Gold-Nanorod-Based Plasmonic Nose for Analysis of Chemical

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Cite This: ACS Appl. Nano Mater. 2019, 2, 3897−3905

Gold-Nanorod-Based Plasmonic Nose for Analysis of Chemical Mixtures Huzeyfe Yilmaz,† Sang Hyun Bae,† Sisi Cao,† Zheyu Wang,† Baranidharan Raman,*,‡ and Srikanth Singamaneni*,† †

Department of Mechanical Engineering and Materials Science, and Institute of Materials Science and Engineering, and Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, Missouri 63130, United States

‡

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ABSTRACT: We introduce the “plasmonic nose” as a novel approach for detection, recognition, and quantification of mixtures of chemical species. Using a paper substrate and a calligraphy-based fabrication approach, we generated an array of surface-enhanced Raman scattering (SERS)-active sensors with distinct chemical functionalities. Each sensor is composed of gold nanorods (AuNRs) functionalized with a macromolecule that determined its sensitivity and specificity. We show that the SERS-active sensor array is capable of detecting and discriminating a wide variety of chemical species. To validate this approach, we exposed the sensor array to individual analytes and their binary/ternary mixtures. We found that each mixture generated a multivariate fingerprint that varied with identity (vibrational frequency) and intensity. Statistical analysis of SERS spectra from multiple sensors allowed us to not only recognize components of mixtures but also estimate their mixing ratios. In sum, our study presents a highly practical, low-cost sensing approach for quantitative chemical analyte detection for a wide variety of applications including life sciences, environmental monitoring, and homeland security. KEYWORDS: plasmonic calligraphy, SERS, multiplexed sensing, dimensionality reduction, quantitative SERS

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recognition based-sensing (i.e., “electronic, optical, or chemical nose”).25−28 Despite intensive research with cross-reactive array sensors, SERS remains underappreciated as a transduction mechanism.29 In fact, very few generalized quantification methods have been demonstrated for SERS-based sensors so far.30−32 In addition to differential functionalization of transducers, development of arrays of SERS-based sensors would require robust, cost-effective fabrication of substrates to steer this class of sensing technologies into the real-world settings.33−35 Among many alternatives, a substrate that offers a number of advantages for creating SERS-based sensing arrays is “paper”.36−39 It is inexpensive, mechanically flexible, easy to use, and compatible with traditional printing techniques. Plasmonic calligraphy or pen-on-paper is a method that has proven to be highly effective in generating uniform patterns of plasmonic nanoparticles on paper substrates,40−42 transcending immersion method,43,44 and circumventing time-consuming and costly approaches such as plasmonic inkjet printing.45,46 In addition, calligraphy of functional plasmonic nanoparticles can easily be scaled up and automated with robotic arms.

INTRODUCTION Surface-enhanced Raman scattering (SERS) is considered to be a powerful platform for ultrasensitive chemical and biological sensing1−6 and trace detection.7,8 Design and synthesis of plasmonic nanostructures with large enhancement factors allow detection of target analytes at extremely low concentrations.9−20 To take advantage of Raman scattering enhancement, it is necessary that molecules of the analyte are in close proximity to the surface of plasmonic nanostructures.9,21 This proximity requirement could be achieved through both physical (i.e., entrapment close to the plasmonic nanostructure surface) or chemical interactions. Analyte specific coatings that utilize antigen−antibody or analyte− receptor interactions have been successfully employed22−24 to capture molecules from chemical species of interest (i.e., impart selectivity) and take advantage of sensitivity enhancements in plasmonic nanosensors. While having specific chemical targets are often desirable, in most diagnostic or detection problems a unique single marker/ analyte is not present. Instead, a number of species of interest coexist, which require the use of arrays of sensors as differential receptors. A cross-responsive SERS-sensor array can detect, identify, and quantify species of interest in complex mixtures. Various transduction methods have been utilized with crossresponsive sensing elements as an alternative to “lock−key” © 2019 American Chemical Society

Received: April 24, 2019 Accepted: May 14, 2019 Published: May 14, 2019 3897

DOI: 10.1021/acsanm.9b00765 ACS Appl. Nano Mater. 2019, 2, 3897−3905

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ACS Applied Nano Materials

Figure 1. Plasmonic nose based on cross-responsive SERS sensors calligraphed on a paper substrate. (A) Chemical structures of polymers coated on AuNRs and the dominant interactions with target analytes are shown. Note that four different sensors were synthesized by using the following polymeric coatings: two polyelectrolytes (PAH, PSS), PVP, and PEG. (B) Zeta-potentials of polymer-coated AuNRs in aqueous solution. (C) TEM image of AuNRs. (D) Illustration of plasmonic calligraphy showing how the cross-responsive SERS-sensing strips were fabricated on flexible paperbased substrates. (E) Optical image of an ∼2 cm test strip with each line corresponding to AuNRs coated with a distinct organic molecule, i.e., four distinct sensors. (F) SEM image of a typical sensor exhibiting uniform and dense distribution of AuNRs.

Here we introduce the “plasmonic nose”, which consists of an array of plasmonic nanostructures functionalized with a variety of polymers. Each polymer offers a distinct set of chemical interactions which imparts partial selectivity and allows us to create a cross-responsive sensor. Arrays of such SERS-active sensors are fabricated on paper substrates using a calligraphy-based approach. We validate our plasmonic nose approach on the problem of identifying and quantifying the components of binary and ternary mixtures. In sum, we combine nanoparticle array-based SERS spectroscopy with statistical methods to realize a quantitative sensor for mixture analysis.

more information). Likely interactions and chemical structures of polymer coatings are shown in Figure 1A. Functionalization of the AuNRs with different polymers resulted in a relatively small red- or blue-shift corresponding to an increase or decrease in the effective refractive index of the medium surrounding the AuNRs (Figure S1). It is important to note that the surface functionalization procedures did not result in uncontrolled aggregation of the nanostructures as evidenced by the absence of a large red-shift or LSPR peak broadening. Further confirmation of the functionalization was obtained through the zeta-potential values: −51 mV for PSSAuNRs, 46 mV for PAH-AuNRs, −20 mV for PEG-AuNRs, and −13 mV for PVP-AuNRs. Polyelectrolyte (PAH and PSS) coatings with large (positive and negative) zeta-potentials facilitate electrostatic interactions, while PEG and PVP allow noncovalent interactions such as hydrogen-bonding and polar interactions (Figure 1B). Polymer-coated AuNRs were concentrated to form plasmonic inks that were calligraphed onto a filter paper using a 0.7 mm ballpoint pen separately to create distinct, cross-responsive plasmonic sensor array as shown in the schematic in Figure 1D. Ballpoint pens are cost-effective and simple tools for conception of fine and uniform structures on ordinary laboratory papers since plasmonic inks prepared from polymer-capped AuNRs can be easily adjusted (by controlling the concentration of the AuNRs) to have suitable viscosities. Each sensor observed in the optical image was obtained by two strokes with the ballpoint pen (Figure 1E). The uniformity of the distribution of AuNRs was confirmed via scanning electron microscopy (SEM) images (Figure 1F and Figure S2). After

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RESULTS AND DISCUSSION To serve as SERS-active medium, we chose gold nanorods (AuNRs) as plasmonic nanostructures due to the strong electromagnetic fields at their sharp ends and tunable longitudinal localized surface plasmon resonance (LSPR) wavelength.47−50 Dimensions of AuNRs were measured from transmission electron microscopy (TEM) images to be 55 ± 5 nm in length and 18 ± 1 nm in diameter (Figure 1C). Modification with polyelectrolytes was initiated with negatively charged poly(styrenesulfonate) (PSS) through electrostatic interactions since as-synthesized AuNRs are capped with positively charged cetyltrimethylammonium bromide (CTAB). Positively charged poly(allylamine hydrochloride) (PAH) was similarly coated on PSS-modified AuNRs. Poly(vinylpyrrolidinone) and poly(ethylene glycol) coatings were obtained via ligand exchange (see experimental details for 3898


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Figure 2. (A) Chemical structures of the model analytes used in the study. SERS spectra of ternary mixtures of MO, R6G, and FITC collected from (B) PAH-AuNRs, (C) PEG-AuNRs, (D) PSS-AuNRs, and (E) PVP-AuNRs domains. Each spectrum is an average of ten measurements. Note that each spectrum is obtained for a ternary mixture with a particular mixing ratio. For example [3, 9, 0.6] indicates a mixture of 3 μM MO, 9 μM R6G, and 0.6 μM FITC. Also note that the spectra were offset to show multiple spectra in the same plot, and only the location of peaks carries analyte specific information.

To create binary and ternary mixtures that were not dominated by any one analyte, we characterized the dose− response curves of each analyte. Based on the dose−response curves (FigureS5), analyte concentrations were varied between 3−12 μM for MO and R6G, 0.3−1.2 μM for FITC in binary mixtures, 3−18 μM for MO and R6G, and 0.3−1.8 μM for FITC in ternary mixtures. The SERS spectra obtained using these three target analytes and their binary and ternary mixtures are shown in Figure 2. Note that the SERS spectra obtained from each SERS sensor was quite different and that, different SERS sensors were effective at capturing different analytes. For example, R6G has characteristic Raman bands at 612 cm−1 (C−C−C ring in-plane vibration) and 1363/1509 cm−1 (aromatic C−C stretching vibration)51 that were

printing, AuNR sensing domains preserved their optical properties since the extinction spectra collected from all four domains on the paper substrate were similar to that from aqueous solutions except for a blue-shift in the LSPR wavelength due to the change in the refractive index of the medium surrounding the nanostructures (Figures S1 and S3). To test the multiplexing capability of the plasmonic sensor array, a set of representative analytes capable of interacting with the SERS sensors through distinctive interactions were chosen to test the plasmonic nose. Negatively charged methyl orange (MO), positively charged rhodamine 6G (R6G), and fluorescein isothiocyanate (FITC) provided a wide range of interactions that can also be found in many biological systems, presenting a candid challenge for our plasmonic nose. 3899


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Figure 3. Principal component (PC) analysis of SERS spectra based on sensor type. The entire data set composed of ten measurements made from 28 mixtures using four different plasmonic sensors (1120 spectra in total). (A) Top three eigenvectors (with largest eigenvalues) of the covariance matrix of the entire data set. (B) Each high-dimensional Raman spectrum after dimensionality reduction is shown as a 3D sphere in the PC space. The measurements are color-coded to indicate which type of nanosensor was used to obtain a specific spectrum. (C) Spectra were color-coded according to analyte concentration on the respective favorable sensor (darker colors refer to higher concentrations). Three orthogonal directions validate the cross-responsive nature of SERS sensor array. (D) SERS spectra reconstructed (dark colors) from three concentration points in the PC space for each analyte−sensor pair are plotted with corresponding actual SERS spectra (light colors).

each analyte. Note that the contribution from the fourth SERS sensor (PVP-AuNR) is not highlighted as it overlaps with PSSAuNR and PEG-AuNR sensors. Overall, these results demonstrate the cross-responsive nature of the SERS sensor array. PCA of the (1120 × 1489) entire data set did not reveal a clear variation between the Raman bands of each of the three analytes in the mixtures; instead, it captured the variation among sensors (Figure 3B). Nevertheless, PC analysis revealed that each SERS sensor provided separate information. Furthermore, each of the three eigenvectors with largest eigenvalues resembled the spectrum of a separate analyte (i.e., each axis encodes identity of one target analyte). Hence, projection of spectra along axis vectors allowed us to quantify analytes based on their distance from origin. Therefore, both identity and quantity could be determined by our analysis of SERS spectra. To systematically verify that the projections of SERS spectra along PC axes were according to the analyte concentration, three points (12, 9, and 3 μM) from each direction were picked and used to reconstruct the rank-3 SERS spectra. Reconstructed spectra (dark colors) were compared to the original SERS spectra (light colors) in Figure 3D, and peak positions and intensities were closely matched. The mismatching low-intensity peaks can be attributed to the fact that only three eigenvectors were used in reconstruction, capturing 81% of the variation. Next, we sought to examine how separable were the different analytes and their mixtures based on the SERS spectra obtained using all four plasmonic sensors. Therefore, for each analyte mixture, we first concatenated the

detected only by using PSS-AuNR and PVP-AuNRs. MO has bands at 1117 cm−1 (Ph−N stretching) and 1143 cm−1 (C−H deformation) that were picked up only in the SERS spectra measured using the oppositely charged PAH-AuNRs52 (Figure 2). FITC Raman bands at 1186 cm−1 (phenolic OH) and 1604 cm−1 (xanthene C−C stretching) were noticeable in all four AuNRs with varying SERS intensities (Figure 2 and Figure S6).53 The entire data set included 1120 spectra (28 analyte combinations, ten measurements each, and four different plasmonic sensors). Each Raman measurement was 1489 wavenumbers long (in the range of 400−1800 cm−1 with a resolution of 0.94 cm−1). To qualitatively understand whether different plasmonic sensors were indeed contributing nonredundant information, we performed a principal component analysis (PCA). The top eigenvectors (with three largest eigenvalues; principal components or PCs) of the covariance of the above-described data matrix (1120 × 1489) were computed (Figure 3A). Each 1489-dimensional SERS spectrum was projected onto the PCs and visualized as shown in Figure 3B. Note that each SERS spectrum is color-coded to identify which of the four plasmonic sensors was used to obtain the measurement. By visual inspection, it can be observed that each functionality is clustered separately even though for certain scores (PVP) the separation is small. This is expected since a fine concentration gradient was used in the training set (for instance, for FITC, concentration steps were 0.3 μM). Based on the color-coded scores in Figure 3C, three orthogonal directions indicate concentration gradients for 3900


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Figure 4. SERS data reorganization and PC analysis based on chemical mixture. (A) Average of ten concatenated Raman spectra for each ternary mixture is shown. (B) Top eigenvectors of the covariance matrix corresponding to two largest eigenvalues is shown. Note that only the 130 Raman measurements involving the ternary mixtures are used in this analysis. The first principal component vector (black solid line) has characteristic FITC peaks in the negative direction and characteristic MO and R6G peaks in the positive direction. The second principal component vector has characteristic MO peaks in the positive direction and characteristic R6G peaks in the negative direction. (C) PCA scatter plot obtained by multiplying (dot product) the two eigenvectors shown in (B) with each row of the concatenated Raman matrix shown in (A). Each point is colorcoded according to the mixture from which the spectra were measured. (D) PCA scatter plot including the blind test measurements. The scatter points of the two test cocktails are shown as hollow squares and hollow triangles.

Raman spectra from the four sensors. This converted the original 1120 × 1489 Raman spectra matrix into 280 × 5956 concatenated Raman spectra matrix (Figure 4A). We performed principal component analysis to examine the predominant source of variance in the Raman spectra for each binary or ternary mixture (Figure 4B and Figures S8B, S9B, and S10B) and to visualize the Raman spectra generated by each analyte across the plasmonic sensor array (Figure 4C and Figures S8C, S9C, and S10C). For the ternary mixtures, the two primary sources of variance in the data set (the “eigenspectra”), i.e., two eigenvectors of the covariance matrix corresponding to largest eigenvalues, are shown in Figure 4B. The first eigenspectrum captured 68% of SERS spectra variance, which contained all the Raman bands of all analytes. A separation of Raman bands that were specific to MO, R6G, and FITC was observed (i.e., MO and R6G Raman bands had positive values where FITC Raman bands had negative values). Projecting the Raman spectra onto this eigenspectra (i.e., computing their dot product) therefore separated FITC from the other two analytes (MO and R6G). This result is expected

as FITC interacted with all four sensing domains and contributed the most variance in this data set. The second eigenspectrum captured 18% of the variation. Note that the second eigenspectrum does not have any of the FITC bands but clearly separated R6G and MO Raman bands (Figure 4B). When the same analyses were repeated but focusing on each binary mixture (Figures S8−S10), we find that the first eigenvectors always separated the Raman bands of the two components. Next, we visualized each concatenated Raman spectrum in two dimensions by projecting the same onto the top two eigenspectra (dot product between the 5956-dimensional concatenated Raman spectra shown in Figure 4A and the eigenspectra shown in Figure 4B). The Raman spectra after this dimensionality reduction were color-coded and shown as a scatter plot (Figure 4C; training data set). Note that exposures of the plasmonic sensor array to the pure analytes generated Raman spectra that mapped onto distinct clusters of points after PCA (blue cluster: FITC; black cluster: MO: and red cluster: R6G). Binary mixtures of two analytes resulted in spectra that were linear combinations of the two-component 3901


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Figure 5. Results from the linear regression analysis. The three axes correspond to the concentrations of each analyte used in the study (MO, R6G, and FITC). The actual composition of each testing-phase mixture used in the study is shown as red cubes. The predicted composition is shown as blue spheres (mixture [10, 4, 0.4]). Similarly, the other unknown ternary mixture (marked with hollow squares and mixing ratio [4, 11, 0.3]) was mapped closest to the [6, 9, 0.3] mixture in the training data.

were the components of various binary and ternary mixtures robustly recognized, but their concentrations could also be calculated with good agreement to the actual values. In addition, we provided evidence for the contribution of each functionality and validated the use of four functionalities in our SERS sensor array by measuring the percentage error with only two or three functionalities (Figure S11). We found that, on an average, the errors in quantitative blind test measurements were 22.3% and 12.6% with PAH−PSS and PAH−PEG−PSS functionalities, respectively. Because the percentage error varied for each mixture (Figure S11), we also provided a visual observation of the accuracy of the plasmonic nose sensor based on mixtures, as shown in Figure S12. In sum, we have demonstrated differential and quantitative chemical sensing based on arrays of polymer functionalized gold nanorods. We used paper as a substrate to fabricate flexible, disposable, and low-cost arrays of plasmonic nanosensors. Chemical diversity of polymers used in functionalization yielded a differential response which was confirmed by statistical analysis. Raman spectra collected from the sensor array exposed to different analytes allowed us to not only recognize the components of various binary and ternary mixtures but also quantify the mixing ratios. Taken together, our results provide a first demonstration of the plasmonic nose concept where SERS-based measurements are used for chemical analyte detection. We have demonstrated a chemical sensing framework that is practical, selective and capable of quantitative analysis. Our sensor combines multivariate

spectra. Therefore, these binary mixture measurements projected onto space between the two-component pure analyte clusters (for example, a binary mixture of MO and R6G [9, 9, 0] (shown in dark green) can be spotted between the twocomponent clusters shown in red and black). Qualitatively similar results were obtained when different binary mixtures were individually analyzed (see Figures S8C, S9C, and S10C). Ternary mixtures included contributions from all three analytes. The exact location of any mixture depended on the ratios with which the three components were mixed. To determine whether the mixing ratios of an unknown mixture could be determined based on the concatenated Raman spectra, we prepared two cocktails not used in the training data (shown in Figure 4D; test data shown as black cluster of points with hollow squares and triangles). Note that the nearest cluster of points to one of unknown ternary mixture (shown in hollow triangles) was the [9, 6, 0.3] mixture. This is indeed compositionally the most similar to the actual mixing ratio of this mixture ([10, 4, 0.4]). Similarly, the other unknown ternary mixture (marked with hollow squares and mixing ratio: [4, 11, 0.3]) was mapped closest to the [6, 9, 0.3] mixture in the training data. To further quantify our predictions, we used a simple linear regression (after dimensionality reduction). A total of eight blind tests were performed with binary and ternary mixtures, and the average error was calculated to be 11.4%. Quantitative results of principal component regression from every blind test are shown in Figure 5. These results indicate that not only 3902


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two strokes over the same region. After each stroke, the paper was left to dry to prevent damages in the fiber. The width of the pen tip was 0.7 mm, as specified by the manufacturer. The substrates were then immersed and shaken in nanopure water for 10 min to release any loose nanorods and then dried naturally in air. Plasmonic inks were calligraphed soon after they were prepared. Extinction spectra of calligraphed plasmonic lines were measured from 10 random points on each polymer−AuNR sensor (Figure S3). The uniform distribution of AuNRs on the filter paper suggests stable plasmonic inks and a uniform deposition from the ballpoint pen tip. Because each polymer interacts with the filter paper differently, a small variance between extinction values of AuNR−polymer domains is expected. Ultimately, the viscosity values observed from AuNR− polymer plasmonic inks fell within the compatible viscosity range for ballpoint pens to achieve uniform calligraphy.41 Once dried, they were cut into strips 5 mm across (Figure 1E). The plasmonic sensors on the paper substrate were subsequently dipped in mixtures of different concentrations of MO, R6G, and FITC aqueous solutions of different concentrations for 1 h. The samples were then shaken in 5 mL of ethanol for 10 min, then shaken in 5 mL of nanopure water for 10 min, and dried with nitrogen for 30 s. Neither final washing steps with ethanol and water nor immersion into aqueous analyte solutions discernibly affected the adsorption or distribution of AuNRs on the paper substrate as confirmed by apparent Raman background from polymer coatings throughout the synthesis and measurement (Figure S4). SERS spectra from the substrates were collected using a Renishaw InVia Raman microscope and Wire 3.0 software on a dedicated computer, with a 785 nm laser focused using a 20× objective lens with 10 s of exposure. Ten spectra were collected from different locations across each substrate. Custom-written programs in MATLAB were used to perform data processing and statistical analysis. Prior to statistical analysis, background subtractions were performed as shown in Figure S13. Characterization Techniques. Transmission electron microscopy (TEM) micrographs were recorded on a JEM-2100F (JEOL) field emission instrument. A drop of the solution was dried on a carbon-coated grid that was previously made hydrophilic by glow discharge. A FEI Nova 2300 field emission scanning electron microscope was used to obtain SEM images at an accelerating voltage of 10 kV. Plasmonic paper was sputtered with gold prior to SEM imaging. The zeta-potential was measured using a Malvern Zetasizer Nano ZS dynamic light scatterings system. UV−vis extinction spectra of nanorod solutions were collected using a Shimadzu UV-1800 UV−vis spectrophotometer. Extinction spectra of nanorods on paper substrates were collected using a CRAIC microspectrophotometer (QDI 302) coupled to a Leica optical microscope (DM 4000M) with 10× objective in the range of 450− 800 nm with 50 accumulations and 0.144 s exposure time in reflection mode.

statistical analyses with SERS sensors and has the potential to serve as a reference for researchers in the field. Robust detection, identification, and quantification provided by plasmonic nose can be utilized in real-world applications in the future.

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MATERIALS AND METHODS

Cetyltrimethylammonium bromide (CTAB), chloroauric acid (HAuCl4), ascorbic acid, sodium borohydride (NaBH4), rhodamine 6G (R6G), methyl orange (MO), fluorescein isothiocyanate (FITC), poly(styrenesulfonate) (M w 70000 g mol −1 ), and poly(vinylpyrrolidone) (Mw 29000 g mol−1) were purchased from Sigma-Aldrich. Poly(allylamine hydrochloride) was purchased from Alfa Aesar, and methoxy PEG thiol (Mw 5000 kDa) was purchased from JenKem Technology. Silver nitrate and filter paper (Whatman #1) were purchased from VWR International. All the chemicals have been used as received, with no further purification. Pilot G1 retractable ballpoint pens (0.7 mm) were bought from Amazon. Synthesis of Gold Nanorods (AuNRs). Gold nanorods were synthesized using a seed-mediated approach. The seed solution was prepared by adding 0.75 mL of an ice-cold sodium borohydride solution (10 mM) into 10 mL of 0.1 M CTAB and 2.5 × 10−4 M HAuCl4 solution under 10 min of vigorous stirring at 800 rpm at room temperature. Once the sodium borohydride solution was added, the gold seed solution immediately changed from yellow to brown. The seed solution was stirred for an additional 5 min before use. The growth solution was prepared by mixing 1.8 mL of HAuCl4 (10 mM), 38 mL of CTAB (0.1 M), 0.415 mL of silver nitrate (10 mM), and 0.22 mL of ascorbic acid (0.1 M), in that order. The solution was vortexed after each addition for homogenization. To the resulting solution, 48 μL of freshly prepared seed solution was added and set in the dark undisturbed for 14 h. The AuNR solution was centrifuged twice at 9000 rpm for 30 min to remove excess CTAB and redispersed in nanopure water (18.2 mΩ·cm).47,54 Preparation of Polymer-Coated Gold Nanorods (AuNRs) and Corresponding Plasmonic Ink. AuNRs were coated with selected polymers as previously reported.41 Forty milliliters of twicecentrifuged AuNR solution was added dropwise to 40 mL of PSS (0.2% w/v) in NaCl aqueous solution (6 mM) under vigorous stirring of 1000 rpm, followed by stirring for 1 h. Then the solution was sonicated for another hour. The above solution was centrifuged to remove excess PSS at 9000 rpm for 30 min and concentrated to 40 μL solution, after which 10 μL of 2% PSS was added. Forty milliliters of PSS encapsulated AuNR solution was added dropwise to 40 mL of PAH (0.2% w/v) in NaCl aqueous solution (6 mM) under vigorous stirring of 1000 rpm, followed by stirring for 1 h. Then the solution was sonicated for another hour. To remove excess PAH, the above solution was centrifuged at 9000 rpm for 30 min and concentrated to 40 μL solution after which 10 μL of 2% PAH was added. Forty milliliters of twice-centrifuged AuNR was added dropwise to 40 mL of PVP (w/v) in nanopure water, then shaken, and sonicated for an hour with the same conditions as PSS- and PAHcapped AuNR solutions. During the second centrifuge before the dropwise addition, only 80% of the supernatant solution was removed. Once the sonication was complete, the PVP-capped AuNR solution was centrifuged at 9000 rpm for 30 min and concentrated to 50 μL solution. Eight milliliters of 1% mPEG-Thiol solution was mixed with 200 μL of 100 mM NaCl. The resulting solution was added to 40 mL of twice-centrifuged AuNR, where during the second centrifuge only 80% of the supernatant was removed. The solution was sonicated for 1 h and then immediately centrifuged at 9000 rpm for 30 min to remove excess PEG. The solution was concentrated to 50 μL. First, ballpoint pen cartridges were emptied and washed with ethanol and nanopure water thoroughly via sonication. Then concentrated solutions of each polymer-capped AuNR were injected into separate ballpoint pen cartridges. SERS Spectra Measurements. Whatman #1 laboratory filter paper was calligraphed with each polymer-capped AuNR solution at four distinct regions. Each line was written with a straight edge with

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ASSOCIATED CONTENT

* Supporting Information S

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acsanm.9b00765.

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Figures S1−S13 and Table S1 (PDF)

AUTHOR INFORMATION

Corresponding Authors

*E-mail: [email protected]. *E-mail: [email protected]. ORCID

Huzeyfe Yilmaz: 0000-0003-1595-7019 Srikanth Singamaneni: 0000-0002-7203-2613 Notes

The authors declare no competing financial interest. 3903


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ACKNOWLEDGMENTS We acknowledge support from Office of Naval Research (Award # N00014-16-1-3030). The authors thank Nano Research Facility (NRF) and Institute of Materials Science and Engineering (IMSE) at Washington University for providing access to electron microscopy facilities.

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