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Sensors and Materials, Vol. 34, No. 6 (2022) 2089–2104
MYU Tokyo
S & M 2958

Smartphone-based On-chip Homogenous Sensing
by Analysis of Dynamics of Oscillating Polystyrene Particles
under Dielectrophoretic Forces
Jaiyam Sharma, Taisuke Ono, Naohito Satake, and Adarsh Sandhu*
Graduate School of Information and Engineering, University of Electro-Communications,
1-5-1 Chofugaoka, Chofu, Tokyo 182-8585, Japan(Received February 28, 2022; accepted May 6, 2022)

Keywords: biosensing, medical diagnostics, point-of-care testing, functionalized particles

Compact, rapid, and inexpensive smartphone-based point-of-care diagnostics is developing
rapidly to replace conventional medical diagnostic procedures that necessitate specialized skills
and bulky instruments. Smartphone-based biosensing protocols proposed to date involve“heterogenous” interactions of labels with functionalized sensing surfaces. In contrast,“homogenous” protocols do not require surface functionalization but are bulky and unsuitablefor point-of-care diagnostics. In this work, we demonstrate a smartphone-based homogenous
biosensing protocol that is portable, rapid, and low cost. Currents passed through microelectrodepatterns fabricated onto silicon substrates produced dielectrophoretic forces that induced
harmonic oscillations of 1-μm-diameter carboxyl-coated polystyrene particles suspended inaqueous solutions at well-defined heights above the surfaces of the chips. Next, we introduced
200-nm-diameter polystyrene particles functionalized with complementary amine compounds
into the solution, which interacted with the larger particles, thereby significantly changing theoscillation dynamics of the larger beads. A smartphone was used to record the motion of thebeads. Particle tracking and filtering algorithms were used to extract information about theoscillation of the particles and detect changes in their dynamics. The results showed that ourapproach enables the detection of the concentration of aminated particles with a smartphone
within 1 min without requiring any prior functionalization of the biochip.

1.

Introduction

Smartphone-based point-of-care diagnostics has developed rapidly in the last decade(1,2) withthe goal of replacing conventional medical diagnostic technologies, which are time-consuming,bulky, and expensive, limiting their applications in remote locations and public healthemergencies. However, most diagnostic protocols based on smartphones developed to dateinvolve the complementary interaction between targets and labels with a solid sensing surface ina so-called heterogenous interaction. The heterogenous approach requires the preparation ofhigh-quality sensing surfaces with selectively immobilized probes. This has two disadvantages.
*Corresponding author: e-mail: sandhu@uec.ac.jp

https://doi.org/10.18494/SAM3881

ISSN 0914-4935 © MYU K.K.
https://myukk.org/

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First, sensors with solid sensing surfaces often require numerous (3,4) and expensive
microfabrication steps. Second, the heterogenous approach leads to nonspecific interactions(5) of
the biological labels with the sensing surface in addition to the sensor noise and environmental
noise inherent to the measurement setup. These factors limit the practical applications ofheterogenous biosensors for smartphone-based point-of-care testing.
The limitations of heterogenous biosensing approaches have accelerated the development ofprotocols that do not require a solid sensing surface and where the biochemical reaction between
the target and probe molecules occurs completely within a homogenous liquid medium.(6,7)
These “homogenous” biosensing protocols offer significant advantages such as low nonspecificinteractions and simpler sample preparation because sensing surfaces do not require
functionalizion with probe molecules. However, most homogenous protocols demonstrated todate are not portable and require a bulky experimental setup, making them unsuitable for
integration with smartphones. Park et al.(7) demonstrated the quantitative detection of avidin bymeasurement of the optical transmission from chains of magnetic particles in a homogenous
medium under a rotating magnetic field. The magnetic field was rotated by sinusoidally varying
the currents in a pair of large Helmholtz coils, which makes this method too bulky for point-ofcare diagnostics. DuVall et al.(8) demonstrated the detection of the C. difficile pathogen based onmagnetic bead aggregation in a homogenous medium under a rotating magnetic field. Although
the analysis of magnetic particle aggregation in their study was performed on a smartphone,their experimental setup required four industrial stepper motors, long steel guiding rails, and alarge power supply, making the protocol generally unsuitable for portable diagnostics.
Here, we propose a portable and rapid smartphone-based homogenous biosensing protocol
utilizing polystyrene particles, sometimes referred to as “polystyrene beads”, as labels to address
the limitations of the previous approaches(6,7) discussed above. Specifically, our method
involves:(a) inducing specific interactions between polystyrene particles moving freely in a homogenous
medium using horizontal on-chip dielectrophoretic forces,(b) recording high-resolution videos of thousands of polystyrene particles during the biochemical
reaction, and(c) analyzing the motion of every particle in the video with submicron resolution to identify
which particles have interacted specifically with the target molecules of interest.
Experimental results on a simple carboxyl–amine (COOH–NH2) model system showed thathomogenous chemical interactions can be detected with the proposed approach using on-chipforces within 1 min and that the quantitative detection of amine targets can be achieved. We
obtain a limit of detection of 50 μg/ml for NH2. The results from this research demonstrate that
our method is promising for realizing innovative smartphone-based point-of-care diagnostics.
Magnetic-particle-based homogenous biosensing protocols proposed to date have focused on
having a large 3D volume of the sample under analysis and obtaining a signal from the wholevolume. Although analyzing a large volume has inherent advantages such as low statistical noise
margins, producing uniform mixing forces throughout the volume requires large and bulky
equipment as discussed above. In contrast, heterogenous biosensing methods are able to produce“on-chip” electromagnetic forces in a small region above the sensing surface, thereby resultingSensors and Materials, Vol. 34, No. 6 (2022)

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in small, portable systems and high reproducibility of the forces. A quantitative analysis of theforces on magnetic particles suspended in a liquid medium over a solid substrate9 showed thatthe various vertical forces acting on the particles cancel out, resulting in the particles being
suspended at an equilibrium height above the substrate, as shown in Fig. 1.
This poses a challenge for heterogenous methods, especially those employing magnetic
sensors. High-gradient vertical magnetic fields are required to bring the particles close to thesensing surface because the sensor output strongly depends on the distance of the particle fromthe surface.(10,11)
In contrast to heterogenous methods, we utilize this property to our advantage by noting that
if horizontal forces are applied to functionalized particles suspended at an equilibrium height
above the substrate, homogenous interactions of polystyrene particles with complementarytargets can be achieved using only on-chip forces, thereby enabling the development of a
portable homogenous system. A constant equilibrium height of the particles from the substrate
results in the particles lying on a plane, which can be imaged via a smartphone. We use thisproperty to obtain measurements from a thin plane rather than from the entire volume as inprevious homogenous approaches. As we discuss in Sect. 2.8, this limitation of the proposed
method is compensated for by its provision of much more specific details about the motion ofeach particle under observation, rather than providing gross statistical estimates from a volume
as in previous approaches(6,7). Therefore, this method can in principle achieve very low limits ofdetection down to the interaction of a single polystyrene particle with complementary targets.

Fig. 1. (Color online) Free-body diagram of a magnetic particle suspended in a liquid medium over a substrate(adapted from Ref. 9).

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2.

Methods

2.1

Proposed protocol

A schematic of the proposed approach is shown in Fig. 2. The method involves two types offunctionalized polystyrene particles: large 1-μm-diameter particles whose surface wasterminated with a carboxyl group and small 200-nm-diameter particles whose surface wasterminated with the complementary amine group. The particles were prepared separately andintroduced one after another in a reaction well on an actuator consisting of four linear electrodes:
AB, CD, PQ, and RS. The extent of the reaction well is not shown in Fig. 2, but can be seen in
Fig. 3 as discussed later. Points B and C were electrically connected via an external jumper, andpoints Q and R were similarly connected. After the particles were introduced, sinusoidal
currents were applied to the electrodes. Current I x = I 0 sin(ωt ) was applied across electrodes AB
and CD and current I y = I 0 cos(ωt ) was applied to electrodes PQ and RS, as shown in the topview of Fig. 2. As shown in the side view of Fig. 2(a), the flow of currents induced harmonic
oscillations of the polystyrene particles due to dielectrophoretic forces (see Sect. 2.2). The
harmonic oscillations caused the two types of solutions containing particles with complementarysurfaces to mix and interact with each other. As a result, the amplitude of motion of the particles
changed when specific interactions occurred, as shown in Fig. 2(b). During the application of theFig. 2. (Color online) Schematic of the proposed homogenous sensing protocol. Orange crosses represent chemicalinteractions between carboxyl and amine groups. (a) The application of currents causes harmonic oscillations inmagnetic particles, whose amplitude depends on the interaction between the particles. (b) When particles interact
with each other, the amplitude of motion changes. Although the particles lie on a horizontal plane, as discussed
above, they are shown staggered in the side views for ease of illustration. The actuator also contains connector pads,
which are not shown in this illustration.

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Fig. 3.

2093(Color online) Photograph of the fabricated actuator with the reaction well installed.

currents and the biochemical reaction, the particle plane was imaged with a smartphone-based
microscope and a high-resolution video was recorded. The particle dynamics were analyzed via
optical tracking and the changes in the amplitude of oscillations of the particles with respect to acontrol sample were quantified. The extent of the changes in the amplitude allows quantitative
detection of the target.
2.2

Dielectrophoretic forces and design of the actuator

Previous biosensing approaches, especially heterogenous ones, have relied on the applicationof magnetic forces to enable the manipulation of magnetic particles across and above the sensing
surface. From basic electromagnetic theory, it is well known(12,13) that the amplitudes of electric 
and magnetic fields of a plane wave traveling in free space satisfy the relation E B = c, wherec is the speed of light in vacuum. Therefore, the amplitude of the electric field is several ordersof magnitude greater than that of the magnetic field. The magnetic force acting on a particle
suspended in a nonmagnetic medium (χm ≈ 0) is given by(9,14)
 V χ p  =FmB ⋅ ∇B ,µ0(1)where V is the volume of the particle, χ p is its magnetic susceptibility, μ0 is the permeability ofvacuum, and B is the external magnetic field. The dielectrophoretic (DEP) force on a particle of
a dielectric material placed in an inhomogenous external electric field E arises from theformation of induced dipoles in the dielectric and is given by(15) 3V ε (ω )  =
FDEPE ⋅ ∇E ,
2

(2)

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where V is the volume of the particle and ε(ω) is the Clausius–Mossotti factor at the frequency ω
ε m* − ε *p
, where εp is the relative permittivity of the particleof the applied field, given by ε (ω ) = *ε m + 2ε *pand εm is the relative permittivity of the surrounding medium. Typically, a biological medium
such as water has negligible magnetic susceptibility (χm ≈ −10−5) but a large relative permittivity(εm ≈ 80). Magnetic particles are composed of iron oxide nanoparticle cores, which impart themwith magnetic susceptibility of χ p = 1–3 depending on the type of magnetism (superparamagnetic
or ferromagnetic) and the size of the particle. The polymer material, typically polystyrene,
composing the particles has a relative permittivity of εp = 2.4–2.7. Thus, from the above
discussion, we conclude that the DEP forces on polystyrene particles are much greater than themagnetic forces. Therefore, the DEP forces produced by a current line actuator extend to muchgreater distances than the magnetic forces.
Previously, we demonstrated a current line actuator optimized for the application of highgradient magnetic forces.(16) However, for currents of 20 mA, the magnetic field extends only toa maximum distance of ≈8–10 µm from the current line. In contrast, DEP forces allow us toincrease the extent of the field by approximately two orders of magnitude at a given current.
Thus, we made the following design decisions:(a) The distance between AB and CD (as well as between PQ and RS) was 550 µm.
(b) The width of the current lines was 10 μm and the thickness of the electrode film was 200 nm.
(c) The amplitude I0 of the applied sinusoidal current was 30 mA (RMS 21 mA) and thefrequency was ω = 0.5 Hz.
These design parameters were selected to achieve acceptable DEP forces over large distances
while reducing Joule heating and the number of steps required to fabricate the actuator. For
example, most heterogenous sensors require a passivation layer to shield the biological fluid
from the Joule heating of the underlying sensors, which would denature the proteins and prevent
biological interactions. However, our design contains a simple current line actuator, and the low
Joule heating eliminates the need for a passivation layer, resulting in a simpler fabrication
process than that of magnetoresistive sensors integrated with actuators.(17) When sinusoidal
currents are applied, DEP forces dominate over a large surface area and produce harmonic
oscillations, whereas magnetic forces dominate close to the electrodes and induce anharmonic
motion of the particles close to the electrodes.
2.3

Fabrication of the actuator

The actuator consists of a silicon nitride substrate with four linear current lines as describedin Sect. 2.2. The substrate was coated with AZ5214E photoresist and the current line pattern was
created using conventional photolithography. After development, a single electron beam
deposition step was performed to deposit films of titanium (100 nm thickness) followed by gold
(100 nm thickness). Lift-off was carried out to remove excess photoresist. As a result, only theelectrode pattern remained on the substrate. Notably, no passivation layer was deposited. Finally,
the prepared substrate was cleaned with acetone and ethanol and treated under oxygen plasma
for 1 min to facilitate the installation of a reaction well fabricated from polydimethylsiloxane

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2095(PDMS). Uniformly sized reaction wells were cut with a laser cutter to ensure that a constantvolume of 3 μL was under analysis. The completed assembly is shown in Fig. 3.
2.4

Preparation of magnetic particles

As stated in Sect. 2.2, two types of polystyrene particles of different sizes were used. The
large polystyrene particles (Dynabeads, Thermo Fisher Inc.) were 1 μm in diameter and thesmaller particles were 200 nm in diameter (FG beads, Tamagawa Seiki Co. Ltd.). Both the 1-μmand 200-nm-diameter particles contained iron oxide nanoparticles giving both types
superparamagnetic properties, whereby the particles become magnetized and align along thedirections of external magnetic fields, but disperse in aqueous solutions exhibiting random
Brownian motion without external magnetic fields. The behavior of the particles described inthis paper only rely on the dielectric properties of the particles, that is the action of electric fields
on the polystyrene particles. The particle behavior is not due to magnetic effects. The reason weused these particles is because we have a wide selection of such particles from our legacy of 20
years or so of research on medical diagnostics using superparamagnetic particles. We wanted tosee if the same particles—that we are able to functionalize and whose physical properties we arevery familiar with—could be used for biosensing based on their dielectric properties.
The surfaces of the Dynabeads were terminated with carboxyl (COOH), whereas those of the
FG beads were terminated with amine (NH2). The particles were used as received from the
manufacturer without surface modification. The Dynabeads were diluted to 2% by volume in asolution of deionized water and the FG beads were diluted with deionized water to five different
concentrations: 100, 200, 240, 300, and 400 μg/mL. In addition, a solution of pure deionizedwater was prepared with no amine-functionalized particles as a control. During the experiment,a solution containing 1.5 μL of the amine-coated particles was first introduced into the reaction
well, followed by a solution of 1.5 μL of the carboxyl-coated particles. Thus, the concentrationsof amine in the final solutions were half of the initial concentrations, i.e., 50, 100, 120, 150, and200 μg/mL.
2.5

Circuit design for application of currents

A printed circuit board (PCB) was designed to allow the rapid formation of contacts for theapplication of current. A chip holder was designed using CAD software and 3D-printed using acommercial FDM printer. The final completed assembly of the chip holder and PCB is shown in
Fig. 4.
2.6

Optical assembly and application of current

The actuator and chip holder assembly was placed under a microscope (Olympus BX51).
Although the microscope has an in-built camera, we utilized only the lens (20×, 0.4 NA) and thefocusing apparatus of the microscope and not the camera. A smartphone was mounted on theeyepiece of the microscope and the substrate was imaged without using any advanced imaging

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Fig. 4. (Color online) Photograph of the PCB and chip holder assembly. The actuator chip is underneath the PCB,
below the hole milled out in the middle.

or color correction functionality of the microscope camera, as shown in Fig. 5. Although a lowcost microscope would have been more suitable for fully demonstrating the potential of ourapproach for point-of-care diagnostics, we note that a large number of low-cost, highmagnification, smartphone-based microscopes have been demonstrated in the literature.(18–21)
Thus, we leave the development of a smartphone-based microscope as future work.
Similarly, a low-cost circuit can be developed for the application of sinusoidal currents.
However, we used a commercial function generator (NF WF1974) in the experiments discussed
here. The power consumed by the actuator was 0.126 W with a peak voltage of ±4.2 V andfrequency of 0.5 Hz. Therefore, a low-cost circuit can be designed because no amplifiers,
frequency synthesizers, or cooling equipment is necessary at such a low power and frequency.
As with the optical setup, we leave this for future work.
The smartphone (iPhone 8, Apple Inc.) was used to record a 4K video (2160 × 3840 pixels) of
the substrate at 24 fps. The recording was started at the time that the application of current was
initiated. A video of just over 1 min (63–65 s) was recorded and a 10 s clip from 50 to 60 s wasanalyzed. After 1 min, the currents and video recording are halted. The recorded video wastransferred wirelessly via a local SMB server connection to a PC for analysis.
2.7

Analysis of particle dynamics

Extracting biologically relevant information about the dynamics of particles from a video is achallenging problem. To summarize the challenges discussed in our previous work,(22) mostparticle tracking algorithms and software developed to date were developed for the study ofcellular biology. Therefore, they focus on accurately tracking a single or a few fluorescent tags,
which can be easily distinguished by their color, in stacks of a few images. Moreover, natural
cells have very simple dynamics, such as Brownian or linear motion, which are explicitly
incorporated into the tracking software. We developed an alternative particle tracking software,
which can track tens of thousands of particles simultaneously and does not require any

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Fig. 5.

2097(Color online) Schematic of the optical setup.

assumptions about the statistics of the motion of individual particles. This software is available
as an open-source platform with a graphical interface(23) that can be freely used without
requiring any knowledge of programming or the algorithms.
In this work, we build upon the capabilities of this software to extract information relevant tohomogenous biosensing. As discussed in Sect. 2.3, DEP forces produce harmonic oscillations,whereas magnetic forces produce anharmonic motion. Thus, the trajectory of a particle is acombination of many types of motion. This makes it necessary to filter out the effect of particles
that do not show harmonic oscillations as well as anharmonic effects in the trajectories ofharmonically oscillating particles. Consider the set S of all particle trajectories along the x and yaxes obtained by optical tracking, i.e.,=S{( x , y ) : 0 ≤ i ≤ N , 1 ≤ t ≤ 240} ,
i ,t

i ,t

(3)

where i represents the particle number, N is the total number of particles in the field of view, andt represents the discrete time step represented as the frame number of the video being analyzed.
Since the video is recorded at 24 fps and a 10 s clip is analyzed, the maximum value of t is 240.
We perform the following steps to extract the amplitude of oscillations of particles, which areillustrated on experimental data in Fig. 6:(a) A single-sided discrete Fourier transform (DFT) of the particle trajectories is performed for2both the x- and y-trajectories. The power spectrum PS ( S (i, x) ) = DFT ( xi ,1:240 ) (and similarly
for y) obtained by the DFT shows a peak at the frequency of the applied field (ω = 0.5 Hz) if
the particle is oscillating. This peak is detected in an automated way by performing linear
regression on the normalized log-log plot of the power spectrum, as shown in Fig. 6(b). The
random sample consensus (RANSAC) algorithm is used while performing linear regression
to avoid outliers resulting from the peak at ω = 0.5 Hz and noise at high frequencies. Note

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Fig. 6. (Color online) Processing steps described in Sect. 2.8. In all parts, red curves correspond to the x axis andblue curves correspond to the y axis. (a) Raw trajectory of a particle obtained by tracking. Drift in the trajectory
superimposed on harmonic oscillations can be observed, (b) Single-sided DFT power spectrum for the trajectory
shown in (a). The peaks observed for the x and y axes at 0.5 Hz are above the linear regression model of the power
spectrum (not shown) by the threshold; thus, this particle is deemed to exhibit harmonic oscillations. (c) Drifts along
the axes estimated by applying a moving average filter and fitting a linear regression model (using RANSAC) to theresulting curves. (d) Original trajectory detrended using the estimated linear drift model along each axis, where
curve fitting is used to estimate the amplitude of motion of the particle along the two axes.

that since the sampling rate of our measurements is 24 Hz, by the Nyquist theorem, themaximum frequency in the single-sided DFT is 12 Hz. If the power spectrum at ω = 0.5 Hz is
higher than the linear regression value by a given threshold T = 0.2, the particle is considered
to be oscillating. Thus, the set O of indices of the oscillating particles is given bySensors and Materials, Vol. 34, No. 6 (2022)

2099=
O {i : PS ( S (i, j ) ) > LR ( S (i, j ),ω ) + T , j ∈ ( x, y )} ,(4)

where LR ( S ( i, j ) , ω ) represents the expected value of the power spectrum at the appliedfrequency ω obtained by the RANSAC linear regression model in the absence of harmonic
oscillations.
(b) Once the oscillating particles have been identified, a sinusoidal curve can be fitted to the
trajectories to find the amplitude of oscillation, but an additional step is necessary to reduce
anharmonic motion in the particle trajectory before curve fitting. The trajectories ofoscillating particles may drift over time due to the nonuniform nature of the fields. Thus,
detrending of the drift is necessary. A median filter ml (of kernel size l = 5) is applied to the
trajectory, and subsequently, a moving average filter μw with a window size of one period of
oscillation (w = fps/ω = 48) is applied to the trajectory, i.e.,=
M (i, j ){µ ( m ( S (i, j ) ) ) : i ∈ O, j ∈ ( x, y)}.
w

l

(5)

The filtered trends M are further filtered by fitting a linear regression model LR (withRANSAC) to reduce the deviations at the beginning and end of the time series data due to themoving average filter. This is shown in Fig. 6(c). The linear regression model is used todetrend the trajectories as follows:D(i, j )= {S (i, j ) − LR ( M (i, j ) ) : i ∈ O, j ∈ ( x, y )} ,(6)(c) The amplitude of motion Ai,j and the phase ϕi,j of oscillating particle i along the j axis are
estimated by fitting a sinusoidal motion model Ci,j to the detrended trajectory data, as shownin Fig. 6(d), where=
Ci , j Ai , j sin(ωt + φi , j ) ∀ i ∈ O, ∀ j ∈ ( x, y ),(7)(d) Next, we note that some particles are subjected to both DEP forces and magnetic forces. The
amplitude of oscillations of these particles can be markedly different from those of the
majority of particles and they can thus skew the statistics of motion of the particle population.
Thus, the resulting amplitudes obtained by curve fitting are further filtered to reduce theeffect of such particles. The average µ Aj and standard deviation σ Aj of the amplitudes arecalculated for both the x and y axes ( j ∈ ( x, y )), and the particles whose amplitude differsfrom the average by more than one standard deviation are rejected. The resulting low-noise
amplitudes are thus given by=
L{A : A − µ ≤ σ
k, j

k, j

Aj

Aj

}

∀j ∈ ( x, y ), k ∈ O ,(8)(e) A histogram of the low-noise amplitudes L obtained after the above four steps is plotted andthe mode of the histogram, i.e., the amplitude with the highest probability, is considered as

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the amplitude of motion of the particles. As is clear from the above discussion, two oscillation
amplitudes are obtained, one each for the x and y axes. These two amplitudes serve as a selfconsistent metric for our method. The two amplitudes should be within the measurement
resolution of one pixel, and a large difference between the two may indicate an experimental
error. In our experimental results, the x and y amplitudes were always found to be within thislimit, thereby passing this self-consistency test.

3.

Results

Supplementary video V1 shows the trajectory of particles obtained by tracking on an
experimentally obtained video. Here, we demonstrate the effectiveness of the processing
described in Sect. 2.8. The red/yellow trajectory paths denote particles that are identified to
exhibit harmonic oscillations (set O in Sect. 2.8), whereas the green paths denote particles that
do not show harmonic oscillations. Here, the oscillation recognition step [step (a)] is run for eachframe, i.e., the DFT calculations, power spectrum analysis, and RANSAC curve fitting are
performed at each time step for each particle. Our efficient implementation of these routines
allows us to perform the advanced analysis of particle trajectories and extract biologically
relevant information from the raw video. The results show that the algorithm can recognize
oscillating particles even with limited data (a few frames).
We now explain the results obtained with carboxyl-functionalized particles with various
amounts of amine-functionalized particles. The amplitude of harmonic oscillations of 1-μmdiameter carboxyl-functionalized particles decreases with increasing concentration of amine
particles in the solution. Since there is no external vertical electric or magnetic field and thesurface of the substrate is not functionalized in any of our experiments, we conclude that thedecrease in the oscillations is due to the increased mass of the chemically bonded beads in thehomogenous suspension. The harmonic oscillations induced in carboxyl-functionalized particles
enhance their probability of interacting with a complementary nanoparticle.
Supplementary video V2 shows a side-by-side comparison of the oscillations induced in the
carboxyl-functionalized particles (without NH2) when 200 μg/mL of amine-functionalized
nanoparticles are present in the solution. The left panel (only COOH particles) shows that theparticles have a large amplitude of motion, whereas the right panel shows that the oscillations aremuch less pronounced when complementary particles are present in the solution. A small part of
the total sensing area is shown in the video for clarity of illustration because a collage of two
full-resolution 4K video streams cannot be viewed on most displays without significant downsampling. Figure 7 shows a comparison of the trajectories of one particle selected randomly fromthe two panels. It is clear from these results that the oscillations of 1-μm-diameter carboxyl
particles are significantly reduced in the presence of amine-functionalized nanoparticles.
Figure 8 shows normalized histograms of particle oscillation amplitude in the x direction forvarious concentrations of amine-coated particles. The x axis represents the oscillation amplitude(in μm), whereas the vertical axis represents the probability of a given amplitude. The area undereach curve is 1. The mode of the oscillation amplitude, i.e., the peak of the histogram, shifts tothe left (decreases) as the concentration of amine-functionalized particles is increased. Thus, theSensors and Materials, Vol. 34, No. 6 (2022)

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Fig. 7. (Color online) Comparison of the trajectories of two randomly selected particles, one from the left panel
and the other from the right panel, in supplementary video V2.

Fig. 8. (Color online) Histograms of particle oscillation amplitudes in x direction for various concentrations ofamine-functionalized particles. Carboxyl particles (without NH2) have the highest oscillation amplitude, which
decreases as amine-functionalized particles are added.

peak position of the histogram is negatively correlated with the concentration of amine particles
in solution.
Figure 9 shows a plot of the peak position for various concentrations of amine particles forboth the x and y axes. We observe that both the x and y peak positions show excellent agreement
and are within ±1 pixel of each other. The average amplitude decreases with increasing

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Fig. 9. (Color online) Amplitudes of histogram of oscillation amplitudes along x and y axes for various
concentrations of amine-functionalized nanoparticles.

concentration of amine particles. However, the decrease is not uniform. For example, the average
oscillation amplitude decreases by about 0.95 μm between 50 and 100 μg/mL, whereas it
decreases by 4.07 μm between 100 and 150 μg/mL and the oscillation is nearly 0 (0.005 μm) at aconcentration of 200 μg/mL.

4.

Discussion and Conclusion

In this work, we proposed a smartphone-based on-chip homogenous medical diagnostics
protocol. Our method combined aspects of both homogenous and heterogenous approaches
proposed previously to achieve the advantages of portable heterogenous protocols and rapid
homogenous protocols. This was achieved by utilizing the well-known fact that the various
forces acting on microparticles in a suspension cause them to reach an equilibrium height above
a substrate. Thus, horizontal DEP forces were applied by on-chip electrode patterns to enhance
the probability of interactions between the particles. The planar arrangement of particles allowed
video imaging (2D + time) by a smartphone, as opposed to volumetric observations (3D)
obtained in conventional homogenous methods. Particle tracking was performed on highresolution videos obtained with a smartphone and several signal processing algorithms wereused to extract biologically relevant information from raw video observations.
A major feature of our work is that the forces produced by the actuators are DEP in nature
and, thus, the magnetic properties of the particles are not used. The relative homogeneity of the
DEP forces allows 2D imaging of the particles, which would not be possible under inhomogenous
magnetic forces produced by on-chip actuators. We therefore conclude that homogenous sensing
can be achieved entirely without the use of magnetism and, thus, magnetic particles are notnecessary. Since polystyrene particles have a considerably lower cost than magnetic particles,

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2103

we expect our method to have a much lower cost after commercialization than competing
magnetic-particle-based approaches in the literature.
Experiments performed on 1-μm-diameter carboxyl-functionalized particles and 200-nmdiameter amine-functionalized particles showed that the oscillation amplitude of the 1-μmdiameter particles decreased as the number of complementary particles was increased. These
results serve as the proof of principle of our unique approach to smartphone-based on-chiphomogenous sensing with potential applications for biosensing.
We envisage applications of the proposed approach in point-of-care diagnostics, especially inrural locations in developing countries where conventional medical healthcare infrastructure isseverely lacking. Going forward, three specific challenges must be addressed to make thisapproach useful for real-world applications:(a) The proposed approach must be tested with clinically relevant biomarkers.
(b) The electrical power delivery and optical assembly must be miniaturized to achieve operationin remote locations.
(c) The data processing algorithms must be integrated with cloud computing to allow anyone to
upload data onto a cloud server and receive results.
We plan to build upon this work and address all these challenges in the future.

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About the Authors
Jaiyam Sharma received his B.Tech degree from Indian Institute ofTechnology Delhi in 2014, his master’s degree from Toyohashi University ofTechnology, Japan, and his Ph.D. degree from the University of ElectroCommunications, Japan, in 2020. His research interests are in biosensing,
especially point-of-care diagnostics. (jaiyamsharma@gmail.com)

Taisuke Ono received his bachelor’s degree from the University of ElectroCommunications, Japan, in 2018 and his master’s degree from the University
of Electro-Communications in 2020. His research interests are in biosensing,
especially point-of-care diagnostics. (Taisuke.ono@uec.ac.jp)

Naohito Satake received his bachelor’s and master’s degrees from the
University of Electro-Communications, Japan, in 2019 and 2021, respectively.
His research interests are in biosensing and the fabrication of liquid flow cells
for wet transmission electron microscopy. (s2033050@uec.ac.jp)

Adarsh Sandhu received his B.Sc. degree from the University of Leeds in1982 and his Ph.D. degree from the University of Manchester in 1986. From1986 to 1995, he was a research scientist at Fujitsu Laboratories Ltd.; from1992 to 1993, he was a visiting scholar at Cavendish Laboratory, Cambridge
University; from 1995 to 2002, he worked at the Department of ElectricalEngineering, Tokai University; from 2002 to 2010, he was at QNERC, Tokyo
Institute of Technology; from 2010 to 2015, he was at EIIRIS, Toyohashi
University of Technology. Since 2015, he has been a professor at the University
of Electro-Communications, Tokyo. His research interests include 2DEG
magnetic biosensors and MEMS for liquid flow TEM. (sandhu@uec.ac.jp)