Glucose detection using SERS with multi-branched gold nanostructures in aqueous medium

Abstract

Gold nanoparticles (AuNPs), multi-branched gold nanoparticles (MBGNs), and silica-coated MBGNs (MBGNs-silica) were studied for rhodamine B (RB) and α-glucose detection at low concentration. The MBGNs SPR band in the NIR, which is tunable, is useful for SERS that was demonstrated using rhodamine B and α-glucose as probe molecules with detection limits of 50 pM and 5 mM (90 mg dL⁻¹), respectively, much lower than that using regular AuNPs. The SERS signals of RB and α-glucose using MBGNs-silica are further enhanced with respect to AuNPs and MBGNs, which is attributed to the aggregation of the MBGNs and a stronger interaction. In the case of α-glucose, the functionalization process performed to both, α-glucose molecules and MBGNs, improves the interaction and allows measurements at low concentration.

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1. Introduction

The detection of molecules at low concentration in solution with high sensitivity and specificity has been of great interest in fields such as biomedical research and diagnosis.^1–4 In particular, surface-enhanced Raman scattering (SERS) is a powerful, ultrasensitive, and non-destructive spectroscopic technique that can detect analytes down to the single molecule level while simultaneously providing molecular specific information.^5–7 Many studies have demonstrated enhancement factors of 10⁵ or higher, leading to Raman signals that are comparable to or even higher than those of the fluorescent organic dyes.^8–10 SERS is a technique resulting in strongly increased Raman signals of molecules at or near metal nanostructures, typically noble metals such as gold and silver. In particular, SERS has been exploited to detect low concentration of biological samples such as different types of cancers,^11–16 Alzheimer's disease (β-amyloid peptide),^17,18 the hepatitis C virus,¹⁹ and Parkinson's disease (dopamine depletion).^20,21 In the study of SERS processes, it is generally accepted that electromagnetic enhancement²² and chemical enhancement²³ mechanisms are the principal phenomena involved in the amplification of Raman signals. Effective SERS depends on absorption of incident light by the metal nanostructures based on their surface plasmon resonance (SPR)²² where the excitation wavelength is resonant with the metal–molecule charge transfer electronic states.²³ The success of SERS is highly dependent on the interaction between adsorbed molecules and the surface of plasmonic nanostructures. In the last years, many studies have strived to optimize substrate structure and configuration to maximize enhancement factors such as new plasmonic materials^24–26 with different shapes and sizes^27,28 that support increased SERS enhancement. The SPR of a metal nanoparticle may be tuned throughout visible and near-infrared (NIR) wavelengths by varying the size and shape or the aspect ratio.^29–32 Particularly, the plasmons of metallic nanostars, nanoshells, and nanorods can be used to tune the SPR into the NIR region,^33–39 which is desired for in vivo biomedical applications due to deeper tissue penetration.^40–42

Gold nanoparticles have been widely studied for bio detection applications due to their unique optoelectronic properties. Various types of AuNPs,^43–46 both in aqueous^47–49 and organic solutions,^50,51 have been developed to serve as excellent SERS substrates. Moreover, the silanization of various metal nanoparticle systems has shown great success in protecting their surface characteristics and facilitating bioconjugation.^52,53 The SERS activity of gold and silver nanostructures has been experimentally verified using rhodamine B as an analyte due to its distinct Raman features and adsorbability onto nanoparticles.^54,55

One of the important applications of SERS is detection of glucose. Glucose in particular is interesting as proper monitoring of diabetes mellitus requires effective screening of glucose levels within human blood. Several studies have been reported previously.^56–59 Van Duyne and coworkers have done extensive work on SERS detection of glucose^60–62 in which they observed that SERS was successful in the detection of glucose at physiological concentrations using in vitro and in vivo sensing techniques. Yang and coworkers proposed the use of a photonic crystal fiber as a container of a different concentration solutions of D-glucose, and obtained a low concentration detection via Raman spectroscopy.⁶³ Dinish et al. implemented a nanogap SERS substrate with a deep-UV lithography technique for glucose sensing.⁶⁴ Very recently, Al-Ogaidi worked with gold nanostar@silica core–shell nanoparticles conjugated with glucose oxidase (GOx) enzyme molecules developed as the SERS biosensor for label-free detection of glucose, by examining SERS peak of H₂O₂.⁶⁵ However, it is still challenging to achieve high reproducibility, good uniformity, and long-term stability of SERS substrates. As mentioned, glucose is extremely difficult to detect through conventional SERS methodologies because of its small Raman cross-section and weak absorption to bare metal surfaces.

In the present work, we have carried out a systematic study of the SERS signals of rhodamine B (RB) and α-glucose adsorbed on colloidal multi-branched gold nanostructures (MBGNs) and MBGNs with a silica coating. The MBGNs demonstrated their ability to detect these two molecules at low concentrations, compared to AuNPs where no Raman signal was observed. While MBGNs serve to enhance the Raman signal, the MBGNs-silica coating induce aggregation and then affords strong interaction with RB and α-glucose resulting in an increase of hot spot density that improve the SERS signal. Since SERS detection of α-glucose in water is generally challenging, the successful detection within a clinically relevant concentration range shows the promise of the MBGNs and MBGNs-silica as potential SERS substrates for detecting molecules that strongly interact with silica coating or MBGN surface itself.

2. Experimental

2.1 Materials

All chemicals were reagent grade. HAuCl₄, sodium citrate, silver nitrate, ascorbic acid, 3-aminopropyltriethoxysilane (APTES), rhodamine B and α-glucose were purchased from Sigma-Aldrich. HCl was purchased from Karal, (∼38% in H₂O), deionized water was purchased from Quimicurt, and ethanol was purchased from Jalmek.

2.2 Preparation of MBGNs and MBGNs-silica

Multi-branched gold nanostructure (MBGNs) were prepared by a seed-mediated growth method following previously reported protocols with some modifications⁶⁶ where a gold seed solution was synthesized using the Turkevich method.⁴⁹ Briefly, the addition of a 1% citrate solution to a boiling solution of 1 mM HAuCl₄ under stirring yielded a red color characteristic of gold nanospheres (AuNP). The stable particles were filtered by Whatman filter papers of 110 nm, and then kept at 4 °C for long-term storage. To synthesize MBGNs, 200 μl of the previously prepared seed solution was added to 10 ml of 0.5 mM HAuCl₄ solution with 40 μl of 1 M HCl at room temperature under moderate stirring. Quickly, 40 μl of 0.01 M silver nitrate and 126 μl of 0.1 M ascorbic acid were added simultaneously under 700 rpm stirring. It is believed that the major role of Ag⁺ is to assist the anisotropic growth of Au branches on certain crystallographic facets on multi-twinned citrate seeds.^67–70 The color rapidly turned from light red to greenish-black, indicating the formation of MBGNs. MBGNs-silica were prepared by adding 10 μl of 10 vol% APTES solution to the MBGNs solution that had been previously prepared.

2.3 AuNPs and MBGNs linked with rhodamine B and α-glucose

High concentration of RB aqueous solution (1 × 10⁻² M) was prepared to obtain the characteristic Raman spectra of concentrated RB. RB–MBGNs solutions were prepared by dissolving solid RB in distilled water and three different concentrations were obtained (1 × 10⁻⁷ M, 1 × 10⁻⁸ M and 1 × 10⁻¹⁰ M), then 500 μl of these RB solutions were mixed with 500 μl of colloidal MBGNs having RB–MBGNs final concentrations of 0.5 × 10⁻⁷ M, 0.5 × 10⁻⁸ M and 0.5 × 10⁻¹⁰ M respectively. Furthermore, RB–MBGNs-silica was prepared as follows: first was prepared RB–APTES solutions adding 10 μl of 10 vol% APTES solution to the three concentrations of RB solutions (1 × 10⁻⁷ M, 1 × 10⁻⁸ M and 1 × 10⁻¹⁰ M). Subsequently 500 μl of colloidal MBGNs-silica, were added to 500 μl of each RB–APTES concentrations. And finally 500 μl of colloidal AuNPs and AuNPs–APTES were added to a 500 μl of the highest concentrated simple (1 × 10⁻⁷ M) RB and RB–APTES solutions, to obtain RB–AuNPs and RB–AuNPs-silica respectively.

A sample of high concentration of α-glucose was prepared by dissolving 50 wt% of α-glucose in water. Additionally, α-glucose–MBGNs solutions were prepared by dissolving solid α-glucose in 100 μl of distilled water and mixed with 900 μl of colloidal MBGNs obtaining final concentrations of 5 mM, 10 mM and 20 mM (90, 180 and 360 mg dL⁻¹) respectively. The α-glucose–MBGNs-silica was prepared by dissolving solid α-glucose in 100 μl of distilled water and adding 10 μl of 10 vol% APTES solution and mixed with 900 μl of colloidal MBGNs-silica (5 mM, 10 mM and 20 mM respectively). And α-glucose–AuNPs-silica was prepared adding 900 μl of colloidal AuNPs (with and without APTES) to a 100 μl of α-glucose and α-glucose–APTES solutions obtaining a 20 mM concentration, respectively.

2.4 Morphology and optical characterization

The morphology and size of the AuNPs and MGBNs-silica were analyzed by transmission electron microscopy (TEM) using a FEI Titan 800-300 with accelerating voltage set to 300 kV. UV-Vis absorption measurements were carried out using a Perkin Elmer Lamda 900 spectrometer with a spectral resolution of 2 nm. Raman spectra were collected using a Renishaw Raman System (inVia Raman Microscope) with a 20× objective lens and the excitation laser was operated at 785 nm. The integration time for each Raman measurement was 20 s. For the Raman signal detection, the laser excitation light was directly focused onto the surface of the sample solution (250 μl) with a laser power of ∼5 mW and the aforementioned integration time.

2.5 Zeta potential

Zeta potential measurements were carried out using a Malvern Instrument Zetasizer Nano (red laser 633 nm) to the following samples, α-glucose in water (100 mM), α-glucose–APTES (100 mM), colloidal MBGNSs and colloidal MBGNs-silica. The samples were dispersed in distilled water (1 mM, pH = 7.4) with a concentration of 1 mg ml⁻¹.

3. Results

The size and morphological characterizations were performed by TEM images, and are shown in Fig. 1(a)–(d), where it was confirmed that AuNPs were obtained with an overall outer diameter of ∼20 nm, see Fig. 1(a) and (b). These AuNPs were mixed with silver nitrate at a 2 : 1 volume ratio and used as seeds to synthesize the MBGNs with an average size of 200 nm, as is shown in Fig. 1(c) and (d). It is observed that the MBGNs are surrounded by a cloud of silica that promotes the agglomeration, having MBGNs-silica clusters of ∼1000 nm, see Fig. 1(d).

Fig. 1 TEM image of AuNPs (a and b) with average size of ∼20 nm, which were used to prepare MBGNs (c) with average diameter 200 nm. Aggregates of MBGNs-silica (d) resulting in an average size of 1000 nm.

Fig. 2 shows the UV-Vis spectrum of (a) AuNP, (b) MBGNs and (c) MBGNs-silica, dispersed in aqueous solution. For AuNP, the SPR is centered at 522 nm, as is shown in Fig. 2(a) and for the MBGNs and MBGNs-silica the SPR was red-shifted to 850 nm, see Fig. 2(b) and (c). The absorption spectrum of the MBGNs-silica sample shows a wide band, which is consistent with the agglomeration of the particles, due to the interplay between the silica coating and the underlying MBGNs resonances, about which more will be discussed later. This is consistent with the dimensional results gleaned from the TEM images of Fig. 1(a)–(d). These large spectral shifts from the nanospheres are manifested as color changes in the colloidal solutions of the nanoparticles, as is shown in Fig. 3. In the image, AuNP are represented by the red solution and the MBGNs-silica solution is the greenish black solution, as seen in Fig. 3(a) and (b), respectively. The color changes are not significant between MBGNs and MBGNs-silica solutions.

Fig. 2 UV-Vis absorption spectra of (a) AuNPs, (b) MBGNs and (c) MBGNs-silica, dispersed in aqueous solution.

Fig. 3 Color comparison of the solutions. (a) AuNPs (red solution), used as a seed for the synthesis of (b) MBGNs-silica (green-black).

SERS activity of the AuNPs, MBGNs and MBGNs-silica was examined using RB as Raman marker. The comparative SERS spectra of varying the concentration of RB, and the effect of APTES addition in AuNP and MBGNs is displayed in Fig. 4. The characteristic Raman signals obtained from a RB high concentrated solution (0.01 M) that yields peaks at 620, 1195, 1275, 1358, 1431, 1506, 1527, 1591 and 1647 cm⁻¹ is displayed in Fig. 4(A) and is in agreement with results reported recently.⁶⁹ In Fig. 4(B) are shown the resulting Raman spectra of using AuNPs with and without silica in RB detection and compared with the AuNP 0 M spectra, where the characteristic peaks were not observed even at relative high concentration (1 × 10⁻⁷ M), however for AuNPs-silica an small peak at 850 cm⁻¹ is observed. The Raman signal of three concentrations of RB (10⁻¹⁰, 10⁻⁸ and 10⁻⁷ M) adsorbed on MBGNs and MBGNs-silica are displayed in Fig. 4(C)(b–d) and (e–g), respectively. The Raman signal of RB adsorbed on MBGNs show peaks at 628, 1284, 1364, 1516, 1534 and 1655 cm⁻¹, see Fig. 4(C)(b–d) and RB adsorbed on MBGNs-silica at 620, 1196, 1268, 1349, 1452 and 1503 cm⁻¹, see Fig. 4(C)(e–g). It is observed that band positions present small changes with the presence of the silica coating about which more will be discussed later. Fig. 4(D) shows the increase of the Raman signal by using MBGNs and MBGNs-silica with different concentration of RB (10⁻¹⁰, 10⁻⁸ and 10⁻⁷ M), each data point represents the average value from three SERS spectra and error bars show the standard deviations. There is a linear relationship between the intensity of the 628, 1284 and 1516 cm⁻¹ bands and RB concentration. The silica coating enhanced the Raman signal by ∼3 times in comparison when using samples without silica coating.

Fig. 4 (A) Raman spectra obtained from a concentrated solution (0.01 M) of rhodamine B in distilled water. (B) Raman signal of AuNP 0 M and AuNP and AuNP-silica with RB solution at 1 × 10⁻⁷ M. (C) Representative SERS spectra following 785 nm excitation obtained from: (a) target MBGNs-silica, and RB at different concentration, 0.5 × 10⁻¹⁰, ×10⁻⁸ and ×10⁻⁷ M on MBGNs (b), (c) and (d), and (e–g) for silica coated MBGNs. (D) A plot of the integrated Raman signal for three peaks, (628, 1284 and 1516 cm⁻¹) vs. the RB concentration as RB on MBGNs (RB–MBGNs) and RB–APTES solution on MBGNs-silica (RB–MBGNs-silica). Each point represents the average value from three SERS spectra and error bars show the standard deviations.

Fig. 5 shows SERS spectra of the α-glucose using colloidal AuNPs, AuNPs-silica, MBGNs and MBGNs-silica as the SERS substrates. In Fig. 5(A) is presented the spectrum of a high concentration α-glucose in water (50 wt%) and several vibrational peaks were seen at 512, 845, 912, 1033, 1114 and 1333 cm⁻¹. These are the typical bands of α-glucose–water.⁷¹ Fig. 5(B) shows the Raman results of testing the detection of α-glucose (20 mM) using AuNPs and AuNPs-silica and compared with the AuNP 0 M spectra, where the characteristic Raman signal is not observed, however for AuNPs-silica only an small peak at 850 cm⁻¹ is observed. Fig. 5(C) displays several SERS spectra taken at varying α-glucose concentrations (0, 5, 10 and 20 mM), showing the potential limit of detection using the MBGNs and MBGNs-silica, see Fig. 5(C)(a–d) and (e–g) respectively, where Fig. 5(C)(a) corresponds to 0 mM. It is interesting the small yet consistent and reproducible blue-shift of the 512 and 1114 cm⁻¹ vibrational bands observed in samples of α-glucose adsorbed in MBGNs-silica. The reason for this small shift will be discussed later. As expected, they increase in intensity with increasing the α-glucose concentration. This relationship is shown clearly in Fig. 5(D), which shows a plot of the integrated signal of the 512, 1033 and 1114 cm⁻¹ vibrational bands. A linear relationship between the intensity of the signal and α-glucose concentration was observed. Each data point represents the average value from three SERS spectra and error bars show the standard deviations. It is observed a ∼2 times increment of the Raman signal for samples using MBGNs with silica coating compared to uncoated nanoparticles.

Fig. 5 (A) Raman spectra obtained from a concentrated solution of α-glucose in distilled water (50 wt%). (B) Raman signal of AuNP 0 M and AuNPs and AuNP-silica with α-glucose in water at 20 mM, (C) Representative SERS spectra following 785 nm excitation obtained from: (a) target MBGNs-silica, and α-glucose at different concentrations (5, 10 and 20 mM) on MBGNs (b–d), and (e–g) for silica coated MBGNs. In this last case, α-glucose was functionalized with APTES. (D) A plot of the integrated Raman signal (512, 1033 and 1114 cm⁻¹) vs. α-glucose concentration as α-glucose on MBGNs (α-glucose–MBGNs) and α-glucose–APTES solution on MBGNs-silica (α-glucose–MBGNs-silica). Each point represents the average value from three SERS spectra and error bars show the standard deviations.

Fig. 6 shows a schematic diagram for the preparation of α-glucose bonded to silica coated MBGNs. It is schematized the preparation of MBGNs-silica bonded to α-glucose–APTES. First AuNPs were prepared to be used as a seed to obtain the MBGNs with a positive surface charge of +34 mV, the process is explained in the experimental section. After that, APTES was added to the colloidal MBGNs binding through amines groups, obtaining MBGNs-silica (+25 mV), having the OH– groups exposed. On the other hand, α-glucose–APTES was prepared as mentioned in the experimental section. The α-glucose surface charge is −18 mV, increasing negatively for α-glucose-APTES to −46 mV, suggesting OH– in the surface. Therefore we are proposing the binding of α-glucose–APTES to the MBGNs-silica through electrostatic forces and hydroxyl groups bonds.

Fig. 6 Schematic diagram showing hypothetical addition of α-glucose to MBGNs. APTES solution was added to the MBGNs, and α-glucose–APTES solution was prepared and subsequently both solutions were mixed. This facilitated the α-glucose incorporation through the bonds of hydroxyl groups and electrostatic forces.

4. Discussion

To interpret the Raman signal enhancement properties of MBGNs, it is necessary to analyze the structural changes and optical properties of these nanostructures. The Fig. 1(a) and (b) shows the spherical gold nanoparticles (AuNPs) with an overall size of ∼20 nm prepared by following the Turkevich method.⁷² Such particles were used as seeds that in combination with HCl, silver nitrate, and ascorbic acid promote the anisotropic growth of Au branches on certain crystallographic facets on multi-twinned citrate gold seeds resulting on a multibranched nanoparticle (MBGNs) obtained in the absence of surfactant, see Fig. 1(c). As has been reported the presence of Ag⁺ ions, and the Cl⁻ produced during the Au reduction of HAuCl₄, precipitate with the Ag⁺ ions forming AgCl on the surface of the growing AuNPs. The growth process and the morphology of the final Au product are affected inevitably, so that the AuNPs could not isotropically expand to large gold spheres but form the MBGNs.⁷³ Fig. 1(d) shows the MBGNs aggregation enveloped by a gray coating of SiO₂, induced by small amount of the addition of APTES.

The SPR of AuNPs situated in 522 nm, was consistent with the particle size and the color of AuNPs solution obtained with the Turkevich method. In the case of MBGNs and MBGNs-silica the SPR is shifted to 850 nm, which is indicative of the MBGNs nature.⁶⁶ It is in accordance with the results reported in the literature where the red-shift was produced from the deviations from spherical geometry.⁷⁴ This is related with the interaction of electric field of the incoming radiation and the nanoparticle, in which it induces the formation of a dipole in the nanoparticle, and there is a restoring force that tries to compensate it, so that a unique resonance frequency matches this electron oscillation within the nanoparticle. For non-spherical particles, such as rods or branches, the resonance wavelength depends on the orientation of the electric field relative to the particle.⁷⁵ The optical properties of non-spherical particles are highly affected due to size variations of anisotropic shapes and this is because the quite differences in frequencies associated with the various resonance modes. These resonances has been modeled via Mie for small spheres⁷⁶ and their modification by Gans for ellipsoids.⁷⁷ As seen in Fig. 2(b) and (c), it is observed that the silica coating expand the width in the green region of SPR peak.^78,59 This is most likely due to the polydispersity of ∼22% of MBGNs-silica agglomerates and is consistent with the results collected from the TEM images in Fig. 1 and with the color changes in Fig. 3.

As can be seen in Fig. 4(B) it was difficult to obtain the characteristic Raman signal of RB adsorbed on AuNPs and AuNP-silica, suggesting that the enhancement factor is weak as has been reported for a spherical nanoparticle, but an small peak is observed at 850 cm⁻¹ for AuNP-silica, corresponding to Si–OH stretching and bending as has been reported.⁷⁹ However, when MBGNs and MBGNs-silica were used, the characteristic Raman signal of RB was clearly observed, see Fig. 4(C). Such enhancement of the Raman signal is the result of the electromagnetic field improvement probably produced by the anisotropic structures increasing the density and number of hot spots, especially at the tip of the branches as a result of the nano antenna effect.⁸⁰ The three times improvement of the Raman signal observed with the introduction of silica coating is probably due to the proximity of the MBGNs, inducing the formation of hot spots with greatly enhanced localized electromagnetic field.^81,82 It has been reported that the carboxylic group of RB is conjugated with APTES through a condensation reaction, and yields a silanized RB which was covalently incorporated into silanol groups present on surface modified of gold nanostructures by Si–O–Si bonds.⁸³ The assembly of MBGNs with silica coating as a reproducible method has been widely applied to SERS studies. Furthermore, improvement of the Raman signal by silica coating is also result of the augmentation of the bandwidth by which resonance with pumping signal is stronger. It is observed that band positions are blue-shifted with the presence of the silica coating in addition to the concomitant increases in the Raman signal. The increment of individual Raman signal peaks (628, 1284 and 1516 cm⁻¹) as a function of RB concentration follow a linear relationship with slopes of 0.5, 2 and 3 for MBGNs respectively and 1, 3 and 5 for MBGNs-silica, respectively, see Fig. 4(D). This mean, MBGNs-silica coated is almost twice sensitive than uncoated nanoparticles making possible the detection of analyte concentration as lower as 10⁻¹⁰ M of RB, not detected with uncoated nanoparticles. Such result shows the relevance of the coating of MBGNs improving the interaction with analyte and increasing the hot spot.

As in the case of RB, the detection of α-glucose in water (20 mM) by using colloidal AuNPs and AuNP-silica was unsuccessful as displayed in Fig. 5(B) and the 850 cm⁻¹ peak is due to the presence of Si–OH stretching and bending.⁷⁹ However, α-glucose was measured with colloidal MBGNs for concentration as lower as 5 mM. As expected according to results described before on RB, the Raman signal was improved when silica coated MBGNs were used, see Fig. 5(C). The agglomeration induced by APTES served to increase SERS signals due to the higher density of SERS hot spots.⁸⁴ As shown in Fig. 5(D), there is a linear relationship between the individual Raman signal peaks (512, 1033 and 1114 cm⁻¹) and α-glucose concentration. The slopes of the linear relationship for uncoated MBGNs is 6, 10 and 10 respectively, and 8, 30 and 20 respectively for silica coated MBGNs. Notice the small blue-shift due to the bonding between the hydroxyl groups of α-glucose–APTES and the free hydroxyl groups of MBGNs-silica particles⁸⁵ and through unions by electrostatic forces, as is proposed in Fig. 6. Therefore, the functionalization proposed here is an effective way to improve interaction between MBGNs and glucose molecule, resulting on an strong enhancement of the Raman signal. These results confirm that colloidal MBGNs is an effective tool for measuring clinical concentration of α-glucose, and it is three time more sensitive when such particles are silica coated. The methodology proposed here for α-glucose measurement is simple, very reliable and cheaper because does not require special instrumentation other than Raman spectrometer.

5. Conclusions

In this work, we have demonstrated that MBGNs are highly SERS-active for quantitative RB and α-glucose detection in low concentrations in aqueous media. We compared the Raman enhancement when using MBGNs and MBGNs-silica and the signal is increased by ∼250% and ∼350%, respectively. Such enhancement is attributed to the increase of hot spot because of the morphology and the strong interaction between the analyte (RB and α-glucose) and MBGNs-silica. The functionalization process performed to both glucose molecules and MBGNs improved such interaction and make possible the measurement of concentration as lower as 5 mM (90 mg dL⁻¹). This is very important since α-glucose has been notoriously difficult to detect by SERS due to its small Raman cross-section and weak interaction with bare metal surfaces. We believe this crucial adsorption problem has been overcome due to the chemical interplay between hydroxyls on the silica surface and the α-glucose structure which facilitates the chemical adsorption. The measurement of α-glucose in water is important because mimics well the chemical environment of the human body. Future endeavors will center on the detection of glucose in body fluids like blood, urine or tears.

Acknowledgements

We acknowledge to CONACyT for financial support through Grant 134111, project CIO-UGTO 2013 and 2014, and scholarship for Andrea Ceja-Fdez. JZZ is grateful to the US NSF for financial support. Thank Dr Juan Vivero Escoto for Zeta Potential analysis.

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