Novel Approach for Battery Type Determination: A Mere Electrical Alternative - Research ...

Novel Approach for Battery Type Determination: A
Mere Electrical Alternative
İsmail Can Dikmen (  can.dikmen@inonu.edu.tr )
 Inonu University
Teoman Karadağ
 Inonu University

Research Article

Keywords: Novel Approach, Battery Type Determination, Electrical Alternative, electrical energy, technical
challenges, electric vehicles, batteries, current and voltage

Posted Date: September 3rd, 2021

DOI: https://doi.org/10.21203/rs.3.rs-858317/v1

License:   This work is licensed under a Creative Commons Attribution 4.0 International License.
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Novel Approach for Battery Type Determination: A Mere
 Electrical Alternative
İsmail Can Dikmen1,*,⸸, and Teoman Karadağ2,⸸
1
 Inonu University OSB Vocational School, Department of Electric Vehicles, Malatya, 44200, Turkey
2
 Inonu University, Department of Electric Electronics Engineering, Malatya, 44200, Turkey
*
 can.dikmen@inonu.edu.tr
⸸
 the authors contributed equally to this work

Abstract
 Today, the storage of electrical energy is one of the most important technical challenges. The
increasing number of high capacity, high-power applications, especially electric vehicles and grid
energy storage, points to the fact that we will be faced with a large amount of batteries that will need
to be recycled and separated in the near future. An alternative method to the currently used methods
for separating these batteries according to their chemistry is discussed in this study. This method can
be applied even on integrated circuits due to its ease of implementation and low operational cost. In
this respect, it is also possible to use it in multi-chemistry battery management systems to detect the
chemistry of the connected battery. For the implementation of the method, the batteries are connected
to two different loads alternately. In this way, current and voltage values are measured for two
different loads without allowing the battery to relax. The obtained data is pre-processed with a
separation function developed based on statistical significance. In machine learning algorithms,
artificial neural network and decision tree algorithms are trained with processed data and used to
determine battery chemistry with 100% accuracy. The efficiency and ease of implementation of the
decision tree algorithm in such a categorization method are presented comparatively.

Introduction
 It is obvious that electric vehicle technologies and the innovations it brings are one of the
leading issues that touch our lives for today and for the next decades. The biggest obstacle to the
realization of this technology was the energy storage problem. Although many electric vehicles have
been produced since the 19th century, mass production was only possible in the 1990s. However, the
foundations of this technology were laid a long time ago and could be developed in a short time. For
example, automotive technology in Britain in the 19th century was advanced far ahead of its time.
According to the articles published in reputable engineering journals on this technological
development; A special electric vehicle powered by lead acid batteries was built by the Immisch & Co
company in 1887 for the 34th Ottoman Sultan Abdülhamid II 1–3. It can easily be said that at the end
of the 19th and the beginning of the 20th century, electric vehicles are technically far ahead of
gasoline vehicles. For example, “Compagnie Internationale des transports automobiles électriques”
company produced the vehicle named “La Jamais Contente” in 1899, which broke the record of 1 mile
per minute and at the same time reached a speed of 106 km/h. Becoming the first land vehicle in the
world to exceed the limit of 100 km/h 4. In the intervening period of almost a century, there has been
no significant development in electric vehicle technology. The first mass-produced electric car after
this long pause is the EV1 model produced by "General Motors" between 1996 – 1999 5.
 The main reason for this pause is the lack of technological advances for an efficient electrical
energy store device. Studies on this subject started in the mid-18th century. The first electrical energy
storage device was the Leyden jar, developed simultaneously by German inventor Ewald Georg von

Kleist and Dutch physicist Pieter van Musschenbroek. 6,7. Subsequently, the voltaic battery developed
by Alessandro Volta at the end of the century is a major development in storing electrical energy. 8.
Volta's work was developed, and the French physicist Gaston Planté developed the first rechargeable
battery in history in the mid-19th century. 9–11. At the end of the 19th century, Swedish scientist
Waldemar Ernst Jungner developed rechargeable Silver-Cadmium and Nickel-Cadmium batteries, but
inventor Thomas Alva Edison got the patent for the Nickel-Iron battery within a few years. 12,13.
 Batteries, which are widely used in medium and large-scale energy storage systems today,
were developed in the 20th century. As an example, Klaus Beccu patented Nickel Metal Hydride
batteries in 1970. 14. Lithium-ion battery technology, on the other hand, was founded by Michael
Stanley Whittingham in the mid-1970s and was developed largely by four scientists in the 1980s.
15,16. These scientists are John Goodenough, Michael Stanley Whittingham, Rachid Yazami and
Akira Yoshino 15,17–19. However, the commercialization of these batteries was only realized in 1991
by the Sony company 20. NCA Li(Ni,Co,Al)O2) batteries are the most widely used version today 21.
Following the invention of lithium-ion batteries, potential candidates as cathode materials were being
studied. The LiMPO4 (M: Transition metal) family was the most popular and promising cathode
material candidate. LiFePO4 batteries (LFP) belonging to this family were developed in 1997 by John
Goodenough, who was part of the team that developed lithium-ion batteries 22. Compared to batteries
previously developed for electric vehicles or large energy storage systems, these batteries are more
applicable than small applications when evaluated in terms of cost, safety and energy density 23.
While graphite is used as the anode material in lithium batteries for its reliability, different anode
materials have been investigated in line with the idea that the number of ions leaving the anode can be
increased with a different anode material 24. As a result of studies carried out by Ohzuku et al., it was
presented in 1995 that Li1.33Ti1.67O4 is a zero voltage anode material with a very flat charge and
discharge plateau of around 1.5 volts 25,26. This battery chemistry, called lithium titanate oxide
(LTO), was introduced to the market in the early 2000s with different patents from different
companies. Thanks to its high-speed charge/discharge capabilities compared to other battery
chemistries, it has gained a wide market from wristwatches to electric bicycles and automotive 27–30.
Another matured battery chemistry is lithium sulphide (LiS) batteries that commercialized slowly 31.
Although LiS batteries first emerged as a concept in 1970, the development process was quite slow 32.
It is planned to launch commercial products for systems with high energy requirements, such as
electric vehicles, after 2022, and for a wide range of applications by 2025 31.
 Electric vehicle technology can actually be realized as a whole by bringing together the battery
management system (BMS), powertrain, vehicle control unit, inverter, electric motor and many other
components, just like the organs that make up the human body. Here, the battery and BMS system is a
vital system that electrically connects all other components to work as a whole. There is no complete
consensus on the definition of the battery management system. While BMS can be defined as a system
that measures potential problems with the battery of an electric vehicle 33; It can also be defined as a
mandatory equipment to monitor, control and balance the battery pack 34. BMS can be briefly defined
as an integrated hardware and software body that enables the battery to operate within a specified
safe/optimal operating envelope in terms of parameters such as current, voltage, temperature and SoC.
At this point, the functions of the BMS are a very important fact. The operating parameters of these
functions vary. In most cases, BMS software and hardware depend on the battery type/chemistry for
which it is designed. However, BMS hardware, software and functions differ according to different
applications. For example, battery type determination can be defined as a necessary function for multi-
chemistry battery packs. 35–38.
 In the last decade, the rapid increase in interest in electric vehicles and the development of this
technology have increased sales considerably. The traction batteries of an increasing number of
electric vehicles on the market are reaching the end of their useful life. When the battery packs on an
electric vehicle do not meet the required performance criteria and when they reach the end of their

useful life, using them in areas other than automotive is defined as the second life. Separating batteries
according to their chemistry for use in second-life applications is a problem we face. Considering the
environmental effects in addition to the limited reserves of the rare earth elements and lithium used in
batteries; it is also a fact that batteries that have reached the end of their useful life will be added to the
waste stream with other types of batteries that are currently collected for recycling 39. Second-life
battery capacity is projected to exceed 275 GWh per year by 2030 40. This offers great opportunities
for large-scale energy storage (grid applications, etc.). However, there are many technical, economic
and regulatory challenges that can complicate this. In the recycling process and in the sale of recycled
products, it is required to classify and separate batteries according to their chemical composition 39.
The difficulty in determining the chemistry of the battery is due to the lack of proper labelling,
standard design or clear marking on the package 41. Currently, there are methods such as X-ray
radiographic scanning, computer vision, artificial intelligence and blockchain that are used to separate
and classify batteries according to their chemistry/type. 41–44.
 In this study, a battery chemistry determination method is proposed. This method can be used
as a determination function of battery chemistry in second life applications and multi-chemistry BMS
applications. In addition, it can provide application flexibility for users and manufacturers who want to
replace vehicle batteries. Even for all battery-powered devices, it offers the opportunity to design
without being dependent on battery chemistry. It has the potential to be used as a tool that can be used
in solving technical problems that may be encountered in the future. The crucial feature of the
proposed method is allowing the applications to support battery chemistries that are likely to be
released in the future by means of mere electrical determination. The biggest challenge in the process
of battery type determination is overlapping current and terminal voltage measurements due to
different state of charge, state of health or its chemistry. In order to eliminate this problem, a
separation function based on statistical significance has been developed. With this developed function,
it was concluded that the overlapping values can be successfully separated and thus, it can determine
the battery chemistry with 100% accuracy with the help of machine learning algorithm. It has been
concluded that with this developed function, overlapping values can be successfully separated and thus
battery chemistry can be defined with 100% accuracy with the help of machine learning algorithm.

Method
 Since the scope of the study includes both multi-chemistry battery management systems and
second life and/or recycling processes of batteries, the data of healthy batteries and batteries aged at
different rates are discussed together. Two hundred and fifty thousand samples were collected from
battery cells with five different chemistry. The first four of these are NCA (Nickel Cobalt Aluminium
Oxide), LFP (Lithium Iron Phosphate), NiMh (Nickel Metal Hydride) and LTO (Lithium Titanate
Oxide) batteries, which are currently commercialized and widely used. Due to the increasing number
of patents and commercialization potential in recent years, the fifth battery chemistry has been
determined as LiS (Lithium Sulphide) 45. The data of NCA, LFP, NiMh and LTO batteries were
obtained from the experimental setup prepared for this study in a laboratory environment; The data of
LiS batteries were obtained by simulation prepared in MATLAB/SIMULINK environment.
 The experimental setup consists of the Prodigit 3300F programmable electronic load and the
interface prepared for this study in LabVIEW. Two different loads, whose value is determined by the
software interface, are switched from one to the other during a specified period. Meanwhile, current
and voltage values are recorded at a frequency of 1 Hz. In the study, 10 ohm and 5 ohm resistors were
used as a load to keep the discharge current between 0.1C and 0.2C. In order to stabilize the measured
values at each load, it was kept at that load for 10 seconds and then switched to the other load. The
reason for such a design is to obtain current-voltage pairs for different loads under constant stress
without allowing the battery to relax.

The data obtained are converted into data frames instead of being evaluated as current-voltage
pairs. In this way, small pieces of information reflecting the battery chemistry are brought together to
obtain more accurate results. To begin with, the current values in the first and second loads (10 data in
10 seconds for 1 Hz), then the voltage values are combined side by side to form 40 data frames as in
Figure 1.

 40 Data
 Figure 1 Data Frame

 A model is designed in the simulation environment for the LiS battery data. To do that, a
SIMULINK model developed by Auger et al., which is based on experimental data, is used. 46. The
model was run using the same algorithm as the software designed for the experimental setup. The data
obtained were formed into data frames like the data obtained from experimental studies.
 In its raw form, it is not always possible to determine battery chemistry from current and
voltage data alone, due to overlapping. Therefore, the data needs to be pre-processed. For this, a
separation function has been developed.
 The main goal of the Separation function is to transform overlapping data frames into disjoint
sets so that the classification problem can be solved with full accuracy by using a machine-learning
algorithm. In order to achieve this, in the first place it is necessary to determine which parts of the data
are statistically more significant. The data are summarized with minimum, 1st quartile, median, 3rd
quartile and maximum statistics in Table 1. The conformity of the numerical variables to the normal
distribution on the basis of the group was examined with the Shapiro-Wilk test. The Kruskal-Wallis H
test was used to determine whether there was a statistically significant difference between the groups.
After the Kruskal-Wallis H test, pairwise group comparisons were made with the Conover test 47.
 Table 1. Data statistics

Group Minimum 1st Quartile Median 3rd Quartile Maximum
 Voltage 2,7985 3,1546 3,2026 3,2309 3,6069
 LFP
 Current 0,2930 0,3320 0,4610 0,6460 0,6980
 Voltage 2,0364 2,09636 2,10201 2,1080 2,4245
 LiS
 Current 0,2061 0,2236 0,2994 0,3987 0,4822
 Voltage 1,3946 2,3194 2,3875 2,4737 2,8955
LTO
 Current 0,1550 0,2510 0,3210 0,4840 0,5720
 Voltage 2,4966 3,4121 3,62325 3,8518 4,2417
NCA
 Current 0,264 0,377 0,516 0,727 0,849
 Voltage 0,8683 1,2447 1,2612 1,2845 1,3913
NiMh
 Current 0,116 0,137 0,195 0,262 0,287

 According to the results obtained, it was concluded that the voltage values were statistically
more significant. In this context, a function has been derived that will enable the separation of current
values by amplifying the voltage in the data frames as in Eq.1.

 : , → , , ∀ , ∈ ℝ and ∀ , ∈ ℝ

 ( + ) × 
 ≤ , ( ) =
 2 
 ( ) =
 ( + ) × × 1)
 > , ( ) =
 { 2 

Here,
 : Row number
 : Maximum number data in a frame

 : Offset
 : Upscaling factor
 : Significance factor
 : Downscaling factor

 , = ( , ) 2)
 = ( )
 3)
 Where j is the sample count in a frame, is the total frame count, is collected data and is
processed data. These data are formed a matrix which consist of data frames in columns. Since the
number of samples in a frame is 40, frames of 40 units consisting of current and voltage data can be
written as columns, as in Eq.4. This formation is used in separation functions’ application. Resulting,
the separation function ( ) can be written in matrix format as in Eq.5.

 1,1 1,2 1, −1 1, 
 ⋯ 1,1 … 1, 
 2,1 2,2 2, −1 2, 
 = ⋮ ⋮ → =[ ⋮ … ⋮ ]
 39,1 39,2 39, −1 39, 40,1 … 40, 4)
 [ 40,1 40,2 ⋯ 40, −1 40, ]

 After obtaining the separation function in matrix form, the primary issue for its application is
to determine the parameters used in the function. Since the voltage portions of the overlapping data are
found to be statistically significant, ρ has to take a value other than one. For the determination of the
parameters, the "Firefly Optimization" algorithm, which was first proposed by Xin-SheYang, was
chosen among the metaheuristic optimization algorithms due to its ease of implementation. 48. In this
study, the cost function presented in Eq.6 was used in the firefly optimization algorithm to determine
the parameters of the separation function. Here, the data of five types, namely batteries with five
different chemistry, are used separately. For this, it is designated as Type 1 NCA, Type 2 LFP, Type 3
NiMh, Type 4 LTO and Type 5 LiS.

 
 1
 = ∑ , 
 5)
 =1

 40
 2 2
 = [10 − ∑ (√( 1 ( ) − 2 ( )) − √( 4 ( ) − 3 ( ))
 6

 =1
 2
 2 6)
 − √( 2 ( ) − 5 ( )) )]

 Thanks to the data processed with the separation function, the problem of determining the
battery chemistry has been transformed into a categorization problem. There are many algorithms in
the literature to solve this problem. In this study, artificial neural network (ANN) and decision tree
algorithms among machine learning algorithms were used.
 Artificial neural network is a widely used algorithm especially in solving categorization
problems. Its theoretical background dates back to the 1940s and 1950s 49,50. The first functional
artificial neural network “The Perceptron” was presented by Rosenblatt in 1957 51. Artificial neural
networks, which have developed since then, are now used in many different fields vary from machine
learning, natural language processing, image and speech recognition 52.
 In this study, the artificial neural network was used for the categorization of the processed data
with the separation function. For the design of the network, the number of hidden neurons and output
neurons were chosen as 10 and 5 fixed. Input neuron numbers are used as 2 or 40 depending on
whether the data is used in voltage-current pairs or frame form. In addition, the classification tree
algorithm, one of the machine learning algorithms, was used for the same purpose. The term
classification tree was first used by Breiman et al. (Breiman et al.) in 1984 53. Classification trees
predict answers to questions posed to the data, also known as decision trees. So, decisions in the tree
are tracked from the beginning (root) node to a leaf node containing the response to predict a response.
Classification trees give boolean responses like 'true' or 'false'. According to these responses, they
either branch to other leaf nodes or arrive at the conclusion that includes the data in a category.
 In Figure 2 the workflow of the study is presented. Here Pre-processing and Classification
sections are fully conducted in MATLAB 2021b. Simulation part of the Data acquisition section is
conducted in SIMULINK 2021b. Experimental study part of the Data acquisition section is conducted
with a programmable electronic DC load controlled via LabVIEW software.

Figure 2 Workflow of the study.

Results
 The data collected from experimental and simulation studies are presented in Figure 3 in the
form of current-voltage pairs. Here, more than 500000 measurements are seen that the data of batteries
with different chemistry and aged at different rates from 25 to 2500 cycles overlap with each other in
certain areas. In particular, the data of NCA and LFP batteries and LTO and LiS batteries overlap.
Since the data were collected for two different loads, a pattern separated by two sharp linear lines can
be seen.

 Figure 3 Raw data

 When the data is used in pairs, the artificial neural network design consists of 2 input neurons,
10 hidden neurons and 5 output neuron configuration. With this configuration, the results presented in
Figure 4a were obtained. Here, the distribution of the datasets is as follows; training 70%, verification
15% and testing 15%. With this ANN, no matter how much fine tuning the network parameters are
made, the result cannot be improved; due to overlapping data.
 Similar results presented in Figure 4b were obtained with the decision tree algorithm. When
Figure 4a and Figure 4b are examined together, a result confirming the overlap in Figure 3 is seen.
Here, it is seen that, for NCA and LFP batteries, with artificial neural network algorithm 6406 data and
with the decision tree algorithm 6851 data were incorrectly predicted. For LTO and NCA, 619 data
with the artificial neural network algorithm and 474 data with the decision tree algorithm were
predicted incorrectly batteries. Likewise, for LTO and LiS batteries 1950 data with the artificial neural
network algorithm and 5769 data with the decision tree algorithm were predicted incorrectly. At this
point, it has been observed that the artificial neural network algorithm gives results that are more
accurate for NCA-LFP and LTO-LiS pairs. While the artificial neural network algorithm made 9064
wrong predictions, the decision tree algorithm made 13212 wrong predictions in total.

(a) (b)

 Figure 4 Neural Network and Decision Tree confusion matrix for binary couples.

 By bringing small pieces of information, reflecting the electrical and chemical properties of
the battery together it is intended to obtain precise results. To achieve this, the data is framed like
presented in Figure 1, as the first step of the pre-processing.

 (a) (b)

 Figure 5 Neural Network and Decision Tree confusion matrix for data frames.

 When framed data is used; the results obtained by the artificial neural network algorithm are
presented in Figure 5a, and the results obtained by the decision tree algorithm are presented in Figure
5b. The artificial neural network algorithm made 100 and the decision tree algorithm made 393
incorrect predictions in total.
 In both cases where the data is used in pairs and frames, the results obtained from artificial
neural network and decision tree algorithms show that battery chemistries cannot be detected with
100% accuracy. However, when the data were evaluated as frames, it was observed that the algorithms

made relatively more accurate predictions. As a result, a separation function is proposed to determine
battery chemistry with zero error using framed data.
 Firefly optimization algorithm was used to determine the optimal values of the offset,
significance, upscaling and downscaling factor parameters in the decomposition function. For this,
optimization was made with the cost function given in Eq.6, which was chosen to maximize the
difference between the mean values of the data sets. The parameters calculated according to the
optimization results are = 38.03; = 5.051; = 1.3523 and = 50.502. The graph of the data
processed with the separation function is presented in Figure 6 as current-voltage pairs. Separation
function provides separation of current values since voltage values are statistically more significant. In
this way, the current values corresponding to the same voltage value are separated according to a
certain offset value. Resulting, it becomes easily predictable with classification algorithms. Here, it is
seen in Figure 6 that the data of the overlapping LTO-LiS and NCA-LFP batteries are separated very
clearly. This shows the effectiveness of the separation function.

 Figure 6 The effect of Separation Function.

 For determining the battery chemistry with 100% accuracy, which is the aim of the study, it
has been concluded that using the data sets in frames and pre-processing the data with the separation
function gives satisfactory results. With the pre-processed data, both the artificial neural network and
the decision tree algorithm were run separately. With both, battery chemistry could be determined with
100% accuracy. The results obtained are presented in Figure 7.

(a) (b)

 Figure 7 ANN algorithm and Decision Tree algorithm results after pre-processing data frames with
 the separation function

 Graphic description of the decision tree algorithm, which can detect battery chemistry with
100% accuracy and less computational cost, is presented in Figure 8. The pseudo code of the
algorithm is as follows.
 x1: ( )
Here } representing;
 x2: ( )

node 1 if x1=737.29 then node 3
node 3 if x2=142.609 then node 5
node 4 if x1=757.004 then LFP
node 5 if x1=752.418 then NCA

 Figure 8 Decision Tree Algorithm

 As can be seen from the pseudo code of the decision tree algorithm, this algorithm can be
easily implemented on any hardware, even with integrated circuits with low processing power. How

the decision tree algorithm categorizes the data processed with the separation function is presented in
Figure 9 comparatively. Figure 9a shows the effect of the separation function. Thanks to pre-
processing, it is seen that current-voltage pairs with the same voltage value have different
 ( ) values although they have the same ( ). Thus, the decision tree classification
areas of the batteries can be clearly predicted as in Figure 9b.

 (a) (b)

 Figure 9 Preprocessed data and how it is categorized by Decision Tree algorithm

 One of the important factors is computational cost, since this proposed approach is especially
designed for a new generation function in multi-chemistry battery management systems or for
different commercial applications. For this reason, the method is designed to be run on any electronic
hardware with a simple microcontroller. It has been concluded that categorizing the data processed
with the separation function using the decision tree algorithm is much more applicable due to its low
computational cost and ease of implementation.

Conclusion
 Meeting the increasing energy need for electrical systems in parallel with the developing
technology, storing electrical energy in sufficient density is a problem that has been going on for
decades and waiting to be solved. Great steps towards the solution of this problem were taken in the
20th century. The development of lithium-based batteries has made high-power applications such as
electric vehicles and grid-scale batteries widespread. Due to the increasing environmental awareness,
legislative changes, incentives and critical decisions made in line with the policies of countries to
reduce carbon emissions; the trend towards electric vehicles has increased significantly. In addition,
renewable energy systems, green energy and large-scale storage systems used in grids have also
increased demand on batteries. However, this increase has revealed the problem of using end-of-life
batteries in other applications or processing them in recycling centres. Separation of batteries
according to their chemistry in these processes is a technical problem. In the solution of this problem,
costly, error-prone, difficult to implement and time-consuming methods such as computer vision,
block chain, artificial intelligence and x-ray are used. In addition, studies are carried out in the field of
multi-chemistry battery management systems, which enable the joint use of the superior properties of
different battery chemistries in order to make battery systems more efficient. Moreover, battery
management systems has to have next-generation features to support future battery chemistries. At this
point, determining battery chemistry is a technical challenge.

A robust determination method has been developed in this regard, presented in this study,
applied to a new generation battery management system developed under a Project supported by
Scientific and Technological Research Council of Turkey and Inonu University research fund.
Additionally a patent application has also been made 54,55. In this method, the determination of the
battery chemistry is provided by measuring the current and voltage parameters of the battery only for
20 seconds. It differs from existing methods in terms of using only electrical basic parameters and ease
of application. The main factors that make this possible are framing the collected data and pre-
processing it with the proposed separation function. Because of pre-processing, the current values
corresponding to the same voltage value of batteries with different chemistries are separated from each
other. In this way, the battery chemistry determination problem has been turned into a categorization
problem. Regarding, artificial neural network and decision tree algorithms were applied to solve this
problem. In terms of computational cost, the decision tree algorithm is considerably more
advantageous than the artificial neural network algorithm. It has been concluded that it would be more
appropriate to use this method with the decision tree algorithm in terms of both computational cost and
ease of implementation so that it can be used even in applications with low computational power. As a
result, thanks to this novel approach 100% success has achieved by using mere basic electrical
parameters.
 It is foreseen that adding the proposed method-based determination function into battery
management systems will provide flexibility so that battery system manufacturers can design without
being dependent on battery chemistry. Considering the rapidly increasing use of electric vehicles
around the world, it will be possible to choose traction batteries that have completed their useful life,
regardless of battery chemistry. In general, it allows the use of all existing battery types in the design
and production of all battery-powered systems, and the use of new battery chemistries that will be
released to the market by determining them. In addition, a low-cost, easy-to-apply and fast alternative
solution works with basic electrical parameters so that batteries can be separated according to their
chemistry in the recycling process.

Acknowledgment
 This study was supported by The Scientific and Technological Research Council of Turkey
(TUBITAK) with grant number 2170454 and Research Fund of the Inonu University with project
number FOA-2018-1358. Patent pending 2021/005464.

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