Chemical Papers (2024) 78:8433–8447
https://doi.org/10.1007/s11696-024-03687-w

ORIGINAL PAPER

Exploring the efficacy of eggshell and its pyrolyzed products
for ciprofloxacin removal with machine learning insights
Md. Rezwanul Islam1,2 · Qingyue Wang1 · Sumaya Sharmin1,2 · Weiqian Wang1
Received: 15 March 2024 / Accepted: 8 September 2024 / Published online: 19 September 2024
© The Author(s), under exclusive licence to the Institute of Chemistry, Slovak Academy of Sciences 2024

Abstract
This study investigated the efficacy of utilizing eggshells and their pyrolyzed derivatives, within the temperature rangeof 400–800 °C, as adsorbents for ciprofloxacin (CIP) removal. Experimental data were analyzed using various machinelearning (ML) algorithms, viz. linear regression, random forest, support vector machines, decision trees, and k-nearest
neighbor to predict performance. Results demonstrated that pyrolyzed eggshells at 600 °C (PES-600) exhibited the highest CIP removal rate (86.06 ± 2.25%). Optimal performance was consistently observed at an initial CIP concentration of
125 mg/L, with the order of PES-600 > PES-500 > PES-400 > PES-700 > eggshells > PES-800. Adsorption capacity peakedat pH 5 (5.84 ± 0.1 mg/g), attributed to interactions including hydrogen bonding, π–π interaction, and ion exchange. Scanning electron microscope images revealed that PES-600 had the highest number of pores, resulting in a smoother surface
post-adsorption. Langmuir isotherm model fitting was best for ES, PES-700, and PES-800, while Freundlich isotherm was
suitable for PES-400, PES-500, and PES-600. PES-600 showed the best fit with the pseudo-second-order kinetic model.
Characterization analysis highlighted the significance of functional groups like C = O, C = C, and –CH groups in aromaticrings. ML algorithms demonstrated remarkable performance with an accuracy level of 90.28%. In conclusion, pyrolyzed
eggshells can effectively remove ciprofloxacin (CIP) from wastewater, with optimal performance predicted by the random
forest machine learning algorithm when considering real environmental factors.
Keywords Ciprofloxacin removal · Eggshell · Pyrolyzed eggshell · Adsorption isotherm · Kinetic model · Machine learning

Introduction
The global concern over pharmaceutical residues, particularly antibiotics, has intensified due to their pervasive presence across various environmental compartments (Zhouet al. 2022). Ciprofloxacin (CIP), a widely used fluoroquinolone antibiotic, enters ecosystems through diverse
sources, including sewage, hospital and industrial effluents, livestock farms, and households (Penafiel et al. 2021;
Kim et al. 2020; Huang et al. 2020; Danner et al. 2019).
* Md. Rezwanul Islam
rezwandae@gmail.com* Qingyue Wang
seiyo@mail.saitama-u.ac.jp
1

Graduate School of Science and Engineering,
Saitama University, 255 Shimo‑Okubo, Sakura‑Ku,
Saitama 338‑8570, Japan2

Department of Agricultural Extension, Khamarbari,
Dhaka 1215, Bangladesh

Conspicuously, wastewater effluents from pharmaceuticalplants can contain increased CIP concentrations, reachingas high as 31 mg/L (Larsson et al. 2007). Hospital effluents in Spain have shown even higher concentrations, withlevels reaching 13 g/L (Danner et al. 2019). Moreover, thehigher concentrations of CIP ranging from 0.57 to 84.4 mg/
kg were recorded in soil of China (You et al. 2018). As CIP
is incompletely absorbed by human and animal bodies, theunused portion enters water and soil systems, contributing
to serious environmental pollution (Huang et al. 2020; Begget al. 2000).
CIP concentrations, even at low levels, in water bodies
may pose risks to human health and have adverse effects
on ecosystems, fostering bacterial resistance (Lapworth
et al. 2012). Due to its acute and chronic toxicity, CIP is
recognized as an emergent pollutant with the potential toharm not only ecosystems but also human health (Espinosa
et al. 2015; Li et al. 2014). Consequently, the removalof this toxic pollutant from the ecosystem is imperative.
Among the various methods available, adsorption stands
Vol.:(0123456789)

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out as the most efficient for CIP removal, offering a costeffective, easy, efficient, and environmentally friendly
solutions (Dao et al. 2020; de Andrade et al. 2018; Alahabadi et al. 2023).
CIP was significantly removed from wastewater using
various adsorbents, including olive leaf biomass (Alakayleh et al. 2024), bamboo biomass activated carbon (Guo andWang 2023), banana peel biochar (Patel et al. 2021), PET
microplastics (Enyoh and Wang, 2023), modified bamboo
waste biochar (Meshasha et al. 2022), and sewage sludge
and bamboo waste biochar (Li et al. 2020).
Considering that global egg production reached 86 million tons in 2021, the annual disposal of nearly 9.5 million
tons of eggshells poses a significant environmental concern(FAO 2023). Due to their easy availability, biodegradability,
high porosity, surface properties, and low cost, eggshells
have the potential to be efficient adsorbents. In contrast,
eggshells, composed of 94% calcium carbonate, 1% magnesium carbonate, and 4% organic materials, have demonstrated effectiveness as adsorbents for various contaminants,
including heavy metals (Makuchowska-Fryc 2019; Ozcan
et al. 2018), Hg (II) (Foroutan et al. 2019), Cd (II) (Menget al. 2022; Yehia and Elmakabati 2019), Pb (Liu et al. 2022;
Wang et al. 2018), Cr (Katha et al. 2021), As (V) (Akramet al. 2022), Ni (II) (Kristianto et al. 2019), Cu (Ahmad et al.
2012), phosphorus (Sun et al. 2022), phosphate (Park et al.
2022; He et al. 2020), dyes and metallic ions (de Oliveira
Zonato et al. 2022), monoethylene glycol (Kumi et al. 2020),
toluene and xylene (Kumi et al. 2022), methyl violet (Foroutan et al. 2019), and organic pollutants (Liu et al. 2020).
The environment is a complex system where CIP concentrations and pH levels can vary significantly based on
location, making it challenging to replicate every real-world
scenario in a laboratory setting. To address this, machinelearning algorithms offer an effective solution for predicting
environmental outcomes by identifying optimal models frombatch experiments. These algorithms are particularly valuable for optimizing adsorption efficacy, as they can predict
the best experimental parameters under varying conditions.
Moreover, they excel at analyzing complex datasets, uncovering hidden patterns that traditional methods might miss,and improving accuracy by minimizing human error. Our
hypothesis is that eggshells can effectively absorb emerging
contaminants like ciprofloxacin (CIP). While many studies
have focused on removing CIP using different biomasses, noresearch has yet explored the use of eggshell and its pyrolytic products for this purpose. Furthermore, no studies havecompared batch experiment data specifically for prediction
purposes. Therefore, this study innovatively investigates
the removal efficacy of ciprofloxacin (CIP) using eggshell
powder and its pyrolyzed derivatives as novel adsorbents,
employing machine learning algorithms to address theglobal issue of pharmaceutical residues in aqueous solutions.

Chemical Papers (2024) 78:8433–8447

Methodology
Materials and chemicals
Eggs were purchased from the Maruetsu Urawa Okubo
branch (35.8678°N, 139.6089°E) in Saitama, Japan. Afterconsumption, eggshells were collected, washed, and dried.
Following this, the eggshells were ground and sieved by
the IKA 2870900 MF 10.1 Cutting-Grinding Head (IKA®
Japan K.K., Osaka, Japan), using a 0.25 MF sieve. The
proximate and ultimate analysis results of the raw eggshellhave been previously documented in the work of Islamet al. (2023).
Ciprofloxacin was collected from TOKYO Chemical Industry in Japan. The requisite chemical solutions,
including 10% hydrochloric acid (HCl) and 0.1 M sodiumhydroxide (NaOH), were supplied by Wako Pure Chemical Industries, Co., Ltd, Japan, while sodium chloride wasprocured from Fujifilm Wako Pure Chemical Corporation,
Japan. Argon (Ar) with a purity exceeding 99.9995%,
obtained from Suzuki Shokan Co., Ltd, Japan, was usedas the inert gas throughout the experimental process.

Preparation of adsorbents
The eggshell powder underwent the pyrolysis process
within a laboratory-scale fixed-bed reactor. To achievethis, eggshell powder was carefully positioned in the reactors and subjected to heating at temperatures of 400 °C,
500 °C, 600 °C, 700 °C, and 800 °C, each accompanied bya heating rate of 10 °C/min. Different pyrolysis temperatures are selected to optimize the breakdown of organic
matter and produce high-quality pyrolysis products that
could enhance adsorption. The sample was maintained ateach specified temperature for a duration of 30 min. To
ensure an inert environment, Argon gas with a flow rateof 100 mL/min was employed. Following the pyrolysis
phase, the resultant pyrolyzed eggshell was collected fromthe reaction tube after cooling. The final product, the pyrolyzed eggshell, was stored in sealed containers, ensuring
the preservation of its quality for further analysis.

Characterization of adsorbent
Scanning electron microscopy (SEM) utilizing the SU1510
instrument from Hitachi Co., Ltd, Japan, was employed
to scrutinize the morphological characteristics of boththe eggshell and its pyrolytic derivatives. Concurrently,
Fourier Transform Infrared spectroscopy (FTIR) wasapplied for the interpretation of surface functional groups

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8435

present in PES-600 before and after adsorption. The FTIR
analysis was performed using the IR-6100 apparatus from
JASCO Co., Ltd, Japan, covering the spectral range of
400–4000 ­cm−1 at a resolution of 4 ­cm−1.

Batch adsorption studies
Batch adsorption experiments were conducted, utilizing
20 ± 0.1 mg samples of both eggshells and their pyrolyzed
counterparts. A diverse range of ciprofloxacin (CIP) initialconcentrations—25, 50, 75, 100, 125, and 150 mg/L—were
employed to expose the concentration-dependent impact on
the adsorption process. Furthermore, various contact times(4, 8, 12, 16, 20, and 24 h.) were systematically investigated,focusing specifically on pyrolyzed eggshell (600 °C) and a
CIP concentration of 125 mg/L. To assess the influence ofpH on the adsorption process, distinct pH levels (3, 5, 7,
9, and 11) were considered in conjunction with pyrolyzed
eggshell (600 °C) and a CIP concentration of 125 mg/L.
The prepared sample solutions were securely sealed andsubjected to agitation in a light-protected shaker at a controlled speed of 200 rpm for 1 h. Preceding the analyticalassessment, the samples obtained from the batch adsorption
experiments underwent filtration utilizing a 0.45 μm membrane. To fortify the robustness of the data, the experiments
were replicated thrice, thereby ensuring the accuracy andreliability of the results through error calculation. Quantification of CIP concentration was achieved using a UV–VIS
spectrophotometer (UVmini-1240, Shimadzu, Japan) at
λmax = 285 nm. The point of zero charge (pHzpc) is themeasurement of pH at which the surface of the adsorbent
possesses no net charge. Nine solutions of a 0.01 M NaClwith pH 3–11 were prepared having three replications. Pyrolyzed eggshell (600 °C) was added to each solution, allowing
it to shake for 1 h (200 rpm) equilibrate. Then the final pH
was measured. The change in pH (ΔpH = final pH − initialpH) was measured against the initial pH. The pHzpc corresponds to the initial pH where ΔpH = 0.
The CIP removal efficiency (%) and adsorption capacity(mg/g) were assessed using the equations as follows (Hamadeen and Elkhatib 2022):

Adsorption isotherms
Adsorption isotherms like Langmuir and Freundlich were
employed to explain the adsorption process. The Langmuir
isotherm indicates a dynamic adsorption process on a uniform and non-interacting surface, governing the molecularinteractions at the interface of the absorbent and adsorbate.
The linearized equation of Langmuir adsorption isotherm is
as follows (Guo and Wang 2019):

1
1
1
=
+
qe
KL qm Ce qm(3)

1
1 + KL Co

(4)

RL =

In this equation, Ce (mg/L) signifies the equilibrium concentration of the CIP solution, qe (mg/g) denotes the amountof adsorbate adsorbed at equilibrium, KL stands for the Langmuir constant (L/mg), reflecting the intensity of adsorption,qm (mg/g) represents the adsorption capacity of the adsorbent, and Co (mg/L) denotes the initial concentration of the
CIP solution. Additionally, RL is a dimensionless constant(known as the separation factor) retained to clarify whether
the adsorption process is favorable (0 < RL < 1), unfavorable(RL > 1), or irreversible (RL = 0) (Foo and Hameed 2010).
Conversely, the Freundlich isotherm speculates that
adsorption takes place on a heterogeneous surface characterized by a multilayer adsorption mechanism. The Freundlich
equation is as follows (Baláž et al. 2005):

ln qe = ln KF +

1
ln Ce
n

(5)

where KF represents the Freundlich constant ­(mg1−(1/n)L1/n/g)
and the Freundlich exponent (n) signifies the adsorption
intensity.

Kinetic models(2)

The pseudo-first order (PFO) kinetic model is a widelyused approach in the analysis of adsorption kinetics. In thismodel, the rate of adsorption is directly proportional to thedifference between the initial adsorbate concentration (Co)and the concentration at time t (Ct). The general form of thepseudo-first-order equation is as follows:()
ln qe − qt = ln qe −K1 t(6)

In these equations, Co (mg/L) and Ce (mg/L) represent
the initial concentration and equilibrium concentration of
CIP, respectively. M (mg) denotes the mass of the adsorbent,
and V (mL) signifies the volume of the CIP solution.

where qe represents the equilibrium amount of soluteadsorbed, qt denotes the amount of solute adsorbed at timet, and k1 stands for the rate constant associated with pseudofirst-order adsorption.

Removal efficiency (% ) =Co − Ce× 100
Co

Adsorption capacity (mg/g) =(Co − Ce) × V
M

(1)

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Chemical Papers (2024) 78:8433–8447

The pseudo-second-order (PSO) kinetic model is anotherwidely applied model in the analysis of adsorption kinetics,
providing a more realistic representation of the adsorption
process compared to the pseudo-first-order model. In thepseudo-second-order model, the rate of adsorption is proportional to the square of the difference between the initialadsorbate concentration (Co) and the concentration at time t(Ct). The equation is as follows:

t
1
1
=
+
qe
K2 qe 2 qe

(7)Here, k2 signifies the pseudo-second-order rate constant,qe represents the amount adsorbed at equilibrium, qe2 isanother term associated with the amount adsorbed at equilibrium, and k2 is the rate constant linked to pseudo-secondorder adsorption.

Development of machine learning algorithms
The analysis of machine learning (ML) algorithms was conducted to predict adsorption performance through data processing, model training, and regression using Python withina Jupyter Notebook environment (Fig. 1). One-hot encoding was applied to polytomous variables (such as eggshelland its pyrolyzed products). The workflow began with datanormalization using min–max scaling to mitigate potential

biases arising from variations in pH data scales. This scaling
ensured that all feature columns, including independent anddependent variables such as the type of adsorbent, initialconcentrations, pH, contact time, and shaking speed, were
harmonized, thereby preventing bias during modeling. Following feature selection, the dataset was divided into training (80%) and testing (20%) subsets to evaluate the generalization capabilities of the model.
Five machine learning algorithms—linear regression(LR), random forest (RF), support vector machines (SVM),
decision trees (DT), and k-nearest neighbor (k-NN)—wereexamined to predict adsorption performance against various
independent variables. The selection of LR, RF, DT, SVM,
and k-NN algorithms for predicting adsorption data is strategically based on their diverse capabilities: LR provides abaseline for linear relationships, RF and DT capture complex nonlinear interactions, SVM excels in high-dimensional
spaces, and k-NN effectively identifies local data structures.
Together, these algorithms offer a comprehensive and robust
approach to modeling adsorption behavior across varied
datasets. The actual performance’s average value was compared with the predicted value of the five machine learning algorithms using the 20% test subset. Performance wasassessed using the R-squared score metric. Model optimization was achieved through cross-validation with 5 folds toensure robust performance and mitigate overfitting.

Statistical analysis
Data
Batch Experiment (CIP adsorption)
Independent variables
Pyrolyzed products
Initial Concentration
pH
Contact time
Shaking speed
Dependent variables
Removal efficiency

Value
ES, PES400, PES500, PES600, PES700, PES800
25, 50, 75, 100, 125, 150mg/L
3, 5, 7, 9, 11
1, 4, 8, 12, 16, 20, 24 hours
200rpm

Python within a Jupyter
Notebook environment
One hot encoding
Data Normalization
Feature Extraction

Results and discussion

Optimization
Gradient
Descent

Model

Training
Data splitting
Model fitting
Hyperparameter tuning
Training
Evaluation
Validation

The statistical analysis of the data was conducted utilizing
IBM SPSS Statistics 20, employing statistical methodologies. Graphical representations were generated using two
distinct software packages: OriginPro 2018 and Microsoft
Excel.

Predicting the algorithms
Linear Regression
Random Forest
Decision Tree
Support Vector Machines
K-Nearest Neighbor

Evaluation

Cross validation
Performance metrics
Accuracy

Predicted PerformanceFig. 1  DOME recommendations flow chart for machine learningalgorithm prediction

Characterization of adsorbent
Color
The pyrolyzed eggshell exhibited a range of colors, reflecting the temperature-dependent transformations as mentionedin Fig. 2. As the pyrolysis temperature increased, the colorof the pyrolyzed eggshell shifted from brown (PES-400) tobrownish black (PES-500), then to black (PES-600), followed by whitish black (PES-700), and finally to an offwhite color (PES-800). At lower temperatures, organic
matter undergoes partial decomposition, resulting in the
production of biochar with higher concentrations of volatileorganic compounds and a lighter color. Conversely, as thetemperature rises, more extensive decomposition occurs,

Chemical Papers (2024) 78:8433–8447

Fig. 2  Eggshell powder and its pyrolytic products a eggshell powder (ES), b pyrolyzed eggshell (400 °C) (PES-400), c pyrolyzed
eggshell (500 °C) (PES-500), d pyrolyzed eggshell (600 °C) (PES-

8437600), e pyrolyzed eggshell (700 °C) (PES-700), f pyrolyzed eggshell(800 °C) (PES-800)

leading to the formation of carbon-rich biochar with darker
hues. Additionally, it was observed that the ash content
increased with increasing temperature. This observation isconsistent with the findings of Pimchuai et al. (2010), whoreported that higher pyrolysis temperatures facilitate devolatilization but also lead to an increase in ash content.
Surface morphology
Surface of the studied material was observed by SEM
mentioned in Fig. 3. SEM images exhibit highly porous,
wrinkles, rough surface that provide enough sorption
places and pores (Akram et al. 2022). A lot of pores ofvarious sizes and shapes of the raw adsorbent that is
responsible for binding of sorbate onto the surface ofthe sorbent. Porous and irregular surfaces contribute to
the total surface area that acts as the active site for theadsorption. The image showed that the maximum number of pores was observed with the PES-600 followed by
PES-500, PES-400, respectively. The number of the pore
was increased with increasing temperature up to 600 °C.
The result is comparable with the findings of Onay (2007)
who mentioned highest surface area (­ m2/g) of the biochar(600 °C) over biochar (700 °C). Higher pyrolysis temperature caused higher devolatilization, resulting in more porevolume in the samples and, in turn, a greater surface area
up to 600 °C (Onay 2007). The number of micropores was
reduced with the increased temperature 700 °C and 800 °C
may be due to the partially pulverized, causing surface

Fig. 3  Scanning electron microscopy images of a eggshell powder (ES), b pyrolyzed eggshell (400 °C) (PES-400), c pyrolyzed
eggshell (500 °C) (PES-500), d pyrolyzed eggshell (600 °C) (PES600), e pyrolyzed eggshell (700 °C) (PES-700), f pyrolyzed eggshell(800 °C) (PES-800) before adsorption

8438

melting and further densification, formed layered structure. A similar trend was also reported by Fu et al. (2012),
who described that the Smicro/Stotal (%) decreased withincreasing temperature from 700 °C to 1000 °C. Onay
(2007) also reported lower surface area of the biochar(700 °C) over biochar (600 °C). Batool et al. (2018) also
noted the similar type of findings.
Functional groups
The functional group of the eggshell, primarily composedof calcium carbonate, exhibits characteristic vibrationalmodes in the infrared spectrum, typically manifesting as
absorption bands at specific wave numbers, with carbonate ions ­( CO 32−) contributing to distinct peaks around1420–1480 ­cm−1 (bending), hydroxyl (OH) stretching bandat 3500-4000 ­cm−1, and C–H bending at 3034 ­cm−1 and867 ­cm−1, respectively. The bands at 3034 and 867 ­cm−1
represent C-H vibration, indicating the existence of theorganic layers, built from amino acids in the eggshells as
mentioned in Fig. 4. Calcium carbonate (CaCO₃) undergoes partial thermal decomposition, resulting in the formation of calcium oxide (CaO) and carbon dioxide (CO₂)
observed in PES-400 and PES-500. The process involves
the breakdown of the carbonate functional group that is
also described by the FTIR spectrum. With increasing
temperatures, the carbonate functional group diminishes
for pyrolyzed eggshell at more than 5­ 000C temperature.
As a result, the characteristic absorption bands associatedwith calcium carbonate become less prominent or disappear in the infrared spectrum as the temperature increases.
The chemical reaction can be represented as follows:­C aCO 3 → CaO + ­CO 2. The result was also comparablewith findings of Tizo et al. (2018), Ajala et al. (2018) and
Torit and Phihusut (2019) who also noted characteristics
asymmetric stretch of ­CO3−2 in the band in the spectrum.

Chemical Papers (2024) 78:8433–8447

Effect of initial concentrations and pyrolyzed
temperature on CIP removal
The effect of initial concentrations in CIP removal is mentioned in Fig. 5. Results revealed that the initial CIP concentration had a significant role in adsorption of CIP by
eggshell and its pyrolyzed products. CIP solutions with initial concentrations of 25, 50, 75, 100, 125, and 150 mg/L
were considered for the adsorption studies. Results revealed
that, as initial concentration was increased, the removal efficiency of the CIP increased to 125 mg/L and then it was
decreased. Li et al. (2018) also reported highest removal of
CIP using used tea-leaf biochar with 200 mg/L initial concentration. The results revealed that the highest CIP removal
rate was 86.06 ± 2.25% attained with the PES-600. PES-600
showed highest removal efficiency against all considered
initial concentrations. It might be due to the formation of
a porous structure with increased surface area. The SEM
image indicated the number of the pores was higher in PES600 than other adsorbents. Though Wang et al. (2015) also
reported that the surface area was reduced after 750 °C of
the activation temperature. However, this enhanced structure
facilitates stronger interactions, such as hydrogen bondingand π-π interactions, leading to a more effective removalof ciprofloxacin from water solutions. Li et al. (2018) also
reported highest removal of CIP using used tea-leaf biochar at 450 °C. In all cases, initial concentration (125 mg/L)
showed highest performance and followed the order of PES600 > PES-500 > PES-400 > PES-700 > ES > PES-800. The
study conducted by Li et al. (2018) and Yang et al. (2010)
yielded consistent findings regarding the impact of CIP (ciprofloxacin) and Z­ n2+ on the adsorption of CIP and zinc. The
comparative findings of the adsorption capacity of eggshelland other adsorbents are mentioned in Table 1.
A noteworthy observation from the comprehensive
analysis of the adsorption process is the gradual increasein the equilibrium adsorption capacity (mg/g) across90

Removal efficiency (%)

80
70
ES
PES-400
PES-500
PES-600
PES-700
PES-800

60
50
40
25

50

75

100

125

150

Concentraon (mg/L)

Fig. 4  FTIR spectra of eggshell and its pyrolyzed products before
adsorption

Fig. 5  Effect of initial concentration on CIP removal by eggshell
powder and its pyrolyzed products

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8439

Table 1  Comparison of removal efficiency with other studies
SerialAdsorbents

Contaminants

Recovery (%)

References

1
2

Eggshell
Eggshell

Pyrolyzed eggshell (450 °C)
Pyrolyzed eggshell (500 °C)
Eggshell

6
7
8
9
10
11
12
13

Modified eggshell
Bamboo biomass activated carbon
Sewage sludge and bamboo waste biochar
Modified bamboo waste biochar
Olive leaves biomass
PET Microplastics
Banana peel biochar
Eggshell and pyrolyzed eggshell (400–800 °C)

100
97
95
94
80
96
94
96
97
99
95
89.9
97.7
95
96
77.9
96.29
81
48.65–93.55

Yehia and Elmakabati (2019)
Mashangwa (2016)

3
4
5

Cd (II)
Pb
Cu
Ni
Zn
As
Cd
Reactive Blue 198 (RB)Direct Black 22 (BD)Cd
Cr
Monoethylene glycol (MEG)
CIP
CIP
CIP
CIP
CIP
CIP
CIP

all examined adsorbents, as detailed in Supplementary
Table 1 (ST-1). This consistent trend implies a positive
correlation between concentration and adsorption capacity. According to Srivastava et al. (2006), the higher concentration of CIP serves as a potent driving force, creatinga favorable adsorption concentration gradient. This gradient facilitates the transfer of CIP between the aqueousand solid phases, overcoming resistance and enhancing
adsorption efficiency. However, it is essential to note thatthe subsequent decrease in the removal efficiency of CIP
at higher initial concentrations poses an intriguing challenge. This phenomenon may be attributed to the saturation of binding sites on the eggshell and its pyrolyzed
products. As highlighted by Yang et al. (2010), the limitations in binding sites could hinder effective adsorption,
leading to a decline in removal efficiency.

Table 2  Statistical parametersof isotherm models

Adsorbents

R
ES
PES-400
PES-500
PES-600
PES-700
PES-800

0.972
0.911
0.904
0.898
0.976
0.998

Kumi et el. (2020)
Guo and Wang (2023)
Li et al. (2020)
Meshasha et al. (2022)
Alakayleh et al. (2024)
Enyoh and Wang (2023)
Patel et al. (2021)
This study

Adsorption isotherm
To gain deeper insights into the adsorption process, theexperimental results underwent rigorous fitting using two
distinct isotherm models: Langmuir and Freundlich. The
findings, presented in both Table 2 and Supplementary Fig. 1(SF-1), revealed compelling patterns. Among the materials
investigated, ES, PES-700, and PES-800 exhibited the mostfitting results with the Langmuir isotherm model with ­R2values of 0.9723, 0.9763, and 0.9976, respectively. The ­RLvalue also implies that the adsorption process is favorable(Foo and Hameed 2010). This suggests that the Langmuiradsorption model is most appropriate for understanding theadsorption process in our study. According to this model,
CIP adsorption on eggshell and it pyrolyzed products predominantly occurs through monolayer adsorption, aligningwith previous research (Chandrasekaran et al. 2020). Similar
outcomes were reported in studies involving activated carbon magnetized by iron (III) oxide magnetic nanoparticles

Langmuir isotherm model2

Akram et al. (2022)
Meng et al. (2022)
de Oliveira Zonato et al.
(2022)

Freundlich isotherm model

KL

RL

R2

Kf

1/n

8.66
43.967
4.163
7.456
3.238
43.9760.00200.00030.00390.00210.00500.0003

0.969
0.944
0.929
0.925
0.926
0.992

0.013
0.00040.042
0.01680.04280.05711.71012.9153
1.5566
1.95611.36681.1675

8440(Al-Musawi et al., 2021) and adsorbents derived frompolyethylene terephthalate microplastics (Enyoh and Wang2023).
On the contrary, this analysis indicates that PES-400,
PES-500, and PES-600 align best with the Freundlich isotherm models, boasting R2 values of 0.9438, 0.929, and0.9252, respectively. Furthermore, the higher 1/n value(more than 1) was also noted as indicative of a favorable
adsorption process. In the study by Zhang et al. (2016),
it was observed that a higher Kf value corresponds to anincreased sorption capacity, highlighting the adsorbents’ability to adsorb a greater amount of solute per unit mass.
Additionally, the higher 1/n value suggests a more uniform
distribution of adsorption site energy on adsorbents likePES-400, PES-500, and PES-600. As the 1/n value comparatively exceeds 1 in these adsorbents, the adsorption process
tends to become more linear, signifying a reduced influenceof solute concentration on the adsorption behavior compared to other adsorbents. Upon reaching a temperature of
600 °C, PES-400, PES-500, and PES-600 surfaces undergo
significant development of surface micropores, mesopores,
and macropores. The heterogeneous system adheres to the
Freundlich adsorption isotherm, demonstrating enhanced
adsorption capabilities on the diverse surfaces of PES-400,
PES-500, and PES-600. This model is also found to be applicable in the adsorption of cadmium onto Saccharum arundinaceum surfaces, as evidenced by Batool et al. (2018).

Effect of pH on CIP adsorption
The pH emerged as a pivotal factor influencing the adsorption capacity of the absorbent, as depicted in Fig. 6A and
B. A gradual increase in adsorption capacity was observedat pH 3, reaching its pinnacle (5.84 ± 0.1 mg/g) at pH 5,followed by a subsequent gradual decline. Beyond pH 7,
the adsorption capacity exhibited statistically insignificant variations. Wang et al. (2015) also reported that theeffect of pH on the adsorption of CIP is not so significant. Similar trends were mirrored in the CIP removal rate
(93.55 ± 1.62%). These results align with the study by Li
et al. (2018), reinforcing a consistent pattern of removal efficiency and adsorption capacity. CIP exists in three forms­(CIP+, ­CIP−, ­CIP±/CIP0) in solution, dictated by two dissociation constants (pKa, 5.9 and 8.9) (Carabineiro et al.
2012). The ­pHZPC of PES-600, as revealed in our study, is9.16 (Fig. 6C). This indicates that the surface of PES-600 is
positively charged when pH ≤ ­pHPZC and negatively charged
when pH > ­pHPZC. Notably, lower adsorption efficiency at
pH 3 and 11 may be attributed to electrostatic repulsion
between CIP and pyrolyzed eggshell, sharing the same stateof charge. Maximum removal was observed at pH 5, likely
due to π-π interactions. Despite the potential for electrostatic
repulsion between the positively charged ciprofloxacin andChemical Papers (2024) 78:8433–8447

PES-600 at this pH, secondary interactions such as hydrogenbonding and van der Waals forces may play a significant rolein enhancing adsorption (detailed in Fig. 6C). This findingaligns with Guo and Wang (2023), who reported the highest
CIP removal (97.7%) and adsorption capacity using activated carbon from Moso bamboo at pH 2.

Effect of contact time
The batch adsorption experiments involved a 125 mg/L CIP
solution, and the relationship between the adsorption time
and the removal efficiency of PES-600 is illustrated in Fig. 7.
The removal percentage of CIP exhibited a gradual increase
with prolonged contact time, reaching a plateau after 20 h, atwhich point no further change was observed. As the contact
time increases, CIP molecules progressively fill the available
spaces of PES-600 until equilibrium is reached (Yang et al.
2009). It is noteworthy that, despite variations in removal
efficiency over time, these changes were not deemed statistically significant. This suggests that the observed fluctuations
fall within the territory of experimental variability and may
not carry meaningful or conclusive statistical significance.
According to Enyoh and Wang (2023), similar trends werereported in the context of PET MPs and CIP molecules,
where the removal rate remained unchanged after 12 h. Thisalignment with existing literature adds weight to our findings
and underscores the stability and consistency of the observed
adsorption behavior over the investigated time frame.

Kinetic models
Figure 8 indicates the pseudo-first-order (PFO) and pseudosecond-order (PSO) kinetic models, respectively. The data
reveal that the studied reaction does not conform to a simple
first-order mechanism, as evidenced by the lack of fit with
the PFO model. However, a notable fit with the PSO model
is observed, supported by a higher coefficient of determination (R2 = 0.999). This suggests that the reaction involves
more complex interactions or multiple steps compared to a
pseudo-first-order reaction. The kinetic model of pseudosecond order (PSO) implies that the adsorption process
entails the chemical interactions between the pyrolyzed eggshell and CIP molecules. The chemical interaction is characterized by valence forces and electron exchange between theadsorbent and adsorbate, indicating a more intricate process.
This behavior is attributed to ions on the surface of the pyrolyzed eggshell, which govern the ion exchange process. The
insights gained from the kinetic analysis provide a deeper
understanding of the mechanisms governing the adsorption process, highlighting its complexity. This information
is instrumental for further exploration and application inenvironmental remediation efforts, offering a basis for optimizing adsorption processes involving pyrolyzed eggshellChemical Papers (2024) 78:8433–8447

Removal efficiency (%)

96

A
a

94

b
92

b

b

b

90
2

4

6

8

pH

10

12

Adsorpon capacity (mg/g)

6

B

a
5.9
5.8

b

b

b

b

5.7
5.6
5.5
2

4

6

pH

8

10

12

C

Zeta potential (mV)Fig. 6  A Effect of pH on
removal rate and adsorption
capacity of CIP by PES-600,
B effect of pH on removal rate
and adsorption capacity of CIP
by PES-600, C zeta potential of
PES-600 at different pH values[At Co = 125 mg/L, adsorbent dose 20 ± 0.1 mg, room
temperature] [Mean values on
the line graphs with uncommon
superscript letters differ significantly (p < 0.05)]

8441

8442

Chemical Papers (2024) 78:8433–8447

and CIP molecules. Alakayleh et al. (2024) also mentioned
similar type of findings of fitting with PSO for the adsorption of CIP on the olive leaves biomass.

85

Removal efficiency (%)

Removal efficiency

Adsorbent properties after adsorption
84

Surface morphology
Surface of eggshell biochar and its pyrolytic products after
adsorption is reported in Fig. 9. Adsorption capacity of theeggshell powder and its pyrolyzed eggshells varied withpore space distribution developed in the pyrolytic products
during carbonization process. As the PES-600 contained
the pore space with different size and shape translating theactive site for the adsorption. After adsorption, the craggy
surface of the adsorbent becomes smoother. The finding iscomparable with Zadeh et al. (2018) who studied adsorptionof Cd (II) using eggshell.

83

82

0

240

480

720

960

1200

1440

Time (min)Fig. 7  Removal efficiency of CIP using PES-600 at different contact
time [At Co = 125 mg/L, adsorbent: PES-600, adsorbent dose 20 mg,
room temperature]

Functional groups after adsorption
The functional group of PES-600 manifests as absorption
bands at specific wavenumbers, with the C–C stretching
band (aromatic or aliphatic ethers) at 1013.4094 ­cm−1,

Fig. 8  Kinetic model a pseudofirst order, b pseudo-second
order

Pseudo first order kinetic model

300

Pseudo Second Order Kinetic model-1.5

y = a + b*x
Equationln (qe-qt)
Plot
No W eighting
W eight-1.11146 ± 0.149
Intercept-0.0012 ± 1.6025
Slope
0.10355
Residual Sum of Squ-0.96622
Pearson's r
0.93358
R-Square (COD)
0.91697
Adj. R-Square

-2.5

-3.0

t/qt

ln (qe-qt)

200

-2.0

0

240

480

720

Time (min)y = a + b*x
Equationt/qt
Plot
No W eighting
W eight
2.7419 ± 0.66241
Intercept
0.18636 ± 7.08708
Slope
2.02514
Residual Sum of Squ
0.99997
Pearson's r
0.99994
R-Square (COD)
0.99993
Adj. R-Square

100

960

1200

1440

0

0

240

480

720

Time (min)Fig. 9  Scanning electron microscopy images of a pyrolyzed eggshell (600 °C) (PES-600) before and b after adsorption

960

1200

1440

Chemical Papers (2024) 78:8433–8447

Proposed mechanism of adsorption process of CIP
by pyrolyzed eggshell

OH(Hydroxyl)

PES-600 (Before Adsorption)
PES-600 (After Adsorption)

Transmiance

160
CO3
(Carbonate)

C-C stretching(Aromac or
aliphac ethers)

140

CO3
(Carbonate)

120

100

80
400

800

1200

1600

2000

2400

2800

3200

3600

4000

Wave number (cm-1)

Fig. 10  FTIR spectra of pyrolyzed eggshell-600 before and after
adsorption

carbonate ions ­(CO32−) contributing to distinct peaks around1600 and 2900 ­cm−1, and the hydroxyl (OH) stretching bandat 3350 ­cm−1, respectively, mentioned in Fig. 10. These
results were also comparable with the findings of Tizo et al.
(2018), Ajala et al. (2018), and Torit and Phihusut (2019),
who also noted the characteristic asymmetric stretch of­CO32− in the spectrum. Similar trends were observed fromthe pyrolyzed eggshell at 600 °C, mentioning peaks at 873­(CO3), 1413 (­ CO3), and 2981 (­ CO2), respectively (Demiret al. 2017). However, the peaks shifted after the adsorption of CIP by PES-600. CIP ions are likely to be affixed
to these active sites through either electrostatic attraction
or a complexation mechanism. The reduction of the intensity of C
­ O3 in the FTIR spectrum suggests the possibilityof forming a Ca–NH2 complex between the N­ H2 functionalgroup and the ­CO3 ion of PES-600. Additionally, the intensity of the hydroxyl ion (OH) suggests the formation of acomplex between two ciprofloxacin molecules, forminga –CO–Ca–CO– bond. Similarly, electrostatic attraction
occurs between sorbate ions and carbonate groups, as indicated by studies such as Daraei et al. (2014) and Putra et al.
(2014).
Fig. 11  Proposed mechanism ofadsorption of CIP by pyrolyzed
eggshellIn its original state, ciprofloxacin possesses a pKa (aciddissociation constant) of approximately 5.9. This property
signifies that in environments with a pH below 5.9, many
ciprofloxacin molecules exist in a protonated state. In thisprotonated state, the nitrogen within the piperazine ring of
ciprofloxacin undergoes protonation, resulting in the creation of a positively charged species shown in Fig. 11. In atheoretical scenario involving the interaction of calcium withthe positively charged amino group, it is conceivable that a
Ca-NH2 complex could be formed through an ionic interaction. Moreover, there is a plausible interaction between calcium carbonate (­ CaCO3) and the carboxylic functional groupof ciprofloxacin (CIP), potentially leading to the formationof a complex characterized by the COO–Ca–COO arrangement, which might involve consecutive ciprofloxacin molecules. This exploration into potential interactions provides
insight into the chemical dynamics that may occur between
ciprofloxacin and calcium, shedding light on the formationof various complexes under specific pH conditions.

77

Average removal efficiency (%)

180

8443

76
75
74
73
72
71
ActualRemovalEfficiency

LinearRegression

RandomForestDecision TreeK Nearest
Neighbour

Support
VectorMachineExperimental and machine learning algorithms

Fig.12  Comparison of actual removal efficiency against machinelearning algorithms

8444

Comparison of actual removal efficiency
against machine learning algorithms
Figure 12 provides a comparison of the actual removal
efficiency (%) alongside the predicted values from different machine learning models. The average actual efficiency
stands at 76.11%, considering the 20% tested data set during the machine learning algorithms analysis. The predictions generated by various models include 74.10% for linear
regression, 75.06% for random forest, 75.25% for decision
tree, 72.85% for K-nearest neighbor, and 75.58% for support
vector machine. These results illustrate the varying degreesof accuracy and effectiveness exhibited by each model inestimating the removal efficiency. While some models likedecision tree and support vector machine come close to theactual value, others such as K-nearest neighbor fall slightly
short. This analysis underscores the importance of selecting the appropriate machine learning algorithm tailored tothe specific characteristics of the dataset to achieve moreaccurate predictions.
Machine learning algorithm evaluation
Figure 13 and ST-2 display the accuracy scores of various
machine learning algorithms during cross-validation on the
CIP adsorption dataset by eggshell and pyrolyzed eggshell.
Linear regression achieved an accuracy of approximately
75.49%, indicating moderate predictive performance, while
support vector machine demonstrated a lower accuracy of
73.07%. On the contrary, random forest emerged as thepreeminent performer, attaining a superior accuracy of
90.28%, owing to its capacity to capture intricate relationships through ensemble learning. The decision tree, closely
trailing random forest with an accuracy of approximately
90.01%, similarly thrived in capturing complex patterns.

Chemical Papers (2024) 78:8433–8447

K-Neighbors showcased good performance, achieving an
accuracy of 89.17%, potentially excelling in scenarios whereclear clusters or patterns exist. Therefore, the random forest,
decision tree and K-Neighbors algorithms are considered thebest fitted models for the predicting adsorption performanceof CIP on the surface of the eggshell and its pyrolyzed products. Ali et al. (2022) also used multiple machine learningalgorithms and analyzed their performance to detect cardiovascular disease.

Conclusion
In conclusion, the results of batch adsorption experiments
underscored the importance of factors such as initial CIP
concentrations, pH, and contact time in shaping the adsorption process. Initial concentrations played a notably significant role in CIP adsorption. Isotherm studies demonstrated that Langmuir and Freundlich isotherms accurately
described the adsorption. In addition, kinetic analysis indicated a pseudo-second-order (PSO) model, revealing intricate interactions between pyrolyzed eggshell and CIP andproviding valuable insights for environmental remediation
efforts. SEM images depicted highly porous surfaces, particularly in PES-600, consistent with increased porosity attemperatures up to 600 °C. The RF, DT, and K-NN algorithms showed the better performers with superior accuracy
among the considered algorithms. This highlights machinelearning’s potential to improve predictive accuracy and optimize adsorption by creating new avenues for optimizing
water treatment processes. By integrating machine learningwith experimental research, we can develop more sustainable and effective solutions to tackle water pollution challenges associated with pharmaceutical contaminants.
Supplementary Information The online version contains supplementary material available at https://d​ oi.o​ rg/1​ 0.1​ 007/s​ 11696-0​ 24-0​ 3687-w.
Acknowledgements Not applicable
Author Contributions Md. Rezwanul Islam was involved in the conceptualization, methodology, investigation, data curation, formal analysis,
and writing—original draft preparation. Qingyue Wang contributed
to the supervision, writing—original draft preparation, and review
and editing. Sumaya Sharmin participated in the methodology, writing—original draft preparation, and review and editing. Weiqian Wang
contributed to the methodology and review and editing.
Funding This study was partially supported by the Special Funds
for Innovative Area Research (No.20120015, FY 2008-FY2012) and
Basic Research (B) (No. 24310005, FY2012-FY2014; No.18H03384,
FY2017-FY2020; No.22H03747, FY2022-FY2024) of Grant-in-Aid
for Scientific Research of Japanese Ministry of Education, Culture,
Sports, Science and Technology (MEXT).

Fig.13  Comparison of accuracy score of different machine learningalgorithms

Data availability The datasets of this study are available upon request.

Chemical Papers (2024) 78:8433–8447

Declarations
Conflict of interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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