Coupling of Biocomputing Systems with Electronic Chips: Electronic

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J. Phys. Chem. C 2009, 113, 2573–2579

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Coupling of Biocomputing Systems with Electronic Chips: Electronic Interface for Transduction of Biochemical Information Melina Kra¨mer,†,‡ Marcos Pita,† Jian Zhou,† Maryna Ornatska,† Arshak Poghossian,‡ Michael J. Scho¨ning,‡ and Evgeny Katz*,† Department of Chemistry and Biomolecular Science and NanoBio Laboratory (NABLAB), Clarkson UniVersity, Potsdam, New York 13699-5810, Aachen UniVersity of Applied Sciences, Institute of Nano- and Biotechnologies (INB), Ginsterweg 1, D-52428 Ju¨lich, Germany, and Research Centre Ju¨lich, Institute of Bio- and Nanosystems (IBN2), D-52425 Ju¨lich, Germany ReceiVed: September 18, 2008; ReVised Manuscript ReceiVed: December 3, 2008

Electronic transduction of biochemical signals processed by the enzyme-based OR-Reset/AND-Reset logic systems was achieved using field-effect Si chips. The developed enzyme logic systems produced pH changes as the result of biochemical reactions activated by different combinations of the chemical input signals. Signal transduction was performed by pH-responding gold nanoparticles associated with the chip interface. The transformation of the nanoparticle shells between the dissociated (negatively charged) and protonated (neutral) states was determined using capacitance-voltage or impedance spectroscopy measurements, resulting in an electronic signal that reflects the state of the system corresponding to the logic output produced by the enzymes. The developed systems are the first examples of enzyme-based biocomputing systems interfaced with ordinary Si-based electronics. 1. Introduction Recent multidisciplinary research at the interface between computer science and chemistry resulted in the development of unconventional chemical computing based on the application of various chemical systems processing information and performing Boolean logic operations in response to chemical or physical input signals.1 Chemical,2 electrochemical,3 and photochemical4 systems of different complexity were developed to mimic the operation of various electronic elements of computing circuits such as logic gates,5 switches,6 memory units,7 and other specialized elements (e.g., molecular digital demultiplexers8 and keypad locks9). The information processing of chemical systems could proceed in solutions10 or at chemically modified interfaces.11 Different physical signals, such as light,12 magnetic fields,13 electrical potentials,14 or changes in chemical compositions (e.g., pH value),15 have been used to switch the states of the molecular systems and activate various computing operations. The output signals generated by the biocomputing systems in response to the external signals were transduced and determined by optical16 or electronic17 means. Chemical systems were assembled in “devices” performing simple arithmetic operations,18 e.g., half adder and half subtractor19 or full adder and full subtractor.20 Increasing the complexity of the chemical computing systems resulted in the integration of singlefunctional devices into complex multicomponent and multifunctional chemical computing networks.21 Chemical systems can solve computing problems at the level of a single molecule22 resulting in nanoscales of the computing units23 and allowing parallel computation performed by numerous molecules involved in various reactions.24 * To whom correspondence should be addressed. E-mail: ekatz@ clarkson.edu. Fax: 1-315-2686610. Telephone: 1-315-2684421. † Clarkson University. ‡ Aachen University of Applied Sciences and Institute of Bio- and Nanosystems.

Despite the rapid development of unconventional chemical computing, the research area is still in a very early experimental and theoretical stage; however, great future potential is expected.25 Computing performed by biomolecular systems (biocomputing)26 is one of the most promising approaches in chemical computing because of the complexity of biological materials and their unique properties such as the selectivity of biocatalyzed reactions and specificity of biorecognition processes. Biocomputing systems could include DNA,27 proteins,28 and whole cells.29 Recently pioneered logic gates based on the use of enzyme-catalyzed reactions30 hold promise for the development of complex biocomputing networks because of their compatibility and scalability. Enzyme logic gates were concatenated to a sequence of logic operations processing complex information received from many input signals.31 On the basis of experimental results and theoretical modeling, the enzyme logic gates could be assembled in a sequence of at least 10 concatenated operations with the acceptable noise level in the processed information.32 Output signals generated by the enzyme logic gates or their networks could be used to control properties of signal-responsive materials.33 Another use for the enzyme logic systems includes their integration with electronic transducers to yield multisignal-responding biosensors with the digitized output signal in the form of YES/NO, depending on the combination of all input signals and the “program” installed in the biochemical logic system.34 Coupling enzyme logic systems with electronic transducers could be based on regular conducting electrodes modified with enzyme-electron relay assemblies and electrochemical means traditionally used in biosensors.35 The first experimentally designed electrode-enzyme systems with output signals originating from logically processed information have been reported for soluble36 or immobilized34 enzymes. However, keeping in mind the need for miniaturization and increasing the system complexity, we might prefer Si-based electronic transducers (e.g., field-effect transistors37 and other semiconducting devices38) as interfaces for biocomputing systems.

10.1021/jp808320s CCC: $40.75  2009 American Chemical Society Published on Web 01/21/2009

2574 J. Phys. Chem. C, Vol. 113, No. 6, 2009 SCHEME 1: Enzyme-Based AND-Reset Logic Operation Activated by Biochemical Input Signals, Resulting in pH Changes as Output Signals

Novel signal-responsive materials associated with electrode surfaces (e.g., polymer brushes, membranes) can “gate” electrochemical reactions depending on the presence or absence of various chemicals or on the change of environmental conditions (e.g., pH value),39 thus providing an interface for the transduction of chemical inputs to electrochemical (electronic) signals. Another approach is based on the use of (bio)molecularfunctionalized nanostructured materials [e.g., nanoparticles (NPs), carbon nanotubes] operating as a functional interface between biochemical and electronic systems.40 Self-assembled NPs operating as transducing units associated with electrode surfaces41 can substantially enhance the sensitivity of chemicalto-electronic signal transduction. The present paper demonstrates for the first time electronic transduction of biochemical signals processed by enzyme logic gates to electronic signals generated by Si chips with the use of immobilized functional NPs. 2. Experimental Section Chemicals and Reagents. The enzymes glucose oxidase (GOx) from Aspergillus niger, type X-S (EC 1.1.3.4); esterase (Est) from porcine liver (EC 3.1.1.1), crude; invertase (Inv) from baker’s yeast, grade VII (EC 3.2.1.26); and urease (Ur) from jack beans (EC 3.5.1.5) were purchased from Sigma-Aldrich and used as supplied. Other chemicals purchased from SigmaAldrich or Fluka were analytical quality and used as supplied: β-D-(+)-glucose, urea, sucrose and gold(III) chloride trihydrate (HAuCl4 · 3H2O), ethyl butyrate, 3-mercaptopropionic acid (MPA), and (3-mercaptopropyl)trimethoxysilane (MPTMS). Ultrapure water (18 MΩ · cm-1) from a NANOpure Diamond (Barnstead) source was used in all of the experiments. Au NPs (24 ( 8 nm in diameter, according to AFM measurements) were synthesized by the citrate method.42 EIS-Based Si-Field-Effect Sensor Chip. The capacitive EIS (Electrolyte-Insulator-Semiconductor) sensor43 was made of a p-doped (boron) 3 in. single-crystal silicon wafer (Si-Mat, Silicon Materials, Germany) with orientation (specific resistance, 1-10 Ω cm; thickness, 356-406 µm). A 30 nmthick SiO2 layer was grown on a silicon substrate as an insulating layer by thermal dry oxidation of the Si surface. As a rear-side electrical contact, a 300 nm Al film was evaporated and treated

Kra¨mer et al. SCHEME 2: Enzyme-Based OR-Reset Logic Operation Activated by Biochemical Input Signals, Resulting in pH Changes as Output Signals

by a rapid thermal annealing process. The EIS wafer was cut into 10 × 10 mm2 chips. The SiO2 active surface was cleaned by a solution composed of 1:1:1 ammonium hydroxide (30%), hydrogen peroxide (33%), and water, reacting with the surface for 1 h at 60 °C. Then the surface was thoroughly rinsed with water and dried with Ar flow. The chip surface was functionalized with thiol groups by reacting with 10% MPTMS solution in dry toluene for 1 h at 60 °C. The silanized surface was rinsed sequentially with toluene (5 times), ethanol (5 times), acetic acid (1 mM, 10 min), and finally water. The thiol-functionalized chip surface was reacted overnight with a dispersion of Au NPs (1 mg mL-1) to yield a self-assembled monolayer of NPs on the surface, which was then reacted with an aqueous solution of MPA (1 mM) to generate a thiolated shell containing carboxylic groups. The morphology of the Au-NPs-functionalized surface was studied with atomic force microscopy (AFM). We used a Dimension 3100 microscope by Veeco Instruments (New York), operating in the tapping mode, and BAS-Tap300 silicon probes (Budget Sensors, Bulgaria) with the following characteristics: tip radius of 10 nm, spring constant of 40 N m-1, and resonance frequency of 300 kHz. Electrochemical Measurements. The Au-NPs-functionalized chip was assembled in a pocket of a homemade electrochemical cell.44 The side walls and backside contacts of the EIS sensor chip were protected from the electrolyte solution by means of the sealing O-ring, thus excluding the need for a complex encapsulation process. The contact area of the EIS sensor was determined by the O-ring diameter, about 2 cm2. On the top side of the cell, a Metrohm Ag/AgCl/KCl 3 M was applied as a reference electrode, while the rear-side aluminum electrical contact of the chip was wired through a gold pin. Electrochemical characterization of the EIS chips was performed by means of capacitance-voltage (C-V) and impedance spectroscopy methods using an ECO Chemie Autolab PSTAT-10 electrochemical instrument equipped with a frequency response analyzer. A variable DC-biasing voltage (from -3 to +1 V) was applied between the conducting rear side of the chip and the reference electrode to set the working potential of the EIS chip, and a small alternating voltage (20 mV, 90 Hz) was applied to the system in order to measure the capacitance. Impedance measurements were carried out in 1 mM NaCl in a frequency range varying from 0.1 Hz to 100 kHz. All measurements were

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Figure 1. AFM imaging of the signal-transducing interface. (A) Si chip surface after its silanization and prior to the deposition of the Au NPs. (B) Cross section of the surface image prior to the deposition of NPs. (C) Si chip surface after the deposition of the Au NPs. (D) Cross section of the surface image after the deposition NPs. Insets show the surfaces with a higher magnification.

SCHEME 3: Electronic Scheme of the Signal-Transducing Device (A) and the C-V Curves Theoretically Expected for Variable Charges at the Sensing Interface (B)a

Figure 2. C-V curves obtained for the signal-transducing Si chip functionalized with the MPA-Au NPs upon application of different combinations of chemical input signals processed by the enzyme-based OR-Reset logic system: (a) 0,0; (b) 0,1; (c) 1,0; (d) 1,1; and (e) Reset signal. Capacitance measurements were performed at a frequency of 90 Hz in a 1 mM NaCl aqueous solution at room temperature.

a FRA ) frequency response analyzer, RE ) reference electrode, ∆C ) change of the interfacial capacitance, and ∆VFB ) change of the flat-band potential.

performed at room temperature, and all potential values are referred to the reference electrode. 3. Results and Discussion To prove the feasibility of the electronic transduction of biochemical information processed by enzymes with the use of

the EIS Si chip, we developed two enzyme logic systems. The first logic system is the AND-Reset logic gate composed of three enzymes added to the solution, 1 mM NaCl, over the chip surface: invertase (Inv, 12.5 units mL-1), glucose oxidase (GOx, 5 units mL-1), and urease (Ur, 5 units mL-1). The second logic system, the OR-Reset logic gate, was also composed of three enzymes added to the solution: GOx (5 units mL-1), esterase (Est, 5 units mL-1), and Ur (5 units mL-1). The AND gate was activated by two chemical input signals: sucrose (100 mM) and oxygen (concentration obtained in the solution under equilibrium with air) (Scheme 1). The absence of the respective chemicals was considered as the input signals 0 (Ar was bubbled through the solution in order to remove O2 when needed), while addition of chemicals at specific concentrations was used as the input signals 1. Selected concentrations of the chemical inputs resulted in substantial pH changes upon the biochemical reactions catalyzed by the enzymes in a nonbuffered solution. It should be noted that intermediate concentrations of the chemical inputs were considered as undefined signals similar to the approach used in electronics. The operation of the AND logic gate was based on the biochemical chain reaction proceeding in the

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Figure 3. Electronic signals in the form of ∆C (A) and ∆VFB (B) derived from C-V measurements and obtained from the chip upon processing different combinations of chemical input signals processed by OR logic gate (R ) Reset). ∆C values were derived from the respective C-V curves at -1.2 V, while ∆VFB values were found at C ) 42 nF.

Figure 4. Mott-Schottky plots derived from the impedance spectra obtained for the signal-transducing Si chip functionalized with the MPA-Au NPs upon application of different combinations of chemical input signals processed by the enzyme-based OR-Reset logic system: (a) 0,0; (b) 0,1; (c) 1,0; (d) 1,1; and (e) Reset signal. Measurements were performed in the frequency range of 1 mHz to 100 kHz. Inset: Reversible ∆VFB induced by biochemical signals. VFB values were derived at C ) 1.38 PF.

presence of sucrose and O2 (input signals 1,1). The hydrolytic conversion of sucrose to glucose and fructose catalyzed by Inv was followed by glucose oxidation catalyzed by GOx to yield gluconic acid. The final product, gluconic acid, resulted in the acidification of the solution lowering the pH from the initial value of 6.5 to the final value of about 4 ( 0.1. The biochemical process was not completed in the absence of sucrose or O2 or both (input signals 0,1, 1,0, and 0,0, respectively), thus inhibiting the formation of gluconic acid and keeping the pH value unchanged. The output signal generated by the enzyme logic system was considered a 1, if ∆pH > 1.5, while ∆pH < 1 was considered a 0 output signal. pH changes between the threshold ∆pH values of 1.5 and 1 were considered as undefined (similar to the approach used in electronics). To complete the reversible cycle, the Reset function was activated by the addition of urea (2 mM), resulting in the formation of ammonia biocatalyzed by urease. This resulted in an increase in pH, reaching a pH value of about 6 or higher. The enzyme-operating OR gate function was activated by two input signals: glucose (10 mM) or/and ethyl butyrate (10 mM), considered to be 1 at selected concentrations and 0 in the absence of chemicals (Scheme 2). The OR enzyme logic gate performed two parallel reactions: glucose oxidation biocatalyzed by GOx to yield gluconic acid (O2 serving as a natural electron acceptor was always in the system) and hydrolysis of ethyl butyrate biocatalyzed by Est

producing butyric acid. Any or both of the reactions (input signals 0,1, 1,0, or 1,1) resulted in the formation of acids (gluconic acid, butyric acid, or both), thus lowering the pH from the initial value of 6.5 to the final value of about 3.8 ( 0.1. Solution pH remained unchanged in the absence of both chemicals (input signals 0,0), when both reactions did not proceed. The Reset operation returning the pH to the initial value was activated by the addition of urea similar to that described above. It should be noted that the initial pH value of the enzyme systems and the rate of pH changes could be slightly varied depending on the presence of minor components in the nonbuffered solutions. For example, the presence of CO2 was slightly higher in aerated solutions compared to that in solutions where air was removed by Ar bubbling. However, this did not result in any detectable difference in the enzyme-produced pH changes. The SiO2-sensing surface of the Si chip was functionalized with pH-sensitive Au NPs. The Au NPs were self-assembled on the thiol-terminated silanized thin film associated with the chip surface, and then the Au NPs were functionalized with MPA shells (Scheme SI1 in Supporting Information). The modification steps were followed by AFM characterization. Figure 1A shows the AFM image of the essentially smooth surface of the silanized Si chip prior to the self-assembling Au NPs. An example cross section, Figure 1B, shows a typical surface roughness of about 0.3 nm. Self-assembling of the Au NPs on the thiol-functionalized chip surface yields high density coverage (about 840 NPs per 1 µm2), Figure 1C, with the estimated average height of the Au NPs of about 23.8 nm, which correlates with the diameters of the NPs, Figure 1D. Because of the AFM tip dilation effect, we should assume that the height of the NPs is the true diameter, resulting in an estimated 38% of the surface area coverage with the NPs. The Au NP-functionalized Si chip was embedded into the electrochemical cell, and the DC/AC voltage was applied between the conductive rear side and the reference electrode to allow the capacitance-voltage measurements (Scheme 3A). The pH-sensitive MPA shells around the Au NPs associated with the chip surface allowed alteration of the surface charge upon variation of the solution pH. The density of mercaptopropionic acid in the shell monolayer could be roughly estimated as 7.6 × 10-10 mol cm-2 or 1.26 × 1013 molecules cm-2.46 The pKa value of mercaptopropionic acid in a monolayer configuration is about 5.2 ( 0.1.47 The experiments were always started at pH 6.5 where the carboxylic groups of the shell are dissociated (negatively charged), while the charge was reduced (becoming neutral) upon protonation of the carboxylic groups at lower pH

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Figure 5. Bar diagrams showing the output signals generated by the (A) OR enzyme logic gate and (B) AND enzyme logic gate and transduced by the Si chip in the form of electronic signals as capacitance changes. Dashed lines correspond to the threshold values; output signals located below the first threshold were considered as 0, while the signals higher than the second threshold were treated as 1.

values generated in situ by the enzyme logic systems. The variation of the surface charge should result in a change of the capacitance-voltage (C-V) curve demonstrating the theoretically expected changes in the operating characteristics of the Si chip, e.g., capacitance (C) and flat-band potential (VFB) values (Scheme 3B).48 The ∆C and ∆VFB values could be used as readout signals from the Si chip transducing the biochemical logic operations to the electronic output signals. Figure 2 shows typical experimental C-V curves measured for the system upon operation of the OR enzyme logic gate described above. The C-V curves show the typical behavior of a p-type EIS structure that can be distinguished into three regions: accumulation, depletion, and inversion.49 Application of zero-concentration chemical inputs 0,0 (injection of a background solution) does not produce any change in the initial C-V curve of the chip (Figure 2, curve a). Injection of glucose (10 mM) or/and ethyl butyrate (10 mM) in different combinations (input signals 0,1, 1,0, and 1,1) results in the shift of the C-V curve, indicating the expected changes of C and VFB in the characteristics of the chip (Figure 2, curves b-d). The observed ∆C (about 3 nF) and ∆VFB (about 180 mV) values originate from the change in the electrical charges associated with the shells on the Au NPs immobilized on the transducer surface.49 Later addition of urea (2 mM) resulted in the reset of the electronic properties of the chip to the initial state (Figure 2, curve e). The switching between two distinct states of the chip induced by the biochemical signals shows the reversible character reflected by the cyclic changes of the typical C and VFB values (Figure 3A,B). The capacitance measurements were performed at a single frequency of 90 Hz. To validate the observed electronic changes, we performed impedance measurements in the range of frequencies of 1 mHz to 100 kHz. The obtained results presented in the convenient form of the Mott-Schottky plot (Figure 4) clearly show the VFB changes upon application of different signals activating the OR-Reset gate. The ∆VFB values derived from the impedance measurements correlate with those observed in the C-V curves. The reversibility of the chip transition between two states induced by the biochemical inputs is shown in the inset of Figure 4. Similar switching of the chip induced by the biochemical AND gate is shown in Figure SI1 of the Supporting Information. The difference in the maximum C and VFB values originates from varying the active chip area, i.e., thickness of the siloxane film and density of the Au NPs. Electronic signals generated by the Si chip in the form of ∆C and ∆VFB upon application of different combinations of the chemical signals processed by the enzyme systems reflect the typical behavior of OR/AND logic gates (Figure 5A,B).

4. Conclusions Developed hybrid systems integrate enzyme logic gates processing biochemical information received in the form of chemical input signals and Si chips transducing the information from chemical to electronic signals. The capacitance-potential (C-V) function and impedance spectroscopy were used to determine electronic changes induced in the Si chips by biochemical reactions with enzyme logic operations. Two logic operations (AND/OR) followed by the Reset function were performed in the present study. MPA-functionalized Au NPs associated with the sensing surface operated as the transducing elements converting the chemical ∆pH signal generated in situ by the enzyme logic gates to the electronically readable interfacial changes. This approach allows for tuning in sensing the ∆pH range by selecting the capping thiol acid with the appropriate pKa value. Moreover, the transition of the capping acid from the dissociated (negatively charged) to protonated (neutral) states proceeds in the form of a sigmoid function of the pH value (typical shape of a titration curve). This would allow for a sharp change of the electronic response function from a digital 0 to 1 state, providing minimum noise in the signal transduction process. Global mapping of the logic gate transduction function upon application of variable concentrations of the chemical input signals32 was outside the scope of the present study. If the variable input concentrations were applied, this would allow for the optimization of the gate performance function.32 The flexibility of enzyme logics allowing various logic operations30 and the possibility of enlarging enzyme logic systems into complex logic networks31 promise almost unlimited variability and complexity of biochemical information-processing systems. Coupling of biochemical logics with signaltransducing elements changing the surface material properties in response to enzyme logic operations opens the way to novel signal-processing systems. Application of the nanostructured signal-responsive systems is of particular interest because of the possibility of reducing the responsive elements to a single nanounit (e.g., single-responsive nanoparticle). Variation in the semiconducting properties of EIS chips coupled with nanoparticle systems in response to enzyme logic operations is a promising direction in the general avenue of biochemical logics. Besides fundamental scientific interest, we envisage potential applications in multisignal-responding biosensors with built-in Boolean logic. It should be noted, however, that the present system is only the first simple example demonstrating integration of enzyme logic systems with Si-based electronic devices. The following steps must be included in future development of this fascinating research area:

2578 J. Phys. Chem. C, Vol. 113, No. 6, 2009 (i) Increasing enzyme logic complexity is needed for future integration of biomedical devices with natural in vivo biochemical pathways, and the input signals should be applied at a physiologically relevant concentration. (ii) Enzyme systems processing biochemical signals should be directly immobilized on the signal-transducing interfaces in the form of multienzyme ensembles logically processing biochemical information. (iii) Different mechanisms for the coupling between the signal processing biochemical systems and signal transducing electronic interfaces should be studied. The present system is based on the proton exchange between the enzyme systems and the sensing interface. Other mechanisms might be based on the electron exchange with the use of redox active systems immobilized at the sensing interface. Acknowledgment. This research was supported by NSF Grants Signal-Responsive Hybrid Biomaterials with Built-in Boolean Logic (DMR-0706209) and Biochemical Computing: Experimental and Theoretical Development of Error Correction and Digitalization Concepts (CCF-0 726698). An award from the Semiconductor Research Corporation, Cross-Disciplinary Semiconductor Research (2008-RJ-1839G), is gratefully acknowledged. Supporting Information Available: Scheme of the chip surface modification. Experimental results for the AND logic gate. This material is available free of charge via the Internet at http://pubs.acs.org. References and Notes (1) (a) Adamatzky, A.; De Lacy Costello, B.; Asai, T. ReactionDiffusion Computers; Elsevier Science: Amsterdam, 2005. (b) UnconVentional Computing 2005: From Cellular Automata to Wetware; Teuscher, C., Adamatzky, A., Eds.; C. Luniver Press: Beckington, U.K., 2005. (2) (a) De Silva, A. P.; Uchiyama, S.; Vance, T. P.; Wannalerse, B. Coord. Chem. ReV. 2007, 251, 1623–1632. (b) Magri, D. C.; Brown, G. J.; McClean, G. D.; De Silva, A. P. J. Am. Chem. Soc. 2006, 128, 4950–4951. (c) Balzani, V.; Credi, A.; Venturi, M. ChemPhysChem 2003, 4, 49–59. (3) (a) Gupta, T.; Van der Boom, M. E. Angew. Chem., Int. Ed. 2008, 47, 5322–5326. (b) Katz, E.; Willner, I. Electrochem. Commun. 2006, 8, 879–882. (4) (a) Li, Z. X.; Liao, L. Y.; Sun, W.; Xu, C. H.; Zhang, C.; Fang, C. J.; Yan, C. H. J. Phys. Chem. C 2008, 112, 5190–5196. (b) Pischel, U.; Heller, B. New J. Chem. 2008, 32, 395–400. (c) Ballardini, R.; Ceroni, P.; Credi, A.; Gandolfi, M. T.; Maestri, M.; Semararo, M.; Venturi, M.; Balzani, V. AdV. Funct. Mater. 2007, 17, 740–750. (5) (a) De Silva, A. P.; Uchiyama, S. Nat. Nanotechnol. 2007, 2, 399– 410. (b) Raymo, F. M. AdV. Mater. 2002, 14, 401–414. (6) (a) Zhou, W. D.; Li, J. B.; He, X. R.; Li, C. H.; Lv, J.; Li, Y. L.; Wang, S.; Liu, H. B.; Zhu, D. B. Chem.sEur. J. 2008, 14, 754–763. (b) Zhao, L. Y.; Hou, Q. F.; Sui, D.; Wang, Y.; Jiang, S. M. Spectrochim. Acta, Part A 2007, 67, 1120–1125. (c) Xiao, S. Z.; Yi, T.; Zhou, Y. F.; Zhao, Q.; Li, F. Y.; Huang, C. H. Tetrahedron 2006, 62, 10072–10078. (d) Flood, A. H.; Ramirez, R. J. A.; Deng, W. Q.; Muller, R. P.; Goddard, W. A.; Stoddart, J. F. Aust. J. Chem. 2004, 57, 301–322. (e) Katz, E.; Willner, I. Angew. Chem., Int. Ed. 2005, 44, 4791–4794. (f) Szacilowski, K.; Macyk, W. Solid-State Electron. 2006, 50, 1649–1655. (7) (a) Raymo, F. M.; Alvarado, R. J.; Giordani, S.; Cejas, M. A. J. Am. Chem. Soc. 2003, 125, 2361–2364. (b) Baron, R.; Onopriyenko, A.; Katz, E.; Lioubashevski, O.; Willner, I.; Wang, S.; Tian, H. Chem. Commun. 2006, 2147–2149. (c) Katz, E.; Willner, I. Chem. Commun. 2005, 5641– 5643. (d) Shipway, A. N.; Katz, E.; Willner, I. In Structure and Bonding; Sauvage, J.-P., Ed.; Springer-Verlag: Berlin, 2001; Vol. 99, pp 237-281. (8) (a) Andreasson, J.; Straight, S. D.; Bandyopadhyay, S.; Mitchell, R. H.; Moore, T. A.; Moore, A. L.; Gust, D. J. Phys. Chem. C 2007, 111, 14274–14278. (b) Perez-Inestrosa, E.; Montenegro, J. M.; Collado, D.; Suau, R. Chem. Commun. 2008, 1085–1087. (9) Margulies, D.; Felder, C. E.; Melman, G.; Shanzer, A. J. Am. Chem. Soc. 2007, 129, 347–354. (10) (a) Credi, A.; Balzani, V.; Langford, S. J.; Stoddart, J. F. J. Am. Chem. Soc. 1997, 119, 2679–2681. (b) Pina, F.; Melo, M. J.; Maestri, M.; Passaniti, P.; Balzani, V. J. Am. Chem. Soc. 2000, 122, 4496–4498.

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