# Surface tension effects on immersed electrosprays

Álvaro G. Marín<EMAIL_ADDRESS>Bundeswehr Universität München, Neubiberg.
Ignacio G. Locertales  Escuela de Ingenieros Industriales, Universidad de
Malaga.  Antonio Barrero  Escuela de Superios de Ingenieros, Universidad de
Sevilla.

###### Abstract

Electrosprays are a powerful technique to generate charged micro/nanodroplets.
In the last century, the technique received extensive study and successful
applications, including a Nobel price in Chemistry. However, nowadays its use
in microfluidic devices is still limited mainly due to a lack of knowledge of
the phenomenon when the dispersing fluid is immersed in another inmiscible
liquid. The “immersed electrosprays” share almost identical properties as
their counterparts in air. Things however change when surface active agents
are added to the host liquid, which are normally used in lab-on-chip
applications to stabilize the generated emulsions. In this work, we review the
main properties of the immersed electrosprays in liquid baths with no
surfactant, and we methodically study the behavior of the system for
increasing surfactant concentrations. The different regimes found are then
analyzed and compared with both classical and more recent experimental,
theoretical and numerical studies. A very rich phenomenology is found when the
surface tension is allowed to vary in the system. More concretely, the lower
states of electrification achieved with the reduced surface tension regimes
might be of interest in biological or biomedical applications in which the
free charge is normally hazardous for the encapsulated entities.

##  I Introduction

The generation of emulsion droplets of controllable size and structure
motivates hundreds of publications in the literature and drags attention from
very different types of industries: opticsDrzaic , cosmetics cosmetics  or
biomedicinedropletreview . MMES and Microfluidic technologywhitesides  permit
high control on droplet size and easy post-processing on the produced
particles Barr:rev . Nonetheless, most of the existing techniques based on
these microfluidic technologies for particle production have basically two
serious constraints: one of them is the low production rate and the other the
limitation in particle diameter. The last constraint can be overcome employing
techniques on which the final droplet size does not depend on the channel or
nozzle sizes. Some approaches at this respect can be found on Anna and Mayer
annamayer , who employed surfactants at critical concentrations on a
microfluidic flow-focusing device to find regimes similar to the so-called
tip-streaming. On a different approach, Suryo and Basaran suryobasaran
predicted numerically a regime with the same qualities as tip-streaming in
surfactant-free co-flowing devices under special conditions; Marín, del Campo
and Gordillo Maringordi  recently confirmed it experimentally and demonstrated
that the regime is able to generate particles down to the micron size.

Figure 1: Typical aspect of an immersed electrospray in an inviscid dielectric
liquid: Metallic capillary, Taylor cone, micro-liquid jet and hydrosol with
its characteristic plumed-shape. The picture corresponds to sample G4; the
outer needle diameter is 0.8mm.

Among those techniques able to reach submicrometric sizes, the electrospray is
probably one of the most popular due to its spread use in mass spectrometry,
application which was honored in 2002 with a shared Noble Prize of Chemistry
to Prof. John Fenn. Mass spectrometry has been indeed the only successful
application that the electrospray has found so far. Nonetheless, there is
still an important number of publications every year suggesting interesting
new applications in very different fields, ranging from biomedical to
electronics applicationsalmeria2011multiplexed ; deng2011electrospray ;
gomez2011fundamentals ; lozano2011electrospray .

In a typical electrospray configuration a conductive liquid is pumped at
certain flow rate through a tube, and a meniscus will be formed at its end.
The liquid is connected to one of the poles of a high voltage power supply and
a counter electrode is placed at some distance from the exit of the tube. Due
to the electrical field produced, the free charge in the conductive liquid
meniscus will tend to relax towards the liquid surface, generating a net
normal electric stress that opposes surface tension. As G. I. Taylor explained
in on of his pioneering works, an interesting equilibrium is then reached at a
certain voltage between the two surface forces, which obliges the meniscus to
adopt a conical shape taylor1964disintegration . This equilibrium would only
be stationary if the charges were frozen at the interface, which would only
happen ideally for liquids with infinite conductivity. In contrast, liquids
with finite conductivity manifest electrical shear stresses that lead to the
formation of a liquid jet close to the cone apex which breaks eventually
downstream intro drops due to capillary instabilities, giving rise to an
aerosol of charged droplets (see figure 1). The jet or droplet diameter can
vary substantially depending mainly on the liquid conductivity, ranging from
hundreds of micrometers for the least conducting liquids to a few nanometers
for the most conducting ones. Finer control on the jet diameters can be
achieved by manipulating the injected flow rate Loscermora  or the voltage
gundabala2010current ; larriba2011production  depending on the regime in which
the electrospray is manipulated.

Additionally the technique permits great flexibility in the applications, e.g.
it also permits encapsulation of substances by the combination of two liquids
into the so-called “coaxial-jet electrospray” loscertales2002science . The
technique has proved to be extremely efficient encapsulating substances, not
only as spherical micro-capsules, but also in the shape of micro/nano-
fibersdiaz2006encapsulation ; diaz2006controlled ; loscertales2004electrically
. An interesting perspective would be to implement such a powerful atomization
technique into a microfluidic device to benefit from the enhanced control of
such systems. Barrero et al. Barr:2003  made the first step towards such
objective demonstrating that electrosprays operated in liquid dielectric baths
(from now on “immersed electrosprays”) have analogue properties as their
counterparts in air. Since then, some have attempted to implement the
technique in microfluidic devices KimH ; japos , with positive but only
exploratory results so far. Unfortunately, the implementation of
electrohydrodynamic systems in microfluidics has found problems related with
the control of the generated charged hydrosol. The charged micro/nanodroplets
tend to expand and repeal each other within the whole volume of the
microdevice, and due to the confinement of the system, they end up colliding
and accumulating at the micro device walls. This produces disturbances in the
electrical field surrounding the Taylor cone and has crucial consequences to
its stability. Gundabala et al.gundabala2010current ; ecoflow  solved such
situation by implementing the electrospray in a cylindrical co-flow system
consisting of concentric capillaries. The immersed electroprays are however
extremely sensitive to the interfacial tension and to the hydrodynamic shear
stress that the external medium applies to the Taylor cone which might compete
against the natural electrical shear stresses applied at the Taylor cone
interface. These properties have been only marginally treated in the
literature and there is therefore a lack of understanding on their effects.

A large fraction of the current development on microfluidics is devoted to the
formation of emulsions of controllable size and structure. Emulsions are
energetically metastable and need of stabilizing agents as surfactants.
Although their use is widespread in daily life and in laboratories, very often
its dynamics are complex and unknown, but their use has an impact on
biochemical assaysBaret:2012  as well as in systems as those reported here.
The electrified meniscus in an electrospray maintains a delicate equilibrium
of normal and shear stresses at its interface, and therefore the presence of
surfactants can be extremely harmful for its stability. The aim of the present
work is therefore to study in detail the effect of surfactants and be able to
give some rule of thumb when choosing the experimental parameters for an
immersed electrospray to work according to the needs of the user.

To achieve these objectives, we performed electrical current and droplet
measurements for different liquids, surfactant concentrations and flow rates
in a non-confined device. Such measurements will yield information about the
regimes in which the electrospray is being run, and therefore about mass and
charge transport mechanisms within the liquid. With such information we will
be able to make a proper comparison with the standard electroatomization in
air and to draw conclusions on which regimes would be appropriate for confined
experiments in microfluidics or other different applications.

The article is organized as follows: section II reviews the classical scaling
laws of electrospraying in air. In section III we give all experimental
details of the experimental set-up, liquids employed, and measurement
techniques. Section IV reviews results immersed electrosprays in clean baths
(without surfactants). We continue with baths with surfactants in section V,
which we separate in two extreme cases: high (subsection V.1) and low
conductivity dispersed liquids (subsection V.2). We give a brief description
of the phenomenology when inverted viscosity ratios are used in section VI,
and end up with summary and final conclusions in section VII.

##  II Scaling Laws in Electrosprays

The mass and charge transport mechanisms in the electrospray manifest
experimentally in scaling laws for the electrical current and the droplet size
versus the flow rate. In the last 20 years, many have explored different
electrospray regimes, with different scalings and propertiesMoraReview .
However, it is generally accepted that the regime with the most steady and
robust performance, and which yields the most monodisperse aerosols, is the
so-called _cone-jet_ regimeCloupeau ; gomez2011fundamentals . Recent numerical
simulations have confirmed some of these scaling laws using different
approacheshiguera2004current ; higuera2005electrosprays ;
higuera2010electrodispersion ; higuera2010numerical ; BasaranNature . The
current study will not discuss details of the scaling laws found, but will
only use those most widely accepted to probe intro the physics of the immersed
electrospray.

Within the cone-jet regime Cloupeau , we can find scaling laws to relate the
input parameters of the system, basically the flow rate $Q$ and the applied
voltage drop $V$, with the output parameters, i.e. the electrical current
transported by the liquid $I$ and the droplets size $d$ The scaling laws to be
used as a reference are inspired by the pioneering work of Loscertales and de
la Mora Loscermora  and by Gañan et al.Ganan97 . When high conductivity
liquids with intermediate viscosities are operated at small flow rates in a
gaseous medium, one can find the following scaling laws, expressed in
dimensionless form:

$\frac{I}{I_{o}}=a\left(\frac{Q}{Q_{o}}\right)^{1/2},$ (1)

$\frac{d}{d_{o}}=b\left(\frac{Q}{Q_{o}}\right)^{1/3}.$ (2)

Where we have defined the characteristic values of flow rate $Q_{o}$,
electrical current $I_{o}$ and droplet diameter $d_{o}$ using the density
$\rho$, conductivity $K$ and surface tension $\gamma$ of the conducting liquid
as follows:

$Q_{o}=\frac{\epsilon_{o}\gamma}{\rho K},$ (3)

$I_{o}=\gamma\sqrt{\frac{\epsilon_{o}}{\rho}},$ (4)

$d_{o}=\left(\frac{\gamma\epsilon_{o}^{2}}{\rho K^{2}}\right)^{1/3}.$ (5)

Different other scalings with different exponents can be found for different
range of flow rates, conductivities, viscosity and, as we will see in the
following, of the interfacial tension. The most relevant ones will be
discussed further below.

Figure 2: Comparison of droplet size distributions: glycerol droplets in air
vs glycerol droplets in hexane

To outline some of the advantages of the immersed electrosprays, we compare
the droplet diameters obtained with an immersed electrospray with the same
liquid electrosprayed in air. The sample chosen is G5 since its characteristic
diameter fits well with the range of measurement in the apparatus. As
commented above, when dispersing a conducting liquid into a dielectric in the
cone-jet regime, the characteristic flow rate decreases according mainly to
the decrease in surface tension. Consequently, the droplet diameter decreases
notably as can be clearly seen in figure 2. Operating the electrospray in a
dielectric liquid permits us to decrease the flow rate in almost one order of
magnitude, with the corresponding decrease in droplet size.

##  III Experimental set-up and methods

The experiments were carried out in a small cell (see fig. 3) made of
plexiglass with glass windows to permit visualization. The cell was open in
the upper part to insert the injection needle, whose diameter ranged from
0.8/0.6 mm (O.D./I.D.), for the less conductive liquids, and down to 340/20
$\mu m$ with silica capillaries for the most conductive ones. The electrical
field is generated by applying a high voltage drop from the injection needle
(working as upper electrode) and an electrode placed in the bottom of the
cell. Different types of bottom electrodes were chosen depending on the
characteristics of the generated hydrosol, more details will be given in the
next sections. The liquid was forced through the capillaries using either a
syringe pump (Harvard Instruments) or compressed air, depending on the
viscosity and conductivity of the sample. Higher conductivity samples require
higher control on their flow rates, which can be as small as some nanoliters
per hour (3).

A laser diffraction system (Helos-Sympatec) was employed to perform droplet
diameter measurements. In this method a laser beam passes through the
hydrosol, the diffraction pattern obtained in this process is collected on a
highly sensible multi-element detector. With this patterns as an input, the
system deconvolutes the particle size distribution using the Fraunhofer or the
Mie solutions for light scattering in spherical particles mie . In spite of
being a static optical method, the system has been tested and contrasted with
many different methods with optimum results.

Figure 3: Sketch of the experimental set up employed

In table 1 we show the main physical properties of the liquids employed. On
the first section of the paper, the liquids employed were glycerine as
conductive liquid to disperse and hexane, as host dielectric liquid of the
bath. All the experiments were also repeated with heptane, with no appreciable
differences on the results. Different samples of glycerine with different
conductivities were employed ranging from $10^{-6}S/m$ to almost $10^{-2}S/m$.
These conductivities were achieved by adding different types of salt to the
glycerine samples (basically NaCl or ClH). The inner (glycerine) and outer
(hexane, Hheptane) liquids were mainly chosen for their viscosities since we
will base our studies to cases in which the viscosity ratio
$\lambda=\mu_{i}/\mu_{o}\gg 1$ (where $\mu_{i}$ and $\mu_{o}$ are respectively
the viscosities of the inner and outer liquid) which is also the case of
atomization in air. In the last section VI, we will briefly discuss some of
the effects of the viscosity ratio inversion on the atomization process.

For the electrical current measurements, the injecting needle was connected
indirectly to the positive pole of a high voltage power supply, and different
ampmeters were employed to measure the current supplied through the Taylor
cone depending on the sample: for the highest currents (close to 1 $\mu$A for
the G2 sample), a simple voltmeter with resolution of 1nA was employed. While
for the lowest currents (of the order of 1 nA for sample G6) a Keithley
picoampmeter was used instead.

Controlling the flow rate is a mayor issue on the these experiments. It is
well known that the electrostatic pressure can modify the real flow rate
circulating through the meniscus, and for this reason we employed high
hydrodynamic resistance lines with small in line diameters. In order to force
the liquid through the lines, compressed air was used in most of the cases
except for the lowest conducting one, for which a syringe pump (Harvard
Instruments) was then employed, since it requires the highest flow rates in
these series of experiments (of order of several milliliters per hour).

Table 1: Liquid data |  $\rho$(g/ml) | $\mu(mPa\times s)$ | $\beta$ |  $\gamma$ (mN/m) | I.T.(mN/m)
---|---|---|---|---|---
Glycerol | 1.261 | 1100 | 42.5 | 64 |
Hexane | 0.655 | 0.294 | 1.9 | 30 | 28.3
Table 2: Conducting liquid data Sample | G6 | G4(1) | G4(2) | G3(1) | G3(2) | G2
---|---|---|---|---|---|---
$K(S/m)$ | $1,84\times 10^{-6}$ | $3,30\times 10^{-4}$ | $4,29\times 10^{-4}$ | $2,00\times 10^{-3}$ | $4,90\times 10^{-3}$ | $9.70\times 10^{-3}$

Special care was taken in the measurements of the liquid-liquid interfacial
tension with surfactants. In our case, the surfactant has been added only to
the external dielectric liquid (continuous phase), in order to preserve the
properties of the inner conductive liquid (dispersed phase). For this purpose,
Span 80 (Sorbitan monooleate) was employed in all these series of experiments.
In order to find the appropriate surfactant concentration range, surface
tension was measured for different concentrations using two different
techniques: the Wilhemy plate (Kruss) and pendant drop measurements (KSV
Instruments), the results are shown in figure 4. As can be seen in the plot,
surface tension decreases slowly until it approaches a critical concentration
when the graph becomes more stepped and reaches it minimum value. This value
is reached approximately at $C_{crit}=1.1~{}mol/m^{3}$, which we will call our
Critical Concentration, i.e. when the value of the surface tension reaches its
minimum. Since we are not interested in studying colloidal properties of the
surfactant in these processes, we will avoid using the term Critical Micellar
Concentration, but just Critical Concentration instead defined as stated
above. From now on, all surfactant concentrations $\tilde{C}(mol/m^{3})$ will
be made dimensionless with the Critical Concentration, so that
$C=\tilde{C}/C_{crit}$.

Figure 4: Measured interfacial tension for glycerol/hexane against surfactant
concentration (span80)

##  IV Clean baths

Barrero et al. Barr:2003  confirmed that immersed eletrosprays in clean (i.e.
surfactant-free), insulating and inviscid liquid baths behave basically as
their counterparts in air. Here, we perform extended measurements that will
serve us as a reference for the following ones. Before adding a surfactant to
any of the fluids or alter the viscosities of the liquids we will confirm by
electrical current measurements that the electrospray is certainly being
operated in the cone-jet mode. The liquids employed have been chosen in order
to resemble the conditions of an electroatomization in air, i.e. a viscous
liquid being dispersed in an inviscid fluid ($\lambda=\mu_{i}/\mu_{o}\gg 1$).
In this case, we will disperse different samples of glycerol ($\mu_{i}\approx
1Pa\times s$) in hexane ($\mu_{o}\approx 10^{-6}Pa\times s$). The case of
inverted viscosity will be briefly described at the end of the paper.

As can be observed in figure 1, the aspect of an electrospray of this type is
completely analogous to those in air. Additionally, the electrical current
measurements plotted in figure 5 show that most of the samples follow
reasonably well a classical square root law of the type in equation (1). This
indicates us that the transition from a cone to a jet results from the so-
called charge relaxation effects Loscermora , i.e. surface charges can not
maintain the static cone anymore and they are convected by the liquid flow
towards the cone apex, breaking the electrostatic equilibrium of the cone. The
measurements fit an equation of the type (1) with an slope $a=1.3$ with less
than 10% of error.

Figure 5: Electrical current against flow rate for glycerol samples in hexane.
Insert: Minimum Flow rates for glycerol in hexane. Full circles correspond to
the minimum flow rates of immersed electrosprays, the square corresponds to a
measurement of the minimum flow rate of an electrospray in air for comparison.

The minimum flow rate was also explored for some of the samples employed.
According to several results in the literatureLoscermora ; MoraReview ;
higuera2004current , the dimensionless minimum flow rate $Q_{min}/Q_{o}$
should only depend on the relative dielectric constant of the liquids. Since
we always used the same couple of liquids in the series, the minimum flow rate
should be constant for all the samples. However, the results differs from that
prediction, and as we can see in the insert of figure 5 the minimum flow rate
decreases with the conductivity of the liquid, and with values well below
those predicted. This result is also found by Bon Ki Ku et al. Ku:2001  for
glycerol samples in vacuum, which make us think that it is a characteristic
behavior of glycerol and the nature of the outer medium plays no role.
However, their values where in general much higher than ours. We encounter
here one of the main advantages of the use of electrosprays in liquid mediums:
the value of the minimum is flow rate is at least one order of magnitude
smaller when the liquid is dispersed in liquid dielectric mediums like hexane
instead of air. As an example, the samples with conductivity of order
$10^{-5}S/m$ have a minimum flow rate in air of around $400\mu l/hr$, while
for the same liquid in hexane we can reduce the flow rate to $20\mu l/hr$. The
reason can be found in both, the reduction of surface tension (which decreases
$Q_{o}$) and the reduction of the dielectric constant ratio (which decreases
$Q_{min}/Q_{o}=f(\beta)$). This has important consequences since we can
therefore obtain much smaller droplet diameters by simply dispersing the
liquid into a dielectric medium instead of in air. Another advantage is the
reduction of the needed voltage and current to work the electrospray: while
this particular regime shown in figure 1 requires in air around 6 kV and
around 100 nA, immersed in clean hexane it requires circa 3 kV and a few
nanoamperes.

In order to have precise measurements of the droplet diameters, and since our
laser diffraction system only detects particles between 1 and 80 microns, we
employed only glycerol samples with conductivities above $10^{-3}S/m$, with
diameters ranging from 3 to 45 microns. In table 2 we show the samples
employed, named by their conductivity as G4 (with $K=10^{-4}S/m$, and so on),
G5 and G6. The droplet distributions of the least conducting samples (as in
G5(1) in figure 6a) are found to be bidisperse for all flow rates , even for
the minima observed, being satellite droplets the responsible for the
secondary values. The presence of satellite droplets is a well known
phenomenon in electrosprays, especially for viscous liquids, and is
extensively discussed by Rosell-Llompart and Fernández de la MoraRosell .
Although it is natural to have satellite droplets in these regimes, the
unsteadiness of the jets enhances it. Certainly, in these samples the jets
manifest the so-called “whipping” instabilitytaylor1969electrically ; basset
for all flow rates even for the minimum ones. As Riboux et al. recently
studied in detailriboux2011whipping , the case of the sample G6 is especially
impressive: When we operate pure glycerine in air, the jets are perfectly
steady and they break-up only by capillary instabilities. However, when
inserted in a dielectric liquid bath as hexane, the jet become laterally
unstable and bends developing a spiral-like path, reducing its diameter until
it breaks by capillary instabilities. Therefore, its distribution of droplets
sizes is affected by this violent instability. However, in the sample G4 we
find a different situation (fig 6b), we observe a monodisperse regime without
satellite droplets which is maintained until we increase the flow rate above a
critical value in which the distribution turns bidisperse. Both regimes show
also very distinctive aspects when observed in operation with the aid of a
microscope and special illumination conditions. As shown in figure 7, we can
observe in the bimodal regime the smaller droplets located in the outer part
of the aerosol while the bigger droplets stay in the inner part. This
phenomenon was observed already by Zeleny zeleny  in the first controlled
electrospray experiments ever made and explained later by Tang and Gomez Tang
among others, the reason for this structure in the aerosol can be found in the
higher mobility of the satellite droplets, due to their less inertia they are
electrically repelled farther in the aerosol. Rosell and de la Mora Rosell
made an extensive study for several different samples and noted that it was in
the most viscous and less conducting ones where such dispersion was more often
observed.

Figure 6: Bidisperse droplet distributions for samples G5(1) (left) and G4
(right).  Figure 7: Visualization of the hydrosol plume in sample G4. (a)
Below the critical flow rate, monodisperse hydrosol. (b) Above the critical
flow rate where the droplet size is bidisperse. (enhanced)  Figure 8:
Dimensionless representation of droplet size vs injected flow rate. All
conductivities sampled follow approximately a $d\sim Q^{1/3}$ law as that
described in eq. 2 .

To analyze the results we will make use of the dimensionless diameter and flow
rate defined in equations (5) and (3) respectively. The results are plotted in
figure 8. As we can observed, the data approximately fits a 1/3 law as the one
defined in equation (2). These results are in accordance with those found by
Barrero et al. Barr:2003 , although certainly the dispersion of our data also
would permit a reasonably good fitting with other similar scaling laws as
$d\sim Q^{1/2}$. According to the recent work of
Higuerahiguera2010electrodispersion , a regime in which $I\sim Q^{1/2}$ and
$d\sim Q^{3/8}$ would be a cone-jet mode in which the electric shear stress is
all balanced by the inner liquid viscous drag. On the other hand, a result
with $I\sim Q^{1/2}$ but a higher exponent for the drop diameter on the flow
rate as $d\sim Q^{1/2}$ could be interpreted as a case in which the viscosity
of the outer bath becomes dominant. Given the large viscosity ratio in our
experiment, and the moderate flow rates employed, we do believe that we are in
the first scenario, in which the flow inside the liquid is dominated by the
inner viscosity. It worth noting that all glycerol samples analyzed in hexane
presented a very distinctive behavior at higher flow rates, separating from a
1/3 (or 3/8) exponential tendency law to a steeper one (exponents up to 0.578
for sample G6). Many interpretations can be done at this respect: Rosell-
Llompart et al. Rosell  also reported a similar behavior in their experiments
when the critical flow rate for satellite droplets was reached. Certainly in
our case, the radial distance from the apex at which the inertial effects
become relevant, $R_{i}=({\rho Q^{2}/\gamma})^{1/3}$, becomes of the same
order as the electrical relaxation distance $r_{e}=(\beta Q/K)^{1/3}$. This
argument ignores however other important effects of the inner or outer viscous
stresseshiguera2010electrodispersion  which could be of great importance. We
lack however enough evidence to discern among the competing regimes.

##  V Baths with surfactant

In this section we will use a surfactant in the liquid bath (outer phase). In
order to make proper comparisons, we keep using the same liquids chosen for
the previous chapter. The approach taken in the experiments is the following:
first we perform a qualitative exploration varying independently conductivity
and surfactant concentration. Once the different regimes are identified in the
parameter space constituted by the electrical conductivity K and the
surfactant concentration C, we proceed to make electric current and diameter
measurements in some selected regimes and discuss the results. The range of
the parameters K and C are the following: The conductivity range is the same
used in the clean case in the glycerol samples (table 1). The surfactant
concentration range was chosen in terms of the value of the interfacial
water/glicerine-hexane tension, from its maximum value ($C=0$) to its minimum,
achieved at the Critical Concentration ($C=1$). Due to the large amount of
data and regimes found, we will illustrate two main extreme cases first with
surfactant at its critical concentration value. A lot of intermediate cases
have been also found for different values of the parameters $(K,C)$. However,
they all share similar characteristics with the two limiting cases shown here
and no new phenomena are really introduced. More detailed information about
these regimes can be found in the doctoral thesis of A. G. Marin Alvi .

###  V.1 High Conductivity Cases

To illustrate the extreme case of high conductivity we choose a glycerin
sample of $K=4\times 10^{-3}S/m$. This conductivity is high enough to
illustrate our purposes and is close to the maximum conductivity allowed to
make precise measurements and analyze the jet and droplet diameters. In a bath
clean of surfactants, this sample gives rise to narrow jets and particles
below 1 micron in the low flow rate regime. Adding surfactant in small
quantities does not seem in principle to modify the electrospray properties,
only a mild decrease in the working voltages. However, when we increase the
surfactant concentration above its critical value, what we observe is
something completely different. First of all, the voltages needed to obtain
the Taylor cone have been reduced by a factor of at least 5 (with an electrode
distance of 5 cm, we have 5000 V in a clean bath, while 1000 V in this case).
This lower voltage regimes might be of interest with applications in which the
state of electrification can be detrimental as in biological solutions. We
also note that the flow rate range is much wider. Finally, regarding the
meniscus and spray shape, we do not observe the classical conical spray
anymore, but only a blurred open spray with an almost cylindrical shape (fig.
9).

Figure 9: Visualization of a high K and high C case with regular long
exposures (1ms).

The reason of this peculiar aerosol shape was revealed using a high speed
camera. A sequence of images extracted from a recording are shown in figure
10. As we can see in the images, the jet is much thicker that it was in the
clean bath and it soon becomes laterally unstable and breaks immediately in
filaments. This contrasts strongly with the classical lateral instabilities,
in which the jet performs several “loops” before breaking into droplets
riboux2011whipping .

Figure 10: Visualization of a high K and high C case with high speed imaging
at 2000fps (enhanced)

Due to experimental difficulties it was no possible to use the diffraction
system to measure the droplet diameter in these cases where there was a
surfactant present in the bath, and therefore we could not collect as much
data as in the previous section. The problem found was related to the longer
life of the generated droplets within the cell, which made difficult to
maintain the bath clean of non-desirable droplets in the region of
measurement. A different set-up with a continuous circulating dielectric
liquid would solve the problem and would make possible to make better
measurements, but as we will discuss later, it is still not clear how the flow
in the external liquid would affect the results in this case of extremely low
surface tension. However, lot of information can be obtained from high
resolution images, high speed videos and electrical current measurements.
Depending on the characteristics of the regime, we will proceed with different
techniques.

In section IV, where high conductive glycerine in clean baths was analyzed,
the current to flow rate dependence showed a reasonable good agreement with
the well known $I\sim Q^{1/2}$ scaling laws. In this case we will employ a
single glycerine sample of relatively high conductivity ($K=6\times
10^{-3}S/m$), and let the surfactant concentration ratio vary in two orders of
magnitude, below and above the critical concentration. As we mentioned above,
there is a critical surfactant concentration in which the regime of
atomization changes completely from a regular electrified-capillary breakup to
a regime dominated by wider jets and lateral instabilities, yielding highly
polydisperse emulsions. In figure 11 the data is plotted in dimensional form.
We first note a highly sensitive reaction of the electrical current on the
surfactant concentration (i.e. surface tension). It is also very noticeable
how the range of stable flow rates for the samples $C<1$ are dramatically
decreased due to the reduced surface tension value, as was argued in section
V, specially when this is compared with the case of dispersion in air. The
different dependance on the flow rate of the different samples is also
remarkable: almost all $C<1$ samples seem to follow a similar square root law
(with different offsets that can be explained by their different surface
tensions), while those with $C>1$ present a very reduced dependence with the
injected flow rate. Also notice how the addition of surfactant reduces the
flow rates and therefore the currents initially, but as the concentration
increases the output current surprisingly increases as well; this differs
strongly with the classical models of charge transport developed for regular
electrosprays and therefore indicates that there must be a different mechanism
driving the current in these cases.

Figure 11: Electrical current vs flow rate for different surfactant
concentration from the cases of surfactants-free samples (white circles), mild
concentrations ($C<1$)(bluish circles) to the high concentrations
($C>1$)(reddish circles). Figure 12: Dimensionless Electrical current vs flow
rate for different surfactant concentrations, from the cases of surfactants-
free samples (white circles), mild concentrations ($C<1$)(bluish circles) to
the high concentrations ($C>1$)(reddish circles).

These aspects can be observed in more detail in the dimensionless plot, in
which the characteristics flow rate and the current defined in expressions (3)
and (4) has been used. Especial care has been taken with the interfacial
tension measurements (see section III). First thing to note is that samples
with $C<1$ fit again reasonably well with the well-known square root laws,
with some differences for those cases closer to the critical concentration.
But most important and surprising is the higher concentration cases, those
samples yield electrical currents much higher than those expected for charge
relaxation mechanisms Loscermora . In the case that the current would be
driven by convection, electrical current would be expected to decrease with
surface tension since the characteristic velocities needed to maintain a
stable jet are being decreased. This is observed for the samples of low
surfactant concentration, but not for those with high surfactant concentration
($C>1$) in which the current increases as the surface tension decreases.
Additionally, they show a much reduced dependency with the injected flow rate;
in the best cases the current increases only a 20% when the flow rate is
increased in one order of magnitude. These features might indicate an
electrical current transport for high surfactant concentration samples
different than the classical convection-driven mechanisms. Unfortunately, the
methods employed in previous chapters to obtain droplet diameter distributions
were not suitable for the regimes with presence of surfactants. The presence
of surfactants increased the stability of the generated droplets, which
survived much longer within the liquid bath (they were no absorbed as easily
in the water phase at the bottom of the cell) and hindered the laser
diffraction measurements. On the other hand, regimes as the one depicted in
figure 10, gave rise to a very disordered breakup with wide droplet
distributions that could not give much information. For these reasons, high
speed video techniques will be used instead with enough resolution to make jet
diameter measurements for different surfactant concentration and flow rates,
fixing the liquid conductivity in $K=3.1\times 10^{-4}S/m$ (higher
conductivity would yield jets with diameters that could not be measured
accurately, but the conclusions can be extrapolated to higher conductivities).

Considering classical arguments Ganan97 , one should expect a decrease in jet
diameter as surface tension is decreased as a consequence of the decrease of
the characteristic flow rate (see expressions (5) and (3)). On the contrary,
on figure 13 we observe how the diameter increases as surface tension is being
decreased (for a given conductivity and fixed flow rate). The increase is
timid in the first stages and becomes critical as be reach surfactant
concentration values close to saturation.

Figure 13: Jet diameter variation with interfacial tension for sample of
conductivity $K=3\times 10^{-4}S/m$ at flow rate $Q=20\mu l/hr$ . Insert: jet
diameter is plotted against flow rate, following a scaling law $d\sim
Q^{0.53}$ for a sample saturated with surfactant (minimum $\gamma$).

Regarding the data of $C>1$, not only they showed a reduced dependance with
the flow rate, but the also showed a high dependance with the electrical
voltage. This effect has not been analyzed throughly, but it has several
analogies with the regimes found independently by Gundabala et
al.gundabala2010current  and by Larriba and de la Mora larriba2011production .
In the first case, Gundabala et al.gundabala2010current  used a microfluidic
co-flow system to produce an immersed electrospray, with relatively low
surface tension (no surfactants). They observed regimes in which the current
almost does not depend on the flow rate but it depends strongly on the voltage
applied. Their conclusion is that, due to the reduced size of the micro-
capillary tip, the electrification of the cone-to-jet region was drastically
reduced and the electrospray was driven mainly by conduction. The case of
Larriba and de la Mora is very different, but the results are analogous. They
constructed a coaxial electrospray consisting on an ionic liquid (EMI-BF4)
injecting charge micro/nano-droplets into a insulating liquid menisci of
heptane (also decane). It is indeed a smart and relatively simple way of
atomizing insulating liquids by electrohydrodynamic forces in a controlled
fashion. The authors study the atomization regimes of EMI-BF4 by measuring the
electric current against the flow rate and the applied voltage in a quiescent
bath. They find a very pronounced dependance of the current with the voltage,
and a much weaker one with the injected flow rate. Contrary to Gundabala et
al. gundabala2010current , the authors explain such a behavior by the
appearance of space-charge effects of the charged cloud of droplets in the
quiescent insulating bath, which would screen the electric field felt by the
meniscus.

The regimes found by both groups of authors strongly differ with the classical
cone-jet electrosprays typically found for conducting, inviscid liquids
atomized in air, in which the electrical current depends strongly and almost
exclusively on the flow rate due to the convective dominance of the charge
transport. It would be tempting to identify some of these regimes found in
immersed electrosprays with our results, but their conditions do not apply to
ours. Regarding the experiments of Gundabala, the smallest meniscus to jet
ratio was on the order of 20, which is much higher than their case. On the
other hand, we also reject the hypothesis of the space-charge effects in our
experiments for two main reasons: First of all, such effects would have been
noticed already in clean baths more pronouncedly, but the fact that the
current-flow rate dependence showed a clear $I\sim Q^{1/2}$ discards the
hypothesis. Secondly, if space-charge effects are important, then the
electrical mobility $Z$ of the droplets must relatively low (Larriba and de la
Mora found values of $Z\sim 10^{-7}$). We can estimate the droplet electric
mobility as $Z=2Id^{2}/9\mu Q$, which yields values of the order of $Z\sim
10^{-5}$ in the samples with $C>1$ (figure 10), comparable to the cases in
air, probably due to the relatively large drop size and electric current.

A more likely explanation for such a regime can be found in the recent study
by Higuera higuera2010electrodispersion . In this theorical-numerical study of
immersed electrosprays, a regime is introduced in which most of the electrical
shear stress is equilibrated by the external viscous stresses, which has been
normally ignored in the past. Such regime would take place when the
dimensional parameters
$\Pi=\mu_{e}K^{1/3}/(\rho_{i}\epsilon_{o}\gamma^{2})^{1/3}\sim O(1)$ or $\Pi
R^{1/4}=\lambda(\mu_{i}^{3}K^{2}Q/\gamma^{3}\epsilon_{o}^{2})^{1/4}\sim O(1)$.
In this particular case of high conductivity and $C>1$, the dimensionless
parameters take the experimental values of $\Pi\simeq 0.75\sim O(1)$ and
$R\simeq 7.75$, and therefore $\Pi R^{1/4}\simeq 1.22\sim O(1)$. From which we
can interpret that, besides the large viscosity ratio, the electrical shear
stress on the jet is mostly invested in equilibrate the external viscous
shear. This is experimentally supported by the large velocities induced in the
external liquid bath. In this regime, a critical flow rate
$Q_{M}=\epsilon_{o}\gamma^{2}a/\mu_{o}^{2}K$ is found, where $a$ is the
meniscus radius, and $\mu_{o}$ the outer liquid viscosity. According to this
study, when $Q>Q_{M}$ the charge transfer region becomes large compared to the
meniscus size, the surface charge is therefore limited and the jets are no
longer charged-stabilized. In addition, the current becomes independent on the
flow rate, and the jet size scales with the flow rate as $r_{j}\sim Q^{1/2}$.
This situation fits better with our data of lowest surface tension due to the
strong dependance of $Q_{M}$ on the surface tension. A decrease of almost one
order of magnitude in surface tension in the case of $C>1$ yields $Q_{M}\simeq
0.6ml/h$ which is not so far from the range we operate the electropray. To
give additional support such hypothesis, we measured the jet diameters for
several flow rates for the case of lowest $\gamma$, revealing an exponent of
$0.535$, close enough to the predicted. Other symptoms observed here fit with
the regime described by Higuerahiguera2010electrodispersion , specially those
regarding the lack of stability of the jets due to the reduced surface charge
and the elongated shape of the meniscus (10). The reason for such critical
behavior with the surfactant concentration $C$ above its critical value can be
then explained by the fast decrease of the critical flow rate
$Q_{M}=f(\gamma)$ with the interfacial tension $\gamma$ below the working flow
rates.

We should conclude this section by adding some comments on the stability of
those regimes with higher surfactant concentration. Certainly in these cases
the regimes lack the robustness of the classical cone-jet electrosprays,
either in air or in liquid, manifested by oscillations in the electrical
current and even in the jet diameter. We will point out two possible reasons
for this fluctuations: If we assume that the surfactant concentration is not
homogeneous along the liquid interface, it is then very likely that this
distribution will be also changing in time depending on the amount of
surfactant available in the bath. The liquid in the bath recirculates due to
the electrically-driven movement in the conical interface, so there is no
control on the amount of surfactant contained in the liquid surrounding the
cone-jet. Related to such effect, a curious phenomenon was observed close to
the water phase. This was placed at the bottom of the bath and was used both
as ground electrode and as a droplet collector in order to maintain the bath
clean of charged particles. However, with surfactant present in the hexane
bath, the water-hexane interface was soon covered by surfactant and the
coalescence of the charged droplets into the water phase was hindered. As a
consequence, it could be observed how some droplets approached the water-
hexane interface, “touched” it, and they were immediately repelled back to the
bath. It seems to be a similar phenomenon as that found by Ristenpart et al.
ristenpart2009non : a charged droplet migrates towards an oppositely charged
interface and, in certain circumstances, instead of being trapped and
absorbed, it is repelled from the interface after a short contact. The authors
demonstrate that the time required to equilibrate the charge is much shorter
than the typical coalescence time; therefore, before the coalescence process
is finished, the droplet is already set at the same potential as the interface
and a repulsive electrical force is created among them. In our particular case
the coalescence time can be also further delayed by the presence of
surfactants. Although interesting, the presence of charged droplets
surrounding the electrified meniscus can be extremely disturbing and can be
also responsible of electrical current and jet diameter oscillations. It is
nonetheless a significant problem and can be easily solved using a continuous
external liquid flow as is the case in Gundabala et al.gundabala2010current
and others ecoflow ; japos .

###  V.2 Low conductivity cases

We analyze now a extreme case of low conductivity with surfactant
concentration above the critical. We use here the least conducting glycerol
sample: G6, pure glycerol. It should be reminded that pure glycerine in a
clean bath of hexane presents long laterally unstable jets with diameters
ranging from 10 to 30 microns depending on the flow rate. Adding small
quantities of surfactant gives a similar result as in the high conductivity
case, lower working voltages and certain increase in the jets size. But as we
reach the critical concentration, we find a surprising situation like the one
showed in figure 14.

Figure 14: Visualization of the “dripping-jet” in the limit of low K and high
C (enhanced).

This mode requires only 20% of the voltage needed to stabilize an immersed
electrospray in a clean bath, but instead of having a thin laterally unstable
jet, we find a broad widening jet that will end up growing a droplet in its
end. This droplet increases its size as the jet elongates until the electrical
forces are strong enough to detach it from the jet tip, then the jet retracts
and a new droplet starts to grow again. The size of the droplet is still small
enough to ignore gravity effects: a Bond number defined as
$Bo=\rho_{i}gR_{c}/\gamma$, where $\rho_{i}$ is the liquid density,$\gamma$
its surface tension, and $R_{c}$ its typical drop size, yields values of
$Bo\sim 0.01$. Therefore the detached droplets are directly driven to the
electrode surface. Although the electrical current was extremely low, it was
possible to make some spurious measurements of oscillating electrical currents
varying with the same frequency as the dripping frequency. A relatively thick
jet ($60-100\mu m$) generates and widens in its tip until a big droplet is
created ($200-400\mu m$). Since new liquid is being injected, the droplet
increases its diameter as the jet becomes longer and slender until a certain
point in which the droplet reaches such a size that detaches from the jet. The
liquid ligament then retracts, a new small droplet is formed on its tip, and
the process starts again. The frequency of this cycle, lengths and diameter of
the jet and size of the droplet may vary depending on the controlled
parameters: voltage and flow rate. An increase on voltage will make longer
jets, and therefore (for the same flow rate) smaller droplets, with a slight
increase in the frequency. A reduction in flow rate will create shorter jets,
smaller droplets and a higher frequency. Regarding the electrical current, it
was mostly too low to be measured accurately, but it presented fluctuations
synchronized to the drop generation cycles. The key of this behavior seems to
be in the joint effect of reduced surface tension and low conductivity in the
liquid. Certainly, the electrical field needed to create a conical structure
in the meniscus is so reduced that it fades away not so far from the meniscus
tip and consequently the liquid jet flows downstream practically as a free
jet, feeling no electrical stress in its surface. This differs from regular
Taylor cones in air, where the electrical field close to the tip is so strong
that the jet suffers a tremendous electrical stress still far from the cone.
The electrical stress is responsible of the progressive decrease in size of
electrified driven jets and also explains their resistance against varicose
instabilities PepeHerrera2004 ; Tomotika . In our case, the absence of this
electrical stress explains the progressive widening of the jet. Additionally,
the reduced electrical field on the jet also explains the absence of lateral
instabilities, often related to jets electrically overcharged. However, the
arguments above still can not explain the appearance of a droplet in the tip
of the jet. On the other hand, a similar phenomenon has been observed and
successfully explained in co-flow systems to generate monodisperse emulsions
AlbertoLC . In that case, Utada et al.Utada1  observed how the droplet to jet
diameter ratio increased its value greatly when the inner liquid jet was being
slowed down by the outer slower liquid, the authors identified this phenomenon
as the outcome of an absolute instability due to the decrease in inertia of
the jet, which triggers the classical transition to a “dripping” regime in the
jet at certain distance from the needleclanet1999transition . A classical
example of absolute capillary instabilities can be found in the dripping at
the end of a tube, which is characterized by the growth of perturbations in
fixed points of the interface. Monodisperse droplets are therefore produced
due to the proximity of the injection needle which damps most of the external
perturbations. On the other hand, in the case of convective instabilities the
maximum growth rate of the perturbations is found far from the injection
needles, giving rise to polydisperse droplets due to the effect of external
perturbations. In our experiment, the electrical field is enough to generate
an electrified conical meniscus through which the liquid in being injected at
certain flow rate. Within the meniscus, the fluid is being accelerated towards
the vertex until inertia overcomes the capillary tension a jet is formed. The
jet is then accelerated by the electrical field of the meniscus, however, due
to the low value of both electrical field and surface charge, the tangential
stress over the surface decreases fast with the distance from the tip. At this
point, the only force over the surface of the jet is the viscous drag from the
outer liquid which is not in any way negligible, but we have no control of it,
and therefore its only effect is to slow the jet down, in a similar way as it
does the outer liquid in Utada et al. Utada2  experiments. The jet then widens
up and velocity decreases until inertia can not maintain a stable jet anymore
and finally a droplet appears at its tip. In co-flow experiments, a series of
oscillations in the interface preceded the detachment of the droplet from the
tip; we are not aware of these oscillations in our experiments, but they may
be visible employing higher spatial or temporal resolution techniques.
Nevertheless, it must be noted that the liquid interface in this case presents
certain surface charge (although low when compared to that found in classical
electrosprays, but still not negligible), which seems to give the interface
some resistance to varicose oscillationsBasaranjets . This is of course an
extreme case of low conductivity and therefore wide jets, but the same
phenomenon has been found increasing the conductivity of glycerine with
thinner jets, giving rise to the same type of instability but with smaller
drops.

##  VI Notes on inverted viscosity ratios

So far in this article we have experimentally studied immersed electrosprays
of highly viscous liquids in quiescent and inviscid liquid baths with variable
surface tension, controlled by the use of surfactants. A very different case
that deserves study is that of the atomization with inverted viscosity ratio,
i.e. an inviscid liquid being atomized in a viscous insulating liquid. We will
refer this regimes as $\lambda=\mu_{i}/\mu_{o}<1$, where $\lambda$ is the
viscosity ratio, and $\mu_{i}$ and $\mu_{o}$ are respectively the inner and
outer viscosities. This is the typical case, for example, of water being
electrosprayed in a bath of oil. Barrero et al. Barr:2003  reported successful
experiments of water in oil, but they were not extensively studied.
Unfortunately we can not provide enough data about this case of inverted
viscosities, but we can very briefly discuss the preliminary experiments
performed, specially regarding the stability of the process. Water samples of
different conductivities were atomized in a bath of silicone oil (viscosity of
20 cP) for different surfactant concentrations. For the case of clean baths,
the electrospray was hardly ever stable and the current showed a high
dependance on the voltage applied. In the case with surfactants in the viscous
bath, stability was achieved, but still with a high sensitivity to the applied
voltage. A significant symptom observed was the extremely reduced velocity of
the emitted droplets, which were easily observed with at standard recording
speeds. More data is obviously needed, but the symptoms observed seem to point
out the presence of strong space-charge effects as those described by Larriba
and de la Mora larriba2011production . If this is the case, the presence of
surfactants might have improved the stability due to the increase of droplet
size, which increases the droplet electrical mobility and therefore reduces
the screening of the electrical field. We conclude that, according to our
experiments, space-charge effects would become relevant when the viscosity of
the outer liquid is similar or higher than that of the atomized liquid,
although further research must confirm such hypothesis.

##  VII Summary and conclusions

In this work, we have reviewed the behavior of immersed electrosprays of
viscous conducting liquids into insulating inviscid liquids baths, for
different parametric cases, but mainly focused in the effect of the surface
tension modified by the presence of surfactants. As a summary, we have
confirmed the results by Barrero et al. Barr:2003  regarding the
reproducibility of cone-jet regimes in immersed electrosprays. We also make
emphasis in the advantages of the immersed electrosprays, since the moderate
reduction of surface tension brings lower characteristic flow rates and
therefore a lower droplet size. However, a more pronounced reduction of
surface tension, achieved by the use of surfactants, provokes the loss of the
cone-jet regime, characterized for its reproducibility and monodisperse
sprays. On the other hand, the use of surfactant also brings advantages as the
reduction of the voltage necessary for atomization (of interest when cells or
vesicles are encapsulated inside the conducting liquid), and higher droplet
electrical mobility. The new modes of atomization at minimum surface tension
have been analyzed and compared with different new regimes recently identified
by Higuerahiguera2010electrodispersion  and Larriba and de la
Moralarriba2011production . The nonessential inconveniences originated from
the use of surfactants, as the surfactant accumulation at the base of the
meniscus, or the enhanced stability of the hydrosol, could be easily overcome
by a controlled external flow, in a similar fashion as Gundabala et al.
gundabala2010current  did in their experiments. Other effects need to be
further studied, such as the effect of the outer liquid flow on the stability
of the regimes, specially in liquids of similar or higher viscosity than the
conducting liquid. The control of the flow in the outer liquid could also
solve the hypothetical space-charge effects that could appear when the
viscosity ratio both liquids have similar viscosities. However, the effect of
the external viscous shear stress on the surface of the Taylor cone have been
only marginally studied and would be of special relevance in the case of
reduced surface tension by surfactants, when the electrification of the
meniscus is also the lowest. To conclude, we do believe that
electrohydrodynamic atomization has several features that could be of great
interest for lab-on-chip applications which has to be still explored.
Therefore, the aim of this work has been to study possible solutions to the
current problems that the community has been facing in recent years.

###### Acknowledgements.

A. G. M. and I. G. L. dedicate this work to Prof. Antonio Barrero Ripoll,
respectively mentor and colleague of the authors, who inspired this and many
other research along his life. A. G. M. owes a late acknowledgement to the
Yflow crew: J. E. Díaz, M. Lallave and D. Galán, for their help and their
friendship. A. G. M. also acknowledges the encouragement of Prof. Juan F. de
la Mora to finally publish these results.

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11institutetext: Indian Institute of Information Technology, Allahabad, India
11email: {mit2019018, pse2017002, rsi2017502<EMAIL_ADDRESS>
# Machine learning equipped web based disease prediction and recommender
system

Harish Rajora  0000-0002-7171-0064 Narinder Singh Punn  0000-0003-1175-1865
Sanjay Kumar Sonbhadra  0000-0002-7457-9655 Sonali Agarwal
0000-0001-9083-5033

###### Abstract

Worldwide, several cases go undiagnosed due to poor healthcare support in
remote areas. In this context, a centralized system is needed for effective
monitoring and analysis of the medical records. A web-based patient diagnostic
system is a central platform to store the medical history and predict the
possible disease based on the current symptoms experienced by a patient to
ensure faster and accurate diagnosis. Early disease prediction can help the
users determine the severity of the disease and take quick action. The
proposed web-based disease prediction system utilizes machine learning based
classification techniques on a data set acquired from the National Centre of
Disease Control (NCDC). $K$-nearest neighbor ($K$-NN), random forest and naive
bayes classification approaches are utilized and an ensemble voting algorithm
is also proposed where each classifier is assigned weights dynamically based
on the prediction confidence. The proposed system is also equipped with a
recommendation scheme to recommend the type of tests based on the existing
symptoms of the patient, so that necessary precautions can be taken. A
centralized database ensures that the medical data is preserved and there is
transparency in the system. The tampering into the system is prevented by
giving the no “updation” rights once the diagnosis is created.

###### Keywords:

Disease prediction, Healthcare, Web, Ensemble learning

##  1 Introduction

Organization of the medical data is always a challenging task for betterment
of modern healthcare system. It is evident that mostly the medical data is
exclusive only for the healthcare organizations which is only practised by
elite hospitals. Following this, the idea of the proposed platform is to offer
global reachability of the medical data that is not restricted just to one
hospital and its subsidiaries, but to local hospitals and clinics (if the
doctor is registered), while also securing the patient’s identity. This
platform can be used as an excellent source for the collection of data of the
common symptoms and the underlying disease. The data can be used for various
research activities and to generate new real-life data sets. Insurance
companies can also make use of authentic data to bring down the fraudulent
claims which on an average ranges between $\sim$10 to $\sim$15 [15]. The
system is equipped with a prediction algorithm that works in the back-end to
predict the disease from the given symptoms.

With immense applications of deep learning and machine learning across various
domains and applications such as classification, detection, recognition,
localization, etc. [8, 9, 17, 21]. These techniques have been used for several
application domains such as the future box office movie collections, price of
a house with given parameters or bankruptcy prediction, human face recognition
systems and voice recognition, etc. [4, 20]. The ensemble approach [18, 13]
has been proved to be very effective and more accurate [14] in many medical
applications such as heart disease prediction [3, 2, 6], cancer prediction
[18], hepatitis disease prediction [16], diabetes prediction [7] and
Parkinson’s disease prediction [5]. With this motivation, to predict plausible
disease an ensemble approach is proposed in the present research by combining
the features of three state-of-the-art classification algorithms: naive
bayesian, $K$-NN and random forest. The selection of these algorithms follows
from various research papers [20, 16, 11] which are focused on determining how
the different classification algorithms behave with respect to the different
data chunks and different applications. Following this, the ensemble approach
is combined with the voting mechanism [18, 16, 12] (discussed in the sub-
sections 4.2.1) to provide the best prediction of the disease.

The main contribution of this paper is as follows:

  * •

Disease prediction: This paper provides an algorithm to predict the disease
based on the given symptoms using state-of-the-art machine learning
classification algorithms.

  * •

Analyze ensemble results: Combine the results of the individual algorithms
with voting mechanism discussed in sub-section 4.2.1.

  * •

Web-based interactive platform: The research also presents an interactive
platform (Raahat111https://bdalabiiita.pythonanywhere.com/) integrated with
the disease prediction system along with other important features for a more
robust healthcare management system.

The paper is divided into six major sections. Section 2 describes the
motivation behind the project. Section 3 explains the background knowledge
required to understand the various algorithms used. Section 4 is a brief
introduction to the components of the platform including prediction and voting
mechanism. Section 5 contains the results and output of the algorithm.
Finally, the concluding remarks are presented in Section 6.

##  2 Motivation

A more robust and transparent healthcare infrastructure is the primary global
need of most of the countries. The non-existence of such a system causes
delayed diagnosis, transfer of misinformation and lack of information among
the doctors and people. Healthcare management systems have proved to be
beneficial in some developed countries such as the UK and USA, where a
patient’s complete medical record is accessible to every registered doctor for
better and quicker diagnosis. The system can also help if the patient is
unconscious and has no alibis. Hence, a centralized system helps to minimise
the chances of wrong medication and other such mishaps to ensure better
recovery chances.

The observations and efforts to cover the gaps in the system have also paved
the way to write a disease prediction algorithm and integrate it with the
application. The motivation comes from the fact that people try to diagnose
themselves by their previous knowledge which is not a right way, thereby
risking the severity of the disease. For example, a running nose is instantly
attributed to cold, stomach ache to overeating/acidity etc. While they might
be correct in a small fraction of the cases, the people are unaware of the
consequences of the disease they are suffering from. The diagnosis at the
later stage might result in increased expenditure and higher complexities
which are difficult to treat. A disease prediction module in the system that
can help to diagnose sitting at home and take actions quickly in case the
disease is serious. The disease prediction can also be used for confirmation
of the prediction from the doctor and optimizing it frequently for better
results.

##  3 Literature review

Deep learning and machine learning technologies are playing active role in
various sectors to develop intelligent applications [10, 1]. A disease
prediction system is an important aspect of a developing nation as part of
strong healthcare infrastructure. With this notion, the present research
proposes a machine learning equipped web-based disease prediction system along
with a recommender system to ensure a more strategic and robust healthcare
environment. With deep insight into the existing e-healthcare system, it is
evident that there are few disease specific solutions are available; but a
diverse disease prediction system considering all common diseases is still
missing (to the best of our knowledge).

Kumar et al. [18] has focused on the prediction of the cancer while Bashir et
al. [13] proposed a solution to predict the heart disease for a patient. In
similar approach, Dutta et al. [16] proposed an approach for hepatitis disease
prediction, Rao et al. developed a parkinson’s prediction approach [5] and
Pathirage et al. [7] offered a diabetic prediction system. All these methods
are algorithmic approaches and do not offer user interfaces for end users. The
absence of a GUI creates a gap between the system and the user. Following
this, the present research focuses on the common disease symptom (including
rare) to cater to the needs of a large group of people and offers a user
friendly web application. For disease prediction the present research uses the
classification models: $K$-NN, naive bayes and random forest, and fuses the
results using an ensemble approach with a novel voting scheme to ensure
accurate prediction (covered in following subsection).

###  3.1 Classification strategies

The following section explains the background knowledge required to understand
the proposed methodology. This section describes the classification algorithms
used in the voting mechanism.

####  3.1.1 $K$-NN

The $K$-NN [19] algorithm classifies the new data point (the one that needs to
be classified) based on the most similar classes that are known as neighbors
($N$). In this research, the data set is run multiple times for different
values of $N\in[1,15]$ and the most promising value of $N$ is selected for
further experiments, where the distance is determined by the Euclidean
distance as shown in Eq. 1:

$Dist(X^{n},X^{m})=\sqrt{\sum_{i=1}^{D}(X_{i}^{n}-X_{i}^{m})^{2}}$ (1)

####  3.1.2 Random forest

The random forest [4] algorithm takes decision trees as the basic block in the
construction of the model. The idea behind the implementation of random forest
is that taking output from one decision tree may not be entirely reliable.
Since there is a lot of variance involved in generating the final output, it
may not necessarily be closest to the actual answer. Hence, random forest
creates multiple decision trees and analyze them together. Random forest also
has an edge over decision trees as decision trees are prone to overfitting.
Random forest selects random smaller samples and builds tree from them
overcoming the disadvantage of overfitting in decision trees. Therefore,
multiple decision trees approach used in random forest generates a better
output than a single decision tree.

####  3.1.3 Naive bayes

The naive bayes classification [19] technique works on the Bayes’ theorem
popularly used in probability analysis in mathematics and computer science.
Naive bayes algorithm considers each property as an independent property
contributing to the final classification. Due to this reason, Naive Bayes has
been acclaimed of its accuracy and reliability across various researches. The
Bayes’ theorem used in the Naive Bayes algorithm can be represented using Eq.
2 and Eq. 3:

$P(c|x)=\frac{P(x|c)P(c)}{P(x)}$ (2)

$P(c|X)=P(x_{1}|c)*P(x_{2}|c)*....*P(X_{n}|c)*P(c)$ (3)

####  3.1.4 Ensemble

The voting algorithm has also been used in various researches to improve the
overall efficiency of the model. A voting algorithm uses multiple machine
learning algorithms and a method to leverage the output from these individual
algorithms. Voting algorithms are a better way of devising prediction based
approaches since more than one model is used for providing reliable and
efficient results. Wide variety of voting approaches have been proposed, for
instance, Dutta et al. [16] have used the majority voting technique in their
research. In the majority voting technique, the votes are counted for each
prediction given by the algorithms. Therefore, if a prediction “X” is
predicted by the majority of the algorithms, then “X” disease is the predicted
disease. Xiaoyan et. al [20] has presented two voting strategies; majority and
plurality, where in majority voting the final classification is declared based
on half of the votes, while plurality voting selects the classifier having the
highest number of votes.

The present research takes the confidence score of the three classification
algorithms namely $K$-NN, random forest and naive bayes. The voting algorithm
then creates another model taking the input as the individual models. In this
paper, weighted voting algorithm is used [20, 14] where a weight parameter is
defined to prioritize a classifier. The “weight” used for individual
algorithms is the mean accuracy of the model. A model with higher mean
accuracy shows a better performance compared to other models.

##  4 Proposed methodology

The proposed solution covers two major phases: 1) Web application development
and 2) disease prediction via machine learning. The web application is for the
user to interact with the system while the disease prediction system is used
to predict the disease based on the given symptoms.

The prediction system is based on the ensemble classifier technique using
naive bayes, $K$-NN and random forest. The back-end has been developed in the
python programming language with the support of Django framework. The front-
end and back-end are connected with the Jinja2 engine and the database used is
SQLite. The module completely comprises of a GUI for the user to interact and
a back-end server with integrated machine learning system for disease
prediction. The following section presents the detailed description of the
overall system.

###  4.1 Web application

The web application is developed for the module so that the user can interact
with the platform. The web application consists of two major parts:

  * •

Front-End (majorly developed in JavaScript and HTML)

  * •

Back-End (majorly developed in Python)

The front-end offers a GUI with minimal complexities, whereas the back-end of
the website is developed that enables the communication between the front-end
and the back-end using Jinja2 engine. The disease prediction system uses
asynchronous calls from front-end to back-end to generate the output by taking
the symptoms from the user without generating a new request.

The web application offers the following functionalities:

  * •

Login and registration system \- offers sign-up and sign-in facilities into
the system.

  * •

History lookup \- helps in fetching the history of a patient by the doctors or
patients.

  * •

Creating medical log \- This is only available for doctor user. With the help
of this module, the doctors can create the medical entry for a patient that
will be stored into the database.

  * •

Predicting the disease based on symptoms \- helps people predict their
diseases by selecting the symptoms from the list given on the platform.

  * •

An informative section for basic health related schemes \- provides a scheme
structure to manage scheme beneficiaries.

  * •

Quick diagnosis for the over-the-counter medicines \- helps the people in
getting OTC medicines along with the predicted disease.

Figure 1 shows the modules and functionalities described above with a high
level schematic representation. The platform is divided into two major
segments: one is the user’s view and another is the disease prediction system.
Since there are different rights of a doctor and a patient, the user’s view is
further divided into two more views. The prediction system comprises the
python back-end which asynchronously communicates with symptoms and the voting
logic to generate the output.

Figure 1: High level schematic representation of proposed framework.

###  4.2 Disease prediction system

The prediction system is based on the ensemble classifier techniques using
naive bayes, $K$-NN and random forest. To optimize the prediction techniques,
a voting mechanism is used for assigning values to various classifiers which
would act as weights for the system. The higher the weight, the more priority
will be given to the classifier’s output. The voting is done based on the mean
accuracy score of the classifier which is described in the next section. The
final result can then be conveyed to the user through the model and controller
of the Django framework.

####  4.2.1 Voting mechanism

This section focuses on the voting mechanism of the disease prediction system
used in the application as a module. The voting classifier takes into account
the machine learning models used to fit the data set and the weights assigned
to each classifier.

The voting mechanism used in the module takes into account the mean accuracy
score of the classifiers. First, each classifier is run and a mean accuracy
score is generated providing the confidence of the classifier on the result.
The same mean accuracy score is then used as weight for the voting mechanism
and the result along with the accuracy is generated. The prediction results
are provided to the user along with the accuracy so that the transparency is
maintained in the system. Such a voting system is more effective instead of
using any one of the classifiers as standalone for disease prediction.

##  5 Results

The experiments described in this paper are done on the data set taken from
National Centre for Disease Control. The data set contains the data surveyed
exhaustively with the most occurring symptoms in the patients. These symptoms
are taken as final entries to construct the data set. There are a total of
4921 such unique entries in the data set. A single entry may contain a similar
identified disease such as fungal infection but the symptoms in two entries
for the same disease are different. This data set is then organised into
another data set by processing the raw entries.

The new modelled data set contain symptoms as column names and the rows denote
the identified disease. For every symptom occurring for a disease, the column
entry is marked as 1 for that symptom while other entries are marked as 0. The
data set is then split into the test data and train data with a ratio of
20:80. Each individual algorithm is run to fit them on the training data set
and the experimental results are discussed in this section. The benchmark
performance evaluation matrices used are presented in Eq. 4, Eq. 5 and Eq. 6

$Accuracy=\frac{TP+TN}{TP+TN+FP+FN}$ (4)

$Precision=\frac{TP}{TP+FP}$ (5)

$Recall=\frac{TP}{TP+FN}$ (6)

The accuracy defined in Eq. 4 is the measure of correct predictions done by
the models. The precision defined in Eq. 5 signifies the positive instances
are classified as positive in the data set. The recall is the measure of
correctly identifying true positives (Eq. 6). The disease prediction algorithm
is run on $K$-NN, random forest and naive bayes individually before combining
them in the ensemble approach as described in the output section.

For the $K$-NN classifier, 1 to 15 neighbours have been experimented and a
result is generated to bring the maximum confidence score. To find the optimal
value of the neighbours, each value of $N$ has been experimented for $\alpha$
times, where $\alpha=50$. Figure 2 demonstrates the performance of $K$-NN with
different neighbors.

Figure 2: The figure demonstrates the performance of different neighbours (1
to 15) over the data-set matched along the confidence score and number of
neighbors axes.

###  5.1 Experimental analysis

The disease prediction algorithms take a set of symptoms from the user as
input. On the platform, the symptoms can be selected from the drop-down menu
and the algorithms analyze them in the back-end. The symptoms selected by the
user are then analyzed by each of the algorithms providing the output as the
mean accuracy. The output is then used as the weights for those algorithms in
the ensemble classifier. In the present section, the experimental symptoms
considered are swelled lymph nodes, phlegm, redness of eyes, typhos,
unsteadiness, enlarged thyroid. The confidence score values generated by the
algorithms is shown in the Fig. 3 and Table 1.

Figure 3: (a)Accuracy Score (b)Precision Score (c)Recall Score Table 1: Confidence score values of classifiers Classifiers | Accuracy | Precision | Recall
---|---|---|---
Random Forest | 93.65 | 99.7818 | 99.7818
Naive Bayes | 84.02 | 99.7818 | 99.7818
$K$-NN | 93.53 | 99.7823 | 99.7823
Ensemble | 93.65 | 99.7818 | 99.7818

For the experimental symptoms taken in this study, the naive bayes algorithm
generated the least score. The score generated by naive bayes is 84.02, while
random forest generated 93.65 and $K$-NN generated a score of 93.53. These
numbers will be taken as weights in the ensemble model and the prediction will
be run again with defined votes. In this experiment, the ensemble model
generated a score of 93.65 which is the highest score and adapted to the best
model among the three algorithms. The true positives, false positives, true
negatives and false negatives calculated by the model is shown as a confusion
matrix in the Fig. 4.

Figure 4: Confusion matrix testing data Figure 5: Raahat portal: (a)Adding
symptoms on the portal (b) Output as predicted disease

The platform boasts a front-end which takes on the input and present the
output after processing it through the algorithm described in the paper. Once
the symptoms are added, the user can press the “predict” button to pass the
symptoms through AJAX call to the back-end. The predicted disease is then
shown in the output. The screenshot after adding the symptoms in the
application along with the predicted disease is shown in Fig. 5.

##  6 Conclusion

A disease prediction system is a helpful tool in uplifting the healthcare
system of the country. A disease prediction system not only helps in
“predicting a disease” but also in curating medical data, enhance research
activities and control fraudulent activities. The present research offers a
machine learning equipped web-based prediction system along with a
recommendation system for a wide range of users. The ensemble approach is used
as machine learning model that uses a weighted voting mechanism to prioritize
better-performing algorithms on a given set of symptoms. The ensemble used in
this paper involves three individual algorithms namely $K$-NN, naive bayes and
random forest. Ensemble approaches prove to be more effective since the model
uses more than one algorithms for an efficient and reliable result. The
experiment is performed using a data set acquired from NCDC which presents the
symptoms for a corresponding disease. A web-based application is also
developed to help people interact and make use of the disease prediction
system with a GUI. The experimental results show that the ensemble approach is
adaptive to the best model and with the symptoms considered for experiments
the ensemble approach adapted to the random forest.

## Acknowledgment

This research is supported by “ASEAN- India Science & Technology Development
Fund (AISTDF)”, SERB, Sanction letter no. – IMRC/AISTDF/R&D/P-6/2017. Authors
are also thankful to the authorities of “Indian Institute of Information
Technology, Allahabad at Prayagraj”, for providing us with the infrastructure
and necessary support.

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