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A novel micro- and nano-fluidic device stacked with magnetic beads has been developed to efficiently trap, concentrate, and retrieve Escherichia coli (E. coli) from bacterial suspension and pig plasma. The small voids between the magnetic beads are used to physically isolate the bacteria in the device. We used computational fluid dynamics (CFD), 3D tomography technology, and machine learning to probe and explain the bead stacking in a small 3D space with various flow rates. A combination of beads with different sizes is utilized to achieve a high capture efficiency (~86%) with a flow rate of 50 µL/min. Leveraging the high deformability of this device, E. coli sample can be retrieved from the designated bacterial suspension by applying a higher flow rate, followed by rapid magnetic separation. This unique function is also utilized to concentrate E. coli cells from the original bacterial suspension. An on-chip concentration factor of ~11× is achieved by inputting 1,300 µL of the E. coli sample and then concentrating it in 100 µL of buffer. Importantly, this multiplexed, miniaturized, inexpensive, and transparent device is easy to fabricate and operate, making it ideal for pathogen separation in both laboratory and point-of-care (POC) settings.
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Biological and Medical Applications of Materials and Interfaces
Rapid Escherichia coli (E. coli) Trapping and Retrieval from Bodily
Fluids via a Three-Dimensional (3D) Beads Stacked Nano-Device
Xinye Chen, Abbi Miller, Shengting Cao, Yu Gan, Jie Zhang, Qian He, Ruo-
Qian Wang, Xin Yong, Peiwu Qin, Blanca H. Lapizco-Encinas, and Ke Du
ACS Appl. Mater. Interfaces, Just Accepted Manuscript • DOI: 10.1021/acsami.9b19311 • Publication Date (Web): 15 Jan 2020
Downloaded from pubs.acs.org on January 21, 2020
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1
Rapid Escherichia coli (E. coli) Trapping and
Retrieval from Bodily Fluids via a Three-
Dimensional (3D) Beads Stacked Nano-Device
Xinye Chen,1,2 Abbi Miller,3 Shengting Cao,4 Yu Gan,4 Jie Zhang,5 Qian He,2,6 Ruo-Qian Wang,7
Xin Yong,8 Peiwu Qin,6 Blanca H. Lapizco-Encinas,3 and Ke Du 1, 2 *
1Department of Microsystems Engineering, Rochester Institute of Technology, Rochester, NY
14623, United States.
2Department of Mechanical Engineering, Rochester Institute of Technology, Rochester, NY
14623, United States.
3Department of Biomedical Engineering, Rochester Institute of Technology, Rochester, NY
14623, United States.
4Department of Electrical and Computer Engineering, University of Alabama, Tuscaloosa, AL
35401, United States.
5Carollo Engineers, Inc., Seattle, WA 98101, USA
6Center of Precision Medicine and Healthcare, Tsinghua-Berkeley Shenzhen Institute, Shenzhen,
Guangdong Province 518055, China.
7Department of Civil and Environmental Engineering, Rutgers, The State University of New
Jersey, NJ 08854, USA.
8Department of Mechanical Engineering, The State University of New York, Binghamton, NY
13902, USA.
Contact: ke.du@rit.edu
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ABSTRACT
A novel micro- and nano-fluidic device stacked with magnetic beads has been developed to
efficiently trap, concentrate, and retrieve Escherichia coli (E. coli) from bacterial suspension and
pig plasma. The small voids between the magnetic beads are used to physically isolate the bacteria
in the device. We used computational fluid dynamics (CFD), 3D tomography technology, and
machine learning to probe and explain the bead stacking in a small 3D space with various flow
rates. A combination of beads with different sizes is utilized to achieve a high capture efficiency
(~86%) with a flow rate of 50 µL/min. Leveraging the high deformability of this device, E. coli
sample can be retrieved from the designated bacterial suspension by applying a higher flow rate,
followed by rapid magnetic separation. This unique function is also utilized to concentrate E. coli
cells from the original bacterial suspension. An on-chip concentration factor of ~11× is achieved
by inputting 1,300 µL of the E. coli sample and then concentrating it in 100 µL of buffer.
Importantly, this multiplexed, miniaturized, inexpensive, and transparent device is easy to
fabricate and operate, making it ideal for pathogen separation in both laboratory and point-of-care
(POC) settings.
KEYWORDS – Escherichia coli (E. coli), magnetic bead, nano-sieve, computational fluid
dynamics (CFD), optical tomography, machine learning, point-of-care (POC)
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Introduction
Inadequate water supplies and poor sanitation in low- and middle-income settings have elevated
global concern for waterborne disease outbreaks. In 2012, over 500,000 people died due to
diarrheal contamination of drinking water.1 Microorganisms such as Escherichia coli (E. coli) can
cause fecal contamination in recreational and drinking water and post a high risk of disease
transmission. Between 2003 and 2012, 390 outbreaks of E. coli infections in the United States
were reported, which resulted in nearly 176,000 distinct illnesses, more than 2,000
hospitalizations, and 33 deaths.2,3 While E. coli infection can be treated with common antibiotics,
some strains of this bacteria have developed resistance to antibiotics, leading to longer recovery
times and even death. As the development of new antibiotics is slow and challenging, drug-
resistant bacteria are gradually becoming one of the leading public health concerns. Currently,
around 700,000 people are killed each year due to antibiotic-resistant infections. Projected analysis
indicates that if no action is taken to reverse this trend, the global mortality rate caused by drug-
resistant bacteria could rise to 10 million each year worldwide, leading to an annual loss of 100
trillion USD.4,5
The study of drug-resistant strains requires bacteria isolation, purification, and concentration,
followed by molecular characterization such as polymerase chain reaction (PCR),6,7 enzyme-
linked immunosorbent assay (ELISA),8,9 cell plating,10,11 and microscopy.12,13 The rapid
identification of drug-resistant bacteria allows physicians to prescribe a viable drug initially,
resulting in a better prognosis for the patient and increasing the likelihood of survival. ELISA
microarrays can be used to isolate target bacteria via a specific antibody.14 However, the capture
efficiency is low, and the impurities found in bodily fluids can inhibit antibody activity.15
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Membrane-based filtration has been widely utilized and is advantageous because of cost-
effectiveness, simplicity, and rapidness.16 However, the captured bacteria need to be retrieved from
the membrane by iterative buffer washing, which could result in undesired dilution.17 For instance,
100 colony-forming units (CFU)/mL of E. coli in drinking water can cause various infections,
including urinary tract infections and diarrhea.18 Therefore, retrieving the bacteria in a small
volume is required to increase the sample concentration for detection. Membrane-based filtration
is more problematic when dealing with blood samples, as multiple filters with various pore sizes
are required to reduce clogging issues caused by the blood cells.19,20 Cell leakage is another
challenge as the bacteria can deform to pass through the pores. In recent years, microfluidics-based
approaches, such as inertial force separation,21,22 hydrodynamic separation,23,24
electrophoresis,25,26 and acoustics separation,27,28 have been developed to efficiently separate and
detect pathogens; however, all of these methods have limitations and require either sophisticated
microfluidic designs or complicated instruments. Therefore, physical barriers such as “T-junction”
29,30 and micro-obstacle arrays 31,32 were introduced to capture the cells from bodily fluids; yet, the
sizes of most of the bacteria range between 0.5 to 5 µm, making the fabrication process
challenging.33
We previously developed a deformable nano-sieve device for the rapid and size-selective
separation of microplastics.34 Deformation of this device was regulated by flow rate thus allowing
efficient particle trapping and releasing. Therefore, by exploiting the highly efficient particle
trapping of the nano-sieve, stacking of the beads is achieved by hydrodynamic flow at various flow
rates, and the liquid-flow profile of the stack is imaged by optical coherence tomography.35 Then,
a novel machine learning method is applied to automatically reconstruct the 3D topology within
the device.36 Our system can isolate and concentrate E. coli cells from either the bacterial
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suspension or pig plasma by physically capturing the bacteria in the beads assay. Remarkably, the
captured bacteria are easily released from the device with flow rate induced channel deformation,
followed by bead isolation with a magnet. An on-chip concentration factor of ~11× is achieved by
concentrating the bacteria in 100 µL buffer from a 1,300 µL original sample. Utilization of this
method allows for the rapid collection of the intact bacteria from patient samples for down-stream
molecular diagnosis and imaging. More importantly, multiple nano-sieve devices can be patterned
on a small chip by using standard microfabrication techniques and operated by a small syringe
pump, leading to a simple, inexpensive, and multiplexed instrument for bacteria sample
preparation, intended for point-of-care (POC) settings.
Results
Our method of bacterial isolation and retrieval is presented in Fig. 1a. Magnetic beads with a
diameter ranging from 2.8 to 10 µm are pumped into the nano-sieve device at a flow rate of 50
µL/min. Beads with a large volume are stacked tightly within the 3D space (Fig. 1a-i). Then, the
bacterial solution is pumped into the bead-stacked channel, and the bacteria are captured in the
bead assay as the buffer filters into the waste reservoir (Fig. 1a-ii). As the bacteria continue to pass
through the nano-sieve device, an accumulation of trapped cells occurs in the 3D space. Finally, a
high flow rate is applied to heave the nano-sieve and release the beads/bacteria mixture (Fig. 1a-
iii) to the assigned reservoir (Eppendorf tube). To fabricate the nano-sieve device, standard
photolithography and wet etching techniques were used to pattern a rectangular feature on
tetraethyl orthosilicate (TEOS) (L: 8 mm; W: 2 mm). Then, a thin layer of positive photoresist
(PR) with a thickness of ~1 µm was coated uniformly on the substrate and patterned by
photolithography. This resulted in PR coverage in the trench of TEOS. The nano-sieve channel
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was sealed by bonding it to a flat Polydimethylsiloxane (PDMS) sheet (5 mm), followed by acetone
rinsing. The on-chip experiment setup is shown in Fig. 1b-i. The magnetic beads and bacteria are
pumped into the nano-sieve by a multi-channel syringe pump. A magnetic rack rapidly separated
the mixture of released beads/bacteria. A scanning electron microscope (SEM) was used to
characterize the bead stacking within the nano-sieve device (Fig. 1b-ii to Fig. 1b-iv). The beads
are uniformly stacked and heaved the nano-sieve into an arch shape. We used fluorescence
microscopy to image the entire nano-sieve device before (Fig. 1c) and after (Fig. 1d) bacteria-
trapping. As shown in Fig. 1d, stained bacteria are trapped within the interspaces of the beads
assay.
The beads stacking process with a flow rate of 40 µL/min is shown in Fig. 2a. Initially (0 s), the
nano-sieve is empty (Fig. 2a-i). Under the filling process (24 s), the beads begin to stack and form
an arch shape (Fig. 2a-ii). However, as the pressure drop builds up in the nano-sieve (48 s), beads
are pushed to the sides of the channel and burst out to the outlet from the central path of the channel
(Fig. 2a-iii). This process also moves the beads pack closer to the outlet. As the channel
deformation is smaller at the outlet, it allows for filling of the beads (72 s) into the device without
bursting, and a denser bead stacking in the 3D space is achieved (Fig. 2a-iv). The video of this
process is shown in the supporting information. To understand this time-dependent process, we
developed a computational fluid dynamics (CFD) model to study the magnetic bead transport in
the deformed nano-sieve. We assumed that the deformation of the PDMS roof is dominated by
pumping pressure and that the effect of the stacked beads and heaving of the roof is negligible.
Fig. 2b-i depicts with a flow rate of 20 µL/min, the highest flow velocity in the channel is 0.035
m/s, and the maximum height in the channel is 19 µm at the middle cross-sectional (Y-Z) plane.
The velocity at the centroid of the channel increases with the increased flow rate (Fig. 2b-i to Fig.
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2b-iv). At a flow rate of 50 µL/min, the highest flow rate is 0.055 m/s, and the maximum height
is 30 µm at the middle cross-sectional plane. This high flow rate and large deformation of the
channel cause the beads to move to the sides of the channel and to the outlet reservoir. This result
matches our experimental observation and explains the initial bead stacking and burst process (Fig.
2a-i to Fig. 2a-iii).
Following the initial burst, the beads move closer to the outlet. Moreover, the inflow of beads
starts to reconstruct the 3D space, which is further used for bacteria-trapping. We used 3D
tomography technology to scan the entire channel after the bead stacking and applied machine
learning tools to extract the topology-related data (Fig. 3a). The scanning results and quantitative
measures of the microbead array are depicted in Fig. 3a. An overlay of the 3D mask (in pink) and
the raw data are found to be dependent on the flow rate ranging from 20 to 50 µL/min. Inspired by
the machine learning-based segmentation task in 2D-cell microscopy image37 and 2D-material
visualization,38 we generated a 3D mask using a novel segmentation method, which was a
combination of an unsupervised machine learning method, K-mean clustering,39 and
morphological operation.40 A typical example in a cross-sectional plane is depicted in Fig. 3b. The
white region corresponds to bead stacking, and the black region corresponds to the background
and the coverslip. The maximum height of the bead stack was measured from the mask. For each
volume, 3D topology was generated by aligning 2D masks in 3D space. The volume of deposited
beads and the maximum height versus flow rates are presented in Fig. 3c. With a flow rate of 20
µL/min, the total volume and the maximum height in the channel were ~2.50×105 voxel and ~60
µm, respectively. We found that the total volume and maximum height show an uptrend that
corresponded to an increased flow rate. For example, with a high flow rate of 50 µL/min, the
volume and maximum height were increased to ~1.83×106 voxel and ~132 µm, respectively. As
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the original channel height was only 200 nm, a ~650× increase in channel height was observed
without showing channel failure.
Fig. 4 presents the trapping efficiency and retrieval factor of the bacteria in a nano-sieve device.
Two hundred microliters of the stained bacteria sample (concentration: 1.11E8 CFU/mL) were
pumped into the beads-stacked nano-sieve device, and the fluorescence signal of the filtered
supernatant was measured by using a spectrofluorometer at an excitation wavelength of 480 nm.
By introducing 10 µm magnetic beads in the nano-sieve, the measured relative fluorescence
intensity of the filtered supernatant is ~140 counts, demonstrating that most of the bacteria (~65%)
are trapped in the nano-sieve device (Fig. 4a). Alternatively, the measured fluorescence intensity
of the supernatant was only ~30 counts by introducing a bead mixture with various sizes (2.8 µm,
5 µm, and 10 µm), indicating that almost all of the bacteria (~92%) were captured in the nano-
sieve device. The bacteria-trapping efficiency versus flow rate was then explored. As depicted in
Fig. 4b, without any stacked beads, the bacteria-trapping efficiency is between 18%-38% at flow
rates ranging from 8 to 70 µL/min. The addition of the beads mixture to the nano-sieve device
significantly increases the trapping efficiency. The bacteria-trapping efficiency is above 86% at
flow rates of 8-50 µL/min. At a flow rate of 70 µL/min, a bacteria-trapping efficiency of 64% is
achieved, which is higher than that observed within the nano-sieve device without stacked
beads. Following bacteria-trapping, 200 µL phosphate-buffered saline (PBS) was pumped into the
nano-sieve device at a flow rate of ~900 µL/min. This high flow rate induces significant
deformation of the PDMS roof, thus releasing the beads and bacteria into an Eppendorf tube (Fig.
1b-i and Fig. 4c-i. The retrieval process of microbeads/bacteria is shown in Fig. S1. A fluorescence
microscope was used to image the original bacteria sample (Fig. 4c-ii), filtered supernatant (Fig.
4c-iii), and retrieved sample (Fig. 4c-iv). Only a few bacterial cells were observed in the filtered
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supernatant, again proving negligible bacteria leaking. The retrieved bacteria solution (cells in the
unit area: 0.142 cells/µm2) has similar cell numbers to the original sample (cells in the unit area:
0.163 cells/µm2), indicating highly efficient bacterial retrieval. Our platform is capable of trapping
and retrieving bacteria from various media, including bodily fluids. We achieve a bacteria-trapping
efficiency of ~60% and a retrieval rate of 80% from pig plasma at a flow rate of 8 µL/min (Fig.
4d).
The bead-stacked nano-sieve can be applied to concentrate bacteria samples when the original
cell number is low. To explore this, we pumped 1,300 µL bacteria with a concentration of 1.11E7
CFU/mL into the bead-stacked nano-sieve device. Then, we retrieved the bacteria by introducing
100 µL PBS buffer at a flow rate of ~900 µL/min, to obtain an on-chip concentration factor of
13×. As depicted in Fig. 5a, the original sample with a concentration of 1.11E7 CFU/mL only
shows ~60 counts. Notably, the retrieved sample shows a fluorescence intensity of ~480 counts,
indicating a dramatic increase in sample concentration. The integrated fluorescence signal of Fig.
5a with a wavelength from 520-640 nm was plotted and is presented in Fig. 5b, showing an
excellent linear relationship. A ~11.2× on-chip concentration factor was achieved, which is closely
matched to the intended 13× on-chip concentration. This powerful on-chip concentration
capability was further confirmed by fluorescence microscopy. The retrieved bacterial sample
shows a much higher concentration than the original sample, when compared to the filtered
supernatant, indicating an efficient on-chip concentration (Fig. 5c).
Discussion
Drug-resistant bacteria have become a severe public health concern. Fortunately, this risk can be
reduced via the correct use of prescriptions, and by avoiding unnecessary prescriptions, and over-
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prescription of antibiotics.41,42 In this regard, rapid isolation of the target bacteria from various
samples is an essential step toward the identification of antibiotic resistance and providing early-
treatment.43 Reported here is the development of a magnetic beads-stacked nano-sieve device to
separate, concentrate, and retrieve bacteria from both the buffer solution and pig plasma samples.
Leveraging the deformation capability of PDMS, our novel system is able to stack microbeads in
a large 3D space to capture microorganisms from the various media. The bacteria can be trapped
in the voids of the microbeads stack without altering their properties. Our system is simple to build
and cost-effective as it does not require expensive and time-consuming nanolithography. Thus,
this novel method should be ideal for high throughput, multiplexing, and inexpensive clinical
applications. High capture efficiency (86%) is achieved by using a high flow rate (50 µL/min),
indicating that the system can process a large sample volume in a short amount of time. In addition,
the trapping efficiency is high 85%) regardless of the bacteria concentration, further
demonstrating the robustness of our system (Fig. S2).
Unlike conventional membrane-based filtration, which is challenging to recover captured
pathogens,44 our system is able to retrieve the captured bacteria into different bacterial suspensions,
designed for lysis-free diagnostics.45 Even though several approaches based on immunoaffinity
separation have been widely used and show high target specificity, they require expensive and
delicate antibodies to bind with the surface antigens.46,47 In addition, immunoaffinity typically has
a low capture efficiency and cannot detect the presence of pathogens when the cell number is low,
and it also requires time-consuming sample preparation, which is not suitable for POC
applications.48 Our approach entirely relies on physical separation, which is robust and does not
interfere with the host cellular materials. Following rapid sample retrieval and separation, the
samples are ready to be used for genotype analysis (PCR6,7) and standard phenotype analysis (cell
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plating10,11). Furthermore, unlike other microfluidic-based approaches that necessitate the use of
complicated instruments for operation,23,25–28,49–51 our approach only requires a small syringe pump
and the miniaturized nano-sieve device; thus, it could be used for both lab-based or POC diagnosis.
A unique design that incorporates tightly stacked magnetic beads to trap the bacteria was
employed, and the efficiency of bacteria-trapping depends on the configuration of the particle
stacking. Instead of using complicated micro-and nanofabrication to physically isolate the
bacteria,52,53 our approach simply relies on bead stacking at various flow rates and does not require
expensive and time-consuming nanolithography processes.54,55 As the beads are pushed to the
outlet of the channel; they begin to stack in the 3D space (Fig. 2a-iv). The channel height reaches
to ~132 µm at 50 µL/min, which is ~650× higher than the original channel height (~200 nm). This
sizeable 3D space created by bead stacking provides numerous voids for bacteria-trapping. This is
crucial for bacteria-trapping, especially for on-chip concentration, as more voids within the bead
array are required to capture bacteria. Moreover, tension caused by deformation of the PDMS roof
is responsible for locking the bead array into position. Even with a flow rate at 50 µL/min, we did
not observe beads leaking from the nano-sieve, enabling the great capability of processing a large
sample volume.
We found that the bead size is also an essential factor for efficient bacteria-trapping. As shown
in Fig. 4, mixing 5 µm and 2.8 µm beads with the 10 µm beads significantly improves the capture
efficiency. Since E. coli has a dimension less than 2 µm and has great deformability,56,57 it can
pass through the small voids at a higher flow rate. Thus, we first applied the 10 µm beads into the
nano-sieve to occupy the 3D space, followed by the application of smaller beads. The tightly
stacked smaller bead array has smaller voids, thus enhancing the bacteria-trapping efficiency. We
observed a slight reduction in capture efficiency at a flow rate of 70 µL/min due to bead leaking
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caused by the large hydrodynamic deformation. This problem could be easily resolved by
designing a multi-channel nano-sieve device to reduce the deformation and beads leaking.
One of the main advantages of microfluidics is the miniaturized size, enabling multi-device
operation on a small scale and reducing the consumption of applied samples.58,59 Leveraging the
small size of our nano-sieve device (2 mm × 8 mm), it is possible to pattern hundreds of the nano-
sieves on a 4-inch wafer scale for high throughput multiplexing detection. This could also be used
to screen multi-resistant organisms by applying many different antibiotics to the isolated samples.
The small size of the nano-sieve device also enables us to work with sample volumes ranging from
nanoliters to milliliters; thus, it is compatible with either a finger prick test or a blood draw.
The microbial load of drug-resistant bacteria could be as low as ~10-100 CFU/mL in the bodily
fluids;60 thus, concentrating the target is always necessary to reach the detection threshold. By
introducing 1,300 µL of the E. coli sample into the nano-sieve device and then retrieving the
bacteria in 100 µL of the buffer, we demonstrated an on-chip concentration factor of ~11×, which
was evaluated by the excellent linearity of the bacterial concentration (1.11E7 CFU/mL to 1.11E8
CFU/mL). Thus, our system is a useful platform for dealing with low concentration samples and
could be applied to extend the detection limit. The on-chip concentration factor could be further
increased by the application of a larger sample volume and by lowering the volume used for
retrieval. By using our novel approach, bacteria could be easily separated, concentrated, and
retrieved into any buffer solution and would be available for molecular diagnostic testing of
suspects in a POC setting.
Conclusions
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In order to produce a rapid and reliable drug-resistant bacterial separation and detection method,
our work developed the first miniaturized system for the physical separation and concentration of
bacteria from the buffer solutions and pig plasma. This was achieved via 3D stacking of the
unmodified magnetic microbeads in a strained nano-sieve device with numerous voids at the
interface, which were characterized by optical tomography, machine learning reconstruction, and
fluid dynamics simulation. We demonstrated that this system exhibits a very high bacterial capture
efficiency and is capable of on-chip aggregation when dealing with lower concentration samples.
The bacteria are simply retrieved and concentrated in the designated buffer without altering the
properties of bacteria. We believe that our system can be used for a wide spectrum of medical and
technological applications, including rapid diagnosis of drug-resistant bacteria in bodily fluids,
drinking water monitoring, and food safety.
Materials and Methods
Polydimethylsiloxane (SYLGARDTM 184) was purchased from Krayden Inc., CO, USA. Glass
wafer (D263, 550 µm, double side polished) was received from University Wafer, MA, USA.
Magnetic beads with a diameter of 5 µm and 10 µm were ordered from Alpha Nanotech Inc,
Vancouver, Canada. Magnetic beads with a diameter of 2.8 µm were purchased from Thermo
Fisher Scientific, MA, USA. PBS (1× without calcium and magnesium, PH 7.4 ± 0.1) was
purchased from Corning Inc, NY, USA. The fluorescent dye (BacLightGreen Bacterial Stain,
excitation/emission: 480/525) was obtained from Thermo Fisher Scientific, MA, USA. The plasma
solution (P2891-10 mL) was purchased from Sigma Aldrich, MO, USA, which was diluted with a
ratio of 1:10, before running experiments.
Nano-sieve device fabrication. A thin layer of TEOS was deposited onto a cleaned glass wafer
with a thickness of 200 nm by using Plasma Enhanced Chemical Vapor Deposition. Then, positive
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resist (PR: AZ Mir 701) was spin-coated onto the TEOS layer with a thickness of ~1 µm. Standard
lithography was used to pattern the nano-sieve, followed by the development with CD-26
developer. Buffered oxide etching (BOE) was used to etch the TEOS layer, with an etching rate of
~2.72 nm/s for 75 s. Following that, PR was completely removed using the acetone solution,
followed by isopropyl alcohol (IPA) rinsing for 15-20 s and then nitrogen drying. A sacrificial
layer of PR was patterned in the etched channel via standard lithography. PDMS base and curing
agent were mixed at a ratio of 10:1 and then cured in an oven at 85 °C for 50 mins. PDMS was
casted to a final thickness of ~5 mm. The inlet and outlet holes were punched using a biopsy punch
(diameter = 1 mm). Eventually, the PDMS was fused onto the fabricated glass substrate via oxygen
plasma treatment (Electro-Technic Products). The tubing with a diameter of 1.09 mm was press
fit into the 1mm punched hole, without showing any leaking problems.
Cell culture and labeling. Escherichia coli (E. coli, ATCC 25922) cells were cultured for 14-
15 hours in LB broth at 37 °C in a shaker incubator, until they reached an optical density (OD) of
0.5-0.6 measured at 600 nm, which corresponded to an approximate cell concentration of ~1.71 ×
108 cells/mL. E. coli cells exhibited a prolate shape and were 2.38 ± 0.32 µm long and 1.20 ± 0.21
µm wide. The cells were then stained with fluorescent dye to enable visualization. Briefly, 1 mL
of cell culture was centrifuged in a microcentrifuge (VWR Galaxy Mini C1213) at 2000g for 5
mins; then, the supernatant was discarded. The fresh PBS was applied in order to rinse and re-
suspend the condensed cells. Subsequently, 4 µL of the BacLight dye was added into 1 mL of the
PBS-based bacteria solution for staining the E. coli cells; then, the sample solution was vortexed
for 10-15 s. The stained cells were incubated at room temperature for approximately 20 mins,
followed by centrifugation at 2000g for 5 mins. Afterward, the supernatant was discarded, and the
pellet of cells was rinsed again using fresh PBS to remove the excess dye. Finally, the cells were
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re-suspended in 0.5 mL of fresh PBS and ready for use. Cell concentration expressed as CFU was
determined by performing a series of dilutions of overnight liquid culture (OD = 0.92, ~2.71 × 108
cells/mL). This culture was diluted 1:10 stock/mL four consecutive times. The most diluted culture
(0.0001 stock/mL) was then plated on a solid LB agar petri dish and incubated at 37 °C for 24 hrs.
Following growth overnight, the number of colonies was counted visually, and the resulting
concentration was estimated as 1.00 × 108 CFU/mL (Refer to Table S1 in the Supporting
Information).
Bead stacking and bacteria-trapping. A syringe pump (WPI, SP220I Syringe Pump) was used
to inject magnetic beads into the nano-sieve channel. Subsequently, the bacteria solution was
pumped into the channel using a syringe pump at various flow rates. The filtered waste was
collected in a centrifuge tube. Afterward, fresh PBS was applied to wash the magnetic beads and
bacteria out of the nano-sieve channel, then collected in another centrifuge tube that was held on
a magnetic rack.
CFD modeling. The Eulerian multiphase model in FLUENT 19.2 was used to simulate the
transport of the beads through the nano-sieve. Water flow was assumed as laminar flow since the
maximum Reynolds number was less than 30. Velocity inlet and pressure outlet boundary
conditions were applied to the inlet and outlet. The diameter and density of the beads used in the
simulation were 10 µm and 2500 kg/m3, respectively.
Tomography scanning. Volumetric topology was evaluated with an optical coherence
tomography system (OCT, Thorlab, Ganymede). This system used a near-infrared light source
(790 nm to 990 nm) to illuminate the bead sample. Volumetric structural information was
reconstructed through the processes involved in background subtraction, λ domain to k domain
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conversion, apodization, and inverse Fourier Transform. It was a label-free process, which covered
a region of 3 × 6 × 1.94 mm3.
Segmentation via machine learning. In order to quantitatively measure the volume of beads in
each flow rate, we devised an unsupervised machine learning tool for segmentation. Specifically,
we integrated k-mean clustering with the morphological operations, to automatically extract the
boundary of the bead stack in 3D space. K-mean clustering39 learns the structural similarity within
two clusters (groups) of pixels, background, and beads. Within the intensity space, Euclidean
distance between the two groups of pixels was iteratively learned by adaptive justification, which
determined if an unknown pixel belonged to the beads or not. The segmented boundary was further
enhanced when morphological operations were applied to eliminate the noisy pixels at the edge of
the beads.
Spectrofluorometric characterization. A spectrofluorometer (JASCO FP–8500) was used to
measure the fluorescence intensity of the stained cells. The excitation wavelength was set at 480
nm, and the sensitivity was set at level “high” to measure all of the samples in this study. Spectral
analysis software (Spectra Manager, JASCO corporation) was used to collect data from the
spectrofluorometer.
Fluorescence microscopy imaging. The magnetic beads and bacteria samples were imaged
with a high-speed camera (AxioCam MRc, Zeiss) mounted on the microscope (AmScope). The
fluorescence power source and GFP filter kit were used to visualize the E.coli bacteria, which was
stained with a fluorescent dye. The lens magnification was set at 2.5× and 100×, with an exposure
time of 100 ms.
Scanning electron microscopy (SEM). SEM (Tescan Mira3) was used to image the magnetic
beads in the nano-sieve channel. Samples were mounted onto a sample holder. Then, ~20 nm of
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metal film was coated on the sample by using a sputter coater (SPI-Module™ Sputter Coater). For
SEM imaging, the voltage was set at 20 kV.
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FIGURES
Figure 1. (a) Illustration of the magnetic beads stacked nano-sieve device for bacteria isolation: (i) beads trapped in the nano-
sieve; (ii) pumping bacteria into the nano-sieve; (iii) bacteria retrieval via high flow rate buffer washing. (b) Magnetic beads
trapping: (i) Experimental setup. Inset: photograph of beads stacked nano-sieve device. SEM images of the beads stacked nano-
sieve channel: (b-ii) Left; (b-iii) Middle; (b-iv) Right. Fluorescence microscope image of the channel before (c) and after (d)
bacteria-trapping. The white arrow indicates the flow direction.
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Figure 2. (a) Beads stacking process during 72 s with a flow rate of 40 µL/min. The dashed purple box indicates the channel. The
arrow indicates the flow direction. Scale bar: 4 mm. (b) Top: CFD calculation of nano-sieve deformation and beads flow pattern;
bottom: flow velocity with a flow rate of (i) 20 µL/min; (ii) 30 µL/min; (iii) 40 µL/min; (iv) 50 µL/min.
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Figure 3. (a) Optical coherence tomography scan of the beads stacked nano-sieve with the flow rate ranging from 20 - 50 µL/min.
(b) Cross-section view of beads stacking: raw 2D image (top) and segmented 2D mask (bottom). (c) Experientially measured
volume of the deposited beads and maximum height in the channel after beads stacking.
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Figure 4. (a) Uncorrected emission curve of filtered solution from nano-sieve with a flow rate of 8 µL/min: Original solution
(pink); Stacked with 10 µm beads (green); Stacked with mixed beads with sizes of 2.8 µm, 5 µm, and 10 µm (blue). The emission
peaks are centered at ~520 nm. (b) Bacteria-trapping efficiency versus flow rate of nano-sieve only (yellow) and beads stacked
nano-sieve (patterned green). (c-i) Photograph of the bacteria samples: Original (left); Filtered (middle); Retrieved (right).
Fluorescence microscope image of original solution (c-ii), filtered solution (c-iii), and retrieved solution (c-iv). Scale bar: 10 µm.
(d) Bacteria-trapping efficiency and retrieval efficiency from PBS (brown) and pig plasma (patterned blue). The applied flow rate
is 8 µL/min. Error bars indicate standard deviation of the mean.
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Figure 5. (a) Uncorrected emission curve of bacteria solution with various concentrations. The emission peak is centered at ~520
nm. The input bacteria concentration and volume are 1.11E7 CFU/mL and 1,300 µL, respectively (solid pink line). The retrieved
sample is in 100 µL PBS (solid red line). Fluorescence intensities of stained bacteria stock solution with a concentration of 2.22E7,
4.44E7, and 1.11E8 CFU/mL are presented. These references are used to calculate the concentration factor. Inset: Uncorrected
emission curve of filtered solution. (b) Fluorescence intensity versus bacteria concentration. The estimated concentration factor is
13 and the evaluated concentration factor is ~11. Error bars are standard error of the mean. (c) Fluorescence microscope image of
original solution (left); filtered solution (middle); retrieved (right). Scale bar: 10 µm.
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AUTHOR INFORMATION
Corresponding Author: ke.du@rit.edu
Present Addresses: 76 Lomb Memorial Drive, Rochester, NY 14623
Author Contributions: Xinye Chen, Qian He, and Ke Du designed the experiments. Xinye
Chen, Abbi Miller, Shengting Cao, Yu Gan, and Qian He conducted the experiments. Jie Zhang
and Ruo-Qian Wang conducted CFD modeling and calculation. Xinye Chen and Ke Du wrote the
manuscript. All the authors commented on the manuscript.
Funding Sources: This research is supported by the start-up fund provided by Rochester
Institute of Technology.
Acknowledgment: We are grateful to the lab members in the 3N laboratory for fruitful
discussions. The authors would like to thank Wenrong He and Personalize Healthcare Technology
(PHT180) at RIT for schematic design. Part of the nano-sieve device fabrication was conducted in
The Semiconductor & Microsystems Fabrication Laboratory at RIT.
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... For efficient detection of E. coli a micro-nanofluidic device functionalized with magnetic beads was utilized to capture the food pathogens through computational fluid dynamics (CFD), 3D-tomography, and machine learning to probe and bead stack the pathogen. Moreover, this nano-device is multiplexed, downsized, cost-effective, transparent, and easy to fabricate and operate which is making it ideal for pathogen separation in both laboratory and point-of-care (POC) settings [128]. ...
... magneto-immunoassay and AuNPs for electrochemical detection of Salmonella enterica by functionalizing a screen-printed carbon electrode (SPCE) with a permanent magnet. An anti-Salmonella magnetic beads (MBs-pSAb) were used to trap Salmonella-containing samples (skimmed milk), sandwiched with AuNPs-modified antibodies (sSAb-AuNPs) for detection using differential pulse voltammetry (DPV) causing 83% and 94% efficiency [128]. A label-free impedimetric aptamer-based bio-nano-sensor has been utilized based on electrochemically and chemical grafting of a diazonium-supporting layer onto SPEs which initiates immobilization of chemicals of aminated-aptamer, for capturing and detection of Salmonella. ...
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... To date, McFarland turbidity standards [20][21][22] and spectrophotometer measurements [23][24][25] are the two commonly adopted low-cost practices, which give rapid and reasonable estimations of high-density microbes (in the order of 10 8 CFU/mL in less than 15 min, including spectrophotometer warm-up time) [26,27]. To detect low-density microbes, filtration-based methods through membrane and lab-on-chip devices offer great advantages in concentrating large sample volumes [28][29][30][31]. This, combined with fluorescence microscopy [32,33] or adenosine triphosphate (ATP) bioluminescence [34,35], pushes the detection limit to as low as 10 3 CFU/mL in a few hours' time. ...
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In this article, we present a microfluidic technique for the rapid enumeration of bacterial density with a syringe filter to trap bacteria and the quantification of the bacterial density through pressure difference measurement across the membrane. First, we established the baseline differential pressure and hydraulic resistance for a filtration membrane by fully wetting the filter with DI water. Subsequently, when bacteria were infused and trapped at the pores of the membrane, the differential pressure and hydraulic resistance also increased. We characterized the infusion time required for the bacterial sample to achieve a normalized hydraulic resistance of 1.5. An equivalent electric-circuit model and calibration data sets from parametric studies were used to determine the general form of a calibration curve for the prediction of the bacterial density of a bacterial sample. As a proof of concept, we demonstrated through blind tests with Escherichia coli that the device is capable of determining the bacterial density of a sample ranging from 7.3 × 106 to 2.2 × 108 CFU/mL with mean and median accuracies of 87.21% and 91.33%, respectively. The sample-to-result time is 19 min for a sample with lower detection threshold, while for higher-bacterial-density samples the measurement time is further shortened to merely 8 min.
... The use of MT as a sample preparation approach assisted in the efficient isolation of the target bacterial cells (more than 87%) from the human specimens, allowing the cleaning of the interfering components in a fast and simple way, without the need for laborious centrifugation steps. MT has been shown as a promising sample preparation approach in diverse systems (Favrin et al., 2001;Schmelcher et al., 2010;Wang and Alocilja, 2015;Ngamsom et al., 2016;Chen et al., 2020), including some using RBPs to functionalize the MNPs (Kretzer et al., 2007;Denyes et al., 2017;Cunha et al., 2021). The results from the MT also revealed some capture for the non-target bacteria S. aureus. ...
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Escherichia coli is a problematic pathogen that causes life-threatening diseases, being a frequent causative agent of several nosocomial infections such as urinary tract and bloodstream infections. Proper and rapid bacterial identification is critical for allowing prompt and targeted antimicrobial therapy. (Bacterio)phage receptor-binding proteins (RBPs) display high specificity for bacterial surface epitopes and, therefore, are particularly attractive as biorecognition elements, potentially conferring high sensitivity and specificity in bacterial detection. In this study, we elucidated, for the first time, the potential of a recombinant RBP (Gp17) to recognize E. coli at different viability states, such as viable but not culturable cells, which are not detected by conventional techniques. Moreover, by using a diagnostic method in which we combined magnetic and spectrofluorimetric approaches, we demonstrated the ability of Gp17 to specifically detect E. coli in various human specimens (e.g., whole blood, feces, urine, and saliva) in about 1.5 h, without requiring complex sample processing.
... Metallic and magnetic NPs, such as gold and iron oxide NPs, have been widely investigated so far and demonstrated improved testing accuracy, specificity, time, and reliability. 86,87 Gold NPs coupled to complementary DNA sequences demonstrated a color change from red to blue indicating the formation of a tertiary complex with the viral antigen after the immobilization and agglomeration of the NPs. 88 Metal oxide NPs in complement with a silicon-on-insulator nanowire sensor showed a rapid and ...
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A serious viral infectious disease was introduced to the globe by the end of 2019 that was seen primarily from China, but spread worldwide in a few months to be a pandemic. Since then, accurate prevention, early detection, and effective treatment strategies are not yet outlined. There is no approved drug to counter its worldwide transmission. However, integration of nanostructured delivery systems with the current management strategies has promised a pronounced opportunity to tackle the pandemic. This review addressed the various promising nanotechnology-based approaches for the diagnosis, prevention, and treatment of the pandemic. The pharmaceutical, pharmacoeconomic, and regulatory aspects of these systems with currently achieved or predicted beneficial outcomes, challenges, and future perspectives are also highlighted.
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A pneumatic controlled nano-sieve device is demonstrated for the efficient capture and release of 15 nm quantum dots. This device consists of a 200 nm deep glass channel and a PDMS-based pneumatic pressure layer to enhance target capture. The fluid motion inside the nano-sieve is studied by computational fluidic dynamics (CFD) and microfluidic experiments, enabling efficient target capture with a flow rate as high as 100 µL/min. In addition, micro-grooves are fabricated inside the nano-sieve to create low flow rate regions, which further improves the target capture efficiency. A velocity contour plot is constructed with CFD, revealing the flow rate is lowest at the top and bottom of the micro-grooves. This phenomenon is supported by the observed nanoparticle clusters surrounding the micro-grooves. By changing the morphology and pneumatic pressure, this device will also facilitate rapid capture and release of various biomolecules. This is the author's peer reviewed, accepted manuscript. However, the online version of record will be different from this version once it has been copyedited and typeset. PLEASE CITE THIS ARTICLE AS
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The outbreak of corona virus (COVID-19) has imposed serious concern all over the world as many part of the globe have been severely affected by this. It has become essential to develop efficient methods for the treatment and detection of this virus. Among the new approaches, the nanosensor has played a vital role in tracing and detecting the virus. Sensors are tools to assist detect events or changes in the environment while also sending data to other electronics, most commonly a computer processor. This chapter contains the approach followed and development in several biosensors, wearable sensor, and colorimetric sensors toward the identification of corona virus.
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This study reports a microfluidic device for whole blood processing. The device uses the bifurcation law, cross-flow method, and hydrodynamic flow for simultaneous extraction of plasma, red blood cells, and on-chip white blood cell trapping. The results demonstrate successful plasma and red blood cell collection with a minimum dilution factor (0.76x) and low haemolysis effect. The extracted red blood cells can also be applied for blood type tests. Moreover, the device can trap up to ~1,800 white blood cells in 20 minutes. The three components can be collected simultaneously using only 6 μL of whole blood without any sample preparation processes. Based on these features, the microfluidic device enables low-cost, rapid, and efficient whole blood processing functionality that could potentially be applied for blood analysis in resource-limited environments or point-of-care settings.
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This chapter proposes a novel approach towards extraction of brain tumor images from T1-type magnetic resonance imaging (MRI) scan images. The algorithm includes segmentation of the scan image using a rough set-based K-means algorithm. It is followed by the use of global thresholding and morphological operations to extract an image of the tumor-affected region in the scan. This algorithm has been found to extract tumor images more accurately compared than existing algorithms.
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