Microparticle transfer onto pixel electrodes of 45 μm pitch on HV-CMOS chips—Simulation and experiment

Sensors and Actuators A 172 (2011) 533–545

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Sensors and Actuators A: Physical journal homepage: www.elsevier.com/locate/sna

Microparticle transfer onto pixel electrodes of 45 ␮m pitch on HV-CMOS chips—Simulation and experiment J. Wagner a,b,d,∗,1 , K. König a,b,c,1 , T. Förtsch a , F. Löffler a,b,2 , S. Fernandez b , T. Felgenhauer b , F. Painke a , G. Torralba a , V. Lindenstruth d , V. Stadler e , F.R. Bischoff b , F. Breitling c , M. Hausmann a,3 , A. Nesterov-Müller b,c,4 a

Kirchhoff-Institute for Physics (KIP), Chair of Computer Science, Im Neuenheimer Feld 227, 69120 Heidelberg, Germany German Cancer Research Center (DKFZ), Chip-Based Peptide Libraries, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany c Karlsruhe Institute of Technology (KIT), Institute for Microstructure Technology, Herrmann von Helmholtzplatz 1, 76344 Eggenstein-Leopoldshafen, Germany d Frankfurt Institute for Advanced Studies (FIAS), Ruth-Moufang-Str. 1, 60438 Frankfurt, Germany e PepPerPrint GmbH, Rischerstr. 12, 69123 Heidelberg, Germany b

a r t i c l e

i n f o

Article history: Received 8 December 2010 Received in revised form 8 April 2011 Accepted 21 June 2011 Available online 18 July 2011 Keywords: CMOS chip Solid-phase peptide synthesis Microparticle deposition Microarray Parameter optimisation COMSOL

a b s t r a c t Spatially selective deposition of electrically charged microparticles onto integrated circuits that generate electrical fields in programmable patterns using electrodes on their surface was previously limited to a pixel pitch of 100 ␮m. Now, we demonstrate spatially selective deposition onto pixels of 45 ␮m pitch in experiments on a test chip allowing arbitrary patterns, but being of limited size and of fixed characteristics, complemented by COMSOL simulations. Experiments on a prototype high voltage CMOS chip demonstrate the feasibility of miniaturisation in the first place, imply simulations of interest that cannot be tested experimentally and, conversely, complement the simplified simulation models by reality checks. Using COMSOL for the optimisation of the setup parameters, particles of decreasing average diameter in a number of aerosol and electrical field geometries are simulated with particular attention to minimising contamination (deposition of particles on undesirable locations). Combining these results, the average particle diameter is decreased from 10 ␮m to less than 3 ␮m and the deposition voltage is reduced from 100 V to 30 V, when using pixels with a pitch of 45 ␮m. Optimising these parameters allows for more than quadrupling the spot density compared to the previous chip, on which combinatorial particle deposition with minimal contamination is achieved. Peptide arrays, having been previously shown to be a major application for this method, benefit in particular, as the increase in density from 10,000 pixels/cm2 to approximately 50,000 pixels/cm2 promises a significant decrease in cost-per-peptide and amount of test specimens required. © 2011 Elsevier B.V. All rights reserved.

1. Introduction Selective particle transfer onto high-voltage microelectronic chips is a rather novel tool, but has already been successfully

∗ Corresponding author at: Kirchhoff-Institute for Physics (KIP), Chair of Computer Science, Im Neuenheimer Feld 227, 69120 Heidelberg, Germany. E-mail addresses: [email protected] (J. Wagner), [email protected] (K. König), [email protected] (T. Förtsch), f.loeffl[email protected] (F. Löffler), [email protected] (S. Fernandez), [email protected] (T. Felgenhauer), [email protected] (F. Painke), [email protected] (G. Torralba), [email protected] (V. Lindenstruth), [email protected] (V. Stadler), [email protected] (F.R. Bischoff), [email protected] (F. Breitling), [email protected] (M. Hausmann), [email protected] (A. Nesterov-Müller). 1 These authors contributed equally to this work. 2 Tel.: +49 6221 42 1543; fax: +49 6221 42 1744. 3 Tel.: +49 6221 54 9824; fax: +49 6221 54 9112. 4 Tel.: +49 7247 82 9253; fax: +49 7247 82 4331. 0924-4247/$ – see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.sna.2011.06.017

applied e.g., in peptide array synthesis with very high density of synthesis sites [1,2]. In this method, particles similar to laser printer toner are loaded with protected and activated amino acids. These particles are negatively charged and selectively deposited onto peptide synthesis sites on a chemically activated surface. After deposition, the particles are melted, initiating the coupling step of the amino acids contained therein to the synthesis sites. The remnants of the particle matrix are washed from the support after coupling, followed by chemical processing steps to cap (permanently terminate the synthesis of) growing peptide chains to which no amino acids have coupled in this deposition steps and to deprotect the newly coupled amino acids so that the next deposition step can be started. Applying this variant of Merrifield synthesis, array production with a laser printer has been demonstrated by Stadler et al. [3], and is now commercially available at up to 400 peptide spots/cm2 , offering an alternative to established peptide library synthesis techniques such as SPOT technology [4]. These peptide libraries on solid supports are used in numerous biochem-

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Fig. 1. Left: coarse fraction of microparticles with mean diameter of 15.6 ␮m. Right: fine fraction of microparticles with mean diameter of 2.3 ␮m.

ical applications where a large number of peptides is exposed to labelled molecules in solution, i.e., immunoassays, examinations of enzyme-substrate bindings or the search for peptidic medications. Particle-based deposition on CMOS5 chips as solid supports allow an even higher level of miniaturisation than liquid-based or particle-based laser printing approaches: each synthesis location on a CMOS chip support, also called pixel or spot, corresponds to an electrostatic actuator that can be programmed to attract or deflect the triboelectrically charged particles including the logic circuitry required to switch it. Thus, monomer deposition and the subsequent synthesis are self-aligned and the mechanical effort of ensuring alignment is avoided. This allows reducing the spot sizes for synthesis without loss of precision in the deposition process. Increasing miniaturisation not only allows for a reduced cost-perpeptide, but also enables new applications, where test material, e.g., blood serum, is limited to small amounts so that a high density of spots is required to obtain satisfactory results. Using a chipbased system, a spot density of 10,000 peptides/cm2 has already been achieved [1]. As an additional advantage over liquid-based approaches, in which coupling of the monomers in the solution to the surface is immediately initiated upon contact, particle-based systems allow for verification steps [5] before the coupling process is started by melting the particles. Producing peptide arrays with spot densities beyond 10,000 peptides/cm2 using CMOS chips requires further miniaturisation of the synthesis sites and a decrease in toner particle diameter, as the size of the particles becomes comparable to the size of the synthesis spot. While purely experimental approaches to optimise spatially selective particle transfer from the aerosol resulted in good quality for spots of 100 ␮m pitch, a theoretical analysis and systematic optimisation of the deposition process and the setup becomes necessary when further miniaturising [6]. Since experimentally rebuilding each possible setup for testing is expensive due to the time-consuming development and mask costs for each new design, simulation supported theoretical modelling of the deposition process is performed. In order to do so, we first prove feasibility of a spot pitch of 45 ␮m on a prototype chip with a limited number of pixels using the two particle batches that were already successfully deposited onto the chip with 100 ␮m pixel pitch. The tested electrical field configurations on the prototype chip then indicate the experimental limitations of the method that cannot be modelled in the simulations and show which simulations are of interest that cannot be experimentally tested. In the simulation software COMSOL, simulations of different miniaturised setups are implemented to select an optimal setup with respect to finding the minimum amount of incorrectly deposited particles (contamination) and simultaneously guaranteeing well-covered spots to obtain high densities of correctly assembled peptides in the array. Combining the results obtained by experiments and simulations, the optimum setup (particle size, particle charge, voltage applied to the spots, etc.) for an applica-

5

Complementary Metal Oxide Semiconductor.

tion chip with 45 ␮m pixel pitch can be determined and used for peptide synthesis in the future. 2. Materials and methods: microelectronics For experimental verification of the simulation results and proof-of-principle experiments, high-voltage CMOS chips designed in AMIS I2T100 0.7 ␮m technology with triple metal option as described in [1] were used. These chips provide a number of different arrays, each consisting of 8 × 8 identical square pixels, which are suitable to study the influence of pixel size and geometry on particle transfer. Each pixel contains an electrode which can be set to the HV supply voltage, up to 30 V in the case of the pixels used for this work, or to ground. The pixel electrodes are located on the topmost metal layer, covered only by passivation (silicon nitride, approx. 1 ␮m thick), and thus project an electric field in the volume above the chip. The pixel electrode is set to HV or ground using an HV inverter, which is controlled by a low-voltage memory cell. Each memory cell can be configured using an I2 C interface and a notebook PC, allowing the generation of an arbitrary pattern on the whole chip in less than 5 s. Combinatorial array synthesis applications pose conflicting requirements on the pixel geometry: While a smaller spacing (and grid) increases the pixel size and may thus ease detection, a larger spacing may make it easier to separate adjacent spots. In addition, analysis software (e.g., reused from liquid-based methods) may favour round synthesis sites, which are common in liquid-based methods, while square pixels offer a larger synthesis area at the same pixel pitch. Therefore, pixels with square electrodes, pitches of 44 ␮m, spacings of 10 ␮m or 16 ␮m and 6 ␮m or 12 ␮m grid width to separate the spots are tested in Section 5. Additionally, circular electrodes with 33.4 ␮m diameter are investigated. The grid is common for all pixels, also located on the top metal layer, and set to a reference voltage that can be chosen arbitrarily (up to more than 100 V). 3. Materials and methods: particle transfer environment 3.1. Microparticles Similar to toners that are used in laser printing, the microparticles for on-chip synthesis consist of a solvent (resin) with a density of 1100 kg/m3 in which the preactivated amino acids (Fmoc–amino-acid–OPfp–ester), which are commercially available, are contained. Embedded in the solid solvent, they are relatively stable [2,3]. In addition, small amounts of charge control agents improve triboelectrial charging behaviour of the particles, and dye molecules improve visibility, as the resin is transparent. Measuring the particle size distribution of one sort of particles with a Malvern Mastersizer (Malvern Instruments Ltd.), two types of batches can be distinguished as shown in Fig. 1, the coarse fraction with a mean diameter of 15.6 ␮m (left) and the fine fraction with a mean diameter of 2.3 ␮m (right).

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Fig. 2. Left: experimental setup photograph. Right: experimental setup schematic. An ejector, driven by pressurised (transport) air, siphons the amino acid particles out of the particle reservoir. The particles dispersed in air are guided through a tube to the outlet and thus reach the electric fields of the chip.

3.2. Deposition apparatus The particle deposition onto the chip is handled by the setup shown in Fig. 2 (left). Fig. 2 (right) shows the principle of aerosol generation: An air ejector, driven by pressurised (transport) air, siphons the amino acid particles out of a reservoir and generates an aerosol. The aerosol is then guided through a tube to an outlet, where the particles have to pass a metal sieve with 10 ␮m pore size and finally reach the electric fields of the chip. The sieve is set to a high negative voltage of −1 kV, which ensures a highly reproducible aerosol quality: The sieve destroys particle agglomerates and restrains them from reaching the chip surface. Furthermore, the high negative voltage effectively controls the charge of the particles. For further details on the deposition process and the sieve, see [6]. 4. Materials and methods: simulation of particle transfer 4.1. Prerequisites The aerosol in the vicinity of the chip surface is modelled as a laminar decoupled 2 phase flow. The gaseous phase is described as an incompressible, isothermal, continuous stream, while each particle of the solid phase to be transported by the airstream is tracked individually. Assuming that the particles are diluted in the airstream, i.e., particle–particle interactions are negligible, they do not influence the airstream, and thus induce a decoupling. If a refining of this first approach is necessary (weak) coupling can be introduced over an exchange of energy or momentum. This ansatz is supported by calculating the relevant numbers to characterise the experimental setup shown in Figs. 2 and 3: • The Reynolds number Re =

vL 

(4.1)

is 66 for the airstream with velocity v = 0.1 m/s, density  = 1.204 kg/m3 at room temperature and dynamic viscosity  = 18.36 × 10−6 Pa s in the vicinity of the chip surface, with L = 0.01 m being the distance between the outlet of the chamber and the chip surface, indicating that the flow is laminar. • The Knudsen number Kn =

 L

(4.2)

for an airstream with the mean free path length  = 69.5 nm of the air molecules divided by the characteristic length of the geom-

etry L = 0.01 m (the distance between the outlet of chamber and the chip surface) is much smaller than 1, which shows that the equations of motion for the airflow can be formulated by means of continuous fluid mechanics instead of the molecular, statistical approach. • The Mach number Ma =

v vs

(4.3)

for an airstream of v = 1 m s−1 in the vicinity of the chip surface is also much smaller than 1, as the velocity of sound in air at room temperature is given by vs = 343 m s−1 , which allows to model the airstream as an incompressible flow. • The Stokes number St =

p 16p (rp )2 = f 3CD Re

(4.4)

is defined as the quotient of the characteristic response time of the particles  p to the characteristic time of the particle flow  f characterising the degree of interaction between the two phases. Expressing  p and  f as described in [7], the right hand side of Eq. (4.4) can be deduced, where p = 1100 kg/m3 is the density of the particle matrix, rp = 1 ␮m their radius and CD the drag coefficient, which can be approximated by CD = 0.1 for rough, spherical particles. This leads to Stokes numbers in the range of 10−5 , implying that the particles in the transport air actually form a dilute phase. Hence, particle-particle interactions can be neglected, leading to a large Knudsen number for the particles, which motivates the statistical mechanics model for them. Further assumptions are that the particle motion can be approximated by the trajectory of their centre of mass and that the gravitational force can be neglected in a first order approximation, being at least one order of magnitude smaller than the two forces included in the calculation: • The Coulomb force FC = −q∇ (x, y, z)

(4.5)

acting on a particle with triboelectric charge q in the electric potential (x, y, z) of the chip electrodes can be estimated as FC = 2.3 × 10−10 N by inserting q = −2.3 × 10−17 C and approximating the average electrical field strength by |||| = 10 V/␮m. The value for q originates from the fact that the average q/m value of these particles is in the range of −2 to −5 mC/kg as measured in [8,9] and the assumption that the charge of one particle is proportional to its volume. The estimate of the average electrical field will be motivated in the simulations shown in Fig. 5. • The Khan–Richardson force FKR = (rp )2 (v − vp )2 (1.84(Re2 )−0.31 + 0.293(Re2 )0.06 )

3.45

(4.6)

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according to [10] describes the total force the airstream with density  and velocity v exerts on the spherical particles travelling at velocity vp . In this case, the Reynolds number Re2 is not identical to the one in (4.1) but is the Reynolds number of the dispersed two phase flow, given by: Re2 =

|v − vp |rp 

(4.7)

taking into account the relative velocity v − vp between the particles and the airstream. Calculating an estimate of the strength of this force is difficult, as the relative velocity between the two phases varies from coordinate to coordinate and hence, has to be calculated for each point in the geometry individually. 4.2. Simulation of the airstream According to the characteristic numbers calculated in Eqs. (4.1)–(4.4), the equations of motion for the airstream of constant density are given by the incompressible, isothermal Navier–Stokesequations, stating conservation of momentum (first equation) and conservation of mass (second equation): 

∂ v − ∇ (∇ v + (∇ v)T ) + (v∇ )v = −∇ p + fext ∂t

div(v) = 0

(4.8a) (4.8b)

The first term in the first equation describes the acceleration over time, the second is the viscous net force and evolves as a result of the resistance to the rate of deformation of fluid elements. The third term is the convective acceleration, which is caused by a change in velocity over position. On the right hand side, the pressure gradient p and external forces per volume are listed. In the special case of the particle deposition, the external forces are zero. For fully developed flows, the first term can be omitted as the velocity change over time is negligible. This is the case when the time to reach the chip surface is much smaller than the time of the entire deposition. Since the latter is in the range of seconds, the time independent version is supported, as the velocity of the pressurised air containing the particles injected into the chamber is in the range of 1 m/s, implying transport times in the range of 0.05 s for the distance between the injector and the chip surface. Furthermore, the geometry of the deposition chamber (Fig. 2) in the vicinity of the chip has to be taken into account, providing the boundary conditions for the Navier–Stokes-equation. Assuming that the adhesion force to the chamber walls and the chip is stronger than any other force at first contact to the boundary, the velocity of the airstream is set to zero there (which is sometimes called the no slip boundary condition). Taking the estimates of Section 4.1 into account, the boundary and initial conditions shown in Fig. 3 are reasonable to assume.

Solving the Navier–Stokes-equation given the boundary conditions is impossible in closed analytical form due to the non-linearity on the left side of Eq. (4.8a). A very detailed analysis of the problem formulation described above as well as algorithmic implementations of an approximated solution can be found in [11]. The method of choice for solving Eq. (4.8a) and Eq. (4.8b) according to [11] is based on the discretisation of the geometry by finite elements and an iterative solution of the linearised equation on the single elements. Finding an approximative solution of Eq. (4.8a) and Eq. (4.8b), the prerequisites calculated in Section 4.1 allow to select a stationary solver that linearises Eq. (4.8a) and Eq. (4.8b) according to the prescriptions detailed in [12]. Then, the linearised equation is solved numerically by a damped Newton method (see [13] for implementational details) on the discretised geometry of the finite elements. The latter are created as the standard triangular Lagrange finite elements of secondary order for the velocity and of first order for the pressure by respecting that the mesh size h of the elements must be such that the Péclet number: Pe =

|ˇ|h 2c

(4.9)

is smaller than one, which provides the necessary regularity of the solution and ensures numerical stability. ˇ is given as the coefficient of the diffusion term and c as the coefficient of the convection in the linearised Navier–Stokes-equation. Since the Péclet number must be calculated for each coordinate in each optimisation step, there is no average or estimate to be calculated here. A mesh consisting of 50,868 finite elements, that has been refined in a region of interest around 20 pixels, with average element area ratio in the range of 10−6 discretises the geometry of the vicinity of the chip in 1 cm distance (shown on the left side of Fig. 4) and UMFPACK (see [14] for implementational details) with a relative error below 10−6 is used to calculate the numerical solution to Eq. (4.8a) and Eq. (4.8b), which is given by the velocity distribution shown on the right side of Fig. 4.

4.3. Simulation of the electrical potential Due to the linearity of the electro-magnetic theory, the electric potential in the proximity of the chip consists of the superposition of the electric potentials of all pixel electrodes at their locations (sj , tj ) in the x–y-plane for a constant height z0 . Without loss of generality, z0 = 0. The electric potential of one pixel electrode that is assumed to be homogeneously charged can be calculated by formulating Poisson’s equation and the constraints at the borders of the cell. Let ∈ C0∞ (˝ ⊂ R3 , R) be a test function and j ∈ C 2 (˝ ⊂ R3 , R) the electric potential of one pixel electrode (or a grid section) at position

Fig. 3. Left: extensions of the region of interest in the vicinity of the chip surface. Right: boundary conditions and initial values for the Navier–Stokes-equation.

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Fig. 4. Left: finite element discretisation of the x–z-plane of the geometry. Right: numerical solution for the velocity function in the x–z-plane obtained by COMSOL.

(sj , tj ,0). Poisson’s equation, which must be valid for all , reads:





j (x, y, z) (x, y, z)d˝ = −4ε0˝

˝

(x, y)ı(z) (x, y, z)d˝ (4.10a)

The boundary conditions are: / 0) = 0 j (x < sj , y, z =

4.4. Simulation of the particle motion

j (x, y < tj , z = / 0) = 0

/ 0) = 0 j (x > sj + a, y, z =

j (x, y > tj + b, z = / 0) = 0 (4.10b)

which yield the integral equation:



˝

 ı(z) (x − sj ) (sj + a − x) (y − tj ) (y − tj )

×(tj + b − y) (x, y, z)d˝

After convergence of the airstream calculation and modelling the electric potential, the traces of the particles can be determined by inserting their parameters (diameter, mass, charge) and initial conditions (start points, initial velocity) in the particle tracking module of COMSOL that simply solves Newton’s equation of motion md2 x =F dt 2

j (x, y, z) (x, y, z)d˝ = −4ε0˝

the non charged pixels is set to 20 V, as experiments revealed that charging the grid between the pixels prevents contamination on the non charged pixels [1]. Furthermore, COMSOL facilitates the inclusion of other supplementary devices in the model, e.g., the installation of a sieve at the inlet (see Fig. 3), which has been shown to reduce contaminations [6].

(4.11)

where is the constant surface charge density, a the length and b the width of the pixel electrode. Using COMSOL to calculate an approximation to the electric potential of the chip, (x, y, z) must be replaced by a Lipschitz continuous charge density in order to obtain a unique solution of this Dirichlet problem. The result shown in Fig. 5 can be obtained by formulating Poisson’s equation as a linear system of equations, using about 51,000 Lagrange elements with element area ratio in the range of 10−6 to triangulate the region and solving the linear optimisation problem up to a tolerance of 10−6 with the stationary UMFPACK solver. In Fig. 5, two pixels with surface charge density are separated by seven pixels that are not charged. The picture is drawn for a profile through the x–z-plane. Apart from setting the potential at the chip surface to 30 V for the charged pixels, the grid between

 t, x, dx  dt

(4.12)

where F are the external forces acting on the particle with mass m. At first, the electrical force in form of Eq. (4.5), i.e., determining the derivative of the electric potential of Section 4.3, is added to the right hand side. Second, the Khan–Richardson force, Eq. (4.6), as a generalisation of the drag force is also included. Eq. (4.12) is rewritten as a system of ordinary differential equations and solved in COMSOL by using a pair of Runge–Kutta methods of orders 4 and 5, as described in [12]. 5. Results: particle transfer experiments 5.1. Experimental parameters In order to ensure satisfactory deposition for peptide synthesis, arbitrary patterns must be depositable with good quality. This is verified by testing a number of representative deposition patterns on the chips described in Section 2. Due to the limited size of the 8 × 8 pixel matrices, only repetitive patterns of 4 × 4 pixels are tested, so that for each pattern, four deposition results are

Fig. 5. x–z-profile of the electric potential of two pixels at 30 V (black) separated by seven pixels at 0 V (grey), shown are equipotential lines for every 2 V. The circular equipotential lines in between the two pixels at 30 V belong to the grid electrode (dark grey) set to 20 V.

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Fig. 6. Deposition of particles in different patterns: (a) checkerboard deposition, 1:1, (b) 15 active pixels out of 16, 15:1, (c) one active pixel out of 16, 1:15, (d) chip structure with grid electrode (highlighted) and sample regions of interest for contamination or coverage (circles). All depositions are achieved with the particle batch shown in Fig. 1 (right) with 30 V pixel voltage and 20 V grid voltage. Locations of increased deposition at the edges of the matrices are caused by high voltage supply tracks.

shown. This has the advantage that boundary effects in the pattern are minimised when focusing on the pattern in the centre of each 8 × 8 matrix. Unless noted otherwise, active pixels are set to 30 V, inactive pixels to 0 V, while the grid is kept at 20 V. The sieve voltage is −1000 V for all experiments (see Section 3). The photographs of the depositions are acquired with a Zeiss Axiovert 35 equipped with a 5×/0.12 objective in combination with a Zeiss Progres C5 camera on the C-mount behind a 0.63 TV-adaptation.

5.2. Particle transfer in representative patterns As representative patterns for particle deposition, we show particle transfer onto a checkerboard pattern, transfer in a pattern of one active pixel out of 16, and 15 active pixels out of 16. These patterns cover the range between areas with very few and almost all pixels in a region of the chip to be deposited with particles. The deposition results are shown in Fig. 6, with very few contaminating particles discernable on the pixels. On the checkerboard pattern and the pattern of one pixel in 16 deposited, pixels to be deposited on are completely covered. However, the deposition onto 15 out of 16 pixels shows that only few particles are deposited onto each individual pixel. This effect persists even if the chip is exposed to the aerosol repeatedly. While such patterns are occasionally required in combinatorial syntheses, this limitation can easily be overcome by depositing such patterns in two sequential

steps, dividing the large area into two complementary checkerboard patterns.

5.3. Comparison of grid widths Ideally, particle transfer would be possible over a range of grid widths, and for both square and circular pixels, without loss of quality, so that the actual pixel geometry can be decided considering only the needs of the detection system used. Thus, we analyse particle transfer onto pixels with different ratios between grid and electrode size, and circular electrodes in addition to square ones (s. Section 2). The comparisons are preformed for a pattern of one active pixel out of 16, as this pattern is most prone to contaminations. The images are taken from a single deposition procedure in order to ensure that the aerosol conditions are equal for all matrices. The deposition results (Fig. 7) show that the desired pixels are fully covered and contaminations are practically absent. Hence, all 3 types of pixels are equally suitable for particle deposition. Yet, taking into consideration the results of sequential particle deposition gained in Section 5.4 (s. Fig. 8), a square electrode of 6 ␮m grid width seems to be the best one, as it shows the least amount of particles deposited on the grid, followed by the circular pixels. In order to show the influence of the grid width on particle deposition, the simulation part (s. Section 6.5) systematically investigates

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Fig. 7. Particle deposition onto pixel matrices with different grid geometries: (a) square electrodes, grid width 6 ␮m. (b) Square electrodes, grid width 12 ␮m. (c) Circular electrodes of 33.4 ␮m diameter, encircled by the grid electrode (with an inner diameter of 37.4 ␮m around each pixel).

the coverage of the spots and the grid for various grid widths and particle characteristics. 5.4. Sequential particle transfer In a combinatorial synthesis, several particle depositions are required for each layer in order to synthesise a variety of molecules. This is implemented by sequentially depositing the particles containing different amino acids onto the chip until each spot is covered with one sort of amino acid particles, i.e., there is no intermediate coupling or washing step between depositions of particles containing different amino acids. To test the influence of particles already deposited on chip electrodes and the grid upon subsequent depositions, 16 depositions are performed in sequence. The results (Fig. 8, images from all deposition steps in the supplementary online materials) show that the amount of particles deposited on the grid electrode steadily increases over the 16 depositions performed. Nevertheless, the level of contamination on undeposited pixel electrodes remains negligible after 12 depositions, and low enough to be acceptable after 15 depositions, since the region of interest, a circle in the spot centre as defined in Fig. 6 and further detailed in [5], still remains uncovered. There are some insufficiently covered spots, marked by red squares in Fig. 8, while others are sufficiently covered but seem to be insufficiently covered. Comparing the degree of coverage after layer 8 with the same spot after layer 15, it becomes clear that the increasing height of the particle accumulations on the grid cause this impression (see green squares in Fig. 8) (For interpretation of the references to colour in this figure text, the reader is referred to the web version of this article.).

If the amount of particles deposited on the grid in each deposition could be reduced even further, e.g., by lowering the grid voltage, a larger number of depositions could be performed before a significant amount of particles accumulates on the grid, making quality analysis very difficult. 6. Results: particle transfer simulation 6.1. Correspondence of simulation and experiment At first, the models simulated with COMSOL are tested with respect to their ability to explain and thus predict the effects observed in microparticle deposition experiments. To do so, depositions performed with the coarse and the fine fraction microparticles described in Section 3.1 on a 100 ␮m pixel pitch CMOS chip (s. [1] for details) are compared to COMSOL simulations. As proved in [6], experiments and simulations show very good coincidence: in both cases, contaminated pixel electrodes occur when using particles with diameter in the range of 15 ␮m, the pincushion effect which arises due to boundary effects of the pixel electrode is observed in theory and experiment and furthermore, the typical partial coverage of one half of each pixel electrode in the vicinity of other covered pixels can also be reproduced by simulations. Due to this high degree of coincidence, modelling with COMSOL demonstrably is a reliable tool to predict the optimum configuration of the next chip generation. Following up on the experiments performed in Section 5, the simulations in Sections 6.2–6.6 aim at systematically investigating the effects observed experimentally and connecting them to the-

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Fig. 8. Sequential particle deposition with particle batch shown in Fig. 1 (right) for 30 V pixel voltage and 20 V grid voltage (a) after 2 depositions, (b) after 8 depositions, (c) after 12 depositions, (d) after 15 depositions. (First deposition shown in Fig. 7a, other deposition steps in supporting online materials). Both the number of insufficiently covered pixels (squares) and the impression of insufficient coverage for sufficiently covered pixels (circles) increase.

ory. Additionally, the change in magnitude or scale of the effects is analysed by varying the causative parameters. For clear visualisation purposes, a sparse configuration of two pixels at 30 V separated by seven pixels at 0 V is chosen. This also enables to test sparse deposition patterns with respect to their feasibility, which is not possible experimentally due to the limited number of spots (see Section 2).

can therefore be manipulated more easily by external forces. The smaller the particle size, the more particles are correctly deposited on the pixel electrodes and the fewer particle trajectories end on the grid or on incorrect pixels. Hence, particle sizes should be in the range of less than 10 ␮m in order to avoid contamination and maximise the number of correctly deposited particles on the pixels. 6.3. Influence of particle charge

6.2. Influence of particle size In analogy to the experiments carried out in [6], the influence of the particle size on the deposition quality is investigated. In the simulation model, the boundary conditions and initial values for the Navier–Stokes-equation are given as shown in Fig. 3. The boundaries to calculate the electric potential are given by the pixel voltage, set to 30 V or 0 V, depending on whether the pixel is to be covered, the grid voltage, set to 25 V, and the voltage of the sieve, modelled as a conducting plate at the inlet and set to −1000 V. Fig. 9 shows the simulated deposition results for particles with a q/m value of −3 mC/kg for 2 ␮m, 3 ␮m, 5 ␮m, 7 ␮m and 10 ␮m diameter (from top to bottom). Two pixels are set to 30 V separated by 7 pixels at 0 V and the grid electrode between adjacent pixels. As can be observed, contamination increases from top to bottom, starting for particles with a diameter of more than 7 ␮m. Along with contamination, the number of particles deposited on the grid increases. Both of these effects can be explained by the fact that smaller particles have less inertia than the larger ones and

Using the same boundary conditions and initial values as in Section 6.2, the influence of the charge or the q/m value of the particles on the deposition quality is analysed. For particles with diameter 5 ␮m, the q/m values are varied from 0 mC/kg (for reference purposes) over −0.1 mC/kg, −1mC/kg, −3 mC/kg and −15 mC/kg to −30 mC/kg as shown in Fig. 10 from top to bottom. The simulations imply that with increasing absolute charge6 , contamination also increases. As expected, the particle trajectories for smaller charges are dominated by the path of the air stream, while with increasing charge, the trajectories tend to follow the electric field lines. Noting that the Khan–Richardson force in Eq. (4.6) has the same value for all three q/m values, Fig. 10 shows the influence of increasing Coulomb force (Eq. (4.5)) in the equation of motion for the particles defined in Eq.

6 For the sake of simplicity, charge means the absolute value of charge throughout this article.

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Fig. 9. Top to bottom: COMSOL simulations for particles with a q/m value of −3 mC/kg and diameter of 2 ␮m (a), 3 ␮m (b), 5 ␮m (c), 7 ␮m (d) and 10 ␮m (e) onto a chip with 45 ␮m pixel pitch, 2 ␮m insulator and 2 ␮m grid width. Two pixels are set to 30 V, separated by 7 pixels at 0 V with the grid electrode at 25 V.

(4.12). As the deposition results indicate, at very small q/m values, the Coulomb force is so weak that the Khan–Richardson force dominates the motion of the particles such that no particles are deposited onto the chip. For q/m values in the range of −1 mC/kg to −3 mC/kg, Coulomb and Khan–Richardson force are balanced, leading to homogeneous depositions onto the pixels at 30 V. Particles with a q/m value of more than −3 mC/kg follow the electric field lines as the Coulomb force dominates the Khan–Richardson force, which then leads to worse deposition results as the particles are focused to the centre of the pixel electrodes and the grid. In the worst case, for particles with high q/m value and a large diameter, this effect leads to contaminations as shown in Fig. 10f.

6.4. Influence of grid voltage Upon investigating the influence of the particle parameters on the deposition quality, the parameters of the chip can also be finetuned to achieve minimal contamination. At first, the grid voltage is varied from 0 V over 5 V, 10 V, 15 V, 20 V to 25 V with a fixed pixel voltage of 30 V and all other conditions also fixed as described in Section 6.2. From Fig. 11, which shows particles with 5 ␮m diameter and q/m = −30 mC/kg, it can be clearly observed that increasing the grid voltage to 15 V or above reduces contamination. Since an increase in the grid voltage increases the electric potential (and hence also its gradient) in the vicinity of the chip surface, the

electrical force gains importance over the non-charge-dependent Khan–Richardson force and, thus, reduces contamination.

6.5. Influence of grid width As another influence on deposition quality, the pixel electrode size and grid widths for fixed spot density are varied. Decreasing the pixel electrode size from 39 ␮m over 35 ␮m and 32 ␮m to 29 ␮m in Fig. 3 and leaving the width of the insulator as 2 ␮m, the grid electrode between adjacent pixel electrodes is enlarged from 2 ␮m to 12 ␮m. All other boundary conditions and initial values remain the same as described in Section 6.2. Comparing the deposition on these chip geometries for particles with 5 ␮m diameter and a q/m value of −30 mC/kg, as shown in Fig. 12, the degree of contamination decreases for an increased width of the grid electrode, yet, the resulting deposition quality is not significantly improved for grid widths above 6 ␮m. Therefore, the optimum grid width is 6 ␮m, being large enough to avoid contamination from medium sized highly charged particles on the one hand and being small enough to allow for a large spot for peptide synthesis on the other. In case of particles with 3 ␮m diameter and a low q/m value of −3 mC/kg under the conditions described in Section 6.2, even a 2 ␮m grid is sufficient to achieve a contamination free deposition.

Fig. 10. Top to bottom: COMSOL simulations for particles with diameter 5 ␮m and q/m value 0 mC/kg (a), −0.1 mC/kg (b), −1 mC/kg (c), −3 mC/kg (d), −15 mC/kg (e) and −30 mC/kg (f) onto a chip with 45 ␮m pixel pitch, 2 ␮m insulator and grid width. Two pixels are set to 30 V, separated by 7 pixels at 0 V with the grid electrode at 25 V.

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Fig. 11. COMSOL simulations for particles with diameter 5 ␮m and q/m value −30 mC/kg in electrical field with grid voltage (a) 0 V, (b) 5 V, (c) 10 V, (d) 15 V, (e) 20 V, and (f) 25 V.

Increasing the grid width, no effect on the deposition quality can be observed.

sieve voltage is to be adjusted large enough to filter them out and small enough to avoid contamination of highly negatively charged particles, which occurs at −1000 V.

6.6. Influence of the sieve voltage In a final simulation, the influence of the sieve voltage at the inlet (s. Fig. 3) on the deposition quality is investigated. Fig. 13 shows simulations with particles of 5 ␮m diameter and q/m value of −30 mC/kg under the conditions of Section 6.5, i.e., with the optimal grid width of 6 ␮m. Varying the voltage from 0 V over −30 V, −500 V and −1000 V to −2000 V, it can be read off (a) to (e) that the particle trajectories come closer to the chip surface, leading to contamination in the case of −2000 V. To explain this phenomenon, the Coulomb force as defined in Eq. (4.5) must be split into a near field force in the vicinity of the chip and a far field force ranging over the entire deposition region. The former is caused by the pixel and grid voltage, the latter by the electric field between the potential of the chip and the sieve. Due to the homogeneity of the potential more than 60 ␮m far away from the chip (s. Fig. 3), the electric far field can be calculated as the quotient of the voltage difference of the chip and the sieve and their distance. Hence, the larger the absolute value of the sieve voltage, the stronger the particles are accelerated towards the chip. Being two orders of magnitude larger than the voltages applied to the pixels and grid, the far field effects dominate, and hence lead to contaminations for highly charged particles, as already observed in Fig. 10. Thus, the optimum sieve voltage would be 0 V, if there were no agglomerations or particles of positive charge. Taking into account these suboptimal particles, the

7. Discussion and outlook: miniaturised particle transfer in simulations and experiments 7.1. Scope of simulations and experiments With the prototype CMOS chip as described in Section 2 and the fine fraction particle batch of Fig. 1 (right), a first particle deposition can be performed. Further experiments include the investigation of different deposition patterns, as [6] found out that different patterns can lead to variations in the coverage of the pixels. This effect is indeed observed on the prototype chip for dense deposition patterns, analogous to [6], proving that effects occurring for a pixel pitch of 100 ␮m can also be found on smaller pixels of 45 ␮m pitch. Additionally, a sequential particle deposition of 16 steps is performed to determine the influence of 16 single pixel particle depositions on the overall deposition quality. The latter is hard to estimate in this experiment (shown in Fig. 8) as the particle accumulations on the grid increase with every deposition step, leading to underestimates of the pixel coverage. Contaminations, however, only occur at the borders of the pixels due to particles on the grid melting into that region. But, since the corners of the spots are not inside the main synthesis region as defined in [5], they are acceptable.

Fig. 12. COMSOL simulations for pixel electrode size of 39 ␮m and grid width of 2 ␮m (a), 35 ␮m pixel electrode and 6 ␮m grid width (b), 32 ␮m pixel electrode and 9 ␮m grid size (c) and 29 ␮m pixel electrode and 12 ␮m grid width (d), separated by 2 ␮m insulator.

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Fig. 13. Top to bottom: COMSOL simulations for particles with 5 ␮m diameter, q/m value of −30 mC/kg and a pixel electrode size of 35 ␮m and grid width of 6 ␮m for sieve voltages of 0 V (a), −30 V (b), −500 V (c), −1000 V (d) and −2000 V (e). Note that at low sieve voltages, the trajectories onto the activated pixels converge already at a distance from the chip greater than the 50 ␮m shown here.

The influence of particles already deposited on the chip upon subsequent depositions is not simulated, as it is hard to estimate which fraction of their charge is retained after contact with the surface. However, repeated particle depositions are essential for combinatorial syntheses of peptides, taking into account that the grid electrode may become saturated with particles over numerous depositions. During peptide synthesis, this effect can be avoided by dividing each layer of the chip into sequential depositions with 12 or less steps, then performing a melting step to decrease the height of the accumulations on the grid, and thereafter continuing depositions for the remainder of that layer. A good parameter to compare between experiment and simulation is the grid width. Due to the different grid widths on the prototype chip, the influence of the grid width on the deposition result can be analysed experimentally and then compared to the results gained in the simulations. In the experiment as well as in the simulation, sparse deposition patterns are chosen to investigate this parameter, because they are most prone to contaminations. Influence of particle charge is not verified experimentally, as a real aerosol generated using our particles always contains a mix of particles of different size and charge. Different particle sizes were already examined experimentally in [6], with much better results obtained for smaller particles, in agreement with the simulation results presented here. 7.2. Synopsis of simulation and experimental results From Section 6, the feasible combinations of grid widths and voltages, particle sizes and charges can be deduced as summarised in Table 1. These results indicate that the width of the particle distribution shown in Fig. 1 should be kept as narrow as possible around 2 ␮m as these particles lead to the best deposition results. Hence, the fine particle batch (Fig. 1 (right)) with its lower mean value and its smaller distribution shows improved deposition compared to the coarse one (Fig. 1 (left)). Table 1 Summary of tested and optimal conditions for particle deposition onto the chip surface. Quality

Tested range

Optimum combination

Particle size Particle charge Grid width Grid voltage Sieve voltage

Radius ∈ [2 ␮m, 10 ␮m] q/m ∈ [−30 mC/kg, 0 mC/kg] Width ∈ [2 ␮m, 12 ␮m] Ugrid ∈ [0 V, 25 V] Usieve ∈ [−2000 V, 0 V]

Radius = 2 ␮m −3 mC/kg Width = 6 ␮m Ugrid ≥ 15 V Usieve ≤ −1000 V

Assuming a particle charge proportional to the particle volume, the smaller particles will also have smaller absolute charge, approaching the optimum value without any further experimental tuning. Concerning the grid width and voltage, the simulations are rather robust against variations as demonstrated in Sections 6.4 and 6.5. The results from experiments, however, indicate that particle accumulations on the grid, not included in the simulations, are decisive to determine the optima for those parameters. Depending on the purity of the particle ensemble concerning size and charge distribution, the sieve voltage should be reduced to the minimum as heavily charged particles with large radii are accelerated towards the chip surface in the high voltage field, leading to contamination. Yet, from the experimental side, a sieve voltage in the range of −1000 V is currently required due to the difficulty of reproducing conditions in particle creation. Thus, the sieve voltage is the most decisive parameter to be adjusted to control the amount of contamination. As can be read off Table 2 and Sections 5 and 6 in combination with the results gained in [6] for the coarse particle fraction (s. Fig. 8 (left)) on the 100 ␮m chip, the simulations are in good accordance with the experiments, hence, the assumptions made in Section 4, especially the one on neglecting particle-particle interactions, are justified. Furthermore, most of the parameters determined experimentally are already close to their theoretical optimum, comparing the optimum combinations in Tables 1 and 2. Finally, the parameters that can only be determined experimentally due to the simplified simulation model, are listed in Table 2. Sparse deposition patterns lead to a good quality in particle deposition (s. Figs. 6–8), i.e., they show full coverage of spots actually to be covered and minimum contamination on the region of interest of the others. For dense depositions, it is best to choose a combination of checkerboard patterns. These patterns have already led to successful results [1,2] and, as demonstrated in [6], it is possible to split

Table 2 Summary of experimentally tested and optimal conditions for particle deposition onto the chip surface. Quality

Tested range

Optimum combination

Particle size Grid width Grid voltage Sieve voltage Patterns Sequential depositions

Radius ≈ 2.3 ␮m (see Fig. 1 right) Width ∈ [6 ␮m, 12 ␮m] Ugrid = 20 V Usieve = −1000 V 1:1, 1:15, 15:1 n = [1.16]

See [6] Width = 6 ␮m Ugrid = 20 V Usieve = -1000 V 1:1, 1:15 n = [1.12]

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any dense deposition pattern into a combination of checkerboards in order to avoid undesired insufficiencies in pixel coverage. Sequential deposition of particles has been successfully shown. This still leads to high particle accumulations on the grid after 15 depositions, therefore one melting step may have to be performed every 12 depositions in order to avoid contaminations due to particles melting from the grid onto the spot.

[7] [8]

[9]

7.3. Conclusion and outlook [10]

As could be shown, simulation and experimental particle depositions onto electrically programmable surfaces can be used to find optimum conditions for minimum contamination. The results from simulation and experiments are promising that particle transfer on 45 ␮m pixels is possible, using both simulation-driven parameter optimisation and experiments on a dedicated chip which allows to test large-area patterns. Such a chip with a total of over 80.000 synthesis sites on an area of 1.28 cm × 1.28 cm, intended to succeed the present chip with 10,000 spots/cm2 in peptide synthesis applications, is currently being designed, and could compete with the state of the art in laser-printer based systems with 156,000 spots per 20 cm × 20 cm glass plate [3,15], while additionally offering the advantage of much higher spot densities. Not only would this increased density reduce production cost due to lower reagents consumption, but it may also enable applications where only minimal amounts of test substance are available, e.g., in diagnostic screenings of patients. Funding and grants The financial support of the German Ministry of Education and Research (BMBF) in the framework of the National Genome Research Network (NGFN) is acknowledged. We acknowledge financial support from the Baden-Württemberg Stiftung. Acknowledgements The authors thank R. Achenbach, V. Kiworra, M. Dorn, D. Rambow, and S. Hess for technical assistance. We also thank C. Schirwitz, Y.-C. Cheng, F. Märkle, N. Boztepe, G. Pilarczyk and E. Schmitt for helpful discussions. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.sna.2011.06.017 References [1] K. König, I. Block, A. Nesterov, G. Torralba, S. Fernandez, T. Felgenhauer, K. Leibe, C. Schirwitz, F. Löffler, F. Painke, J. Wagner, U. Trunk, F.R. Bischoff, F. Breitling, V. Stadler, M. Hausmann, V. Lindenstruth, Programmable high voltage CMOS chips for particle-based high-density combinatorial peptide synthesis , Sens. Actuators B 147 (2010) 418–427. [2] M. Beyer, A. Nesterov, I. Block, K. König, T. Felgenhauer, S. Fernandez, K. Leibe, G. Torralba, M. Hausmann, U. Trunk, V. Lindenstruth, F.R. Bischoff, F. Breitling, V. Stadler, Combinatorial synthesis of peptide arrays onto a microchip , Science 318 (2007) (1888). [3] V. Stadler, T. Felgenhauer, M. Beyer, S. Fernandez, K. Leibe, S. Güttler, M. Gröning, K. König, G. Torralba, M. Hausmann, V. Lindenstruth, A. Nesterov, I. Block, R. Pipkorn, A. Poustka, F.R. Bischoff, F. Breitling, Combinatorial synthesis of peptide arrays with a laser printer , Angew. Chem. Int. Ed. 47 (2008) 7132–7135. [4] R. Volkmer, Synthesis and application of peptide arrays: quo vadis SPOT technology , ChemBioChem 10 (2009) 1431–1442. [5] J. Wagner, F. Löffler, K. König, S. Fernandez, A. Nesterov-Müller, F. Breitling, F.R. Bischoff, V. Stadler, M. Hausmann, V. Lindenstruth, Quality analysis of selective microparticle deposition on electrically programmable sufaces , Rev. Sci. Instrum. 81 (2010), 073703-1–073703-6. [6] F. Löffler, J. Wagner, K. König, F. Märkle, S. Fernandez, C. Schirwitz, G. Torralba, M. Hausmann, V. Lindenstruth, F.R. Bischoff, F. Breitling, A. Nesterov,

[11]

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High-precision combinatorial deposition of micro particle patterns on a microelectronic chip , Aerosol Sci. Technol. 45 (2010) 65–74. C. Kleinstreuer, Two-Phase Flow Theory and Applications , Taylor & Francis, NY, 2003. A. Nesterov, F. Löffler, K. König, U. Trunk, K. Leibe, T. Felgenhauer, F.R. Bischoff, F. Breitling, V. Lindenstruth, V. Stadler, M. Hausmann, Measurement of triboelectric charging of moving micro particles by means of an inductive cylindrical probe , J. Phys. D 40 (2007) 6115–6120. A. Nesterov, F. Löffler, K. König, U. Trunk, K. Leibe, T. Felgenhauer, V. Stadler, F.R. Bischoff, F. Breitling, V. Lindenstruth, M. Hausmann, Noncontact charge measurement of moving microparticles contacting dielectric surfaces , Rev. Sci. Instrum. 78 (2007), 075111-1–075111-7. A.R. Khan, J.F. Richardson, The resistance to motion of a solid sphere in a fluid , Chem. Eng. Commun. 62 (1987) 135–150. R. Rannacher, Finite Element Methods for the Incompressible Navier–Stokes Equations , Institute of Applied Mathematics, University of Heidelberg, 1999 (Tech. Report). COMSOL AB, Comsol Multiphysics User’s Guide, 2008 ed. S. Boyd, L. Vandenberghe, Convex Optimization , Cambridge University Press, NY, 2004. http://www.cise.ufl.edu/research/sparse/umfpack/. www.pepperprint.com.

Biographies Jenny Wagner studied physics, mathematics and computer science at Heidelberg University and received her diploma about “data compression for the ALICE detector at CERN” in 2008. She currently pursues her PhD research about image processing, pattern analysis and computer vision focussing on automated deposition quality analysis. Kai König studied physics at Heidelberg University from 1999 to 2005, focusing on microelectronics and physics in biology. In 2005, he received his degree in physics. In 2010, he completed his doctoral thesis “CMOS-based Peptide Arrays” at the Kirchhoff-Institute for Physics, Heidelberg University and the German Cancer Research Centre, in which the first large-area combinatorial peptide synthesis was demonstrated. His areas of expertise include high-voltage ASIC design and testing, design of electrostatic actuators and integration of CMOS chips into systems for combinatorial synthesis including microparticle transfer. Tobias Förtsch is studying physics at the University of Heidelberg and currently working as student research assistent at the German Cancer Research Centre. His study interests include physical simulation. Felix Löffler has earned his degree in physics 2009 at the University of Heidelberg and is currently a PhD student at the German Cancer Research Centre. His general research interests include Biophysics and in particular the manipulation of bio micro particles. Simon Fernandez graduated PhD at the Institute of Organic Chemistry as a chemist from the University of Heidelberg with the thesis titled “Säurekatalysierte Umlagerung von 3,3-disubstituierten und von 3,4-spiroverknüpfen Pyrazolo[5,1c]-1,2,4-triazolen”in 1989. From 1990 to 1991, he was senior scientist in the department of Molecular Genetics at the Limbach Laboratory in Heidelberg. He was in employment as senior scientis at the Fraunhofer Institute for Chemical Technology in Pfinztal in 2002 for the application of micro particle generation for peptide synthesis. Since 2003, Dr. Simon Fernandez works as senior scientist in the DKFZ research group “Chip-Based Peptide Libraries”, where he is again consigned with the particle generation. Thomas Felgenhauer studied chemistry at the University of Heidelberg from 1990 to 1996. His Diploma Thesis (1997) and his PhD Thesis (2002) at the Applied Physical Chemistry Group dealt with the characterization, chemical modification and nanostructuring of self-assembled monolayers on noble metal electrodes. Since 2002 he is engaged in the German Cancer Research Centre in the BMBF junior research group “Chip-Based Peptide Libraries”, with the emphasis on combinatorial peptide synthesis and process development. Florian Painke graduated April 2007 in Physics at the University of Heidelberg, Germany. At present he is doing his doctorate in Computer Science and Biophysics at the Kirchhoff Institute for Physics in collaboration with the German Cancer Research Centre. The focus of his studies is automation of solid phase peptide synthesis on a microchip. Gloria Torralba received the MS in physics and the M.S. in electrical engineering degrees from the University of Valencia, Spain, in 1992 and 1995 respectively, and the PhD degree in electronic engineering from the University of Valencia, Spain, in 2004. From 1996 to 2002 she joined the electronic engineering department of the University of Valencia as a research assistant, and moved to the department of technical computer science of the University Heidelberg, Germany, in 2002, where she works as a postdoctoral researcher with focus on hardware description languages, microelectronics and sensors design. Volker Lindenstruth studied physics and received his PhD from Frankfurt University in 1993. He spent five years as a PostDoc at the LBNL (California). In 1998, he was appointed professor by the Heidelberg University and was head of the Com-

J. Wagner et al. / Sensors and Actuators A 172 (2011) 533–545 puter Engineering Department of the Kirchhoff-Institute for Physics. Since 2009, he is a Senior Fellow of the Frankfurt Institute for Advanced Studies and leads the Department of High Performance Computer Engineering at Frankfurt University. He also founded two hardware companies. His main research interests are parallel and cluster computing in real-time high reliability environments, FPGA and ASIC design. Volker Stadler studied chemistry at the University of Heidelberg. He Volker Stadler worked as senior scientist in the German Cancer Research Centre. By winning the BMBF NanoFutur Competition in 2002, he established the junior research group “Chip-Based Peptide Libraries” in 2003. He headed a truly interdisciplinary research team which developed the new combinatorial technique for the synthesis of high density peptide arrays. In 2008, this group was awarded with the highly renowned “Science Award of the German Association of Founders”. He is co-founder and CEO of Heidelberg start-up company PEPperPRINT, which makes this new array technology available to the scientific community. F. Ralf Bischoff studied biology in Heidelberg, receiving his PhD in 1992. He received his habilitation in 2000 for work on transport of macromolecules between nucleus and cytoplasma and mitosis. Since 2001, he is working in the field of combinatorial synthesis of peptide libraries.

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Frank Breitling received his PhD in biochemistry in 1992. He developed recombinant antibody technology, and particle-based peptide synthesis. Since 2009, he is employed as a group leader at Karlsruhe Institute of Technology (KIT). Michael Hausmann studied physics, recieved his PhD in 1988 and his habilitation in 1996. Thereafter, he was appointed leader of the “Farfield/Nearfield Microscopy Lab” at the Institute of Molecular Biotechnology in Jena and worked guest professor at the Universities of Jena and Amsterdam. In 2002, he became research group leader of the Microscopy Group of the Institute of Pathology in Freiburg. In 2004, he was appointed professor by the Faculty of Physics and Astronomy, University of Heidelberg. Since 2005, he is the leader of the research division “Peptide Chips” of the Kirchhoff Institute of Physics, University of Heidelberg. Alexander Nesterov-Müller earned his PhD from German Cancer Research Centre and the University of Heidelberg in 2006, where he became an assistant professor in 2008. His research interests include fabrication sand processing of high density molecular libraries. Since 2010, he works as project leader at Karlsruhe Institute for Technology in the group of Frank Breitling.