\documentclass[10pt]{article}

\usepackage{fullpage}
\usepackage{setspace}
\usepackage{parskip}
\usepackage{titlesec}
\usepackage[section]{placeins}
\usepackage{xcolor}
\usepackage{breakcites}
\usepackage{lineno}
\usepackage{hyphenat}





\PassOptionsToPackage{hyphens}{url}
\usepackage[colorlinks = true,
            linkcolor = blue,
            urlcolor  = blue,
            citecolor = blue,
            anchorcolor = blue]{hyperref}
\usepackage{etoolbox}
\makeatletter
\patchcmd\@combinedblfloats{\box\@outputbox}{\unvbox\@outputbox}{}{%
  \errmessage{\noexpand\@combinedblfloats could not be patched}%
}%
\makeatother


\usepackage{natbib}




\renewenvironment{abstract}
  {{\bfseries\noindent{\abstractname}\par\nobreak}\footnotesize}
  {\bigskip}

\titlespacing{\section}{0pt}{*3}{*1}
\titlespacing{\subsection}{0pt}{*2}{*0.5}
\titlespacing{\subsubsection}{0pt}{*1.5}{0pt}


\usepackage{authblk}


\usepackage{graphicx}
\usepackage[space]{grffile}
\usepackage{latexsym}
\usepackage{textcomp}
\usepackage{longtable}
\usepackage{tabulary}
\usepackage{booktabs,array,multirow}
\usepackage{amsfonts,amsmath,amssymb}
\providecommand\citet{\cite}
\providecommand\citep{\cite}
\providecommand\citealt{\cite}
% You can conditionalize code for latexml or normal latex using this.
\newif\iflatexml\latexmlfalse
\providecommand{\tightlist}{\setlength{\itemsep}{0pt}\setlength{\parskip}{0pt}}%

\AtBeginDocument{\DeclareGraphicsExtensions{.pdf,.PDF,.eps,.EPS,.png,.PNG,.tif,.TIF,.jpg,.JPG,.jpeg,.JPEG}}

\usepackage[utf8]{inputenc}
\usepackage[ngerman,greek,english]{babel}
\usepackage{CJKutf8}



\usepackage{float}








\begin{document}

\title{An integrated digital microfluidic bioreactor for fully automatic
screening of microalgal growth and stress-induced lipid accumulation}



\author[1]{Yunhua Wang}%
\author[1]{Hongyu Zhao}%
\author[2]{Xianming Liu}%
\author[1]{Lin Wang}%
\author[3]{Youwei Jiang}%
\author[4]{Jianfeng Li}%
\author[1]{Guoxia Zheng}%
\affil[1]{Dalian University}%
\affil[2]{Dalian Institute of Chemical Physics}%
\affil[3]{Southern University of Science and Technology}%
\affil[4]{Jiangsu Celyee Cell Technology Research Institute}%


\vspace{-1em}



  
  \date{May 19, 2024}


\begingroup
\let\center\flushleft
\let\endcenter\endflushleft
\maketitle
\endgroup





\selectlanguage{english}
\begin{abstract}
Algae are promising feedstock of biofuel. The screening of competent
species and proper fertilizer supply are of the most important tasks. To
accelerate this rather slow and laborious step, we developed an
integrated high-throughput digital microfluidic (DMF) system that uses
discrete droplet to serve as micro-bioreactor, encapsulating microalgal
cells. Based on the fundamental understanding of various droplet
hydrodynamics induced by the existence of different sorts of ions and
biological species, an incorporation of capacitance-based position
estimator, electrode-saving-based compensation and deterministic
splitting approach was performed to optimize the DMF bioreactor. Thus,
it enables all processes (e.g. nutrient gradient generation, algae
culturing and analyzing of growth and lipid accumulation) occurring
automatically on-chip especially in a high-fidelity way. The ability of
the system to compare different micro algal strains on chip was
investigated. Also, the Chlorella sp. were stressed by various
conditions and then growth and oil accumulation were analyzed and
compared, which demonstrated its potential as a powerful tool to
investigate microalgal lipid accumulation at significantly lower
laborites and reduced time.%
\end{abstract}%



\sloppy


\textbf{1. Introduction}

Microalgae are of particular interest as the most promising source for
sustainable energy which has the potential to meet the increasing global
demand for transport fuels by displacing non-renewable fuels. Although
biodiesel production extracted from microalgae is proved to be
technically practicable, a scalable and commercially viable are far from
competitive~fossil fuels' prices (Jin et al., 2020). Thus, new methods
must be developed to increase yield and decrease the cost of the
production. Currently, the improvements aiming at algal lipid
accumulation mainly include screening suitable microalgal species which
grows fast and/or could produce more oil, and optimizing their growth
conditions (e.g., nutrients, temperature, pH and light condition, etc.),
as well as under-expressing genes involved in lipid biosynthesis
pathways (Chen and Chang, 2016; Khandelwal et al., 2020; Richter et al.,
2018). All screenings are typically done by classical laboratory
culturing and off-line lipid analyzing methods. In many cases, these
processes are costly and labor-intensive. It is especially true in
cultivation conditions optimizing experiments. Large scale bioreactors
and repetitive dispensing induced by parallel experiments and
concentration gradients generation make it extremely physical space
exhausting and labor-intensive. Thus microalgal lipid screening could
benefit from a more miniaturized, streamlined and integrated approach.
The maneuverable electrowetting-on-dielectric (EWOD) behavior of
micro-droplets, on variable-electrized surface, has impelled the
development of digital microfluidic (DMF) systems for high-throughput
micro-reactors (Gao et al., 2013), which have prop up a wide range of
biological and chemical applications in sub-microliter/or nanoliter drop
scales, such as cell-based screenings (Han et al., 2019), immunoassays
(Rackus et al., 2015), polymerase chain reactions (Schell et al., 2012),
and single crystal synthesis and crystallization (Josep et al., 2014).
DMF achieves a droplet manipulation via surface-tension modulation
induced by electrodes coated with dielectric and hydrophobic materials.
It belongs to a two-dimensional planar electronic control system (Fan et
al., 2011), in which complex experimental procedures (eg. concentration
gradient generation, cell staining and analyzing, etc.) can be
effectively carried out through basic fluidic operations including
droplet dispensing, moving, splitting, mixing and merging (Wang et al.,
2017). Their open structure (i.e., no preset microchannel), scalability
and high compatibility with diverse aqueous and different operational
protocols, thus offering a more flexibly controllable platform for
lab-on-a-chip applications (Choi et al., 2012).

Despite these tremendous potentials, only a few labs have reported
application of DMFs-based platforms in microalgae screening at present.
Reliability and robustness are still great challenges of DMF based
micro-bioreactors due to the co-existence of different sorts of
chemicals and biological species. Our experiments data substantiated
that droplets containing various nutrient ions, salinity and algae
species ran at dissimilar velocities and so the optimal charging times.
Especially, low-moving algae droplets usually could not reach the target
electrodes due to insufficient charging time, halting the entire system.
To overcome potential driven failures, a feedback control loop,
especially independent from droplet constituents should be included into
DMF control system. All droplets were highly desirable to be addressed
by real- time position-dependent maneuver mode to avoid manual tuning,
and a self-directed expert control to guide multi-droplet manipulation.
Furthermore, for stressor-induced lipid assays, which involve many steps
of component dilution, the consistency of dispensed droplet volumes is
also pivotal. However, currently, in most DMF applications highly
sensitive to sample volume variations (e.g., immunoassays (Vergauwe et
al., 2011), DNA amplification (Chang et al., 2006; Malic et al., 2009)
and cell analyzing (Bogojevic et al., 2012), droplet generation
approaches showed a \textasciitilde{}10\% variability. These
applications leave the cutting and generating process subject to many
uncertainties, and thus affect reproducibility.

Herein, we demonstrated an improved DMF-based algae micro-bioreactor
enabling a comprehensive implement of culturing and stressors triggering
lipid accumulation analyses in microalgae, especially a high-fidelity
screening performance of fertilization applied in brackish/salt water,
which will make microalgae-based biofuels more attractive and
competitive. Three optimizations were involved in the development of the
DMF-bioreactor. The first one is that the imperfect profiling of droplet
hydrodynamics are inspired by the capacitance-sensing concept. In this
work, the droplet-derived capacitance was determined in real time and
was used to track position (Zhang et al., 2019). Thus, droplets can be
automatically addressed in real time, without human observation,
pre-calibration (like in voltage feedback, Shih et al., 2011 ), or image
capturing/processing (like in visual feedback loop, Shin and Lee, 2010).
The second optimization is electrode-saving-based compensation. More
electrodes of the DMF will lead to increasing of complexity of control
system and feedback circuit. To compensate the system complexity and
cost-adding, an electrode-saving scheme with inherent simplicity and
fault tolerance was applied here. In this scheme, electrodes for algae
culturing are connected with ``finger-fringe'' geometry rather than one
or more interval electrodes. The third improvement of the developed
bioreactor is deterministic splitting of liquid. A simple approach
called ``bi-directional stretching spiting was used to achieve accurate
splitting of liquids. It can significantly reduce the splitting volume
variation and avoid abnormal droplet dividing, which is usually occurred
in traditional unidirectional stretching-split process.

We evaluated the DMF prosperities by measuring actuation fidelity,
volumetric variation in splitting and fluorescence intensities of target
droplets, as well as DMF compatibility in algae. The ability of the
system to compare different microalgal strains on chip was investigated.
Also, the selected \emph{Chlorella sp.} was successfully stressed by
various fertilizer conditions and then growth and oil accumulation were
well analyzed and compared. Thus the presented system allows, for the
first time, the entire proceed in of quantifying microalgal
proliferation and stressor-induced lipid enrichment were performed on a
microchip, smoothing the way towards more reliable and automated algae
lipid screening.

\textbf{2. Materials and Methods}

\textbf{2.1 DMF screening system}

The developed DMF screening system consists of three subsystems: (a) the
chip, (b) the operation unit, and (c) the detector element (Fig. 1). The
chip is composed of two parallel plates and integrated with a
temperature controller. The photograph of 24 electrodes chip
demonstration was shown in Fig.1a. In 24 electrodes, there are
3reservoir electrodes(for nutrients sample, blank and algae/probe,
respectively, with the size of 2\selectlanguage{ngerman}×2mm), 1 waste electrode for discarding
droplet (with the size of 4\(\times\)4mm), 2 preparing
electrodes for droplet waiting (with the size of 2×2mm), 6 culturing
electrodes for addressing algae droplets (with the main size of 2×2mm
and ``finger-fringe'' geometry of 2 mm length) and 12 actuation
electrodes to implement basic fluidic operations (moving, splitting,
mixing and merging, with the size of 2×2mm). To increase the culturing
throughput of a single chip, we applied a simple scheme that only a
culturing electrodes array was added on-chip and other electrodes were
the same as 24 electrodes demonstration. A 6×3 culturing electrodes
array chip were shown in Fig. 1d and the culturing electrodes were
connected with ``finger-fringe'' bridges in each line. To further saving
electrodes, 6×\emph{n} culturing electrodes were connected with
lengthwise ``finger-fringe'' bridges (Fig. 1e). The materials and
fabrication procedure of chip were described in details in Fig. S1 and
supporting information. The operation unit is FPGA (field programmable
gate array) based actuation system, which is responsible for applying
voltage to the electrodes, with a real-time capacitance-based position
feedback circuit to insure the accuracy of actuation (Fig. 1b). The
Hardware of DMF operation unit and operating principle of the
capacitance-based position feedback circuit as well as the entire
performance of the DMF micro-bioreactor were described in details in
Fig. S2 and S3 and supporting information..

\textbf{2.2 Microalgae strains}

Four dominant sea species of microalgae in Chinese coastal waters
including \emph{Dicrateria zhanjiangensis Hu. Var. sp., Chlorrela. Sp.,
Platymonas helgolandica var. tsingtaoensis and Platymonas
subcordiformis} were used in our experiments. Their main characteristics
were shown in Table S1 and supporting information.

\textbf{2.3 Droplet actuation complexity and reliability}

To assess EWOD actuation complexity and fidelity, a droplet was
programmed to move to the adjacent electrode repeatedly for one hundred
times. In schema without feedback, actuations were performed using
different charging time (\emph{T\textsubscript{a}} = 100, 200, 300, 400,
500, 600, 700, 800, 900 ms) for ion samples and
(\emph{T\textsubscript{a}} = 300, 600, 900, 1200, 1500, 1800, 2100 ms)
for algae samples. In feedback mode, an initial charging
time\emph{T\textsubscript{a}} of 300 ms was used, with an error-sensing
step between two charging durations of \emph{T\textsubscript{c}} =100ms.
A ``\% completion'' parameter was set as the number of successful
droplet movements divided by the total number of movements.

\textbf{2.4 Dilution properties of DMF}

\begin{equation}
E_{n}=\sqrt{n\times E^{2},}E=\frac{\sum_{i=1}^{m}{\left|\frac{(S_{i}-{1/2S}_{0})}{{1/2S}_{0}}\right|\times 100\%}}{m}\nonumber \\
 \end{equation}

Where, four merge-split steps were the ultimate of our dilution to meet
the demand of minimum concentration generation. Therefore, when n=4,
E\textsubscript{n} will be the maximum total error of the
diluter.E\selectlanguage{ngerman}  is the average value of split volumetric
error.\emph{S\textsubscript{i}} is the area of a divided droplet of
\emph{i}step.

\textbf{2.5 Characterizing of On-chip BODIPY staining}

A stock solution of BODIPY 493/503 (Inc. Eugene, OR, USA) prepared at
10\selectlanguage{greek}μ\selectlanguage{english}g/ml in dimethyl-sulfoxide (DMSO) was use as a fluorescent probe for
algae lipid staining. This probe was used for their high selectivity to
the hydrophobic tri-acyl-glycerolsaccumulatedin stressedalgal cells
(Gocze et al., 1994; Hu et al., 2008). The capability of in-situ lipid
analysis in DMF system was characterized in details in Fig S4 and SI
text.

\textbf{2.6 DMF growth and stressing experiments}

To demonstrate the streamlined analyzing function of the developed
system, growth and oil accumulation analyses in algae were performed
on-chip. Four marine species of \emph{D. zhanjiangenis} ,
\emph{Chlorella sp.} , \emph{P.helgolandica} and \emph{P.subcordiformis}
were candidates for algal strains screening. 5-day culture (N-replete
culture with f/2 medium under a light intensity of 100 \selectlanguage{greek}μ\selectlanguage{english}mol
photons\selectlanguage{ngerman}·m\selectlanguage{english}\textsuperscript{-2}*s\selectlanguage{english}\textsuperscript{-1} with a 12 hour
light-dark cycle) with the initial cell density of
5x10\textsuperscript{4}cells/ml were implemented for growth profile
acquiring, and were followed by on-chip oil staining/quantification. The
algae growth were characterized using apparent biomass increase rate,
respectively:

\(V=\frac{\text{LnN}_{t}-\text{LnN}_{0}}{T_{t}-T_{0}}\)

Where \emph{V} represents the apparent biomass increase rate
(10\textsuperscript{5}cells
ml\textsuperscript{-1}d\textsuperscript{-1}), N\textsubscript{0} and Nt
are the cell density at the beginning and at the end, respectively
(10\textsuperscript{5}cells ml\textsuperscript{-1}) and
(T\textsubscript{t}-T\textsubscript{0}) during the test (d). Data are
showed as mean +- SD. The lipids were analyzed in the same way as
described above. These results were compared with off-chip experiments,
in which algal were cultured in 96-well plate and analyzed by total
lipid extraction (Bligh et al., 1959; Gim et al., 2016). The following
experiments were to evaluate the suitability of the DMF system for
stressor trigging and in-situ analyzing algal oil accumulation. 5day-DMF
culturing of selected (oil-rich/high growth rate) strain
(\emph{Chlorella. sp.} ) was stressed in concentration gradient (CG)
droplets and then BODIPY fluorescence responses were observed to screen
the optimal fertilizations. The optimized fertilizer conditions include
nitrogen-depleted conditions (in the form of nitrate,
NaNO\textsubscript{3}) and phosphorus-depleted conditions (in the form
of dihydrogen~phosphate, NaH\textsubscript{2}PO\textsubscript{4}). Three
replicates were tested on one chip. All Data are presented as mean +- SD.

\textbf{3. Results and discussion}

\textbf{3.1 Actuation complexity and fidelity in DMF algae screening
system}

DMF device shown in Fig.1 was designed to generate arrays of droplets
containing algae, which were exposed to different fertilized salt
waters, and interrogated with microalgal growth as well as lipid
production measurements. For implement of a high-fidelity EWOD
controlled algae micro-bioreactor, we firstly evaluated the droplet
actuation reliability without feedback control. As demonstrated in Fig.
2, the displacements of droplets containing multiple liquid evolved in
DMF-bioreactor versus the charging time presents a nonlinear
relationship. The alterations of the position of the droplets with
nutrients during this state are distinct from each other even though
with the same equivalent concentration (Fig.2a).The moving velocities
for droplets containing DI water,0.1
moll\textsuperscript{-1}NaNO\textsubscript{3} and 0.1 mol
l\textsuperscript{-1} NaH\textsubscript{2}PO\textsubscript{4} were 4.99
mm s\textsuperscript{-1}, 6.52 mm s\textsuperscript{-1} and, 3.51 mm
s\textsuperscript{-1}, respectively. Also, as shown in Fig.2b, the
transport distance of salty droplets are different during the actuation
time. The movements of sea water with salinity of 10.9\selectlanguage{english}%0, 32.2\selectlanguage{english}%0 and 37.3\selectlanguage{english}%0
(mainly NaCl) which normally exist in brackish/salt water environments
(i.e. brackish lake/estuary, costal area and high salt lagoon/open sea),
were observed with velocities of 5.12 mm s\textsuperscript{-1}, 6.47 mm
s\textsuperscript{-1}and 6.62 mms\textsuperscript{-1}, respectively
(Fig.2b). All these profiles of electrolytes, which are usually applied
in algae culture condition screening, revealed that the differences in
the ions sorts/contents of the liquid droplets also lead to different
optimum charging times. Interestingly, the observed velocity-trend is a
function of ion kosmotropicity, which represents the capability of an
ion to strengthen the water structure (Hribar et al., 2002; Marcus,
1994, 2015). The movements of droplets containing algae suspension are
shown in Fig. 2c, with velocities of 2.15 mm s\textsuperscript{-1}
for\emph{Chlorella. sp.} (\emph{C cell} ) droplets and 1.38 mm
s\textsuperscript{-1} for \emph{P.helgolandica} (\emph{P cell} )
droplets. Regarding the slow movement of algae suspension (vs. DI water
of 4.99 mm s\textsuperscript{-1}), it was found that procrastination
occurred more seriously in droplets containing \emph{P} cells than those
containing \emph{C} cells even though with the same cell densities.
However, when the mass of the content was taken into account, the mass
of \emph{P} cells in solution will be much higher than that of
\emph{C}cells due to their larger cell size. It has been reported that
microalgae (usually 5-100\selectlanguage{greek}μ\selectlanguage{english}m in diameter) can be defined as a suspension
of organic particles in a fluid. The concentration, size and shape of
the microalgae will influence the fluid rheological behavior (Dalmazzo
et al., 2010). Particle concentration is main parameter influencing the
fluid viscosity. In particular, an increase of particle concentration
determines an increase of fluid non-Newtonian behavior instead of the
Newtonian one, which is typical for water and solutions with low
particle concentration (Mueller et al., 2010). More frustratingly, these
non-Newtonian droplets were always observed to fail to reach the
expected destination and divide into satellite droplets, halting the
entire system.

Our experiments data substantiated that different behaviors in the
droplet movement occurred with various interactions of chemical and
biological samples. These serve as the rationale for embedding an
automation of fluidic protocol. This protocol requires a feedback
controller whose sensing mode is independent of liquid composition. In
this work, we introduce a capacitance-based indicator that tracks the
droplet position, even in the domain between two adjacent electrodes.
This estimator is independent of the liquid composition because the
droplet position is calculated based on a dimensionless ratio of
adjacent electrodes' capacitances. Fig. 2d compared the actual position
and experimental droplet position estimated from the capacitances for
different liquids. The actual droplet position was calculated from the
top view images. The experimental droplet position was estimated using
imbed protocol. As illustrated in Fig.2d, a precise tracking scheme was
achieved in our work that the position estimates of multi-ion solutions
almost lay along the ideal line. The positions estimated from algae
suspensions are significantly deviated from the ideal line. The negative
deviation behavior occurred in the first half and positive in the second
half. However, the estimates are accurate in the start, the middle, and
the end points, which are likely key points for feedback control. To
further confirm the proposed capacitance-based droplet movement control
protocol for algae screening system, the percentage of completion of
different inclusions without additional calculation has been studied
(the threshold value R\textsubscript{thresh} was set to 0.75). As listed
in Table 1, DI water, nutrients and salty water can be transported with
100\% completion with feedback controllers. Especially, droplet
actuation reliability is dramatically improved for liquids containing
algae (e.g. \textless{}5\% completion without feedback and 98\%
completion with feedback for \emph{P.helgolandica} ).Thus, this proposed
approach significantly inspires imperfect droplet hydrodynamics of
multiple liquid and algae samples in the algae screening system.

\textbf{3.2 Architecture and characterization of the DMF bioreactor}

Several design iterations were required to develop a device capable of
completion of the complex sequences in algae culture and screening.
Firstly, establishment of the hydrodynamic profiles by our experiments
enforced an introduction of a feedback controller independent of liquid
composition. However, this introduction requires complex circuitry and
signal processing. The cost-adding and system-complicating continued to
be problematic for DMF screening applications. To address this issue, an
innovative electrode-saving scheme was applied here. In this scheme,
electrodes for algae culturing are connected with
``finger-fringe-bridge'' geometry rather than one or more interval
electrodes (Fig.3a). With these geometries, algae droplets can be
addressed individually without cross-talk even when a droplet walking
past in the absence of spacer electrodes (Fig.3b). Besides, an
``interdigitated finger bridge'' formed by finger fringes of two
neighbor electrodes guaranteed algae droplets transporting more smoothly
from non-charged electrode to charged one. With this ``bridge'',
droplets are more liable to spread and increase the contact area and
thus increasing the successful transportation chance (Fig.3c). The
interval between two usual designed electrodes is negligible, and
therefore at least one or two electrodes need to be inserted into two
culturing electrodes to implement isolating and connecting functions. In
6\selectlanguage{ngerman}×1 culturing electrodes, at least 5 intervals were needed. In
6×\emph{n} culturing electrodes, at least 5×\emph{n} intervals were
needed (Fig.1e). With the finger geometries of culturing electrodes, our
DMF designing was an electrode-saving scheme whose driving controller
holding inherent simplicity and high fault tolerance will made our DMF
system a great potential screening platform.

Besides using a smart control system compensated with electrode-saving
scheme, a simple approach for a highly accurate deterministic splitting
of liquids was also demonstrated.\begin{CJK}{UTF8}{gbsn}　\end{CJK}\selectlanguage{english}In our work, the droplet division on
the electrode of splitting region was performed by powering on/off
horizontal adjoining electrodes and thus activating vertical adjoining
electrodes and so-called ``bi-directional stretching split'' (Fig. 3d).
Under equilibrium conditions, the droplet maintains of equal Laplace
pressures at the whole oil--water interface and can be described as a
function of horizontal and vertical radii of curvatures of meniscus and
interfacial tension \selectlanguage{greek}γ\selectlanguage{english}\textsubscript{ci}:\(p=\gamma_{\text{ci}}(\frac{1}{R_{n}}+\frac{1}{R_{v}})\) (Banerjee et
al., 2012).When redistributing of volume from the splitting region to
the horizontal adjoining voltage-activated regions, extra Laplace
pressure will be accumulated across the highly deformed aqueous-oil
interface. If the voltage at the horizontal splitting electrode is
switched off abruptly, the momentum transfer (interfacial energy to
internal energy) inside droplet cause an initial transient impulse,
which makes droplet deformation along its horizontal axis toward the
center. With this horizontal confining force, we observed that more
deterministic fluid vertical divisions were achieved from the splitting
region to the vertical adjacently electro-wetted segments. The deviation
data results of DI water from 5 independent bi-direction or
uni-direction splitting were shown in Fig.3e. The average volumetric
errors of DI water, and salty waters with low to high salinity were all
lower than 1.5\% for ``bi-directional stretching split'' and
3\%\textasciitilde{}6\% for common stretching split. Percent of abnormal
droplet dividing (volume deviation [?]\selectlanguage{ngerman}±20\%) in total dividing was 0\% for
``bi-directional stretching split'' and 10\% for common stretching
splitting. To further verify the accuracy of concentration gradient
generation, serial dilution experiments are performed. Figure 3f
demonstrates the DI water droplets (supplemented with fluoresce dye
Rh-123) generated for dilution ratios of 0, 1/16, 1/8, 1/4, 1/2 and 1.
The intensities of Rh-123 at six droplets were measured, and compared
with the theoretical data. There was a good coherence between the
theoretical and experimental data with a correlation coefficient of
0.996. Since there are no significant difference between the splitting
volumetric error of DI water and that of seawaters, dilution performance
will be also valid for salt waters with wide range of salinity on our
DMF chip. Thus, stressors triggering lipid analysis of algae can be
performed in a simple and accurate way.

\textbf{3.3 DMF droplet-based interspecies lipid screening in
microalgae}

Before the verification of the capability of DMF micro-bioreactor in
lipid screening, their biocompatibility was initially evaluated by
microalgal motility measurements and growth experiment. With the DMF
micro-bioreactors, \emph{P.helgolandica var. tsingtaoensis} could be
nondestructively actuated and successfully cultured on-chip. Cell
motility and concentrations for these cultures were determined using
optical microscopy, with a detailed description given in Fig. S5 and
supporting information.

To demonstrate the streamlined analysis functionality of the developed
DMF platform, growth and oil accumulation analyses of four costal
species of \emph{D. zhanjiangenis} , \emph{Chlorella sp.,
P.helgolandica}and \emph{P.subcordiformis} were performed on-chip. For
further validation, off-chip experiments were also performed, in which
the growth data were obtained using 96-well plate and hemocytomete and
the oil production was analyzed by total lipid extraction. As
illustrated in Fig.4a and 4b, both on-chip and off-chip population
measurements showed similar growth resulting for each of these four
algal species. On-chip growth rates are 0.249±0.007
d\textsuperscript{-1}, 0.313±0.004 d\textsuperscript{-1}, 0.248±0.03
d\textsuperscript{-1} and 0.251±0.003d\textsuperscript{-1} for \emph{D.
zhanjiangenis} ,\emph{Chlorella sp., P.helgolandica} and
\emph{P.subcordiformi} , respectively. From these growth data,
\emph{Chlorella sp.} was more suitable for large scale culture to
manufacture biodiesel among the four strains for feature of rapid
growth. Same results were also achieved by the off-chip experiments.

After 5-days culturing, intracellular lipid bodies were stained with
BODIPY and imaged for oil content analyzing. Intercellular TAGs were
visualized as light green fluorescence which was spectrally separated
from the red algal auto-fluorescence and also varied significantly in
intensity between the four species (Fig.4c). We calculated the lipid
content using Image J software which were represented as average
fluorescence intensities per unit cell area and illustrated in Fig.4d.
As illustrated, interspecies lipid contents were significantly
recognizable, although algae were encapsulated in DMF micro-droplets. It
interpreted that oil-rich microalgae can be generally discriminated
using fluorescence-activated cell sorting (FACS) on DMF (Lee et al.,
2013). As expected, the same conclusion was gained from off-chip total
lipid extraction data, where \emph{Chlorella sp.} exhibited higher lipid
content than other three and can be the selection for biodiesel
production (Fig.4e).

\textbf{3.4 Stressor triggered lipid accumulation on-chip}

As we know that algae derived lipid content would be greatly inspired by
proper stimuli supply (Yin et al., 2020). Determination of a specific
stimuli and proper culturing condition for a certain species are costly
and labor-intensive process. The DMF diluter is well suitable for the
rapid and mass creating the culturing conditions responsible for lipid
production. Since stressor triggered lipid accumulation in algea has not
been explored in DMF droplet-based bioreactors, the following will focus
on feasibility study of the DMF system.

Selected strain with high lipid productivity (oil-rich/high growth rate)
of \emph{Chlorella. sp.} was encapsulated in a series of droplets with
nitrogen-depleted gradients and phosphorus-depleted gradients for 5 days
and then the BODIPY fluorescence responses were observed. DMF-based in
situ profiling of the stressor triggered growth and lipid contents
within droplets were shown in Fig.5. As shown in enlarged bright-field
and fluorescence images, N (NaNO\textsubscript{3}) and P
(NaH\textsubscript{2}PO\textsubscript{4}) fertilizers have great
influence on the biomass and lipid content of \emph{Chlorella
sp.}(Fig.5a-d). It was found that the increase of nitrogen and phosphate
concentrations would promote the growth of cells to produce more biomass
in droplets. However, accumulated lipid bodies, exhibited highly
increasing fluorescence signals among cells with the decreasing of
nutrition concentration. The lowest cell density, but the clear/largest
cytosolic lipid bodies are seen in zero concentration of nutrition among
all stressed cultures.

The resulting growths in all droplet-based cultures are shown in Fig.5e
and 5f (blue line). Increasing of nutrition concentration would promote
the growth of cells while inhibit the accumulation of oil. The most
significant effect on the growth rate of \emph{Chlorella sp.} was
obtained when the concentrations of NaNO\textsubscript{3} were between
2.2×10\textsuperscript{-3}mol.l\textsuperscript{-1}\textasciitilde{}8.8×10\textsuperscript{-3}mol.l\textsuperscript{-1}(Fig.
5e, blue line) or when the concentration of
NaH\textsubscript{2}PO\textsubscript{4} were
0.9×10\textsuperscript{-4}mol.l\textsuperscript{-1}\textasciitilde{}3.6×10\textsuperscript{-4}mol.l\textsuperscript{-1}
(Fig. 5f, blue line). Applying excessive amount of NaNO\textsubscript{3}
will not promote cell growth significantly. The cell growth will be
slightly suppressed by extra NaH\textsubscript{2}PO\textsubscript{4}.
Fluorescence intensities for representative cells in both cultures are
shown in Fig. 5e and 5f (red column). As for oil accumulation, the
nitrate-depleted culture
(NO\textsubscript{3}\textsuperscript{-}\textless{}
8.8×10\textsuperscript{-3} mol l\textsuperscript{-1}) has an average
green fluorescence intensity of 104\textasciitilde{}165 unit, while the
nitrate-replete
culturing(NO\textsubscript{3}\textsuperscript{-}\textgreater{}
8.8×10\textsuperscript{-3} mol l\textsuperscript{-1}) has an average
intensity of 70\textasciitilde{}77 unit (Fig. 5e, red column). At the
normal P concentration of 3.6×10\textsuperscript{-5}mol
l\textsuperscript{-1}, the average BODIPY fluorescence intensity
of\emph{Chlorella sp.} was 79 unit. It is equivalent to only 45\% of the
P deprivation cultured cells (Fig. 5f, red column)\emph{.} Higher
cellular lipid contents, associated with higher growth data, have
addressed the optimal fertilizer supply for maximum lipid productivity,
which were
2.2×10\textsuperscript{-3}moll\textsuperscript{-1}\textasciitilde{}4.4×10\textsuperscript{-3}
mol l\textsuperscript{-1}of NaNO\textsubscript{3} or
0.9×10\textsuperscript{-4} mol
l\textsuperscript{-1}\textasciitilde{}3.6×10\textsuperscript{-4} of
NaH\textsubscript{2}PO\textsubscript{4}. Together, these data suggest
that lipid productivity can be stimulated in DMF bioreactors for
microalgae, making screening using this technology readily attainable.

\textbf{4. Conclusions}

We demonstrated a DMF droplet-based microalgae bioreactor suitable for
screening of competent species and proper fertilizer supply. A unique
adaptation of capacitance-based position estimator associated with
electrode-saving architecture was used, which allows for inherent
simplicity and high-fidelity performance of any type of droplet. A
deterministic splitting of liquids to generate nutrient gradient was
also demonstrated. Using this system, microalgae were successfully
cultured and stressed. In all cases, we confirmed that the results in
DMF droplets were comparable to that of off-chip culturing cells, which
exhibit an unparalleled potential in developing an integrated platform
for fully-automatic, high-throughput microalgal lipid screening.

\par\null

\textbf{SUPPORTING INFORMATION} Additional supporting information may be
found online in the Supporting Information section.

\textbf{ACKNOWLEDGMENTS}

This work was supported by National Natural Science Foundation of China
(No. 41476085, No. 81471807), Scientific Research Project of Liaoning
Education Department (LJQ2015005) and Dalian Science and Technology
Innovation Fund (2019J12SN55).

\textbf{CONFLICT OF INTERESTS} The authors declare that there are no
conflict of interests.

\par\null

\textbf{REFERENCE}

Banerjee, A., Liu, Y., Heikenfeld, J., Papautsky, I. (2012).
Deterministic splitting of fluid volumes in electrowetting
microfluidics. \emph{Lab on a Chip} , 12,
5138--5141.\emph{doi:10.1039/C2LC40723J}

Bligh, E.G., Dyer, W.J., Can, J. (1959). A rapid method of total lipid
extraction and purification. Canada \emph{Journal of Biochemistry and
Physiology} , 37, 911-917. \emph{doi:10.1139/o59-099}

Bogojevic, D., Chamberlain,M.D., Barbulovic-Nad, I., Wheeler, A.R.
(2012). A digital microfluidic method for multiplexed cell-based
apoptosis assays. \emph{Lab on a Chip} , 12, 627-634.~\emph{doi:
10.1039/C2LC20893H}

Chang,Y.H., Lee, G.B., Huang, F.C., Chen, Y.Y., Lin, J.L. (2006).
Integrated polymerase chain reaction chips utilizing digital
microfluidics. \emph{Biomedical Microdevices} , 8,
215-225.~\emph{doi:10.1007/s10544-006-8171-y}

Chen, C.Y., Chang, H.Y. (2016). Lipid production of
microalga\emph{chlorella sorokiniana} CY1 is improved by light source
arrangement, bioreactor operation mode and deep-Sea water
supplements.\emph{Biotechnology Journal} , 11(3),
356-362.~\emph{doi:10.1002/biot.201500288}

Choi, K., Ng A.H., Fobel, R., Wheeler, A.R. (2012). Digital
microfluidics. \emph{Annual Review of Analytical Chemistry} , 5,
413-440.~\emph{doi:10.1146/annurev-anchem-062011-143028}

Dalmazzo, D., Santagata, E., Daniel, N., Zanetti, M. (2010). Rheological
characterization of sludge coming from a wastewater treatment
plant.\emph{American journal of environmental sciences} , 6 (4),
329-337.\emph{doi:10.3844/ajessp.2010.329.337~}

Fan S. K., Yang H., Hsu W. (2011) Droplet-on-a-wristband: Chip-to-chip
digital microfluidic interfaces between replaceable and flexible
electrowetting modules. \emph{Lab 0n a Chip} , 2,
343--347.\emph{doi:10.1039/C0LC00178C}

Gao, J., Liu, X., Chen, T., Mak, P.I., Du, Y., Vai, M.I., Lin, B.,
Martins, R.P. (2013). An intelligent digital microfluidic system with
fuzzy-enhanced feedback for multi-droplet manipulation. \emph{Lab on a
Chip} , 13, 443-451.\emph{doi:10.1039/C2LC41156C}

Gim, G.H., Ryu, J., Kim, M.J., Kim, P.I., Kim, S.W. (2016). Effects of
carbon source and light intensity on the growth and total lipid
production of three microalgae under different culture conditions.
Journal of I\emph{ndustrial Microbiology \& Biotechnology} , 43,
605-616.~\emph{doi:10.1007/s10295-016-1741-y}

Gocze, P.M., Freeman, D.A. (1994). Factors underlying the variability of
lipid droplet fluorescence in Ma-10 Leydig tumor cells.\emph{Cytometry}
, 17, 151-158. \emph{doi:10.1002/cyto.990170207}

Hribar, B., Southall, N.T., Vlachy, V., Dill, K.A. (2002). How ions
affect the structure of water.\emph{Journal of the American Chemical
Society}. 124, 12302-12311.\emph{doi:10.1021/ja026014h}

Han S, Zhang Q, Zhang X, Liu X, Lu L, Wei J, Li Y, Wang Y, Zheng G.
(2019). A digital microfluidic diluter based microalgal motion biosensor
for marine pollution monitoring. \emph{Biosensors and Bioelectronics} .
143:111597.\emph{doi:10.1016/j.bios.2019.111597}

Hu, Q., Sommerfeld, M., Jarvis, E., Ghirardi, M., Posewitz, M., Seibert,
M., Darzins, A. (2008). Microalgal triacylglycerols as feedstocks for
biofuel production: perspectives and advances. \emph{The Plant
Journal.}54(4), 621-639. \emph{doi:10.1111/j.1365-313X.2008.03492.x}

Jin H, Zhang H, Zhou Z, Li K., Hou G., Xu Q., Chuai W., Zhang C., Han
D., Hu Q. (2020) Ultrahigh-cell-density heterotrophic cultivation of the
unicellular green microalga Scenedesmus acuminatus and application of
the cells to photoautotrophic culture enhance biomass and lipid
production. \emph{Biotechnology and Bioengineering} ,117,
96--108.~\emph{doi:10.1002/bit.27190}

Josep P L (2014). Microfluidic platforms: a mainstream technology for
the preparation of crystals.~\emph{Chemical Society Reviews}, 43, 7,
2253-2271.\emph{doi:10.1039/C3CS60372E}

Khandelwal, A., Chhabra, M., Yadav, P. (2020). Performance evaluation of
algae assisted microbial fuel cell under outdoor
conditions.\emph{Bioresource Technology} , 310,
123418.~\emph{doi:10.1016/j.biortech.2020.123418}

Lee, D.H., Bae, C.Y., Han, J.I., Park, J.K. (2013).In situ analysis of
heterogeneity in the lipid content of Single Green Microalgae in
alginate hydrogel microcapsules. \emph{Analytical Chemistry} , 85,
8749--8756.\emph{doi:10.1021/ac401836j}

Malic, L., Veres,T., Tabrizian, M. (2009). Biochip functionalization
using electrowetting-on- dielectric digital microfluidics for surface
plasmon resonance imaging detection of DNA
hybridization.\emph{Biosensors and Bioelectronics,} 24(7),
2218-2224.~\emph{doi:10.1016/j.bios.2008.11.031}

Marcus, Y.J. (1994). Viscosity B-coefficients, structural entropies and
heat capacities, and the effects of ions on the structure of
water.\emph{Journal of Solution Chemistry} , 23,
831-848.~\emph{doi:10.1007/BF00972677}

Marcus, Y.J. 2015. The Effect of Complex Anions on the Structure of
Water. \emph{Journal of Solution Chemistry,} 44,
2258-2265.\emph{doi:10.1007/s10953-015-0402-5}

Mueller, S.P., Llewellin, E.W., Mader, H.M. (2010). The rheology of
suspensions of solid particles. \emph{Proceedings of the Royal Society
A} , 466, 1201-1228.\emph{doi:10.1098/rspa.2009.0445}

Rackus DG, Dryden MD, Lamanna J, Zaragoza A, Lam B, Kelley SO, Wheeler
AR (2015). A digital microfluidic device with integrated nanostructured
microelectrodes for electrochemical immunoassays. \emph{Lab on a Chip} ,
15(18), 3776-84.~\emph{doi:10.1039/c5lc00660k.}

Richter, L.V., Mansfeldt, C.B., Kuan, M.M., Cesare, A.E., Menefee, S.T.,
Richardson, R.E., Ahner, B.A. (2018). Altered Microbiome Leads to
Significant Phenotypic and Transcriptomic Differences in a
Lipid\emph{Accumulating. Environmental Science and Technology} , 52,
6854-6863.\emph{doi:10.1021/acs.est.7b06581}

Schell, W.A., Benton, J.L., Smith, P.B. (2012). Evaluation of a digital
microfluidic real-time PCR platform to detect DNA of Candida albicans in
blood. \emph{European Journal of Clinical Microbiology \& Infectious
Diseases} . 31, 2237--2245. \emph{doi:10.1007/s10096-012-1561-6}

Shih, S.C.C., Fobel, R., Kumar, P., Wheeler, A.R. (2011). A feedback
control system for high-fidelity digital microfluidics. \emph{Lab on a
Chip} , 11(3), 535-540.\emph{doi:10.1039/C0LC00223B}

Shin, Y.J., Lee, J.B. (2010). Machine vision for digital
microfluidics.\emph{The Review of Society Instruments,} 81,
014302.~\emph{doi:10.1063/1.3274673}

Shukla, V., Hussin, F.A., Hamid, N.H., Zain Ali, N.B. (2017). Advances
in testing techniques for digital microfluidic biochips. \emph{Sensors
(Basel)} ,17(8), 1719-1726.\emph{doi:10.3390/s17081719}

Vergauwe,N., Witters,D., Ceyssens, F., Vermeir, S., Verbruggen,B.,
Puers, R., Lammertyn, J.J. (2011). A versatile electrowetting-based
digital microfluidic platform for quantitative homogeneous and
heterogeneous bio-assays.\emph{Journal of Micromechanics and
Microengineering}, 21, 054026.\emph{doi:10.1088/0960-1317/21/5/054026}

Wang, Y.B., Huang, J.H., Lee, M.S., Huang, C.Y., Huang, C.S., Yamashita,
I., Tu, Y.Y., Hsu, W., (2017). An EWOD-based micro diluter with high
flexibility on dilution ratio. \emph{Microsystem Technology.} 23,
3645-3651.~\emph{doi:10.1007/s00542-016-3146-2}

Yin, Z.H., Zhu, L.D., Li, S.X., Hu, T.Y., Chu. R.Y., Mo, F., Hu, D.,
Liu, C.C., Li, B. (2020). A comprehensive review on cultivation and
harvesting of microalgae for biodiesel production: Environmental
pollution control and future directions. \emph{Bioresource Technology} ,
301, 122804.~\emph{doi:10.1016/j.biortech.2020.122804}

Zhang, Q., Zhang, X.C., Zhang, X.L., Jiang, L., Yin, J.M., Zhang, P.,
Han, S., Wang, Y.H., Zheng G.X. (2019). A feedback-controlling digital
microfluidic fluorimetric sensor device for simple and rapid detection
of mercury (II) in costal seawater. \emph{Marine Pollution. Bulletin} ,
144, 20-27.\emph{doi:10.1016/j.marpolbul.2019.04.063}

\par\null

\textbf{TABLE}

\textbf{Table 1} Transport of multi-liquid with and without feedback
control

\selectlanguage{english}+No sign represents the percent of successful transportations on
actuation electrodes and * represents that on culturing electrodes

\par\null

\textbf{FIGURE LEGENDS}

\textbf{Fig. 1.} Proposed control-engaged DMF screening technology.
Overview schematic of the DMF screening system that (a) the DMF chip
demonstration is patterned a 24-electrodes array which is connected to
(b) the operation unit with position feedback for actuating and mounted
onto (c) the detection element for detecting. (d) DMF chip with a
6x3culturing electrodes array (e) Schematic of the DMF chip with a
6x\emph{n} culturing electrodes array.

\textbf{Fig.2.} Droplet movement for DMF actuation of droplets of
complex liquids containing (a) Nutrients (b) Salty water and (c) Algae,
without feedback. (d) Actual and estimated droplet position plot from
experiments.

\textbf{Fig.3.} Design of DMF screening system.(a) The configuration of
culturing electrodes. It has one or two finger fringes to form
``Interdigitated-finger Bridge'' for connecting with each other. The
``Bridge'' functions of (b) isolating and (c) transporting droplets. (d)
Droplet bi-directional stretching split way used in this study compared
with common split way. The power goes through the electrode displayed by
yellowsign. (e) Deviation of target volume of DI water on one side
during random 5 merge-split cycles.(f) Dilution performance validation.
As the fluorescence intensity of Rh-123 was proportional to the
concentration, the intensities of Rh-123 at six droplets generated by
DMF diluter were imaged by a fluorescence microscope.

\textbf{Fig.4.} Interspecies comparison of lipid productivity in
microalgae. (a) On-chip growth analysis of four microalgal species. The
number of the algae were counted. The growth rates were calculated. (b)
Off-chip growth analysis of four microalgal species.(c) On-chip
fluorescence images of the four species respectively, stained with
BODIPY. The autofluorescence (red) and oil fluorescence (BODIPY, green)
were shown in upper-row. The oil fluorescence were shown in bottom row.
(d) Algal cells were cultured in droplet (on chip) for 5 days. The cells
were then stained with BODIPY and average fluorescence intensities were
analyzed for measurement of oil accumulation. (e) Off-chip measurements
of oil accumulation for 5 days of culture.

\textbf{Fig.5.} In situ profiling of the stressor triggered growth and
lipid contents within DMF droplets. (a) Enlarged bright-field images of
algae cells in droplets and (b) fluorescence images of Cytosolic lipid
bodies in representative cells cultured for 5 days in nitrate-deplete
mediums. (c) Enlarged bright-field images of algae cells in droplets and
(d) fluorescence images of Cytosolic lipid bodies in representative
cells cultured for 5 days in phosphate-depletemediums.(e)The growth
(blue line) and oil accumulation (red column) of \emph{Chlorella
sp.}cultured in nitrate-depletemediums were analyzed. (f) The growth
(blue line) and oil accumulation (red column) of \emph{Chlorella sp.}
cultured inphosphate-deplete mediums were analyzed.

\textbf{Hosted file}

\verb`Table 1.docx`  available at \url{https://authorea.com/users/347082/articles/472907-an-integrated-digital-microfluidic-bioreactor-for-fully-automatic-screening-of-microalgal-growth-and-stress-induced-lipid-accumulation}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-1/Figure-1}

\end{center}
\end{figure}

\textbf{Hosted file}

\verb`Figure 2.eps`  available at \url{https://authorea.com/users/347082/articles/472907-an-integrated-digital-microfluidic-bioreactor-for-fully-automatic-screening-of-microalgal-growth-and-stress-induced-lipid-accumulation}

\textbf{Hosted file}

\verb`Figure 3.eps`  available at \url{https://authorea.com/users/347082/articles/472907-an-integrated-digital-microfluidic-bioreactor-for-fully-automatic-screening-of-microalgal-growth-and-stress-induced-lipid-accumulation}

\textbf{Hosted file}

\verb`Figure 4.eps`  available at \url{https://authorea.com/users/347082/articles/472907-an-integrated-digital-microfluidic-bioreactor-for-fully-automatic-screening-of-microalgal-growth-and-stress-induced-lipid-accumulation}

\textbf{Hosted file}

\verb`Figure 5.eps`  available at \url{https://authorea.com/users/347082/articles/472907-an-integrated-digital-microfluidic-bioreactor-for-fully-automatic-screening-of-microalgal-growth-and-stress-induced-lipid-accumulation}

\selectlanguage{english}
\FloatBarrier
\end{document}

