Binding Specificity of a Peptide on Semiconductor Surfaces - Nano

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NANO LETTERS

Binding Specificity of a Peptide on Semiconductor Surfaces

2004 Vol. 4, No. 11 2115-2120

Karsten Goede,*,† Peter Busch,‡,§ and Marius Grundmann† UniVersita¨t Leipzig, Fakulta¨t fu¨r Physik und Geowissenschaften, Linne´ strasse 5, D-04103 Leipzig, Germany Received July 22, 2004; Revised Manuscript Received August 19, 2004

ABSTRACT Peptide adhesion on semiconductors is quantitatively investigated by atomic force microscopy. A selected 12-mer peptide is reproducibly found to adhere to various III−V and group-IV semiconductor surfaces in a substrate-specific way. The observed succession Al0.98Ga0.02As, Si, Ge, InP, GaP, GaAs in terms of increasing peptide adhesion coefficients is qualitatively explained by the substrate electronegativity and the acidity of the amino acid side chains. It is shown that peptide adhesion is strongly dependent on the amino acid sequence.

Self-assembly1,2 appears to be the most promising way to build nanometer-scale devices in an applicable way. Due to their self-assembly and recognition properties, which are unmatched by conventional inorganic analogues, biomolecules are extremely powerful and easy-to-use building blocks in nanotechnology,3 with possible applications in photonics, electronics/computing, catalysis, sensoring/detection, and energy storage. Their true potential should evolve especially in combination with the reliable electronic and photonic properties of inorganic materials. However, biomolecules are still technically underused today, since nature has not designed them to deal with technologically important inorganic materials. A major research focus4 lies on selfassembled amino acid-based molecules such as peptides or proteins and on single-stranded DNA. Such molecules can be employed for the assembly process of inorganic nanoparticles.5 Biomolecules also work also as active elements in the devices as shown in principle for small peptides6 and other organic molecules.7 Polypeptides have been successfully employed to govern the morphology of gold nanocrystals in the crystallization process.8 One can think of two device-building approaches.9 First, natural and well-known recognition capabilities between molecules such as single-stranded DNA are exploited in a self-organizing process. Sophisticated two-dimensional DNA crystals have been realized.10 As building blocks in such devices, a variety of substrates and inorganic nanocrystals have been investigated such as polymers,11 metals,12-14 semiconductor quantum dots,15 and carbon nanotubes.16 Second, a hybrid organic-inorganic device is created where * Corresponding author. E-mail: [email protected]. † Institut fu ¨ r Experimentelle Physik II. ‡ Institut fu ¨ r Experimentelle Physik I. § Present address: Institut fu ¨ r Polymerforschung, Hohe Strasse 6, D-01069 Dresden, Germany. 10.1021/nl048829p CCC: $27.50 Published on Web 09/04/2004

© 2004 American Chemical Society

biomolecules adhere to suitable sites of to-be-made nanocircuits. In this approach, inorganic material recognition properties of the biomolecules17 allow for the crucial assembly step. Yet so far, no device functionality has been shown for any such hybrid interface. Even what governs such an assembly microscopically is still largely unclear. Thus it is necessary to empirically select a peptide from a large library17 for good adhesion properties to a specific semiconductor rather than synthesizing it directly to exhibit a certain specificity. Here we show that by selective recognition a semiconductor-organic matter hybrid interface build-up is possible without inserting the crucial peptide into a virus15,17 or employing preparation procedures such as tedious and nonsubtle functionalizing of hydrocarbons on surfaces,18 nanoparticle coating or capping,12,19,20 or electrostatic nanoparticle charging.20 The small peptide size promises a robust procedure and crosstalk-free addressing of a single peptide particle in a future (electronic) device. It will emerge that the specificity of the peptide adhesion to the different semiconductors covers 2 orders of magnitude, which is by far bigger than the involved measurement errors. Such a pronounced specificity might be utilized in a future (peptide or semiconductor) sensing device. A correlation of these binding properties with the chemical structure of the peptide and the semiconductor opens the door for future tuning of the peptide adhesion coefficient (PAC), i.e., the percentage of surface area that is covered by peptide clusters, by choosing the peptide sequence. It will be shown that both chemical composition of the peptide and its spatial conformation have a huge impact on the PACs. In ref 17, peptides, each containing 12 amino acids, with a high binding specificity to (100) GaAs have been selected using a phage-display approach. From there, we have chosen and synthesized21 the clone with the amino acid sequence22

AQNPSDNNTHTH. With an extended conformation being the expected structure of such a small peptide,17 we anticipate an overall size of a single peptide of a few nm, which can be imaged by an atomic force microscope (AFM). To allow for future studies of the cluster ensemble on the surfaces we have studied the peptide as such and not fused it to a coat protein of a phage virus as in ref 17. All GaAs and AlGaAs samples have been grown by metal-organic vapor phase epitaxy. The other samples have been prepared from commercially available substrates. (100)-Surface samples of adequate size have been properly etched, and their suitable cleanness and flatness was confirmed.23 For peptide attachment, samples have been transferred immediately (within seconds) after etching to the respective peptide solution (for concentrations and dwell time see below) in Tris-buffered saline (Carl Roth, Karlsruhe, Germany; pH 7.6). The saline solution was used to minimize peptide cluster lumping in solution. The sample exposure to the peptide (2 h) was followed by a 20 min distilled water bath and water rinse to remove unbound particles. In this way we can exclude the observed effects to result from the different semiconductor surface energies which might otherwise lead to surfacespecific formation of loose peptide clusters. Peptide particles that still appear on the surface after the wash and water rinse can be expected to have formed a strong binding to the surface when the semiconductor was still in solution. For an assessment of the percentage of loose, unbound peptide particles on the surface, we equally determined PACs for unwashed peptide-covered samples. Washing persistence of the peptide clusters was measured by means of AFM images which were recorded after a second distilled water bath and rinse. (110) Surfaces of AlxGa1-xAs samples (the Al content x being 0.0, 0.35, 0.5, and 0.98, respectively, for the different samples) have been obtained by cleaving multilayer GaAs/ AlGaAs/GaAs (100)-grown samples within the peptide solution in order to circumvent the fast Al oxidation in air. Peptide exposure was as above. All samples were allowed to dry for about 6 h in air before they were investigated. Remaining water drops were shaken off beforehand. We have used a Dimension 3000 AFM in combination with a Nanoscope IIIa (Digital Instruments, now Veeco, Woodbury NY) operating in tapping mode.24 In contrast to contact mode, tapping mode is adequate for investigation of soft matter such as peptides because it minimizes sample damage due to lateral shear forces. The PAC for the given conditions was estimated by analyses on eight nominally equivalent square areas of (1.6 µm)2 on two equivalently prepared samples from different preparation cycles (four analysis areas per sample). This choice of size for the micrograph areas accounts for both the aim to investigate rather large areas to obtain representative PACs and for the conflicting necessity to investigate rather small areas to avoid overlooking small clusters due to the fixed number of 512 probe points per scan line. Respective errors were estimated as standard deviation from the mean value of the eight equivalent measurements per sample. The (110) facets of the GaAs/AlGaAs/GaAs samples proved to be too rough to allow for detection of adherent peptide clusters by standard 2116

Figure 1. PAC on once-washed (100) GaAs in dependence of the peptide concentration in solution. The line between the data points is the linear fit of the first four data points. Error bars show the respective standard deviations.

AFM topography measurements. This is mainly because the emerging Al oxides on the air-exposed surface form bulges with heights of some dozen nm. However, analyzing the phase-lag signal which is recorded simultaneously with the topography information provides a suitable alternative. Phase changes of the oscillating tip during scanning are related to energy dissipation during tip-sample interactions25 which can have various causes.26-28 The phase image often provides more contrast than the topography image26,28,29 and is especially useful for compositional mapping of surfaces with different stiffness of the constituting substances.30 Thus, phase imaging is well suited for detecting soft peptide clusters on hard semiconductor surfaces. We note that for the (100) surfaces, all information gained from the AFM topography images is entirely consistent with the AFM phase images. PACs on the (110) AlGaAs samples and their errors have been estimated from four equivalent measurements per sample. Figure 1 shows the dependence of the (100)-GaAs PAC on the peptide concentration in solution. For low concentrations below 2 µg/mL the PAC increases linearly with increasing concentration, while it saturates for higher concentrations. In the latter regime, a peptide layer of increasing thickness forms, which is not investigated in detail here. Since we aim at observing small peptide clusters on surfaces rather than depositing peptide layers on them, the following measurements are taken in the low-concentration regime. A peptide concentration of 1 µg/mL was chosen in the following to guarantee a detectable amount of peptide also on surfaces with a low PAC. A similar dependence can be observed (not shown) for the PAC as a function of the dwell in the peptide solution. For dwells below 1 h, the PAC increases with time, while for longer dwells it remains constant. A dwell of 2 h was chosen. Typical AFM images of (100) surfaces of different peptide-covered III-V and group-IV semiconductors are shown in Figure 2. An etched, clean surface (Figure 2a), roughness rms 0.25 nm, becomes partly covered by the peptide as described above (Figure 2b), and the grain analysis Nano Lett., Vol. 4, No. 11, 2004

Figure 2. Typical AFM micrographs of the investigated surfaces. The scale bar is 1 µm, respectively. (a) Pure, peptide-free (100) GaAs surface. Height scale 1 nm. (b) Peptide-covered, once-washed (100) GaAs surface. Height contrast 5 nm. (c) Visualization of the grain analysis result on the same surface area as in (b). (d-g) Peptide-covered, once-washed (100) surfaces of GaP (d), InP (e), Ge (f), and Si (g). Height scale 5 nm, respectively. Figure 4. Peptide adhesion on (110) AlxGa1-xAs. (a,b) Typical AFM phase images of (110) GaAs/Al0.98Ga0.02As surfaces, without (a) and with (b) standard exposure to the peptide solution. The [100] crystal direction is marked to point up the orientation of the sample. Phase contrast 2°. (c) Typical AFM phase image of a sophisticated multilayer (110) Al0.35Ga0.65As/GaAs sample. The regions of the thin GaAs layers and the beginning of the GaAs buffer are accentuated. The [100] crystal direction is marked to clarify the orientation of the sample. Arrows are skewed to show the layers’ plane. Scale bar 1 µm, phase contrast 6°. (d) PAC on (110) AlxGa1-xAs for x ) 0.0, 0.35, 0.50, and 0.98, respectively, as estimated from AFM phase images. The line between the data points is the linear fit. Error bars show the respective standard deviations.

Figure 3. PACs on various (100) semiconductors. The values for the freshly prepared peptide-covered surface, the once-washed surface, and the surface after a second wash are given. Lines between data points are guides to the eye. The maximum dust coverage found on nominally clean surfaces is marked. Error bars show the respective standard deviations.

is carried out (Figure 2c). The semiconductor-substrate specificity of the peptide coverage is obvious from typical images of the investigated surfaces: GaAs (Figure 2b), GaP (Figure 2d), InP (Figure 2e), Ge (Figure 2f), and Si (Figure 2g). This is quantified in Figure 3, where the PACs on the above five substrates and on (100) Al0.35Ga0.65As for freshly prepared samples, once-washed samples and twice-washed samples are indicated together with the respective standard deviation. PACs on once-washed samples range from 25% on GaAs to 1% on Si.31 Considering the two-step washing process, it follows from Figure 3 that on an average for all surfaces the first wash removes 32% of all present clusters, and the second wash 26%. Hence the binding strength between peptide molecules and substrate is overwhelmed for a nearly constant share of the peptide clusters in any washing process. This confirms that only a small share (about 6%, the difference between the above values) of all clusters on the freshly prepared surface is unbound and only mechanically deposited onto the surface.32 This allows to conclude that the different semiconductor surface energies have only a small impact Nano Lett., Vol. 4, No. 11, 2004

on the PACs. The peptide-sequence specificity of the results will be discussed further below (see Figure 6). Figure 4 visualizes the results of the phase-lag measurements on the (110) AlxGa1-xAs samples. In Figure 4a, such a facet without exposure to the peptide solution is exemplarily shown. Boundaries between GaAs and Al0.98Ga0.02As layers are faintly visible. The sample facet in Figure 4b was cleaved in and subsequently exposed to the peptide solution, and a peptide coverage is clearly visible on the first GaAs layer, next to the edge of the (100) surface. Neither on the adjoining Al0.98Ga0.02As layer nor on the following second GaAs layer is any further peptide visible. This is typical for all Al-rich samples (x ) 0.98). We believe, therefore, that peptide cluster adhesion on such facets starts from the already strongly covered (100) surface toward neighboring sample regions. In this way, the Al-rich layer might act as a barrier to peptide clusters and prevent adhesion also on the following GaAs layer. The stated (110) GaAs PAC therefore refers to the first GaAs layer at the (100) surface edge. The almost entire absence of peptide adhesion on Al0.98Ga0.02As has also been observed in ref 17 for phages displaying the same peptide as employed here. In Figure 4c, a typical adhesion pattern on a (110) Al0.35Ga0.65As/GaAs surface is shown. The dominating Al0.35Ga0.65As on a GaAs buffer is interrupted by two thin GaAs layers. There is a certain peptide adhesion on Al0.35Ga0.65As, while on GaAs it is much more pronounced. It is noted that the above-mentioned assumed adhesion-barrier effect of Al-rich layers seems to be nonexistent for Al0.35Ga0.65As. Also, it is remarkable that the presence of nm-thick GaAs regions is enough to enhance 2117

Figure 5. PACs of the investigated semiconductors in dependence of (a) the lattice constant (no correlation) and (b) the weighted electronegativity difference dEN* of the respective semiconductor to GaAs as described in the text. Lines are guides to the eye for (100) and (110) surfaces, respectively.

peptide adhesion in a region that is more than 10 times larger. Yet actual binding of the these clusters might be restricted mainly to the GaAs layer. These findings are quantitatively summarized in Figure 4d, where also the fit for a linear dependence between Ga content and PAC is given. Now we discuss the correlation between the observed PACs and parameters of the diverse semiconductors. In Figure 5a, clear evidence is given that there is no such correlation between PACs and the lattice constant33 of the respective surface. We note that also between the semiconductor band gap energy33 and PACs no apparent correlation is found. An obvious correlation exists between the measured PACs and the Pauling electronegativity34 (EN) difference of a certain semiconductor to that of GaAs (Figure 5b). In our peptide, amino acids with polar and basic side chains dominate.17 Thus it is expected that this peptide will prefer adhesion to inorganic matter with a certain surface polarity and with appropriate acid binding sites on its surface. Since the peptide has been bred17 to adhere well to (100) GaAs, it is understandable that the GaAs PAC is the highest. The other compounds deviate from this optimal EN value in a specific way. To obtain the weighted EN difference dEN* plotted in Figures 5b and 5c, acidic (Ga, In, Si, Ge, Al) and basic (As, P, Si, Ge) atoms have been accounted for separately, and for each compound the two respective absolute EN differences have been added. To account roughly for the occurrence of nine amino acids with Lewis-base functional groups in their side chains (Q, N, S, N, N, T, H, T, H)22 and just one with an Lewis-acid functional group in a side chain (D)22 in this peptide, any deviations of the substrate-atom EN from the reference values of Ga and As, respectively, to the basic side have been weighted 9-fold.35 It is obvious that high acidity as such does not make a surface suitable for strong adhesion of this peptide. Instead, the smaller the EN difference between its basic and acid atoms on one hand and Ga and As, respectively, on the other, the higher the PAC on that surface.36 As can be seen, the PAC-EN dependence seems to be different for the (100) surfaces and the (110) surfaces (Figure 5b), yet we refrain from a detailed discussion in this regard until sufficient experimental and theoretical data are available. Though we found the PAC difference between (100) and (110) GaAs to be negligible, 2118

Figure 6. PACs for three peptides with different sequences on once-washed (100) surfaces of GaAs and Si, respectively. Left peptide has the original sequence. In the center peptide, amino acid H has been exchanged for A. Sequence of the right peptide is a random permutation of the original sequence. Error bars show the respective standard deviations.

further measurements (not shown here) show (111) GaAs to exhibit a rather low PAC. Such different PACs on different GaAs surfaces have been observed also elsewhere.17 Besides the EN and the chemical composition of a compound, also the spatial arrangement of its surface atoms should have an impact on the PAC. The amino acid sequence specificity of the PAC results is shown in Figure 6, where PACs on once-washed (100) GaAs and Si, respectively, are shown for the investigated peptide (sequence AQNPSDNNTHTH) and two control peptides AQNPSDNNTATA22 and TNHDHSNAPTNQ.22 The first control peptide has been synthesized21 by exchanging the twice-occurring amino acid histidine in the sequence of the original peptide for alanine. Histidine has a basic side chain and is known37 for binding to the two most important transition metals38 in living matter: Fe (in hem of the blood pigments) and Zn. The change from histidine to the unpolar amino acid alanine makes the peptide adhere clearly worse to (100) GaAs and better to (100) Si (Figure 6). This illustrates the huge impact of a crucial amino acid like histidine on the peptide binding behavior. However, in addition to the chemical composition the spatial conformation of the peptide molecules on the surface will also influence their binding specificity. Therefore we synthesized21 a peptide whose sequence was derived from the those of the original peptide by a random permutation. This TNHDHSNAPTNQ peptide is shown in Figure 6 to adhere to GaAs (100) and Si (100) with nearly the same PACs, thereby demonstrating that PACs are specific for a given peptide sequence and determined by both chemical composition and spatial conformation of the peptide. Randomizing the peptide sequence seems to result here in a loss of specificity rather than in a loss of binding capability. Future experimental and theoretical work should concentrate on clarifying systematically how chemical composition and spatial structure of a peptide determine microscopically its adhesion specificity. In conclusion, we have shown that exposing a peptide to clean, flat semiconductor (100) and (110) surfaces leads to quantitatively different, reproducible PACs. These have been explained qualitatively with the semiconductor-specific elecNano Lett., Vol. 4, No. 11, 2004

tronegativity and the acidity of the peptide side chains. Thus, the succession Al0.98Ga0.02As, Si, Ge, InP, GaP, GaAs in terms of increasing PAC is understandable. PACs have been shown to be amino acid sequence specific. This correlation between binding properties and peptide structure should enable future tuning of the PAC by choosing the peptide sequence, possibly allowing for a bottom-up approach to nanoscale hybrid devices. Acknowledgment. We thank V. Gottschalch for providing most samples and H. Herrenberger for help with the etching procedure, both at the Institut fu¨r Anorganische Chemie of Universita¨t Leipzig, and F. Hopfer and R. Sellin of the Institut fu¨r Festko¨rperphysik of Technische Universita¨t Berlin for the Al0.98Ga0.02As samples and one GaAs sample. We thank F. Kremer of the Institut fu¨r Experimentelle Physik I of Universita¨t Leipzig for supplying us with the AFM. M. Lang, S. Tschiedel, A. Bettio and A. Beck-Sickinger of the Institut fu¨r Biochemie of Universita¨t Leipzig have synthesized the peptides. This work has been supported by startup funds (M.G.) of Universita¨t Leipzig.

(22) (23)

Note Added after ASAP Publication. The legend to Figure 6 was corrected. This paper was posted ASAP on 9/4/04. The corrected version was posted on 9/13/04. References (1) Whitesides, G. M.; Grzybowski, B. Science 2002, 295, 2418-2421. (2) Kamien, R. D. Science 2003, 299, 1671-1673. (3) Whitesides, G. M.; Mathias, J. P.; Seto, C. T. Science 1991, 254, 1312-1319. (4) Seeman, N. S.; Belcher, A. M. Proc. Nat. Acad. Sci. U.S.A. 2002, 99, 6451-6455. (5) Zhang, Z.; Horsch, M. A.; Lamm, M. H.; Glotzer, S. C. Nano Lett. 2003, 10, 1341-1346. (6) Santoso, S.; Hwang, W.; Hartman, H.; Zhang; S. Nano Lett. 2002, 2, 687-691. (7) Reed, M. A.; Chen, J.; Rawlett, A. M.; Price, D. W.; Tour, J. M. Appl. Phys. Lett. 2001, 78, 3735-3737. (8) Brown, S.; Sarikaya, M.; Johnsson, E. J. Mol. Biol. 2000, 299, 725735. (9) Cingolani, R.; Rinaldi, R.; Maruccio, G.; Biascio, A. Physica E 2002, 13, 1229-1235. (10) Winfree, E.; Liu, F.; Wenzler, L. A.; Seeman, N. C. Nature 1998, 394, 539-544. (11) Brown, S. Nano Lett. 2001, 1, 391-394. (12) Sastry, M.; Kumar, A.; Datar, S.; Dharmadhikari, C. V.; Ganesh, K. N. Appl. Phys. Lett. 2001, 78, 2943-2945. (13) Alivisatos, A. P.; Johnsson, K. P.; Peng, X.; Wilson, T. E.; Loweth, C. J.; Bruchez, M. P.; Schultz, P. G. Nature 1996, 382, 609-611. (14) Monson, C. F.; Woolley, A. T. Nano Lett. 2003, 3, 359-363. (15) Lee, S.-W.; Mao, C.; Flynn, C. E.; Belcher, A. M. Science 2002, 296, 892-895. (16) Keren, K.; Berman, R. S.; Buchstab, E.; Sivan, U.; Braun, E. Science 2003, 302, 1380-1382. (17) Whaley, S. R.; English, D. S.; Hu, E. L.; Barbara, P. F.; Belcher, A. M. Nature 2000, 405, 665-668. (18) Hata, K.; Fujita, M.; Yoshida, S.; Yasuda, S.; Makimura, T.; Murakami, K.; Shigekawa, H.; Mizutani, W.; Tokumoto, H. Appl. Phys. Lett. 2001, 79, 692-694. (19) Wang, G.; Murray, R. W. Nano Lett. 2004, 4, 95-101. (20) Nakao, H.; Shiigi, H.; Yamamoto, Y.; Tokonami, S.; Nagaoka, T.; Sugiyama, S.; Ohtani, T. Nano Lett. 2003, 3, 1391-1394. (21) The peptide was synthesized by automated multiple solid-phase peptide synthesis (Syro, Multisyntech, Bochum, Germany) using the Wang resin to obtain a peptide acid (30 mg, resin loading 0.6 mmol/ g) and by using the fluorenyl-9-methoxycarbonyl (Fmoc)/tert butyl strategy. The Fmoc-amino acids (10-fold excess) were introduced

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(24)

(25) (26) (27) (28) (29)

(30) (31)

by double coupling procedures (2 × 36 min) using in situ activation with diisopropylcarbodiimide and 1-hydroxybenzotriazol. The Fmoc removal was carried out with 40% piperidine in DMF for 3 min, 20% piperidine for 7 min, and finally 40% piperidine for 5 min. The peptides were cleaved by using a mixture of trifluoroacetic acid/ thioanisol/thiocresol (90:5:5, v/v) for 3 h. Afterwards the peptides were precipitated from ice-cold diethyl ether, collected by centrifugation, and washed four times. Purification of the peptides was achieved by preparative HPLC on a RP C-18 column (Waters, 300 × 25 mm, 5 µm) with a gradient of 20-70% B in A (A ) 0.1% trifluoroacetic acid in water; B ) 0.08% trifluoroacetic acid in acetonitrile) over 45 min and a flow of 15 mL/min. All relevant fractions were collected and further analyzed. The crude and purified peptides were analyzed by electrospray ionization mass spectrometry (ESI-MS) on an API 3000 PE Sciex (Canada, Toronto) and by analytical reversed-phase HPLC on a Vydac RP18-column (4.6 × 250 mm; 5 µm/300 Å) using linear gradients of 10-60% B in A over 30 min and a flow rate of 0.6 mL min-1. The achieved purity of the peptide was g95%. To allow for optical investigations (not presented here), the peptide has been tagged with the fluorescent molecule carboxyfluorescein. However, the omission of labeling17 the peptide with 20-nm long fluorescence-sensitive gold nanoparticles allowed for a direct visualization of the peptide clusters. A: alanine, Q: glutamine, N: asparagine, P: proline, S: serine, D: aspartate, T: threonine, H: histidine. (100)-Grown semiconductor samples with average surface sizes of 50 mm2 have been prepared. Native oxides and other particles covering the surfaces were removed by etching for 1 min in an ammonium fluoride solution of NH4F‚H2O/HF‚H2O (87.5:12.5 v/v) followed by a water rinse and distilled water bath for 2 min. This etch does not attack the pure semiconductor surfaces. Si samples showed typical hydrophobic properties after etching, indicating a clean Si surface. We have investigated the clean semiconductor surfaces by AFM to estimate the cleanness and flatness of the respective surfaces. The maximum particle coverage was 0.2%, which is well below the peptide PACs discussed in the text. After images were leveled by a first-order plane fit when necessary to remove a sample tilt, the maximum roughness rms of the clean (100) sample surfaces was 0.37 nm, while for most samples the roughness rms value was well below 0.30 nm. These values are typical for freshly etched surfaces of the investigated semiconductors. The AFM probe was n+ silicon with a 123-µm cantilever and a spring constant of 59 N/m driven near its resonance frequency of 380 kHz. Scan rates were of the order of 1.5-0.15 µm/s, depending on the image size of 3-0.5 µm (2-3.3 s per image line). Very similar images have been obtained with other probes (226 µm, 188 kHz, 45 N/m). Sample-specific peptide PACs have been obtained by performing a grain analysis for each image using the SPIP program (version 1.9223, Image Metrology A/S, Lyngby, Denmark). Images were leveled using a first-order plane fit when necessary to remove a sample tilt. By doing so it was possible to set the minimum detection height in the grain analysis to 0 nm above the average surface height, thus allowing each cluster to be detected. Cleveland, P.; Anczykowski, B.; Schmid, A. E.; Elings, V. B. Appl. Phys. Lett. 1998, 72, 2613-2615. Bar, G.; Thomann, Y.; Brandsch, R.; Canthow, H.-J.; Whangho, M.H. Langmuir 1997, 13, 3807-3812. Magonov, S. N.; Elings, V.; Papkov, V. S. Polymer 1997, 38, 297307. Raghavan, D.; Gu, X.; VanLandingham, M.; Nguyen, T. Langmuir 2000, 16, 9448-9459. Nguyen, T.; Gu, X.; Van Landingham, M.; Giraud, M.; Dutuc-Rosset, R. Proceedings of the 24th Annual Meeting of the Adhesion Society; Emerson, J. A., Ed.; Adhesion Society: Williamsburg VA, 2001; pp 68-70. Magonov, S. N.; Heaton, M. G. Am. Laboratory 1998, 30, 9-15. Apparent errors arise from a certain arbitrariness in choosing the surface areas for the grain analysis, the uncertainty in estimating the PAC on such selected surface pieces, and the sample reproducibility. However, since the PACs for nominally equal surface areas on different samples show essentially the same value spread as those for such areas on the same sample, it appears that the reproducibility accuracy is higher than the degree of reliance for the PAC on one and the same sample. We believe the natural inhomogeneity of the peptide cluster allocation on the surfaces to be the main source for the estimated errors. 2119

(32) This is not surprising since the peptide solution was not oversaturated. Therefore only a small amount of peptide should have sunk down mechanically in the water during sample preparation. Washing the fresh surfaces as practiced here and in ref 17 to remove unbound particles therefore increases the accuracy of the PAC results but does not change their magnitude. (33) Landolt-Bo¨rnstein Numerical Data and Functional Relationships in Science and Techology, New Series; Springer: Berlin, 1982; Vol. III/17a. (34) Allred, A. L. J. Inorg. Nucl. Chem. 1961, 17, 215-220. Pauling, L. The Nature of the Chemical Bond, 3rd ed.; Cornell University: New York, 1960. (35) For a semiconductor A-B, this corresponds to the formula dEN* ) (ENA - ENGa)‚fA + (ENB - ENAs)‚fB, with fA,B being 1 or -9 if the proceeding difference is positive or negative, respectively. Thus, dEN* values cannot be compared to standard EN values. Values for AlxGa1-xAs mixed crystals have been obtained by interpolating

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between the Al and Ga ENs according to the x/(1-x) ratio. We annotate that also for other parameters derived from the EN of the constituting atoms, a correlation with the PACs (while not as persuasive as here) can be found. (36) In Figure 5b, it appears from a comparison of GaAs and GaP that for high PACs already a very small dEN* increase clearly decreases the PAC, possibly due to suppression of chemical bonds between this peptide and the surface at the interface. However, high dEN* values as for Al0.98Ga0.02As and Si might largely prevent even van der Waals-type binding as the respective PACs are extremely small. Adhesion on Ge and Si surfaces is additionally low because they lack the polar character. (37) Coleman, J. E. Annu. ReV. Biochem. 1992, 61, 897-946. (38) Lippard, S. J.; Berg, J. M. Bioanorganische Chemie; Spektrum, Akad. Verl.: Heidelberg, Germany, 1995.

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Nano Lett., Vol. 4, No. 11, 2004