.acs.org/NanoLett
Identification and Quantification of Organic Vapors by Time-Resolved Diffusion in Stacked Mesoporous Photonic Crystals Timothy L. Kelly, Adrian Garcia Sega, and Michael J. Sailor* Department of Chemistry and Biochemistry, University of California, San Diego, La Jolla, California 92093-0358, United States
bS Supporting Information ABSTRACT: Microsensors for gas-phase analytes are fundamentally limited by their inability to discriminate between analytes. While cross-reactive arrays consisting of multiple different sensor elements provide one means to identify individual analytes, these “artificial nose” devices rely on complicated data processing algorithms and they generally suffer from significant zero-point drift. Herein, we present a single component optical sensor that is capable of identifying chemical compounds at parts-per-million concentrations. The device consists of a stack of three mesoporous silicon-based photonic crystals; a porous “drift tube” is sandwiched between two optically responsive layers. The drift layer temporally separates the optical responses of the other layers, and this difference is shown to be characteristic of the analyte. KEYWORDS: Porous silicon, nanomaterials, chemical sensor, chromatography, photonic crystal, volatile organic compounds
P
hotonic nanostructures provide a simple means of transducing chemical adsorption or binding events into readily measured optical responses. For example, the adsorption of organic vapors on porous Bragg mirrors13 or the wing scales of Morpho butterflies4 produces characteristic color changes that can be measured with low-cost photodetectors, digital imagers, or even the naked eye.5 Principal component analysis of the spectral response from sensor arrays3,613 and time-resolved measurements14,15 have been used to determine the chemical identity of vapors, even at part per million or sub-part per million concentrations.1618 Despite these recent advancements, the fragile nature of many photonic nanostructures remains problematic; many devices cannot withstand the temperatures needed to desorb or oxidize strongly adsorbed, low volatility analytes. Alternative analyte identification mechanisms are of interest in order to provide both improved analyte resolution and simplified calibration and data processing procedures. The optical sensors prepared in this work are based on porous Si films prepared by electrochemical anodization of silicon wafers.19 The electrochemical corrosion reaction generates pores that propagate as a uniform front in the Æ100æ crystallographic direction, and the diameters of the propagating pore tips are proportional to the instantaneous current density applied at any given time. Thus the porosity (and effective refractive index) of the material can be spatially modulated by programmatic adjustment of the currenttime waveform applied during the etch.20 For this work, three distinct photonic layers were sequentially etched into the silicon wafer (Figure 1a). Each layer was etched with a sinusoidally varying porosity; they are one-dimensional photonic crystals known as rugate filters.21 The etching waveform was designed such that each layer possessed the same r 2011 American Chemical Society
average porosity (70%), although the period of the porosity gradient and the total thickness of each layer were different. After the electrochemical etch, the samples were oxidized (450 C for 1 h, in air) to provide a stable nanostructure22 that could withstand repetitive thermal cycling. The entire structure was 244 μm thick, as shown by scanning electron microscopy (SEM); cross-sectional images of individual layers prepared in separate experiments (Supporting Information Figure S1) yielded thickness values of 35, 177, and 36 μm for the top, middle, and bottom layers, respectively. The periods of the etching waveforms were chosen such that these three layers displayed photonic stop bands in the blue, green, and red regions of the spectrum, respectively (Figure 1b,c, Supporting Information Figure S2), allowing simultaneous and independent monitoring of each layer in the optical reflectance spectrum. The porous silicon trilayer films possess a high-surface area and a mesoporous substructure that adsorbs condensable organic vapors. This adsorption causes an increase in the effective refractive index of the material that results in a red shift in the position of the photonic stop band of the relevant layer.2,5,2325 Because the stack is only open on one side, introduction of an analyte generates a sequential response in the physically separated layers that is dependent on the rate of analyte diffusion through the nanostructure. Concentration profiles, calculated using a simple one-dimensional Fick’s second law model (Supporting Information Figure S4 and subsequent derivation) are given in Figure 2. Analyte vapors are first adsorbed on the top Received: April 25, 2011 Revised: July 6, 2011 Published: July 12, 2011 3169
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Figure 1. Fabrication process (a), optical characteristics (b,c) and morphology (d,e) of stacked porous silicon photonic crystals. (a) The materials were fabricated by sequential electrochemical etches in an aqueous electrolyte containing HF and ethanol. A current density waveform consisting of three sequential sinusoids of different periods (“T”) but identical amplitudes was applied. The number of cycles in each photonic crystal is indicated by “N”. The structures were then thermally oxidized to provide a stable surface oxide coating on the pore walls. (b) The process results in a stack of three distinct photonic crystals. (c) The structure displays three distinct stop bands in the optical reflectance spectrum (in air). (d) Plan-view SEM image (secondary electron image) of the sensor surface, showing the mesopore openings. The average pore diameter is 8 ( 6 nm. Scale bar is 200 nm. (e) Cross-sectional SEM image showing a close-up view of several repeats in the bottom layer of the trilayer structure. The series of light and dark bands running horizontally in the image correspond to the porosity modulation of the photonic crystal (spatial period = 300 ( 20 nm). Scale bar is 2 μm. The periods of the top and middle layers (Supporting Information Figure S3) were 178 ( 16 and 229 ( 18 nm, respectively.
(blue) porous layer. The photonic crystal in the middle of the stack is thicker than the top (blue) or the bottom (red) structures, and it functions like a drift tube, delaying transport of analyte into the bottom layer. The time lag between the entry of analyte into the top and bottom layers corresponds to the retention time of the analyte in the middle layer, and it can be observed in the temporal response of the reflectance spectrum. The peak wavelengths of the two stop bands originating from the top and the bottom layers of the structure are presented as a function of time after introduction of ethanol vapor (Figure 3). Analyte adsorption causes a red shift in these photonic features (Figure 3a,b), and the response of the
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Figure 2. (a) Schematic diagram (not to scale) of the stacked photonic crystal sensor element. A fixed surface concentration of analyte (C0) is introduced at time t = 0, and diffusional transport generates a onedimensional concentration gradient within the porous film. (b) Calculated analyte concentration profiles within the sensor at various times (t = 1, 10, 25, 50, 75, 100 s). The data are derived from a one-dimensional Fickian diffusion model (see Supporting Information Figure S4 and subsequent derivation). The position and relative thickness of each photonic crystal layer is indicated by the color shaded areas. (c) Calculated relative analyte concentration as a function of time for positions in the center of the top (blue line), middle (green line), and bottom (red line) photonic crystals.
bottom layer is significantly delayed relative to the top layer (Figure 3c). In order to better quantify this difference, a characteristic response time (τ) for each layer was defined based on the derivative of the peak stop band wavelength with respect to time; the point of maximum rate of change (i.e., the inflection point of Figure 3c) is defined as the characteristic response time, τ, for each layer. The fundamental delay, or the retention time (Δτ), is then expressed as the difference of the two characteristic response times (eq 1). From these data, the retention time of 1700 ppmv of ethanol vapor in the drift tube is found to be 242 s. ðΔτ ¼ τbottom τtop Þ
ð1Þ
Figure 4 shows the three-dimensional relationship between wavelength shift (Δλ), retention time (Δτ), and analyte concentration for seven test analytes. The curves are well-separated in the three-dimensional plot. The traces tend to cluster into two groups based on the hydrophobic (cyclohexane, heptane, and toluene) or hydrophilic (acetone, methanol, ethanol, and isopropanol) nature of the analyte (Figure 4b,c). A projection of the data in the ΔλΔτ plane yields distinct curves for all the analytes tested, indicating that a specific wavelength/retention time combination uniquely identifies both the analyte and its concentration. For all concentrations less than 1000 ppmv, the sensor can clearly distinguish between each analyte; even for concentrations above 1000 ppmv, only the toluene responses were not completely separable. The time-dependent nature of the data is important in distinguishing the analytes; typically, the 3170
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Figure 3. (a) Reflectance spectra as a function of time for the blue photonic stop band. (b) Reflectance spectra as a function of time for the red photonic stop band. At t = 960 s, the sensor was exposed to a 0.5 SLPM flow of 1700 ppmv ethanol vapor in nitrogen carrier gas. All other data were collected under a 0.5 SLPM flow of pure nitrogen. Full spectra are shown in Supporting Information Figure S5. (c) Peak shift of the photonic stop bands from the top (blue) and bottom (red) layers as a function of time. The shift of the photonic stop band (Δλ) is defined as the difference between the equilibrium wavelength of the stop band maximum after exposure to analyte vapor and the wavelength of the stop band maximum under pure nitrogen. Red shifts of the stop band position are denoted as positive. Peak wavelengths were calculated by fitting the photonic stop bands to a Gaussian function. The data have been smoothed using a two-pass moving average (N = 15) in order to facilitate the calculation of the first derivative; unsmoothed data are shown in Supporting Information Figure S5 for comparison. The blueshaded region corresponds to the time of exposure to ethanol vapor. Black arrows denote the beginning and end of the application of a thermal pulse by means of a resistive heater, mounted on the back of the chip, to purge the sensor of residual analyte. (d) The first derivative of the data shown in (c); data for the top and bottom layers are shown in blue and red, respectively. The retention time (Δτ) is denoted by the dashed gray lines.
steady-state response of a low concentration of a strongly adsorbed analyte cannot be distinguished from a high concentration of a weakly adsorbed analyte. In addition to its ability to distinguish significantly different analytes (e.g., a nonpolar hydrophobic analyte, such as heptane, from a polar hydrophilic analyte such as isopropanol), the method is also capable of separating the responses of chemically similar molecules (e.g., methanol, ethanol, and isopropanol). The retention time parameter, Δτ, is affected by two primary analyte properties: concentration and polarity. These effects can be interpreted in the context of a Knudsen diffusion model that takes into account adsorption on the pore walls.26 In the present experiment, analyte is not actively transported through the layers with a mobile phase, as it would be in a gas chromatography column; instead it diffuses through the mesoporous structure under the influence of a concentration gradient. The analyte/ surface interaction involves simple unimolecular adsorption/ desorption at low analyte concentrations, and cooperative adsorbate effects at higher analyte concentrations, yielding a
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Figure 4. (a) The 3D plot showing the wavelength shift of the photonic stop band of the bottom layer (Δλbottom) and the retention time (Δτ) as a function of concentration for the seven analytes indicated. (b) Projection of the data from (a) in the ΔλΔτ plane. (c) Projection of the data from (a) in the Δτconcentration plane. Error bars (three standard deviations, Supporting Information Figure S6) are derived from the level of noise associated with the measurements.
concentration-dependent diffusion coefficient.2628 For a given analyte, the retention time (Δτ) decreases with increasing concentration (Figure 4c), which leads to better resolution with decreasing analyte concentration. The ability of the system to better distinguish analytes at low concentrations is a distinct advantage compared with most point sensors; in this case, the limit of recognition effectively becomes the limit of detection. The effect of analyte polarity on the sensor response is consistent with gas phase Knudsen diffusion as the primary mode of mass transport: the nonpolar analytes cyclohexane, heptane, and toluene display short retention times; their interactions with the hydrophilic silica surface are weak, and they rapidly diffuse through the porous nanostructure (Figure 4b). In contrast, more polar molecules (such as alcohols) are adsorbed more strongly 3171
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LETTER
’ ASSOCIATED CONTENT
bS
Supporting Information. Experimental procedures, crosssectional scanning electron micrographs of porous silicon control samples, reflectance spectra of porous silicon multilayer photonic crystals, cross-sectional scanning electron micrographs of porous silicon trilayer structures, Fick’s Law derivation of relationship between retention time and drift layer length, temporal responses of the sensor to ethanol vapor, and cross-sectional scanning electron micrographs of drift tubes of varying thickness. This material is available free of charge via the Internet at http://pubs.acs.org.
’ AUTHOR INFORMATION Corresponding Author
*E-mail:
[email protected].
Figure 5. The spectral shift (Δλ) as a function of retention time (Δτ) upon exposure to varying concentrations of methanol (120054 400 ppmv), ethanol (90037 700 ppmv), and isopropanol (64027 100 ppmv) are shown for sensors of different drift layer thickness: (a) 29, (b) 87, (c) 177 μm. (d) Plot of retention time (for 1700 ppmv of ethanol vapor) vs k (a geometric parameter related to drift layer thickness, see Supporting Information Figure S4 and subsequent derivation), showing behavior consistent with a Fickian diffusion process.
on the silica surface and are correspondingly less mobile. The data indicate that the surface chemistry used in this work is more efficient at resolving the responses of polar molecules such as alcohols; surface functionalization with hydrophobic moieties29,30 may be more effective at resolving less polar molecules in a manner analogous to reverse-phase chromatography. The effect of drift layer thickness on resolving power was tested with the set of alcohols methanol, ethanol, and isopropanol. Two additional sensor structures were fabricated that were identical to the original, except the thickness of the middle layer was either 29 or 87 μm (Supporting Information Figure S7), instead of 177 μm (Supporting Information Figure S1b). The analyte response shows an increase in retention time with increasing layer thickness, and a concomitant increase in separation between the response curves (Figure 5). These results show that the trilayer nanostructures display responses that are both reproducible and systematically tunable. The relationship between retention time and drift layer thickness follows a Fick’s law dependence (Figure 5d, model derivation is included in Supporting Information Figure S4 and subsequent derivation). The data show that a stacked porous photonic crystal optical vapor sensor is capable of identifying and quantifying volatile organic compounds in the gas phase, and this capability extends to concentrations in the parts per million range. The oxidized porous Si sensor is robust (surviving >100 dose/purge cycles with no sign of degradation) and thermally stable up to 450 C, allowing the sensor to be refreshed by means of thermal cycling. While the present work has focused on vapor sensing, these optical nanostructures should be amenable to the identification of liquid-phase analytes such as proteins, DNA and other biomolecules.
’ ACKNOWLEDGMENT This material is based upon work supported by the National Science Foundation under Grant DMR-0806859 and by Tyco Electronics. T.L.K. acknowledges the Natural Sciences and Engineering Research Council of Canada (NSERC) for a postdoctoral fellowship. ’ REFERENCES (1) Choi, S. Y.; Mamak, M.; von Freymann, G.; Chopra, N.; Ozin, G. A. Nano Lett. 2006, 6 (11), 2456–2461. (2) Snow, P. A.; Squire, E. K.; Russell, P. S. J.; Canham, L. T. J. Appl. Phys. 1999, 86 (4), 1781–1784. (3) Bonifacio, L. D.; Puzzo, D. P.; Breslav, S.; Willey, B. M.; McGeer, A.; Ozin, G. A. Adv. Mater. 2010, 22 (12), 1351–1354. (4) Potyrailo, R. A.; Ghiradella, H.; Vertiatchikh, A.; Dovidenko, K.; Cournoyer, J. R.; Olson, E. Nat. Photonics 2007, 1 (2), 123–128. (5) Sailor, M. J.; Link, J. R. Chem. Commun. 2005, 11, 1375–1383. (6) Persaud, K.; Dodd, G. Nature 1982, 299 (5881), 352–355. (7) Freund, M. S.; Lewis, N. S. Proc. Natl. Acad. Sci. U.S.A. 1995, 92 (7), 2652–2656. (8) Dickinson, T. A.; White, J.; Kauer, J. S.; Walt, D. R. Nature 1996, 382 (6593), 697–700. (9) Rakow, N. A.; Suslick, K. S. Nature 2000, 406 (6797), 710–713. (10) Albert, K. J.; Lewis, N. S.; Schauer, C. L.; Sotzing, G. A.; Stitzel, S. E.; Vaid, T. P.; Walt, D. R. Chem. Rev. 2000, 100 (7), 2595–2626. (11) Rock, F.; Barsan, N.; Weimar, U. Chem. Rev. 2008, 108 (2), 705–725. (12) Janzen, M. C.; Ponder, J. B.; Bailey, D. P.; Ingison, C. K.; Suslick, K. S. Anal. Chem. 2006, 78 (11), 3591–3600. (13) Bohrer, F. I.; Colesniuc, C. N.; Park, J.; Ruidiaz, M. E.; Schuller, I. K.; Kummel, A. C.; Trogler, W. C. J. Am. Chem. Soc. 2008, 131 (2), 478–485. (14) Woodka, M. D.; Brunschwig, B. S.; Lewis, N. S. Langmuir 2007, 23 (26), 13232–13241. (15) Letant, S. E.; Sailor, M. J. Adv. Mater. 2001, 13 (5), 335–338. (16) Wolfrum, E. J.; Meglen, R. M.; Peterson, D.; Sluiter, J. Sens. Actuators, B 2006, 115 (1), 322–329. (17) Lim, S. H.; Feng, L.; Kemling, J. W.; Musto, C. J.; Suslick, K. S. Nature Chem. 2009, 1 (7), 562–567. (18) Feng, L.; Musto, C. J.; Kemling, J. W.; Lim, S. H.; Suslick, K. S. Chem. Commun. 2010, 46 (12), 2037–2039. (19) Lehmann, V. Electrochemistry of Silicon: Instrumentation, Science, Materials and Applications; Wiley-VCH: Weinheim, Germany, 2002; p 286. (20) Vincent, G. Appl. Phys. Lett. 1994, 64 (18), 2367–2369. (21) Lorenzo, E.; Oton, C. J.; Capuj, N. E.; Ghulinyan, M.; NavarroUrrios, D.; Gaburro, Z.; Pavesi, L. Appl. Opt. 2005, 44 (26), 5415– 5421. 3172
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