Stochastic On-Chip Detection of Subpicomolar Concentrations of

Article pubs.acs.org/ac

Stochastic On-Chip Detection of Subpicomolar Concentrations of Silver Nanoparticles Kay J. Krause,† Alexey Yakushenko,† and Bernhard Wolfrum*,†,‡ †

Institute of Bioelectronics (PGI-8/ICS-8) and JARAFundamentals of Future Information Technology, Forschungszentrum Jülich, 52425 Jülich, Germany ‡ Neuroelectronics, Department of Electrical and Computer Engineering, Technische Universität München, Boltzmannstr. 11, 85748 Garching, Germany ABSTRACT: We introduce the stochastic amperometric detection of silver nanoparticles on-chip using a microelectrode array. The technique combines the advantages of parallel and low-noise recordings at individually addressable microelectrodes. We demonstrate the detection of subpicomolar concentrations of silver nanoparticles with a diameter of 10 nm at sampling rates in the kilohertz regime for each channel. By comparison to random walk simulations, we show that the sensitivity of a single measurement is mainly limited by adsorption of nanoparticles at the surface of the chips and the measurement time.

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microelectrodes. However, recently it was demonstrated that the same devices can also be used for amperometric recording of vesicular neurotransmitter release from cells growing on top of the microelectrode array.19−21 In this configuration, each vesicle that releases its neurotransmitter content (e.g., dopamine molecules) close to one of the microelectrodes causes a distinct spike in the current trace due to the Faradaic reaction (e.g., oxidation of dopamine to dopaquinone). For such systems, Ag/AgCl pellets are widely used as reference electrodes. In this case, silver particle contaminations occurring from the surface of the pellet electrodes can cause artifacts in electrochemical measurments.22 Here, we employ this concept to the stochastic detection of individual silver nanoparticles at the surface of a thin-film microelectrode array. Within a fixed time frame, each oxidation event occurring during the collision of a particle with one of the electrodes is monitored. Due to the parallel recording of many electrodes at the same time, it is possible to quantify even low particle concentrations where only a few collisions per electrode occur. We demonstrate the detection of 10 nm silver nanoparticles within a concentration range from 500 fM to 40 pM and a temporal resolution of 0.5 ms using a MEA system with 62 working electrodes. We further analyze the size distribution of the nanoparticles by integration over the peak currents and estimate a limit of ∼9 nm for the smallest particles that can be detected for a bandwidth in the kilohertz range. Additionally, we calculate the peak frequency over time and

ilver nanoparticles have become widely used in recent years for sensing applications1−3 and commercial products due to their antimicrobial properties.4,5 The latter causes a release of several tons of nanoparticles to the environment every year.6 Despite this extensive application, the longtime effects of silver nanoparticles on humans and the environment are not fully understood.7,8 This is partially due to the fact that the detection of small particles at low concentrations remains challenging, making a high-throughput and long-term screening of environmentally relevant water systems difficult.9−12 One approach to detect silver nanoparticles in solution, including seawater, is the oxidation of the particles at a properly biased electrode surface.13−15 The sensitivity of such systems is limited by the diffusive mass transport to the electrode; therefore the detection limit for small concentrations is dependent on the electrode surface area. Using a single carbon fiber with 1 mm length and 7 μm cross-section, the detection of concentrations down to 90 fM has been reported.16 Unfortunately, large electrode areas lead to high capacitance and, therefore, high current noise, which limits the minimal size of particles that can be detected. When a low-pass filter is used to overcome this issue, the time resolution is also decreased. In turn, this limits the maximum number of events that can be detected within a given time span, effectively reducing the maximum concentration that can be detected. To combine the advantages of a large detection area and low-noise recordings, we employ a different approach, based on individually addressable microelectrode arrays (MEAs), which are generally used for the investigation of physiological activity in cellular networks.17,18 These investigations rely on the recording of small extracellular voltage signals generated by the cells in close contact with the © 2015 American Chemical Society

Received: April 20, 2015 Accepted: June 16, 2015 Published: June 16, 2015 7321

DOI: 10.1021/acs.analchem.5b01478 Anal. Chem. 2015, 87, 7321−7325

Article

Analytical Chemistry

this work is 50 nm. The diffusion coefficient for the simulations is calculated from the Stokes−Einstein equation.

show by comparison to random walk simulations that the sensitivity of the sensor is limited by the adsorption of nanoparticles at the electrode passivation.

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D=

METHODS MEAs were fabricated in the clean room using standard photolithography techniques. Borosilicate wafers with a thickness of 500 μm were used as a substrate. A metal stack was deposited for the electrodes consisting of an adhesion layer of titanium with a thickness of 10 nm and a 200-nm-thick platinum layer using electron beam evaporation. For the passivation of the feedlines, a 2 μm photodefineable polyimide layer, HD-8820 using the adhesion promoter VM-652 (HDMicroSystems), was used. A more detailed description of the fabrication process can be found elsewhere.23 After fabrication, the chips were sonicated for 5 min in acetone followed by 5 min in isopropanol. Finally, the chips were rinsed with Milli-Q water. To maintain a reservoir for the experiments, a glass ring with a diameter of 20 mm and a height of 10 mm was glued on top of the chip. A water-based dispersion of citrate-stabilized silver nanoparticles with an average size of 10 nm and a total silver concentration of 0.02 mg/mL was purchased by Sigma-Aldrich. The concentration of the nanoparticles from stock solution is 6000 pM. For the adjustment of the concentration, the nanoparticle solution was diluted with Milli-Q water directly before the measurements. Characterization measurements for the electrodes were performed using a VSP-300 potentiostat from BioLogic Science Instruments. A reservoir of 1 mL of 300 mM KCl solution was used as the electrolyte. For all measurements, a Ag/AgCl reference electrode (Super Dri-ref SDR 2 purchased from World Precision Instruments) was used as a reference electrode. For cyclic voltammetry experiments, a platinum wire was used as a counter electrode. Each electrode was measured for three cycles to achieve a steady state. Amperometric measurements were performed in a 150 mM KCl solution. The potential of the platinum microelectrodes was set to +400 mV vs the Ag/AgCl reference electrode. The data acquisition was performed using a parallel electrochemical amplifier system (PicoAmp) that has been developed at our institute. A detailed description of the system can be found elsewhere.19 Briefly, the system consists of a preamplifying 64channel head stage, followed by a main amplifier and an ADC converter (NI USB 6255, National Instruments). The output of the preamplifier stage used in these experiments was 1 mV/pA and subsequently amplified by a factor of 100 by the main amplifier. The sampling rate of each channel was 10 kHz (640 kHz in total), and for analysis the current traces were filtered using a moving average filter with a width of five data points. The framework used for the simulation of the peak frequency is based on a discrete random walk. Here, every particle is displaced for a certain distance dx along all three Cartesian axes within the time step dt. The temporal and spatial steps of one iteration width are connected by the diffusion equation. dt =

dx 2 2D

k B· T m2 = 4.4 × 10−11 6·π ·η·r s

(2)

Here, kB is the Boltzmann constant, T = 300 K is the temperature, η = 1 mPa·s is the viscosity of the liquid, and r = 5 nm is the radius of the particle. With a simulated measuring time of 22 s, this corresponds to 774 400 iterations. We simulated a single circular microelectrode with a diameter of 8 μm, which is embedded into a 2 μm high passivation layer. The reservoir on top of the passivation opening is 90 μm × 90 μm × 43 μm. The simulated number of particles is 25 500 corresponding to a concentration of 121.5 pM. This is equal to the cumulative concentration from one experimental concentration series. The simulated peak number is then scaled by of factor of 3 to match the correct number of experiments. A more detailed description of the source code can be found elsewhere.24,25

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RESULTS AND DISCUSSION A light microscope picture of the microelectrode structures is shown in Figure 1a. Prior to the nanoparticle detection, we

Figure 1. (a) Light microscope image of a multielectrode array. The diameter of the electrode openings is 8 μm. (b) Exemplary CV curves for two individual microelectrodes on a chip.

characterized the individual microelectrodes using cyclic voltammetry (CV). The potential of the electrode was swept between −0.3 and 0.6 V using a Ag/AgCl reference electrode, a platinum wire as counterelectrode, and a scanning speed of 100 mV/s. Exemplary CV curves of the third cycle can be seen in Figure 1b. For the amperometric detection of silver nanoparticles, 800 μL of 150 mM KCl solution is used as the electrolyte and a Ag/ AgCl reference electrode is inserted into the solution. The platinum working electrodes at the chip surface are biased to an oxidizing potential of 400 mV to make sure that all particles are oxidized when they are in contact with the electrode surface.26 After reaching a steady state current, 200 μL of nanoparticle solution is added to the reservoir. For the evaluation of the current traces, 2 s relaxation time directly after the insertion is neglected, followed by 20 s measuring time. Exemplary current traces from three electrodes are shown in Figure 2a. Individual peaks correspond to individual particles being oxidized. A magnification of a single-particle signal can be seen in the inset of Figure 2b. For evaluation of the peak frequency, an automatic peak detection algorithm is used to count the number of oxidation events per electrode. After every measurement, the chip was rinsed with Milli-Q water. Every

(1)

Furthermore, if a particle hits the electrode, an oxidation event is assumed to happen instantaneously, and the particle adsorbs at the electrode surface. The spatial step width used in 7322


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respectively. It should be noted that the condition described above is only valid for a single measurement of 20 s. Lower concentrations can be measured by simply increasing the number of experiments, the measurement time, or the mass transport of particles to the electrode, e.g., by introducing a microfluidic system. While the concentration correlates with the peak rate, the size of the particle can be inferred from the integral of the oxidation peak.27,28 The total charge (Q) transferred to the electrode within one oxidation event is equal to the number of oxidized silver atoms within the nanoparticle. If all atoms are oxidized, this can be transferred to a particle diameter (d), assuming a spherical shape of the particles.13

⎛ 3·Q ·ρ ⎞1/3 d = 2·⎜ ⎟ ⎝ 4·π ·e0·N ⎠ Figure 2. (a) Exemplary current traces from three electrodes. The blue part of the current (after insertion of the nanoparticles) was used for the analysis. The currents are inverted and shifted by 25 pA for a better illustration. (b) A magnified current spike attributed to the reaction with an individual silver nanoparticle.

(4)

Here, e0 is the elementary charge; N, the Avogadro constant; and ρ, the density of bulk silver. To calculate the total charge, we integrated the current of the individual oxidation peaks. The size distribution for all oxidation peaks measured during the concentration series is shown in Figure 4a with a total amount

concentration was measured three times, and the results of the peak frequencies were averaged. For the measured concentrations between 500 fM and 40 pM, the peak frequency scales linearly with the concentration (Figure 3) as expected. By

Figure 4. (a) Size distribution for 2010 nanoparticle oxidation events. The particle sizes are calculated from the integrals of the oxidation peaks according to eq 4. (b) A histogram of the temporal width of all individual oxidation peaks, calculated from the duration between the intersections of the peak with the baseline.

of 2010 events. The size distribution has a maximum around 10 nm as expected. The distribution is asymmetrical with a higher amount of particles with a diameter larger than 10 nm. This behavior can be explained by two reasons. First, it is known that nanoparticles tend to agglomerate,29,30 and second, the minimum diameter that can be detected is limited by the current noise. The automatic peak detection algorithm we use relies on a peak height that is 5 times larger than the current root-mean-square noise (RMS). Only electrodes with an RMS smaller than 1.6 pA are used for the analysis, and therefore, the requirement for the minimal peak height was set to 8 pA. During the peak integration, we also extracted the peak duration. The results are shown in Figure 4b. It can be seen that the peak duration varies between 1 and 2.5 ms with a maximum at 1.2 ms. Assuming a shortest peak duration of 1.0 ms and a triangular peak shape with a height of 8 pA, the total charge of the smallest peaks that can be detected is 4 aC, corresponding to a particle size of 9.3 nm. This is in line with the cutoff size we see in Figure 4. A detection of smaller particles is possible by applying a low-pass filter with a smaller cutoff frequency to the data. However, in this case, the time resolution would be compromised, leading to a decrease of the maximum

Figure 3. (a) Peak frequency per electrode dependent on the nanoparticle concentration. (b) The oxidation frequency dependence for higher concentrations up to 40 pM.

applying a linear fit, the relation between peak frequency and particle concentration can be extracted. For a single electrode and a measuring time of 20 s, this leads on average to Hz F(c) = 0.01 ·c pM (3) Here, F is the mean peak frequency per electrode and c, the concentration of nanoparticles within the electrolyte solution. This allows us to calculate the reliability for a single measurement of 20 s duration. For 62 working electrodes, a single event corresponds to a number concentration of 80 fM and a weight concentration of 2.7 × 10−10 kg/L. Assuming a Poisson distribution of the number of oxidation events, a 20% standard deviation is reached by an average value of 25 events per measurement. This value corresponds to 2 pM and 6.7 × 10−9 kg/L for the number and weight concentration, 7323


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For the simulation, we assumed the extreme cases of a totally adsorbing surface and a nonadsorbing surface of the passivation (Figure 5). It can be seen that the experimental results agree well with the model of an adsorbing passivation. This effect is limiting the sensitivity of our MEA sensor design for the detection of silver nanoparticles by an order of magnitude. For future applications, different surface modifications and electrode passivation materials should be tested to overcome this issue.

concentration that can be detected due to merging of single peaks. To investigate the influence of adsorption on the stochastic sensing of particles, we analyzed the time dependence of the detected peak frequency. It is known that silver particles can be adsorbed at the electrode passivation leading to a decrease in the concentration of freely diffusing particles and, therefore, the peak frequency.31 In Figure 5, the peak rate vs measurement

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CONCLUSIONS We demonstrated stochastic sensing of silver nanoparticles with a diameter of 10 nm and a wide range of concentrations below 1 pM using microelectrode arrays. The parallel amperometric recordings of each electrode were carried out with a time resolution of 0.5 ms per channel. For future applications, the usage of MEAs for the detection of nanoparticles may have further advantages. This includes the simultaneous stochastic detection of different metal particles like silver and nickel discriminated by different oxidation potentials32,33 or the detection of specific binding events for particles or electrodes with diverse functionalization.34,35

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AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected].

Figure 5. (a) Experimental (blue dots) and simulated time dependence of the oxidation peak rate for nonadsorbing (red) and adsorbing (green) boundary conditions at the chip surface. (b) A magnification of the experimental data trace and corresponding simulation.

Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS We thank Marko Banzet for the fabrication of microelectrode arrays and Norbert Wolters and Jan Schnitker for the development of the electronic amplifier system. We gratefully acknowledge funding by the Helmholtz Young Investigators Program.

time accumulated from all electrodes and measurements is shown (blue dots). The peak rate decreases from approximately 100 Hz at the beginning of the measurement to 15 Hz after 20 s of measuring time. To estimate the effect of adsorption at the passivation on the peak rate, we applied a random walk simulation, which was previously used to describe the influence of sensor geometries on current noise spectra and the effects of adsorption in redox cycling systems.24,25 Here, we simulated a single microelectrode embedded into a 2-μm-thick layer of passivation, which is connected to a bulk reservoir by a cylindrical opening. A sketch of the simulated design is shown in Figure 6; the dimensions are not to scale.

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Figure 6. Sketch of the geometry simulated by a random walk simulation. 7324


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Stochastic On-Chip Detection of Subpicomolar Concentrations of

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