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ionization mass spectrometry
Analytica Chimica Acta 687 (2011) 97–104
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Fabrication of nanostructured silicon by metal-assisted etching and its effects on matrix-free laser desorption/ionization mass spectrometry W.Y. Chen a , J.T. Huang b , Y.C. Cheng c,d , C.C. Chien d,e,f , C.W. Tsao b,∗ a
Department of Chemical & Materials Engineering, National Central University, Jhongli, Taiwan Department of Mechanical Engineering, National Central University, Jhongli, Taiwan Department of Medical Research, Cathay General Hospital, Taipei, Taiwan d Institute of Biomedical Engineering, National Central University, Jhongli, Taiwan e School of Medicine, Fu Jen Catholic University, Taipei, Taiwan f Department of Anesthesiology, Sijhih Cathay General Hospital, Sijhih City, Taipei, Taiwan b c
a r t i c l e
i n f o
Article history: Received 14 September 2010 Received in revised form 8 November 2010 Accepted 18 November 2010 Available online 26 November 2010 Keywords: Mass spectrometry Nanostructured silicon Metal-assisted etching Matrix-free laser desorption/ionization Desorption/ionization on silicon
a b s t r a c t A matrix-free, high sensitivity, nanostructured silicon surface assisted laser desorption/ionization mass spectrometry (LDI-MS) method fabricated by metal-assisted etching was investigated. Effects of key process parameters, such as etching time, substrate resistance and etchant composition, on the nanostructured silicon formation and its LDI-MS efficiency were studied. The results show that the nanostructured silicon pore depth and size increase with etching time, while MS ion intensity increases with etching time to 300 s then decreases until 600 s for both low resistance (0.001–0.02 cm) and high resistance (1–100 cm) silicon substrates. The nanostructured silicon surface morphologies were found to directly affect the LDI-MS signal ion intensity. By characterizing the nanostructured silicon surface roughness using atomic force microscopy (AFM) and sample absorption efficiency using fluorescence microscopy, it was further demonstrated that the nanostructured silicon surface roughness was highly correlated to the LDI-MS performance. © 2010 Elsevier B.V. All rights reserved.
1. Introduction MS is one of the most used analytical techniques in biological analysis. Soft ionization methods, such as electrospray ionization (ESI) and matrix-assisted laser desorption/ionization (MALDI), have been routinely used to analyze a range of biomolecules, including proteins, peptides and DNA. Nonetheless, ESI and MALDI still suffer from disadvantages, such as excessive matrix backgrounds in the low molecular weight range, multiple charge peaks and detection sensitivity limitations. To overcome these limitations, Siuzdak and co-workers [1] proposed a porous silicon-assisted soft ionization method called desorption/ionization on silicon (DIOS). In the DIOS method, electrochemical etching is used to produce high surface area/volume ratio porous silicon surfaces that can be used in
Abbreviations: LDI, laser desorption/ionization; MS, mass spectrometry; ESI, electrospray ionization; MALDI, matrix-assisted laser desorption/ionization; DIOS, desorption/ionization on silicon; D/I, desorption/ionization; FE-SEM, field emission scanning electron microscope; AFM, atomic force microscope. ∗ Corresponding author at: Department of Mechanical Engineering, National Central University, No. 300, Jhongda Rd., Jhongli, Taiwan. Tel.: +886 3 4267343; fax: +886 3 4254501. E-mail address: [email protected] (C.W. Tsao). 0003-2670/$ – see front matter © 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.aca.2010.11.041
place of typical organic matrixes to absorb UV laser energy for LDI-MS analyses. This process eliminates the MALDI-MS matrix background noise at low molecular weights so that small molecules can be analyzed more effectively and accurately. In addition, this matrix-free, high sensitivity approach exhibits a high tolerance to salt and contaminants. Detection levels in the picomole or attomole range are readily achieved [1,2]. Due to these advantages, DIOS-MS has been used to analyze various low molecular weight molecules in polymer analysis [3], protein characterization [4–6] and forensics applications [4]. The porous silicon surface’s ability to ionize biomolecules has been shown to be due to its high surface area/volume ratio silicon morphology [1,7], indicating that creating high surface nanostructures on a substrate may be essential to initiate the surface-assisted LDI-MS process. Various micro/nano-fabrication methods have been reported to generate nanostructured surfaces and several of them have successfully demonstrated for LDI-MS applications. Some experiment demonstrations have utilized porous silicon substrates for DIOS-MS fabricated by electrochemical etching performed in a custom-made Teflon cell. Silicon nanowires are normally generated by the vapor–liquid–solid method (VLS) in a high vacuum physical vapor chamber [8,9]. Dry etching processes, such as reactive ion etching [10] and e-beam lithography [11], can be
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various pore sizes, depths and geometries, and the resulting relationship with LDI-MS efficiencies. A better understanding of the metal-assisted etching process and the role of nanostructured silicon surface morphology in LDI-MS performance will improve the application of these techniques in future research. 2. Experimental 2.1. Materials
Fig. 1. FE-SEM image of 3 nm Au deposited on P-type (1 0 0) 0.001–0.02 cm silicon substrate.
used to create ordered nanocavity arrays on silicon substrates for LDI-MS applications. Other methods, such as porous silicon dioxide fabricated by electrochemical etching and high temperature wet oxidation [12] and nanofilament silicon fabricated by metalassisted etching [13], have also generated effective surface-assisted LDI-MS target substrates. Porous aluminum [14,15], aluminum foil [16], carbon nanotubes [17,18], zinc oxide nanowires [19,20] and germanium nanodots [3,21] have also been used to produce effective surface assisted LDI-MS target substrates. In these surface-assisted LDI-MS methods, the nanostructured surface morphologies were found to correlate with the relative desorption/ionization (D/I) efficiencies, such that higher nanostructure surface area/volume ratios enhance D/I efficiencies as well as detection sensitivities [1,7]. In addition to surface area/volume ratio correlation, nanostructure pore size and depth effects on D/I efficiencies have been reported. In initial reports, smaller pore sizes (2 nm) generated more intense ion signals than bigger pore sizes (2–50 nm) [1]. However, more recent research indicated that smaller pore sizes did not promote stronger MS ion signal intensities. In this research, a silicon surface with an optimized 70–120 nm pore diameter and 200 nm pore depth produced the strongest MS intensity reported [4]. Bohn and Sweedler have reported that a pore size larger than 10 nm produced strong DIOS-MS ion signal intensity, while a 3 nm pore size was too small for efficient D/I detection [22]. Similar pore size effects were reported by Nayak and Knapp, who demonstrated an increase in ion signal intensity with aluminum pore depth up to 600 nm and a decrease in signal intensity with increasing film depth [14]. A more recent publication also reports that sub-micron pitted surfaces are required for surface assisted LDI-MS but that smaller pore size or higher depth is not essential for efficient D/I processes [23]. Thus, further research is required to clarify the relationship between nanostructured surface morphology and D/I efficiency. The nanostructured silicon surfaces presented in this research paper were produced via a metal-assisted etching process [24] and were applied to surface-assisted LDI-MS as first demonstrated by Tsao et al. in 2008 [13]. In this previous study, metal-assisted etching was proved to be a fast and simple method to create nanostructured silicon surfaces exhibiting 2–4 times higher detection sensitivities than DIOS-MS. However, detailed information regarding the effects of nanostructured silicon surface morphology on LDI-MS efficiency was not reported. In this study, we systematically investigated the metal-assisted etching process parameters, their effects on producing nanostructured silicon morphologies of
A gold (Au, 99.999% purity) slug and a P-type (1 0 0) 10 cm silicon wafer with 0.001–0.02 cm and 1–100 cm resistivity were purchased from Summit-Tech Resource Corp. (Taipei, Taiwan). Hydrofluoric acid (HF, 49%), hydrogen peroxide (H2 O2 , 31%) and nitric acid (NHO3 , 70%) were purchased from BASF Corp. (Ludwigshafen, Germany). Ethanol (EtOH, electronic grade), water (HPLC grade), methanol (MeOH, 99.9%, electronic grade), carbonate buffer (0.1 M, pH 9.2) and phosphate buffer (PB buffer, 0.1 M, pH 7.0) were purchased from J.T. Baker (NJ, USA). Des-Arg9-Bradykinin peptide sample and fluorescein isothiocyanate (FITC) were purchased from Sigma–Aldrich (MO, USA). 2.2. Nanostructured silicon substrate fabrication The nanostructured silicon surfaces were fabricated utilizing a metal-assisted etching process composed of some simple steps. First, a thin Au layer was deposited onto a bare P-type silicon substrate by an e-beam evaporator ULVAC EVA-E500 (ULVAC, Taiwan Inc.). The metal-coated silicon wafer was then immersed in an HF/H2 O2 /EtOH mixture, in which etching proceeded for 30–600 s at 25 ◦ C to create nanostructured silicon on the wafer. After etching, the wafer was fully rinsed with a methanol solution, and the wafer surface was blown dry with nitrogen. Because glass containers are not compatible with HF solutions, Teflon containers were used for all HF solutions. Similar to electrochemical etching to create porous silicon, metal-assisted etching acts as a localized electrochemical etching process in which local electrodeless etching occurs at the metal/silicon interface, each nanometer-sized metal particle acts as a local cathode and the silicon surface acts as an anode. The metal particles are critical in the process to promote H2 O2 decomposition and cause electron–hole injection into the silicon surface; silicon is dissolved by HF to create pits or nanostructures on the surface. Fig. 1 shows FE-SEM images of 3 nm thick Au particles deposited on the silicon substrate. The metal film morphology exhibited isolated circular shape particles measured around 10–30 nm in diameter with 5–10 nm gap uniformly distributed across the whole wafer substrate. Because HF is a hazardous acid, which can cause severe eye, skin, and respiratory injuries, etching with HF should only be performed in a well-ventilated fume hood with appropriate safety considerations, and calcium gluconate ointment should be readily available. Care must be taken to avoid contact with all HF solutions throughout the etching process. If contact with HF does occur, contaminated clothing should be immediately removed, and the affected area should be flushed vigorously with cold water and massaged with calcium gluconate to neutralize the HF acid. A physician should be immediately consulted after any HF exposure. 2.3. Sample preparation The single model peptide sample, des-Arg9 Bradykinin (MW = 904), was prepared by reconstituting 5 mg lyophilized powder in 2.7654 mL HPLC-grade water into 2 × 10−3 M followed by subsequent serial dilution to 10−7 M, 10−8 M and 10−9 M with HPLC-grade water. FITC was used to label the peptide sample for
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fluorescence microscopy. First, 3.89 mg FITC was added to 10 mL carbonate buffer (0.1 M, pH 9.2) and diluted 10-fold with carbonate buffer to a concentration of 10−4 M. Then FITC-labeled des-Arg9 Bradykinin was prepared by mixing 50 L 2 × 10−4 M des-Arg9 Bradykinin with 50 L 10−4 M FITC solution. The labeling reaction was processed in dark for overnight. 2.4. Surface morphology characterization Fig. 3. LDI-MS spectra for 10−7 M des-Arg9 Bradykinin.
The nanostructured silicon surface was characterized by field emission scanning electron microscopy, FE-SEM (JSM-6500F, JEOL Co., Japan.). Top and cross-section images of the nanostructured surface were taken to characterize the nanostructured silicon pore size, pore depth and geometry. The FE-SEM was operated with an accelerating voltage of 15 kV in secondary electron mode and a working pressure of 4 × 10−6 Torr. 2.5. Mass spectrometry analysis The nanostructured silicon surface-assisted LDI-MS spectra were obtained from a SELDI mass spectrometer (Proteinchip SELDI System, Bio-rad laboratories, USA). The nanostructured silicon substrate was fixed to the target holder and then loaded to the mass spectrometer for MS analysis. All LDI-MS spectra were recorded in the linear, positive ion mode and averaged over 50 laser pulses
using a 337 nm N2 laser. An optimized laser energy of 600 nJ was set for the maximum signal-to-noise ratio (S/N). It is noted that exposing the nanostructured silicon surface to ambient air over longer periods will lead to surface oxidation which reduces the surface hydrophobicity and MS detection sensitivity. To ensure best detection sensitivity and stability, our nanostructured silicon surface-assisted LDI-MS tests were performed within a day after the fabrication of nanostructured silicon surface. 2.6. Contact angle measurement The water contact angle on the nanostructured surface was measured by a custom-made optical goniometer composed of a high resolution digital camera (Canon EOS 450D/TAMRON Macro 90 mm
Fig. 2. Top (a–c, g–i) and cross-section (d–f, j–l) FE-SEM images of nanostructured silicon surfaces fabricated from 3 nm Au, P-type (1 0 0), 0.001–0.02 cm silicon wafer etching in HF/H2 O2 /EtOH (1:1:1, v/v/v) for 30 s (a, d), 60 s (b, e), 180 s (c, f), 300 s (g, j), 360 s (h, k) and 600 s (i, l).
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F2.8 lens), a z-axis precision stage and a light source. A 10 L droplet was deposited on the nanostructured surface, and the droplet side view was imaged by the digital camera. The droplet image was then processed using an AutoCAD system to determine the water contact angle. 3. Results and discussion The metal-assisted etching process exhibits good process controllability to generate various nanostructured silicon surface morphologies. In this study, etching time, substrate resistivity and etchant composition were investigated in relation to the nanostructured silicon formation and the resulting effects on LDI-MS efficiency. 3.1. Etch time and substrate resistance effects on nanostructured silicon formation and LDI-MS performance A P-type (1 0 0) silicon wafer with a resistivity of 0.001–0.02 cm was used as the substrate material. A 3-nm thick Au catalyst layer was uniformly deposited on the wafer, which was then dipped in a 1:1:1 volume ratio HF/H2 O2 /EtOH solution to create the nanostructured silicon surface. Six different etching times (30, 60, 180, 300, 360 and 600 s) were selected to create different nanostructured silicon surfaces on which to study the etching time effects. Fig. 2 shows top and cross-section SEM images of the resulting nanostructured silicon surfaces fabricated with various etching times. As shown in Fig. 2, porous-like nanostructure morphologies are observed. For both 30- (Fig. 2a and d) and 60-s (Fig. 2b and e) etching times, the top and cross-section images indicate a high surface flatness with pore sizes measuring about 20–50 nm and with a smaller number of larger pores ranging from 100 to 200 nm near the top of the nanostructured silicon. When etching time was increased to 180 s (Fig. 2c and f), the silicon nanostructures near the top of the surface started to break into ragged silicon surfaces. As shown in Fig. 2(g–i and j–l), longer etching time conditions (300–600 s) resulted in highly ragged nanostructured silicon surfaces of much larger size. The nanostructured surface depths measured around 1.1 ± 0.06, 1.6 ± 0.12, 4 ± 0.06, 5.2 ± 0.26, 6.5 ± 0.21 and 8 ± 0.51 m, corresponding to 60-, 180-, 300-, 360- and 600-s etching times, respectively. In metal-assisted etching processes, the depth and size of the nanostructured silicon are highly correlated to the etching time, generally tending to increase with etching time. The effects on surface-assisted LDI-MS efficiency were characterized by measuring the ion intensity exhibited by the single model peptide. Aqueous des-Arg9 Bradykinin (10 L, MW = 904) was deposited on the nanostructured silicon surface, remaining for 5 min to allow sufficient sample absorption of the surface. Then the droplet was pipetted off of the nanostructured silicon surface and loaded into the mass spectrometer for LDI-MS analysis. Fig. 3 shows the typical MS spectra of 10−7 M des-Arg9 Bradykinin averaged from 50 laser pulses using metal-assisted etching nanostructured silicon as the LDI-MS target substrate. A high signal ion intensity (3667 A) was measured at the 905 mass/charge (m/z) value for the low concentration of 10−7 M. Little background noise was detected at low m/z values. A sharp unknown peak at 72 m/z and some noise peaks in the 0–250 m/z range were observed, possibly from contaminates generated during wet etching or within the sample. The metal-assisted etching nanostructured silicon surfaces exhibited high surface hydrophobicity. The water contact angle measured around 135 ± 2.1◦ , 136 ± 0.6◦ , 146 ± 2.5◦ , 125 ± 0.6◦ , 154 ± 0.2◦ and 156 ± 6.0◦ for 60-, 180-, 300-, 360- and 600-s etching times, respectively. These measurements were averaged from three different measurements for each individual condition. It is evident that the
Fig. 4. Ion intensities (a) and S/N ratios (b) of 10−7 M, 10−8 M and 10−9 M des-Arg9 Bradykinin obtained from nanostructured silicon surfaces produced with etching times from 30 to 600 s. Error bars represent the standard deviation obtained from three different measurements.
highly hydrophobic surface efficiently constrains the droplet from spreading on the nanostructured surface, resulting in a higher local sample concentration and, therefore, improved detection sensitivity for LDI-MS analysis. Fig. 4 summarizes the ion intensity and S/N ratio results for the des-Arg9 Bradykinin sample obtained from 30-, 60-, 180-, 300-, 360- and 600-s etching time conditions. Series des-Arg9 Bradykinin concentrations of 10−7 M, 10−8 M and 10−9 M were used to evaluate the detection limit of the nanostructured silicon surfaces. As shown in Fig. 4, high ion intensities of 1287 ± 326, 2689 ± 311, 3012 ± 149, 3665 ± 95, 2008 ± 115 and 925 ± 131 A with S/N ratios of 884 ± 181, 993 ± 273, 786 ± 95, 1182 ± 345, 1058 ± 64 and 762 ± 58, respectively, were measured from 30- to 600-s etching conditions using a 10−7 M peptide sample. When the sample was diluted 10-fold to 10−8 M, the ion intensities decreased to 109 ± 26, 249 ± 71, 639 ± 194, 1836 ± 229, 221 ± 53 and 104 ± 9 A with S/N ratios of 112 ± 19, 152 ± 34, 213 ± 28, 593 ± 98, 165 ± 57 and 126 ± 23, respectively, for 30- to 600-s etching conditions. In the 10−9 M case, no ion signal was detected for the 30-, 60-, 360and 600-s etching time conditions. Weak ion signal intensities of 86 ± 39 and 404 ± 208 A with low S/N ratios of 4.0 ± 0.05 and 5.1 ± 0.26 were detected for the 300- and 360-s conditions. From the sample dilution test, a detection limit of 10−9 M was found under optimized conditions for the single model peptide sample. Good ion signals can be readily detected under 10−7 M and 10−8 M concentrations. Therefore, to ensure good ion intensity quality, 10−7 M des-Arg9 Bradykinin was used in the following LDI-MS tests.
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This detection limit is equivalent or better than other nanostructured surface-assisted LDI-MS methods unless the nanostructured silicon surfaces are chemically modified [2]. The depth variation of the nanostructured silicon surfaces was found to be dependent on the etching time, such that higher etching times resulted in a deeper nanostructured silicon surfaces. Fig. 4a investigates the effect of pore depth on LDI-MS efficiency. The ion intensity increases with the pore depth (dashed line) until 300 s then decreases as etching time approaches 600 s. An optimized pore depth of 4 m (300 s) exhibited the highest signal intensity for all 10−7 M, 10−8 M and 10−9 M conditions. Likewise, the S/N ratios were optimized at 300 s (Fig. 4b). Similar observations were found for the effects on pore size. As shown in Fig. 2, the pore size increased from 20 to 50 nm to 100 to 200 nm as the etching time was increased. In addition to the low resistance (0.001–0.02 cm) silicon substrate, high resistance (1–100 cm) silicon substrates obtained with various etching times were investigated. Etching times were increased from 30 to 600 s, and different silicon surface morphologies were obtained to evaluate nanostructured surface LDI-MS efficiency. Top and cross-section FE-SEM images of the nanostructured silicon surface are shown in Fig. 5. Deep pore depths of 0.6 ± 0.1, 2.0 ± 0.15, 2.5 ± 0.26, 4.5 ± 0.44, 7.9 ± 0.40 and 16.8 ± 1.0 m were created with 30-, 60-, 90-, 120-, 300- and 600s etching conditions, respectively. From the FE-SEM images, it
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appears that porous or filament like silicon nanostructures with flat surfaces are created with 30- to 120-s etching conditions (Fig. 5a–c, g, d–f, and j). When the etching time was increased to 300 s (Fig. 5h and k) and 600 s (Fig. 5i and j), rugged silicon surfaces were created. The nanostructured silicon surfaces fabricated from high resistance silicon substrates also exhibited high surface hydrophobicity with water contact angles of 142 ± 2.6, 142 ± 1.5, 146 ± 4.4, 148 ± 7.6, 149 ± 8.6 and 145 ± 4.6 for 30-, 60-, 90-, 120-, 300- and 600-s etching times, respectively. An amount of 10−7 M des-Arg9 Bradykinin was used to evaluate the LDI-MS performance of the high resistance silicon substrate with etching times of 30–600 s. The LDI-MS test procedures were the same as those previously described. The ion intensity measurement results are shown in Fig. 6. Ion intensities of 1038 ± 253, 1454 ± 54, 1897 ± 314, 2610 ± 147, 2953 ± 142 and 2376 ± 72 A with S/N ratios of 440 ± 45, 942 ± 341, 705 ± 173, 875 ± 20, 701 ± 270 and 777 ± 63 were detected for 30-, 60-, 90, 120-, 300- and 600-s etching conditions, respectively. Compared with the low substrate resistance nanostructure surfaces, no significant variation in ion intensity was observed, suggesting that the nanostructured silicon surface resistivity is not highly correlated to LDI-MS performance. Pore size and pore depth studies also showed an increase in ion intensity with etching time up to 300 s, after which a decrease was observed as the etching time increased to 600 s. As with low resistance nanostructured silicon (Fig. 4), 300 s of
Fig. 5. Top (a–c, g–i) and cross-section (d–f, j–l) FE-SEM images of nanostructured silicon surfaces fabricated from 3 nm Au, P-type (1 0 0), 1–100 cm silicon wafer etching in HF/H2 O2 /EtOH (1:1:1, v/v/v) for 30 s (a, d), 60 s (b, e), 90 s (c, f), 120 s (g, j), 300 s (h, k) and 600 s (i, l).
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Fig. 6. Comparison of ion intensity and pore depth with different metal-assisted etching time conditions. The nanostructured silicon substrates were fabricated from 3 nm Au, P-type (1 0 0), 1–100 cm silicon wafer etched in HF/H2 O2 /EtOH (1:1:1, v/v/v). Error bars represent the standard deviation obtained from three different measurements.
etching provided the optimized value. The pore size also increased from 50 nm to 200 nm as the etching time increased. These findings support previous reports from other research groups [4,13,14,22] regarding the pore sizes and pore depths observed for this specific micro/nano-fabrication technique. Thus, for higher surface area/volume ratios, higher pore depths or smaller pore sizes do not promote stronger ion intensity or detection sensitivity for LDI-MS analysis. 3.2. Etchant composition effects on nanostructured silicon formation and LDI-MS performance The effects of etchant variation on nanostructured silicon formation and ionization efficiency were investigated via changes of the etchant composition utilized with a 3 nm Au-coated, Ptype (0.001–0.02 cm) silicon wafer with 60 s of etching time. Compared with the 1:1:1 volume ratio HF/H2 O2 /EtOH etching composition, we increased the hydrogen fluoride concentration to a 2:1:1 volume ratio HF/H2 O2 /EtOH mixture, etching for 60 s to test the hydrogen fluorine effects. The nanostructured silicon surface became deeper, with a pore depth of 1.96 ± 0.32 m (Fig. 7a and d) because hydrogen fluorine acid is the major etchant that dis-
solves the silicon in the etching process. When the HF concentration increases, a higher silicon etching rate is expected to created deeper and larger nanopores on the silicon surface. In the metal-assisted etching process, the hydrogen peroxide acts as the oxidant that initiates the redox reaction. Increasing the hydrogen peroxide concentration to a 1:2:1 volume ratio of HF/H2 O2 /EtOH resulted in a higher pore depth of 2.4 ± 0.1 m. When the concentration of the wetting reagent, ethanol alcohol, was increased (Fig. 7b and e) to a 1:1:2 volume ratio of HF/H2 O2 /EtOH, a smaller pore depth of 1.03 ± 0.21 m was observed (Fig. 7c and f). As shown in Fig. 7, the surface morphologies obtained under 2:1:1 (Fig. 7a and d), 1:2:1 (Fig. 7b and e) and 1:1:2 (Fig. 7c and d) conditions are similar, with a pore size around 20–100 nm. These nanostructured silicon surfaces also exhibited high hydrophobicities of 132 ± 1.0◦ , 133.67 ± 2.52◦ and 144.33 ± 10.02◦ for the 2:1:1, 1:2:1 and 1:1:2 conditions, respectively. Surface-assisted LDI-MS using the 10−7 M des-Arg9 Bradykinin sample measured 1260 ± 157, 2153 ± 155 and 2954 ± 217 A with S/N ratios of 493 ± 60, 650 ± 32 and 794 ± 79 for the 2:1:1, 1:2:1 and 1:1:2 conditions, respectively. The effects of etchant variation to surface-assisted LDI-MS efficiency does not show increasing tendency with the nanostructured silicon depth while their corresponding pore depth measured around 1.96 ± 0.32, 2.4 ± 0.1 and 1.03 ± 0.21 m. 3.3. Nanostructured silicon surface roughness effects on LDI-MS performance Previous experiments found that the ion intensity efficiency varied with the etching time for both low resistance (Fig. 4) and high resistance (Fig. 6) conditions; the ion intensity first increased from 30 to 300 s and then decreased until 600 s. However, this trend does not highly correlate with the pore depth and size where pore depth and size increase with etching time. Larger pore depth and smaller pore size does not promote higher signal intensity. If the nanostructured silicon depth and size are independent of the ion intensity, other geometric effects may dominate the LDI-MS efficiency. From the FE-SEM image observations, the nanostructured silicon surfaces were flat under short etching time conditions (Figs. 2(a, b, d and e) and 5(a, b, d and e)). When the etching time was increased, lateral etching occurred near the top silicon nanostructure surface, breaking the vertical silicon nanostructure and resulting in larger pore sizes on the silicon surface (Figs. 2(c and f) and 5(c, f, g and j)). The result was a rougher nanostructured silicon surface. With long etching time
Fig. 7. Comparison of the nanostructured silicon surfaces with various etchant compositions.
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Fig. 8. Surface roughness measurements of nanostructured silicon surfaces prepared with 30 s (a), 60 s (b), 180 s (c), 300 s (d), 360 s (e) and 600 s (f) of etching time. The AFM scanning area is 30 m × 30 m.
conditions (Figs. 2(g–l) and 5(h, k, i and l)), some tiny pits (a couple A˚ size) are generated on the nanostructured silicon surface. With the strongest ion intensity, obtained under the 300 s condition (Figs. 2(g and j) and 5(h and k)), a high number of A˚ sized pits were found at the silicon surface. Thus, it appears that pore depth and pore size are not directly related the ion intensity efficiency. Instead, surface roughness may play a more important role in determining the LDI-MS efficiency. The nanostructured silicon surface roughness was measured by atomic force microscopy (AFM, MultiMode Scanning Probe Microscope, Veeco Instruments). The root mean square roughness (Rq ) was used to characterize the surface roughness of P-type 0.001–0.02 cm, 3 nm Au coated nanostructured silicon surfaces, as shown in Fig. 2. The surface roughness measurement results are shown in Fig. 8. Surface roughness values (Rq ) of 2.82 ± 0.43, 9.79 ± 4.17, 152.47 ± 9.29, 178.28 ± 11.58, 109.36 ± 15.25 and 80.20 ± 5.10 nm were obtained for the 60-, 180-, 300-, 360- and 600-s conditions, respectively. Smooth nanostructured silicon with 2.82 ± 0.43 (Fig. 8a) and 9.79 ± 4.17 nm (Fig. 8b) Rq values were measured for the 30 and 60 s etching conditions. The surface roughness increased with etching time until 300 s and then decreased until 600 s. The surface roughness decreases after 300 s due to overetching of nanostructured silicon surfaces. Some tiny pits generated on the nanostructured silicon surface were removed when etching time exceed 300 s. A highest Rq value of 178.28 nm was detected under the 300 s condition (Fig. 8d). This surface roughness variation was analogous to the ion intensity efficiency results in the previous section. A comparison of the nanostructured silicon surface roughness with the surface-assisted LDI-MS is shown in Fig. 9, with the solid line showing the ion intensity variation and the dashed line showing the roughness variation with etching time. These two curve trends highly correlate with each other, especially when the etching time exceeds 180 s. Since the increased surface roughness does not directly correspond to increased surface area, this high correlation implies that the surface roughness has a more direct effect on the ion intensity efficiency than the silicon nanostructure pore size, depth and surface area/volume ratio.
Various aspects such as the nanostructure surface chemistry, physical and optical properties or sample loading amount may affect the nanostructured surface-assisted LDI-MS detection sensitivities. The observation in Fig. 9 shows higher surface roughness results in higher ion intensities or detection sensitivities may be due to better sample absorption on the high-roughness surfaces. To further investigate this assumption, sample absorption experiments using fluorescence microscopy were performed. The model peptide sample was labeled with FITC fluorescence, and a 10 L FITC-labeled peptide sample was then deposited on the 3 nm Au coated, P-type 0.001–0.02 cm nanostructured surface, obtained in HF/H2 O2 /EtOH (1:1:1, v/v/v) with 30–600 s of etching time. After 5 min of deposition time, the fluorescence-labeled sample droplets were rinsed with 1 mL PB buffer and dried with an N2 gas stream. Fluorescence microscopy (Eclipse 80i, Nikon, Japan) was used to image each nanostructured silicon surface. Fig. 10 shows that the
Fig. 9. Comparison of nanostructured silicon surface roughness with surfaceassisted LDI-MS ion intensity. Error bars represent the standard deviation obtained from three different measurements.
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Fig. 10. Fluorescence images of nanostructured silicon surfaces prepared with 30 s (a), 60 s (b), 180 s (c), 300 s (d), 360 s (e) and 600 s (f) of etching time.
fluorescence intensity was proportional to the sample amount obtained from the surface. A greater illuminance intensity indicates that more sample absorbed to the nanostructured silicon surface. It can be observed from Fig. 10 that the fluorescence illuminance increased with etching time until 300 s and then decreased as time increased. The 300-s etching condition (Fig. 10d) produced the highest fluorescence illuminance, which is in accordance with the surface roughness and ion intensity test shown in Fig. 9. These findings demonstrate that higher surface roughness promotes better sample absorption on the nanostructured silicon surfaces, resulting in the detection of higher ion intensities during the LDI-MS analyses. 4. Conclusion Nanostructured silicon surfaces fabricated by metal-assisted etching demonstrated simple preparation and efficient and high sensitivity for matrix-free surface-assisted LDI-MS. Under optimized conditions, the lowest detectable single model peptide concentration was 10−9 M. This study investigated the effects of metal-assisted etching process parameters, namely etching time, substrate resistivity and etching composition, on the formation of nanostructured silicon surfaces and on LDI-MS performance. The process parameter results reported in this paper should encourage and assist researchers in using metal-assisted etching to create nanostructured silicon substrates for high-sensitivity matrix-free LDI-MS applications. The role of nanostructured silicon morphologies in LDI-MS efficiency is also discussed herein. Our experiments show that the signal ion intensity is dependent on the nanostructured silicon surface roughness, as opposed to its depth and size. Rougher nanostructured silicon surfaces can more efficiently promote sample absorption to the nanostructured surfaces, resulting in higher detection sensitivity. Acknowledgements The authors would like to thank the National Science Council, Taiwan grant #NSC 97-2218-E-008-010-MY2, NCU-Cathay General
Hospital joint research program #99CGH-NCU-B1 and the Department of Health, Taiwan grant #DOH98-TD-N-111-007, for financial support of this project. References [1] J. Wei, J.M. Buriak, G. Siuzdak, Nature 399 (1999) 243. [2] S.A. Trauger, E.P. Go, Z.X. Shen, J.V. Apon, B.J. Compton, E.S.P. Bouvier, M.G. Finn, G. Siuzdak, Analytical Chemistry 76 (2004) 4484. [3] T. Seino, H. Sato, A. Yamamoto, A. Nemoto, M. Torimura, H. Tao, Analytical Chemistry 79 (2007) 4827. [4] Z.X. Shen, J.J. Thomas, C. Averbuj, K.M. Broo, M. Engelhard, J.E. Crowell, M.G. Finn, G. Siuzdak, Analytical Chemistry 73 (2001) 612. [5] J.E. Prenni, Z.X. Shen, S. Trauger, W. Chen, G. Siuzdak, Spectroscopy: An International Journal 17 (2003) 693. [6] L.G. Hu, S.Y. Xu, C.S. Pan, H.F. Zou, G.B. Jiang, Rapid Communications in Mass Spectrometry 21 (2007) 1277. [7] W.G. Lewis, Z.X. Shen, M.G. Finn, G. Siuzdak, International Journal of Mass Spectrometry 226 (2003) 107. [8] E.P. Go, J.V. Apon, G.H. Luo, A. Saghatelian, R.H. Daniels, V. Sahi, R. Dubrow, B.F. Cravatt, A. Vertes, G. Siuzdak, Analytical Chemistry 77 (2005) 1641. [9] G.H. Luo, Y. Chen, H. Daniels, R. Dubrow, A. Vertes, Journal of Physical Chemistry B 110 (2006) 13381. [10] N.H. Finkel, B.G. Prevo, O.D. Velev, L. He, Analytical Chemistry 77 (2005) 1088. [11] Y.S. Xiao, S.T. Retterer, D.K. Thomas, J.Y. Tao, L. He, Journal of Physical Chemistry C 113 (2009) 3076. [12] A. Gorecka-Drzazga, S. Bargiel, R. Walczak, J.A. Dziuban, A. Kraj, T. Dylag, J. Silberring, Sensors and Actuators B: Chemical 103 (2004) 206. [13] C.W. Tsao, P. Kumar, J.K. Liu, L. Devoe, Analytical Chemistry 80 (2008) 2973. [14] R. Nayak, D.R. Knapp, Analytical Chemistry 79 (2007) 4950. [15] N. Shenar, J. Martinez, C. Enjalbal, Journal of the American Society for Mass Spectrometry 19 (2008) 632. [16] N.F. Hsu, S.Y. Tseng, C.Y. Wu, C.T. Ren, Y.C. Lee, C.H. Wong, C.H. Chen, Analytical Chemistry 80 (2008) 5203. [17] S.Y. Xu, Y.F. Li, H.F. Zou, J.S. Qiu, Z. Guo, B.C. Guo, Analytical Chemistry 75 (2003) 6191. [18] S.F. Ren, L. Zhang, Z.H. Cheng, Y.L. Guo, Journal of the American Society for Mass Spectrometry 16 (2005) 333. [19] M.J. Kang, J.C. Pyun, J.C. Lee, Y.J. Choi, J.H. Park, J.G. Park, J.G. Lee, H.J. Choi, Rapid Communications in Mass Spectrometry 19 (2005) 3166. [20] W.J. Shin, J.H. Shin, J.Y. Song, S.Y. Han, Journal of the American Society for Mass Spectrometry 21 (2010) 989. [21] H. Sato, A. Nemoto, A. Yamamoto, H. Tao, Rapid Communications in Mass Spectrometry 23 (2009) 603. [22] R.A. Kruse, X.L. Li, P.W. Bohn, J.V. Sweedler, Analytical Chemistry 73 (2001) 3639. [23] S. Alimpiev, A. Grechnikov, J. Sunner, V. Karavanskii, Y. Simanovsky, S. Zhabin, S. Nikiforov, Journal of Chemical Physics 128 (2008). [24] X. Li, P.W. Bohn, Applied Physics Letters 77 (2000) 2572.
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Analytica Chimica Acta journal homepage: www.elsevier.com/locate/aca
Fabrication of nanostructured silicon by metal-assisted etching and its effects on matrix-free laser desorption/ionization mass spectrometry W.Y. Chen a , J.T. Huang b , Y.C. Cheng c,d , C.C. Chien d,e,f , C.W. Tsao b,∗ a
Department of Chemical & Materials Engineering, National Central University, Jhongli, Taiwan Department of Mechanical Engineering, National Central University, Jhongli, Taiwan Department of Medical Research, Cathay General Hospital, Taipei, Taiwan d Institute of Biomedical Engineering, National Central University, Jhongli, Taiwan e School of Medicine, Fu Jen Catholic University, Taipei, Taiwan f Department of Anesthesiology, Sijhih Cathay General Hospital, Sijhih City, Taipei, Taiwan b c
a r t i c l e
i n f o
Article history: Received 14 September 2010 Received in revised form 8 November 2010 Accepted 18 November 2010 Available online 26 November 2010 Keywords: Mass spectrometry Nanostructured silicon Metal-assisted etching Matrix-free laser desorption/ionization Desorption/ionization on silicon
a b s t r a c t A matrix-free, high sensitivity, nanostructured silicon surface assisted laser desorption/ionization mass spectrometry (LDI-MS) method fabricated by metal-assisted etching was investigated. Effects of key process parameters, such as etching time, substrate resistance and etchant composition, on the nanostructured silicon formation and its LDI-MS efficiency were studied. The results show that the nanostructured silicon pore depth and size increase with etching time, while MS ion intensity increases with etching time to 300 s then decreases until 600 s for both low resistance (0.001–0.02 cm) and high resistance (1–100 cm) silicon substrates. The nanostructured silicon surface morphologies were found to directly affect the LDI-MS signal ion intensity. By characterizing the nanostructured silicon surface roughness using atomic force microscopy (AFM) and sample absorption efficiency using fluorescence microscopy, it was further demonstrated that the nanostructured silicon surface roughness was highly correlated to the LDI-MS performance. © 2010 Elsevier B.V. All rights reserved.
1. Introduction MS is one of the most used analytical techniques in biological analysis. Soft ionization methods, such as electrospray ionization (ESI) and matrix-assisted laser desorption/ionization (MALDI), have been routinely used to analyze a range of biomolecules, including proteins, peptides and DNA. Nonetheless, ESI and MALDI still suffer from disadvantages, such as excessive matrix backgrounds in the low molecular weight range, multiple charge peaks and detection sensitivity limitations. To overcome these limitations, Siuzdak and co-workers [1] proposed a porous silicon-assisted soft ionization method called desorption/ionization on silicon (DIOS). In the DIOS method, electrochemical etching is used to produce high surface area/volume ratio porous silicon surfaces that can be used in
Abbreviations: LDI, laser desorption/ionization; MS, mass spectrometry; ESI, electrospray ionization; MALDI, matrix-assisted laser desorption/ionization; DIOS, desorption/ionization on silicon; D/I, desorption/ionization; FE-SEM, field emission scanning electron microscope; AFM, atomic force microscope. ∗ Corresponding author at: Department of Mechanical Engineering, National Central University, No. 300, Jhongda Rd., Jhongli, Taiwan. Tel.: +886 3 4267343; fax: +886 3 4254501. E-mail address: [email protected] (C.W. Tsao). 0003-2670/$ – see front matter © 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.aca.2010.11.041
place of typical organic matrixes to absorb UV laser energy for LDI-MS analyses. This process eliminates the MALDI-MS matrix background noise at low molecular weights so that small molecules can be analyzed more effectively and accurately. In addition, this matrix-free, high sensitivity approach exhibits a high tolerance to salt and contaminants. Detection levels in the picomole or attomole range are readily achieved [1,2]. Due to these advantages, DIOS-MS has been used to analyze various low molecular weight molecules in polymer analysis [3], protein characterization [4–6] and forensics applications [4]. The porous silicon surface’s ability to ionize biomolecules has been shown to be due to its high surface area/volume ratio silicon morphology [1,7], indicating that creating high surface nanostructures on a substrate may be essential to initiate the surface-assisted LDI-MS process. Various micro/nano-fabrication methods have been reported to generate nanostructured surfaces and several of them have successfully demonstrated for LDI-MS applications. Some experiment demonstrations have utilized porous silicon substrates for DIOS-MS fabricated by electrochemical etching performed in a custom-made Teflon cell. Silicon nanowires are normally generated by the vapor–liquid–solid method (VLS) in a high vacuum physical vapor chamber [8,9]. Dry etching processes, such as reactive ion etching [10] and e-beam lithography [11], can be
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various pore sizes, depths and geometries, and the resulting relationship with LDI-MS efficiencies. A better understanding of the metal-assisted etching process and the role of nanostructured silicon surface morphology in LDI-MS performance will improve the application of these techniques in future research. 2. Experimental 2.1. Materials
Fig. 1. FE-SEM image of 3 nm Au deposited on P-type (1 0 0) 0.001–0.02 cm silicon substrate.
used to create ordered nanocavity arrays on silicon substrates for LDI-MS applications. Other methods, such as porous silicon dioxide fabricated by electrochemical etching and high temperature wet oxidation [12] and nanofilament silicon fabricated by metalassisted etching [13], have also generated effective surface-assisted LDI-MS target substrates. Porous aluminum [14,15], aluminum foil [16], carbon nanotubes [17,18], zinc oxide nanowires [19,20] and germanium nanodots [3,21] have also been used to produce effective surface assisted LDI-MS target substrates. In these surface-assisted LDI-MS methods, the nanostructured surface morphologies were found to correlate with the relative desorption/ionization (D/I) efficiencies, such that higher nanostructure surface area/volume ratios enhance D/I efficiencies as well as detection sensitivities [1,7]. In addition to surface area/volume ratio correlation, nanostructure pore size and depth effects on D/I efficiencies have been reported. In initial reports, smaller pore sizes (2 nm) generated more intense ion signals than bigger pore sizes (2–50 nm) [1]. However, more recent research indicated that smaller pore sizes did not promote stronger MS ion signal intensities. In this research, a silicon surface with an optimized 70–120 nm pore diameter and 200 nm pore depth produced the strongest MS intensity reported [4]. Bohn and Sweedler have reported that a pore size larger than 10 nm produced strong DIOS-MS ion signal intensity, while a 3 nm pore size was too small for efficient D/I detection [22]. Similar pore size effects were reported by Nayak and Knapp, who demonstrated an increase in ion signal intensity with aluminum pore depth up to 600 nm and a decrease in signal intensity with increasing film depth [14]. A more recent publication also reports that sub-micron pitted surfaces are required for surface assisted LDI-MS but that smaller pore size or higher depth is not essential for efficient D/I processes [23]. Thus, further research is required to clarify the relationship between nanostructured surface morphology and D/I efficiency. The nanostructured silicon surfaces presented in this research paper were produced via a metal-assisted etching process [24] and were applied to surface-assisted LDI-MS as first demonstrated by Tsao et al. in 2008 [13]. In this previous study, metal-assisted etching was proved to be a fast and simple method to create nanostructured silicon surfaces exhibiting 2–4 times higher detection sensitivities than DIOS-MS. However, detailed information regarding the effects of nanostructured silicon surface morphology on LDI-MS efficiency was not reported. In this study, we systematically investigated the metal-assisted etching process parameters, their effects on producing nanostructured silicon morphologies of
A gold (Au, 99.999% purity) slug and a P-type (1 0 0) 10 cm silicon wafer with 0.001–0.02 cm and 1–100 cm resistivity were purchased from Summit-Tech Resource Corp. (Taipei, Taiwan). Hydrofluoric acid (HF, 49%), hydrogen peroxide (H2 O2 , 31%) and nitric acid (NHO3 , 70%) were purchased from BASF Corp. (Ludwigshafen, Germany). Ethanol (EtOH, electronic grade), water (HPLC grade), methanol (MeOH, 99.9%, electronic grade), carbonate buffer (0.1 M, pH 9.2) and phosphate buffer (PB buffer, 0.1 M, pH 7.0) were purchased from J.T. Baker (NJ, USA). Des-Arg9-Bradykinin peptide sample and fluorescein isothiocyanate (FITC) were purchased from Sigma–Aldrich (MO, USA). 2.2. Nanostructured silicon substrate fabrication The nanostructured silicon surfaces were fabricated utilizing a metal-assisted etching process composed of some simple steps. First, a thin Au layer was deposited onto a bare P-type silicon substrate by an e-beam evaporator ULVAC EVA-E500 (ULVAC, Taiwan Inc.). The metal-coated silicon wafer was then immersed in an HF/H2 O2 /EtOH mixture, in which etching proceeded for 30–600 s at 25 ◦ C to create nanostructured silicon on the wafer. After etching, the wafer was fully rinsed with a methanol solution, and the wafer surface was blown dry with nitrogen. Because glass containers are not compatible with HF solutions, Teflon containers were used for all HF solutions. Similar to electrochemical etching to create porous silicon, metal-assisted etching acts as a localized electrochemical etching process in which local electrodeless etching occurs at the metal/silicon interface, each nanometer-sized metal particle acts as a local cathode and the silicon surface acts as an anode. The metal particles are critical in the process to promote H2 O2 decomposition and cause electron–hole injection into the silicon surface; silicon is dissolved by HF to create pits or nanostructures on the surface. Fig. 1 shows FE-SEM images of 3 nm thick Au particles deposited on the silicon substrate. The metal film morphology exhibited isolated circular shape particles measured around 10–30 nm in diameter with 5–10 nm gap uniformly distributed across the whole wafer substrate. Because HF is a hazardous acid, which can cause severe eye, skin, and respiratory injuries, etching with HF should only be performed in a well-ventilated fume hood with appropriate safety considerations, and calcium gluconate ointment should be readily available. Care must be taken to avoid contact with all HF solutions throughout the etching process. If contact with HF does occur, contaminated clothing should be immediately removed, and the affected area should be flushed vigorously with cold water and massaged with calcium gluconate to neutralize the HF acid. A physician should be immediately consulted after any HF exposure. 2.3. Sample preparation The single model peptide sample, des-Arg9 Bradykinin (MW = 904), was prepared by reconstituting 5 mg lyophilized powder in 2.7654 mL HPLC-grade water into 2 × 10−3 M followed by subsequent serial dilution to 10−7 M, 10−8 M and 10−9 M with HPLC-grade water. FITC was used to label the peptide sample for
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fluorescence microscopy. First, 3.89 mg FITC was added to 10 mL carbonate buffer (0.1 M, pH 9.2) and diluted 10-fold with carbonate buffer to a concentration of 10−4 M. Then FITC-labeled des-Arg9 Bradykinin was prepared by mixing 50 L 2 × 10−4 M des-Arg9 Bradykinin with 50 L 10−4 M FITC solution. The labeling reaction was processed in dark for overnight. 2.4. Surface morphology characterization Fig. 3. LDI-MS spectra for 10−7 M des-Arg9 Bradykinin.
The nanostructured silicon surface was characterized by field emission scanning electron microscopy, FE-SEM (JSM-6500F, JEOL Co., Japan.). Top and cross-section images of the nanostructured surface were taken to characterize the nanostructured silicon pore size, pore depth and geometry. The FE-SEM was operated with an accelerating voltage of 15 kV in secondary electron mode and a working pressure of 4 × 10−6 Torr. 2.5. Mass spectrometry analysis The nanostructured silicon surface-assisted LDI-MS spectra were obtained from a SELDI mass spectrometer (Proteinchip SELDI System, Bio-rad laboratories, USA). The nanostructured silicon substrate was fixed to the target holder and then loaded to the mass spectrometer for MS analysis. All LDI-MS spectra were recorded in the linear, positive ion mode and averaged over 50 laser pulses
using a 337 nm N2 laser. An optimized laser energy of 600 nJ was set for the maximum signal-to-noise ratio (S/N). It is noted that exposing the nanostructured silicon surface to ambient air over longer periods will lead to surface oxidation which reduces the surface hydrophobicity and MS detection sensitivity. To ensure best detection sensitivity and stability, our nanostructured silicon surface-assisted LDI-MS tests were performed within a day after the fabrication of nanostructured silicon surface. 2.6. Contact angle measurement The water contact angle on the nanostructured surface was measured by a custom-made optical goniometer composed of a high resolution digital camera (Canon EOS 450D/TAMRON Macro 90 mm
Fig. 2. Top (a–c, g–i) and cross-section (d–f, j–l) FE-SEM images of nanostructured silicon surfaces fabricated from 3 nm Au, P-type (1 0 0), 0.001–0.02 cm silicon wafer etching in HF/H2 O2 /EtOH (1:1:1, v/v/v) for 30 s (a, d), 60 s (b, e), 180 s (c, f), 300 s (g, j), 360 s (h, k) and 600 s (i, l).
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F2.8 lens), a z-axis precision stage and a light source. A 10 L droplet was deposited on the nanostructured surface, and the droplet side view was imaged by the digital camera. The droplet image was then processed using an AutoCAD system to determine the water contact angle. 3. Results and discussion The metal-assisted etching process exhibits good process controllability to generate various nanostructured silicon surface morphologies. In this study, etching time, substrate resistivity and etchant composition were investigated in relation to the nanostructured silicon formation and the resulting effects on LDI-MS efficiency. 3.1. Etch time and substrate resistance effects on nanostructured silicon formation and LDI-MS performance A P-type (1 0 0) silicon wafer with a resistivity of 0.001–0.02 cm was used as the substrate material. A 3-nm thick Au catalyst layer was uniformly deposited on the wafer, which was then dipped in a 1:1:1 volume ratio HF/H2 O2 /EtOH solution to create the nanostructured silicon surface. Six different etching times (30, 60, 180, 300, 360 and 600 s) were selected to create different nanostructured silicon surfaces on which to study the etching time effects. Fig. 2 shows top and cross-section SEM images of the resulting nanostructured silicon surfaces fabricated with various etching times. As shown in Fig. 2, porous-like nanostructure morphologies are observed. For both 30- (Fig. 2a and d) and 60-s (Fig. 2b and e) etching times, the top and cross-section images indicate a high surface flatness with pore sizes measuring about 20–50 nm and with a smaller number of larger pores ranging from 100 to 200 nm near the top of the nanostructured silicon. When etching time was increased to 180 s (Fig. 2c and f), the silicon nanostructures near the top of the surface started to break into ragged silicon surfaces. As shown in Fig. 2(g–i and j–l), longer etching time conditions (300–600 s) resulted in highly ragged nanostructured silicon surfaces of much larger size. The nanostructured surface depths measured around 1.1 ± 0.06, 1.6 ± 0.12, 4 ± 0.06, 5.2 ± 0.26, 6.5 ± 0.21 and 8 ± 0.51 m, corresponding to 60-, 180-, 300-, 360- and 600-s etching times, respectively. In metal-assisted etching processes, the depth and size of the nanostructured silicon are highly correlated to the etching time, generally tending to increase with etching time. The effects on surface-assisted LDI-MS efficiency were characterized by measuring the ion intensity exhibited by the single model peptide. Aqueous des-Arg9 Bradykinin (10 L, MW = 904) was deposited on the nanostructured silicon surface, remaining for 5 min to allow sufficient sample absorption of the surface. Then the droplet was pipetted off of the nanostructured silicon surface and loaded into the mass spectrometer for LDI-MS analysis. Fig. 3 shows the typical MS spectra of 10−7 M des-Arg9 Bradykinin averaged from 50 laser pulses using metal-assisted etching nanostructured silicon as the LDI-MS target substrate. A high signal ion intensity (3667 A) was measured at the 905 mass/charge (m/z) value for the low concentration of 10−7 M. Little background noise was detected at low m/z values. A sharp unknown peak at 72 m/z and some noise peaks in the 0–250 m/z range were observed, possibly from contaminates generated during wet etching or within the sample. The metal-assisted etching nanostructured silicon surfaces exhibited high surface hydrophobicity. The water contact angle measured around 135 ± 2.1◦ , 136 ± 0.6◦ , 146 ± 2.5◦ , 125 ± 0.6◦ , 154 ± 0.2◦ and 156 ± 6.0◦ for 60-, 180-, 300-, 360- and 600-s etching times, respectively. These measurements were averaged from three different measurements for each individual condition. It is evident that the
Fig. 4. Ion intensities (a) and S/N ratios (b) of 10−7 M, 10−8 M and 10−9 M des-Arg9 Bradykinin obtained from nanostructured silicon surfaces produced with etching times from 30 to 600 s. Error bars represent the standard deviation obtained from three different measurements.
highly hydrophobic surface efficiently constrains the droplet from spreading on the nanostructured surface, resulting in a higher local sample concentration and, therefore, improved detection sensitivity for LDI-MS analysis. Fig. 4 summarizes the ion intensity and S/N ratio results for the des-Arg9 Bradykinin sample obtained from 30-, 60-, 180-, 300-, 360- and 600-s etching time conditions. Series des-Arg9 Bradykinin concentrations of 10−7 M, 10−8 M and 10−9 M were used to evaluate the detection limit of the nanostructured silicon surfaces. As shown in Fig. 4, high ion intensities of 1287 ± 326, 2689 ± 311, 3012 ± 149, 3665 ± 95, 2008 ± 115 and 925 ± 131 A with S/N ratios of 884 ± 181, 993 ± 273, 786 ± 95, 1182 ± 345, 1058 ± 64 and 762 ± 58, respectively, were measured from 30- to 600-s etching conditions using a 10−7 M peptide sample. When the sample was diluted 10-fold to 10−8 M, the ion intensities decreased to 109 ± 26, 249 ± 71, 639 ± 194, 1836 ± 229, 221 ± 53 and 104 ± 9 A with S/N ratios of 112 ± 19, 152 ± 34, 213 ± 28, 593 ± 98, 165 ± 57 and 126 ± 23, respectively, for 30- to 600-s etching conditions. In the 10−9 M case, no ion signal was detected for the 30-, 60-, 360and 600-s etching time conditions. Weak ion signal intensities of 86 ± 39 and 404 ± 208 A with low S/N ratios of 4.0 ± 0.05 and 5.1 ± 0.26 were detected for the 300- and 360-s conditions. From the sample dilution test, a detection limit of 10−9 M was found under optimized conditions for the single model peptide sample. Good ion signals can be readily detected under 10−7 M and 10−8 M concentrations. Therefore, to ensure good ion intensity quality, 10−7 M des-Arg9 Bradykinin was used in the following LDI-MS tests.
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This detection limit is equivalent or better than other nanostructured surface-assisted LDI-MS methods unless the nanostructured silicon surfaces are chemically modified [2]. The depth variation of the nanostructured silicon surfaces was found to be dependent on the etching time, such that higher etching times resulted in a deeper nanostructured silicon surfaces. Fig. 4a investigates the effect of pore depth on LDI-MS efficiency. The ion intensity increases with the pore depth (dashed line) until 300 s then decreases as etching time approaches 600 s. An optimized pore depth of 4 m (300 s) exhibited the highest signal intensity for all 10−7 M, 10−8 M and 10−9 M conditions. Likewise, the S/N ratios were optimized at 300 s (Fig. 4b). Similar observations were found for the effects on pore size. As shown in Fig. 2, the pore size increased from 20 to 50 nm to 100 to 200 nm as the etching time was increased. In addition to the low resistance (0.001–0.02 cm) silicon substrate, high resistance (1–100 cm) silicon substrates obtained with various etching times were investigated. Etching times were increased from 30 to 600 s, and different silicon surface morphologies were obtained to evaluate nanostructured surface LDI-MS efficiency. Top and cross-section FE-SEM images of the nanostructured silicon surface are shown in Fig. 5. Deep pore depths of 0.6 ± 0.1, 2.0 ± 0.15, 2.5 ± 0.26, 4.5 ± 0.44, 7.9 ± 0.40 and 16.8 ± 1.0 m were created with 30-, 60-, 90-, 120-, 300- and 600s etching conditions, respectively. From the FE-SEM images, it
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appears that porous or filament like silicon nanostructures with flat surfaces are created with 30- to 120-s etching conditions (Fig. 5a–c, g, d–f, and j). When the etching time was increased to 300 s (Fig. 5h and k) and 600 s (Fig. 5i and j), rugged silicon surfaces were created. The nanostructured silicon surfaces fabricated from high resistance silicon substrates also exhibited high surface hydrophobicity with water contact angles of 142 ± 2.6, 142 ± 1.5, 146 ± 4.4, 148 ± 7.6, 149 ± 8.6 and 145 ± 4.6 for 30-, 60-, 90-, 120-, 300- and 600-s etching times, respectively. An amount of 10−7 M des-Arg9 Bradykinin was used to evaluate the LDI-MS performance of the high resistance silicon substrate with etching times of 30–600 s. The LDI-MS test procedures were the same as those previously described. The ion intensity measurement results are shown in Fig. 6. Ion intensities of 1038 ± 253, 1454 ± 54, 1897 ± 314, 2610 ± 147, 2953 ± 142 and 2376 ± 72 A with S/N ratios of 440 ± 45, 942 ± 341, 705 ± 173, 875 ± 20, 701 ± 270 and 777 ± 63 were detected for 30-, 60-, 90, 120-, 300- and 600-s etching conditions, respectively. Compared with the low substrate resistance nanostructure surfaces, no significant variation in ion intensity was observed, suggesting that the nanostructured silicon surface resistivity is not highly correlated to LDI-MS performance. Pore size and pore depth studies also showed an increase in ion intensity with etching time up to 300 s, after which a decrease was observed as the etching time increased to 600 s. As with low resistance nanostructured silicon (Fig. 4), 300 s of
Fig. 5. Top (a–c, g–i) and cross-section (d–f, j–l) FE-SEM images of nanostructured silicon surfaces fabricated from 3 nm Au, P-type (1 0 0), 1–100 cm silicon wafer etching in HF/H2 O2 /EtOH (1:1:1, v/v/v) for 30 s (a, d), 60 s (b, e), 90 s (c, f), 120 s (g, j), 300 s (h, k) and 600 s (i, l).
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Fig. 6. Comparison of ion intensity and pore depth with different metal-assisted etching time conditions. The nanostructured silicon substrates were fabricated from 3 nm Au, P-type (1 0 0), 1–100 cm silicon wafer etched in HF/H2 O2 /EtOH (1:1:1, v/v/v). Error bars represent the standard deviation obtained from three different measurements.
etching provided the optimized value. The pore size also increased from 50 nm to 200 nm as the etching time increased. These findings support previous reports from other research groups [4,13,14,22] regarding the pore sizes and pore depths observed for this specific micro/nano-fabrication technique. Thus, for higher surface area/volume ratios, higher pore depths or smaller pore sizes do not promote stronger ion intensity or detection sensitivity for LDI-MS analysis. 3.2. Etchant composition effects on nanostructured silicon formation and LDI-MS performance The effects of etchant variation on nanostructured silicon formation and ionization efficiency were investigated via changes of the etchant composition utilized with a 3 nm Au-coated, Ptype (0.001–0.02 cm) silicon wafer with 60 s of etching time. Compared with the 1:1:1 volume ratio HF/H2 O2 /EtOH etching composition, we increased the hydrogen fluoride concentration to a 2:1:1 volume ratio HF/H2 O2 /EtOH mixture, etching for 60 s to test the hydrogen fluorine effects. The nanostructured silicon surface became deeper, with a pore depth of 1.96 ± 0.32 m (Fig. 7a and d) because hydrogen fluorine acid is the major etchant that dis-
solves the silicon in the etching process. When the HF concentration increases, a higher silicon etching rate is expected to created deeper and larger nanopores on the silicon surface. In the metal-assisted etching process, the hydrogen peroxide acts as the oxidant that initiates the redox reaction. Increasing the hydrogen peroxide concentration to a 1:2:1 volume ratio of HF/H2 O2 /EtOH resulted in a higher pore depth of 2.4 ± 0.1 m. When the concentration of the wetting reagent, ethanol alcohol, was increased (Fig. 7b and e) to a 1:1:2 volume ratio of HF/H2 O2 /EtOH, a smaller pore depth of 1.03 ± 0.21 m was observed (Fig. 7c and f). As shown in Fig. 7, the surface morphologies obtained under 2:1:1 (Fig. 7a and d), 1:2:1 (Fig. 7b and e) and 1:1:2 (Fig. 7c and d) conditions are similar, with a pore size around 20–100 nm. These nanostructured silicon surfaces also exhibited high hydrophobicities of 132 ± 1.0◦ , 133.67 ± 2.52◦ and 144.33 ± 10.02◦ for the 2:1:1, 1:2:1 and 1:1:2 conditions, respectively. Surface-assisted LDI-MS using the 10−7 M des-Arg9 Bradykinin sample measured 1260 ± 157, 2153 ± 155 and 2954 ± 217 A with S/N ratios of 493 ± 60, 650 ± 32 and 794 ± 79 for the 2:1:1, 1:2:1 and 1:1:2 conditions, respectively. The effects of etchant variation to surface-assisted LDI-MS efficiency does not show increasing tendency with the nanostructured silicon depth while their corresponding pore depth measured around 1.96 ± 0.32, 2.4 ± 0.1 and 1.03 ± 0.21 m. 3.3. Nanostructured silicon surface roughness effects on LDI-MS performance Previous experiments found that the ion intensity efficiency varied with the etching time for both low resistance (Fig. 4) and high resistance (Fig. 6) conditions; the ion intensity first increased from 30 to 300 s and then decreased until 600 s. However, this trend does not highly correlate with the pore depth and size where pore depth and size increase with etching time. Larger pore depth and smaller pore size does not promote higher signal intensity. If the nanostructured silicon depth and size are independent of the ion intensity, other geometric effects may dominate the LDI-MS efficiency. From the FE-SEM image observations, the nanostructured silicon surfaces were flat under short etching time conditions (Figs. 2(a, b, d and e) and 5(a, b, d and e)). When the etching time was increased, lateral etching occurred near the top silicon nanostructure surface, breaking the vertical silicon nanostructure and resulting in larger pore sizes on the silicon surface (Figs. 2(c and f) and 5(c, f, g and j)). The result was a rougher nanostructured silicon surface. With long etching time
Fig. 7. Comparison of the nanostructured silicon surfaces with various etchant compositions.
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Fig. 8. Surface roughness measurements of nanostructured silicon surfaces prepared with 30 s (a), 60 s (b), 180 s (c), 300 s (d), 360 s (e) and 600 s (f) of etching time. The AFM scanning area is 30 m × 30 m.
conditions (Figs. 2(g–l) and 5(h, k, i and l)), some tiny pits (a couple A˚ size) are generated on the nanostructured silicon surface. With the strongest ion intensity, obtained under the 300 s condition (Figs. 2(g and j) and 5(h and k)), a high number of A˚ sized pits were found at the silicon surface. Thus, it appears that pore depth and pore size are not directly related the ion intensity efficiency. Instead, surface roughness may play a more important role in determining the LDI-MS efficiency. The nanostructured silicon surface roughness was measured by atomic force microscopy (AFM, MultiMode Scanning Probe Microscope, Veeco Instruments). The root mean square roughness (Rq ) was used to characterize the surface roughness of P-type 0.001–0.02 cm, 3 nm Au coated nanostructured silicon surfaces, as shown in Fig. 2. The surface roughness measurement results are shown in Fig. 8. Surface roughness values (Rq ) of 2.82 ± 0.43, 9.79 ± 4.17, 152.47 ± 9.29, 178.28 ± 11.58, 109.36 ± 15.25 and 80.20 ± 5.10 nm were obtained for the 60-, 180-, 300-, 360- and 600-s conditions, respectively. Smooth nanostructured silicon with 2.82 ± 0.43 (Fig. 8a) and 9.79 ± 4.17 nm (Fig. 8b) Rq values were measured for the 30 and 60 s etching conditions. The surface roughness increased with etching time until 300 s and then decreased until 600 s. The surface roughness decreases after 300 s due to overetching of nanostructured silicon surfaces. Some tiny pits generated on the nanostructured silicon surface were removed when etching time exceed 300 s. A highest Rq value of 178.28 nm was detected under the 300 s condition (Fig. 8d). This surface roughness variation was analogous to the ion intensity efficiency results in the previous section. A comparison of the nanostructured silicon surface roughness with the surface-assisted LDI-MS is shown in Fig. 9, with the solid line showing the ion intensity variation and the dashed line showing the roughness variation with etching time. These two curve trends highly correlate with each other, especially when the etching time exceeds 180 s. Since the increased surface roughness does not directly correspond to increased surface area, this high correlation implies that the surface roughness has a more direct effect on the ion intensity efficiency than the silicon nanostructure pore size, depth and surface area/volume ratio.
Various aspects such as the nanostructure surface chemistry, physical and optical properties or sample loading amount may affect the nanostructured surface-assisted LDI-MS detection sensitivities. The observation in Fig. 9 shows higher surface roughness results in higher ion intensities or detection sensitivities may be due to better sample absorption on the high-roughness surfaces. To further investigate this assumption, sample absorption experiments using fluorescence microscopy were performed. The model peptide sample was labeled with FITC fluorescence, and a 10 L FITC-labeled peptide sample was then deposited on the 3 nm Au coated, P-type 0.001–0.02 cm nanostructured surface, obtained in HF/H2 O2 /EtOH (1:1:1, v/v/v) with 30–600 s of etching time. After 5 min of deposition time, the fluorescence-labeled sample droplets were rinsed with 1 mL PB buffer and dried with an N2 gas stream. Fluorescence microscopy (Eclipse 80i, Nikon, Japan) was used to image each nanostructured silicon surface. Fig. 10 shows that the
Fig. 9. Comparison of nanostructured silicon surface roughness with surfaceassisted LDI-MS ion intensity. Error bars represent the standard deviation obtained from three different measurements.
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Fig. 10. Fluorescence images of nanostructured silicon surfaces prepared with 30 s (a), 60 s (b), 180 s (c), 300 s (d), 360 s (e) and 600 s (f) of etching time.
fluorescence intensity was proportional to the sample amount obtained from the surface. A greater illuminance intensity indicates that more sample absorbed to the nanostructured silicon surface. It can be observed from Fig. 10 that the fluorescence illuminance increased with etching time until 300 s and then decreased as time increased. The 300-s etching condition (Fig. 10d) produced the highest fluorescence illuminance, which is in accordance with the surface roughness and ion intensity test shown in Fig. 9. These findings demonstrate that higher surface roughness promotes better sample absorption on the nanostructured silicon surfaces, resulting in the detection of higher ion intensities during the LDI-MS analyses. 4. Conclusion Nanostructured silicon surfaces fabricated by metal-assisted etching demonstrated simple preparation and efficient and high sensitivity for matrix-free surface-assisted LDI-MS. Under optimized conditions, the lowest detectable single model peptide concentration was 10−9 M. This study investigated the effects of metal-assisted etching process parameters, namely etching time, substrate resistivity and etching composition, on the formation of nanostructured silicon surfaces and on LDI-MS performance. The process parameter results reported in this paper should encourage and assist researchers in using metal-assisted etching to create nanostructured silicon substrates for high-sensitivity matrix-free LDI-MS applications. The role of nanostructured silicon morphologies in LDI-MS efficiency is also discussed herein. Our experiments show that the signal ion intensity is dependent on the nanostructured silicon surface roughness, as opposed to its depth and size. Rougher nanostructured silicon surfaces can more efficiently promote sample absorption to the nanostructured surfaces, resulting in higher detection sensitivity. Acknowledgements The authors would like to thank the National Science Council, Taiwan grant #NSC 97-2218-E-008-010-MY2, NCU-Cathay General
Hospital joint research program #99CGH-NCU-B1 and the Department of Health, Taiwan grant #DOH98-TD-N-111-007, for financial support of this project. References [1] J. Wei, J.M. Buriak, G. Siuzdak, Nature 399 (1999) 243. [2] S.A. Trauger, E.P. Go, Z.X. Shen, J.V. Apon, B.J. Compton, E.S.P. Bouvier, M.G. Finn, G. Siuzdak, Analytical Chemistry 76 (2004) 4484. [3] T. Seino, H. Sato, A. Yamamoto, A. Nemoto, M. Torimura, H. Tao, Analytical Chemistry 79 (2007) 4827. [4] Z.X. Shen, J.J. Thomas, C. Averbuj, K.M. Broo, M. Engelhard, J.E. Crowell, M.G. Finn, G. Siuzdak, Analytical Chemistry 73 (2001) 612. [5] J.E. Prenni, Z.X. Shen, S. Trauger, W. Chen, G. Siuzdak, Spectroscopy: An International Journal 17 (2003) 693. [6] L.G. Hu, S.Y. Xu, C.S. Pan, H.F. Zou, G.B. Jiang, Rapid Communications in Mass Spectrometry 21 (2007) 1277. [7] W.G. Lewis, Z.X. Shen, M.G. Finn, G. Siuzdak, International Journal of Mass Spectrometry 226 (2003) 107. [8] E.P. Go, J.V. Apon, G.H. Luo, A. Saghatelian, R.H. Daniels, V. Sahi, R. Dubrow, B.F. Cravatt, A. Vertes, G. Siuzdak, Analytical Chemistry 77 (2005) 1641. [9] G.H. Luo, Y. Chen, H. Daniels, R. Dubrow, A. Vertes, Journal of Physical Chemistry B 110 (2006) 13381. [10] N.H. Finkel, B.G. Prevo, O.D. Velev, L. He, Analytical Chemistry 77 (2005) 1088. [11] Y.S. Xiao, S.T. Retterer, D.K. Thomas, J.Y. Tao, L. He, Journal of Physical Chemistry C 113 (2009) 3076. [12] A. Gorecka-Drzazga, S. Bargiel, R. Walczak, J.A. Dziuban, A. Kraj, T. Dylag, J. Silberring, Sensors and Actuators B: Chemical 103 (2004) 206. [13] C.W. Tsao, P. Kumar, J.K. Liu, L. Devoe, Analytical Chemistry 80 (2008) 2973. [14] R. Nayak, D.R. Knapp, Analytical Chemistry 79 (2007) 4950. [15] N. Shenar, J. Martinez, C. Enjalbal, Journal of the American Society for Mass Spectrometry 19 (2008) 632. [16] N.F. Hsu, S.Y. Tseng, C.Y. Wu, C.T. Ren, Y.C. Lee, C.H. Wong, C.H. Chen, Analytical Chemistry 80 (2008) 5203. [17] S.Y. Xu, Y.F. Li, H.F. Zou, J.S. Qiu, Z. Guo, B.C. Guo, Analytical Chemistry 75 (2003) 6191. [18] S.F. Ren, L. Zhang, Z.H. Cheng, Y.L. Guo, Journal of the American Society for Mass Spectrometry 16 (2005) 333. [19] M.J. Kang, J.C. Pyun, J.C. Lee, Y.J. Choi, J.H. Park, J.G. Park, J.G. Lee, H.J. Choi, Rapid Communications in Mass Spectrometry 19 (2005) 3166. [20] W.J. Shin, J.H. Shin, J.Y. Song, S.Y. Han, Journal of the American Society for Mass Spectrometry 21 (2010) 989. [21] H. Sato, A. Nemoto, A. Yamamoto, H. Tao, Rapid Communications in Mass Spectrometry 23 (2009) 603. [22] R.A. Kruse, X.L. Li, P.W. Bohn, J.V. Sweedler, Analytical Chemistry 73 (2001) 3639. [23] S. Alimpiev, A. Grechnikov, J. Sunner, V. Karavanskii, Y. Simanovsky, S. Zhabin, S. Nikiforov, Journal of Chemical Physics 128 (2008). [24] X. Li, P.W. Bohn, Applied Physics Letters 77 (2000) 2572.