Received: 29 November 2023

Accepted: 11 March 2024

DOI: 10.1002/saj2.20669

ORIGINAL ARTICLE
Fundam ental Soil Science

Pb2+ adsorption on functionalized biochar nanoparticles: Insightsfrom nanoparticle characterization and kinetic-isotherm analysis
Hedieh Behnam1

Ahmad Farrokhian Firouzi1

1 Department of Soil Science andEngineering, Shahid Chamran University of
Ahvaz, Ahvaz, Iran2 Department of Environmental Sciences,

University of California, Riverside,
California, USA
Correspondence
Ahmad Farokhian Firouzi, Department ofSoil Science and Engineering, Shahid
Chamran University of Ahvaz, Ahvaz, Iran.
Email: a.farrokhian@scu.ac.ir;
farrokhian@gmail.com
Assigned to Associate Editor Rafael
Clemente.
Funding information
Shahid Chamran University of Ahvaz,
Grant/Award Number: SCU.AS99.97

Jiří Šimůnek2

Abstract
There has been an ongoing discussion about whether using functionalized biochar
nanoparticles for pollutant removal is practical. The existing uncertainty surrounding
functionalized biochar nanoparticles raises questions regarding their effectiveness inunraveling this problem. In this study, functionalized biochar nanoparticles were produced from corn (Zea mays L.) residues and Conocarpus erectus L. wood at 400˚C
and 700˚C using the H2 SO4 /HNO3 treatment. The synthesized nanoparticles wereused to explore their sorption properties for Pb2+ . Various adsorption kinetic andisotherm models were evaluated using linear and nonlinear regression techniques.
The functionalized biochar nanoparticles originated from wood at 400˚C had thelargest (80.74 mg g−1 ) Pb2+ adsorption capacity due to their highest O/C and the mostnegative zeta potential. In comparison, nanoparticles fabricated from corn residues at
700˚C showed the lowest (70.47 mg g−1 ) Pb2+ adsorption capacity. Pyrolysis temperature affected the sorption process. Functionalized biochar nanoparticles produced at
400˚C were more successful in sorbing the pollutant than those fabricated at 700˚C.
Linear and nonlinear pseudo-second-order kinetic models described Pb2+ adsorption
kinetics well, indicating the rate-controlling step. The nonlinear Freundlich model
described the equilibrium relationship between adsorbate concentration and capacity, elucidating adsorption site heterogeneity and biochar nanoparticles’ affinity for
Pb2+ . Our study shows that functionalized biochar nanoparticles could help develop
procedures for remediating polluted environments.

1

Abbreviations: FBNP, functionalized biochar nanoparticle; FNCBC400,
functionalized biochar nanoparticles derived from corn residues at 400˚C;
FNCBC700, functionalized biochar nanoparticles derived from corn
residues at 700˚C; FNCBCs, corn residues-based functionalized biochar
nanoparticles; FNWBC400, functionalized biochar nanoparticles derived
from wood at 400˚C; FNWBC700, functionalized biochar nanoparticles
derived from wood at 700˚C; FNWBCs, wood-based functionalized biochar
nanoparticles; NSE, Nash–Sutcliffe efficiency coefficient; RMSE, root
mean square error; SEE, standard error of estimate.

INTRODUCTION

Heavy metals, well-known as prevalent environmental pollutants, emanate from anthropogenic activities, including thechemical industry, mining, and fertilizer applications, andpose severe ecological and human health challenges (Li et al.,2020). Pb2+ is one of the heavy metals with exceedingly harmful consequences on human health. Pb2+ may be present inindustrial wastewater and then enter the human body, causingkidney damage, anemia, and cancer (Zhou et al., 2018). As theWorld Health Organization (WHO) asserted, the highest value© 2024 The Authors. Soil Science Society of America Journal © 2024 Soil Science Society of America.
Soil Sci. Soc. Am. J. 2024;1–18.

wileyonlinelibrary.com/journal/saj2

1

for the permissible concentration of Pb2+ in drinking water is
0.01 mg L−1 (Xiao et al., 2020). So far, various techniques
have been designed to remove Pb2+ in aqueous systems,
including precipitation, ion exchange, reverse osmosis, andadsorption (Chen et al., 2019).
Using carbon-based adsorbents is a promising heavy metal
removal technique in aqueous systems. Carbon nanotubes(CNTs), graphene oxide, and biochar have been used to eliminate environmental pollutants (Yang et al., 2019). Biochar isa porous material developed at pyrolysis temperatures usually
lower than 700˚C and with a limited oxygen supply (Xiao,2022). This carbon-rich product originates from herbs, agricultural wastes, urban materials, animal manures, and wood
(Li et al., 2021). Biochar shows varied physicochemical characteristics depending on the biomass source and the pyrolysis
temperature employed during production. Its usefulness lies
in reducing the bioavailability of heavy metals in soil andwater, significantly mitigating heavy metal pollution. Biochar
simultaneously serves a dual purpose by enhancing the quality of both soil and water contaminated with heavy metals(Kumar & Bhattacharya, 2021).
Despite biochar’s essential qualities, such as its porosity,
surface area, functional groups on the surface, and sorption
capacity, there is considerable potential for further enhancing these attributes. Therefore, investigations have focusedon modifying biochar to improve its ability to adsorb pollutants (Zhao et al., 2018). This enhancement is essential
for maximizing its efficacy in sorbing contaminants andreducing pollution (Kumar et al., 2022). Chemical, physical, and biological modification strategies can change biochar
properties. In particular, acid and alkali, metal salt, organic
materials, microbial intervention, steam activation, and ballmilling represent multifaceted approaches for functionalizing
biochar (Ahmad et al., 2014; Wang & Wang, 2019; Zhang
et al., 2021). In chemical oxidation, active sorption sites ofbiochar may be created, improving electrostatic interactions
and complexation of pollutants with the adsorbent (Liu et al.,2019). Yuvaraja et al. (2019) indicated that complexationand electrostatic interactions between the functional groups
in the adsorbent and Pb2+ could result in adequate pollutant
removal.
Furthermore, using proper oxidants such as H2 SO4 , HNO3 ,HCl, H3 PO4 , and H2 O2 may enhance functional groups inbiochar, increasing the adsorbent capacity for contaminantelimination (Jun et al., 2018). For example, H3 PO4 -activatedpalm kernel shell biochar was prepared as an adsorbent
to remove Pb2+ from polluted wastewater (Dechapanya &Khamwichit, 2023). The results demonstrated that phosphoric acid could enhance Pb2+ sorption due to phosphate
precipitation.
While the frequency of biochar applications for cleaning up environmental impurities is increasing, attention has
recently been drawn to biochar nanoparticles (˂100 nm) as

Core Ideas∙ Pyrolysis temperature had a notable effect on Pb2+
removal using functionalized biochar nanoparticles.
∙ Functionalized biochar nanoparticles derived fromwood at 400˚C had the highest ability to adsorb
Pb2+ .
∙ A 30-min equilibrium demonstrates rapid Pb2+
kinetics.
∙ Nonlinear regression accurately determined model
parameters.
∙ Functionalized biochar nanoparticles derived fromcorn residues at 400˚C excel in cost-efficient Pb2+
removal.

emerging materials with higher potential in adsorbing pollutants than macro-biochars (≥1 μm). In nanoparticles, thecarbon content may decrease, and the surface area, the number of surface functional groups, and negative zeta potential
can increase, paving the way for their better performanceas adsorbents (Ramanayaka et al., 2020). Goswami andKumar (2018) showed that using rice husk-based biochar
nanoparticles succeeded in removing fluoride from an aqueous solution, and the value of the highest adsorption capacity
was 17.3 mg g−1 . On the other hand, their ability to remove
pollutants could increase when treated with agents affecting
their oxygen-containing functional groups (Chausali et al.,2021).
While biochar nanoparticles naturally possess properties
that enhance contaminant adsorption, there exists a lack ofstudies evaluating the adsorption of Pb2+ by functionalizedbiochar nanoparticles (FBNPs) derived from wood and corn
residues at pyrolysis temperatures of 400˚C and 700˚C. Cornis grown widely in Iran and generates considerable residues,
typically disposed of by combustion, contributing to CO2emissions. Addressing this issue is vital for environmental
sustainability. Conversely, utilizing wood from Conocarpus
trees in Ahvaz (Khuzestan, Iran) offers an economic waste
management strategy. This study aims to fill the research voidby studying the adsorption capabilities of FBNPs, providing
insights for pollutant remediation and sustainable waste management practices. This research aims to (I) assess the impacts
of FBNPs fabricated from corn residues and wood at pyrolysis temperatures of 400˚C and 700˚C on Pb2+ adsorption inaqueous solutions; (II) evaluate the effects of pH solution,
adsorbent concentration, contact time, and Pb2+ concentration on Pb2+ adsorption; (III) analyze adsorption kinetics
using various models, such as the pseudo-first-order, pseudosecond-order, or intra-particle diffusion models; (IV) examine

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BEHNAM ET AL.

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adsorption isotherms using the Langmuir, Freundlich, andTemkin isotherm models; and (V) utilize four error functions
to assess the best-fitted models.

The FBNPs were combusted at 750˚C for 6 h to determine
the amount of ash content as follows:
Ash content (%) =2
2.1

MATERIALS AND METHODS
Biochar fabrication

Corn (Zea mays L.) residues gathered at a corn farm (30˚
36′ 01″N, 50˚ 13′ 38″E) and Conocarpus erectus L. wood
litter collected at the campus of the Shahid Chamran University of Ahvaz, Iran, were used for the biochar preparation.
The slow pyrolysis procedure was carried out in a muffle furnace (Nabertherm), using N2 flow at the heating rate of 5˚Cmin−1 at the preferred temperatures (400˚C and 700˚C) for 3 h(Cantrell et al., 2012).

2.2

FBNPs synthesis

FBNPs were extracted from biochars, as other researchers
did (Gao et al., 2017; Guo et al., 2020). Accordingly, thebiochars were crushed and passed through a 270-mesh sieve.
Biochar powder (5 g) was blended with 45 mL H2 SO4 and
15 mL HNO3 at room temperature for 24 h. The mixture
was then diluted to obtain 1 L using deionized water andstirred for 1 min. The suspension then remained stationary for 2 h. The upper layer was siphoned off and filteredusing the 0.22-μm membrane. Subsequently, the suspension’s pH reached approximately 6 after being washed with
deionized water many times. Lastly, the FBNPs suspensions
were dried in an oven at 80˚C. The samples were denoted
as FNCBC400, FNCBC700, FNWBC400, and FNWBC700,
representing functionalized biochar nanoparticles derived
from corn residues at 400˚C, functionalized biochar nanoparticles derived from corn residues at 700˚C, functionalizedbiochar nanoparticles derived from wood at 400˚C, and functionalized biochar nanoparticles derived from wood at 700˚C,respectively.

2.3

Characterization of FBNPs

The morphology of FBNPs was evaluated using the fieldemission scanning electron microscope (FE-SEM, TESCANMIRA3). Hydrodynamic diameters (Dh ) and zeta potential ofparticles were recorded in a 200 mg L−1 suspension using a
Zetasizer (Nano ZS90). The sample yield was calculated asfollows:
Yield (%) =FBNPs (wt)× 100
Biochar (wt)(1)

Ash (wt)
× 100
FBNPs (wt)(2)

Carbon (C), hydrogen (H), nitrogen (N), and sulfur (S)were quantified using an elemental analyzer (FLASH EA
1112 SERIES), and Equation (3) was used to estimate thetotal oxygen (O) content as follows (Domingues et al.,2017):
𝑂 (%) = 100 − ash (%) − 𝐶 (%) − 𝐻 (%) − 𝑁 (%) − 𝑆 (%)(3)
The pH values of FBNPs were assessed using a pH
meter (WTW inoLab 3856B) at the FBNPs/deionized water
ratio of 1:10 (w/v). The cation exchange capacity (CEC)
was measured by mixing 0.5 g FBNPs with 25 mL of
0.01 M NH4 Cl for 24 h at room temperature (Cao et al.,2019). Afterward, the mixture was centrifuged (10,000 g,
10 min), and the concentrations of Na+ , K+ , Ca2+ , andMg2+ in the supernatant were determined using ICPOES (Perkin Elmer). The specific surface area (SSA),
pore volume (Vt ), and pore diameter (Dp ) of FBNPs
were recorded through N2 adsorption-desorption isotherms(BELSORP-mini analyzer). The Fourier transform infrared
spectroscopy (FTIR) analysis (PerkinElmer-Spectrum 65)
was considered to evaluate the chemical composition of
FBNPs.

2.4

Batch adsorption experiments

All experiments were performed in centrifuge tubes, andPb(NO3 )2 was used as an inorganic compound for the
research. All solutions were prepared using deionized water.
Four factors affecting Pb2+ adsorption, such as the initial
pH of the solution, adsorbent dosage, contact time, andadsorbate concentration, were considered. The mixtures ofadsorbent–adsorbate were stirred in a shaker at 25˚C. Afterthe agitation, the mixtures were centrifuged at 10,000 × g
for 10 min, and the Pb2+ concentration in supernatants wasdetermined using ICP-OES (Perkin Elmer) (Bernardo et al.,2013).

2.4.1

Initial pH

A total of 4 mg of each type of FBNPs was added to 10 mL ofa solution containing 10 mg L−1 Pb2+ . The initial pH of Pb2+
solutions was adjusted to 2, 3, 4, 5, 6, and 7 using HNO3 0.1 M
and NaOH 0.1 M. The mixture was held on a shaker for 24 h,and then the Pb2+ concentration was evaluated.

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BEHNAM ET AL.

TA B L E 1

Linear and nonlinear adsorption kinetic and isotherm models.

ModelsNonlinear expression

Linear expression

Pseudo-first-order𝑞𝑡 = 𝑞𝑒 (1 − exp−𝑘1 𝑡 )ln(𝑞𝑒 − 𝑞𝑡 ) = ln 𝑞𝑒 − 𝑘1 𝑡

Pseudo-second-order𝑞𝑡 = 1+𝑞𝑒 2𝑘 𝑡𝑡
= 𝑘 1𝑞2 𝑞1 𝑡
𝑞𝑡
2 𝑒 𝑒

Intra-particle diffusion

𝑞𝑡 = 𝑘𝑝 𝑡1∕2 + 𝑐

𝑞𝑡 = 𝑘𝑝 𝑡1∕2 + 𝑐

Kinetics
𝑞2 𝑘 𝑡𝑒 2

Isotherms
𝑞 𝑘 𝐶

𝐶𝑒
𝐶
= 𝑞 1𝑘 + 𝑞 𝑒
𝑞𝑒
𝑚 L
𝑚

𝑚 L 𝑒
𝑞𝑒 = (1+𝑘𝐶 )Langmuir

𝑒

L

1∕𝑛

Freundlich

𝑞𝑒 = 𝑘𝑓 𝐶𝑒

Temkin𝑞𝑒 = 𝐵 ln(𝐴𝐶𝑒 )𝐵 = 𝑅𝑇𝑏log 𝑞𝑒 = log 𝑘f + 1𝑛 log 𝐶𝑒
𝑞𝑒 = 𝑅𝑇ln 𝐴 + 𝑅𝑇
ln 𝐶𝑒
𝑏
𝑏

Note: 𝑞𝑡 (mg g−1 ) is the amount of Pb2+ adsorbed by functionalized biochar nanoparticles (FBNPs) at time t; 𝑘1 (min−1 ) is the equilibrium constant; 𝑘2 (g mg−1 min−1 )is the second-order rate constant; 𝑘𝑝 (mg g−1 min−1/2 ) is the intra-particle diffusion rate constant; 𝑐 (mg g−1 ) is a constant, 𝑞𝑚 (mg g−1 ) is the maximum adsorption; 𝑘L(L mg−1 ) is Langmuir constant; 𝑘f (L mg−1 ) is Freundlich partition coefficient; 𝐶𝑒 (mg L−1 ) is the equilibrium concentration in water; 𝑞𝑒 (mg g−1 ) is the equilibrium
adsorbed concentration; 1∕𝑛 is the adsorption intensity; 𝐴 (L g−1 ) is an equilibrium binding constant; 𝐵 (J mol−1 ) is a constant being related to the heat adsorption; 𝑇 (K)is the absolute temperature; 𝑅 (8.314 J mol−1 K−1 ) is the universal gas constant; and 𝑏 (J mol−1 K−1 ) is the adsorption constant (Khan et al., 2018; Xu et al., 2019).

2.4.2

2.4.4
Initial Pb2+ concentration andisotherm experiments

Adsorbent dosage

The impact of the FBNP dosage was assessed by choosing fivevarious dosages of 0.2, 0.4, 0.6, 0.8, and 1.0 g L−1 in 10 mLof 10 mg L−1 Pb2+ solution with an optimum pH identified
in the preceding stage. After 24 h of shaking the mixtures, thePb2+ concentration was measured.

2.4.3

Contact time and kinetic experiments

The optimum pH and adsorbent dosage determined in previous stages were used to evaluate the effect of different
contact times (10, 20, 30, 60, 120, 240, and 300 min) on
pollutant removal using 10 mL of 10 mg L−1 Pb2+ solution.
Removal efficiency and Pb2+ adsorbed to the adsorbent (𝑞𝑒 )were calculated using the following equations:
𝐶 − 𝐶𝑒× 100
Removal Ef f iciency = 0
𝐶0

𝑞𝑒 = (

𝐶0 − 𝐶𝑒)𝑉𝑚(4)

Various initial Pb2+ concentrations (10, 20, 30, 40, and
50 mg L−1 ) were measured using the optimum values ofpH, adsorbent dosage, and contact time investigated in theprevious experiments. Common adsorption isotherm models, including the Langmuir, Freundlich, and Temkin models, were analyzed using linear and nonlinear regression
methods to evaluate the adsorption of Pb2+ onto FBNPs(Table 1).
Four error functions were used to evaluate the effectivenessof theoretical parameters to describe experimental data: thecoefficient of determination (R2 ), the standard error of estimate (SEE), the Nash–Sutcliffe efficiency coefficient (NSE),
and the root mean square error (RMSE):
√

∑

SEE =

(𝑌obs − 𝑌f it )2
𝑁 −2

∑𝑁

(5)

where 𝑞𝑒 (mg g−1 ) is the amount of Pb2+ adsorbed by FBNPs
at equilibrium, 𝐶0 (mg L−1 ) and 𝐶𝑒 (mg L−1 ) are initial andequilibrium concentrations, respectively, 𝑉 (L) is the solution
volume, and 𝑚 (g) is the amount of adsorbent.
The widely used adsorption kinetic models, such as thepseudo-first-order, pseudo-second-order, and intra-particle
diffusion models, were applied in a linear and nonlinear
regression to analyze the kinetics of Pb2+ adsorption onto
FBNPs (Table 1).

𝑖
NSE = 1 − ∑𝑁

(𝑌obs − 𝑌f it )22
𝑖 (𝑌obs − 𝑌avg )[∑RMSE =(𝑌obs − 𝑌f it )2
𝑁

(6)

(7)

]1∕2
(8)

where 𝑁 is the number of data points, 𝑌obs is the observed
value, 𝑌f it is the fitted value, and 𝑌avg is the mean of observed
data.

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F I G U R E 1 Field emission scanning electron microscope images (FE-SEM) of functionalized biochar nanoparticles derived from corn residues
at 400˚C (FNCBC400) (a), functionalized biochar nanoparticles derived from corn residues at 700˚C (FNCBC700) (b), functionalized biochar
nanoparticles derived from wood at 400˚C (FNWBC400) (c), and functionalized biochar nanoparticles derived from wood at 700˚C (FNWBC700)
(d).

3
3.1

RESULTS AND DISCUSSIONFBNPs characterization

The morphological aspects of FBNPs were evaluated using
the FE-SEM technology (Figure 1). FNWBC700 consistsof the smallest spherical particles, whereas FNCBC400
includes the largest granular nanoparticles among all FBNPs(Figure 1). The hydrodynamic diameters (Dh ) of FBNPs
counted by Dynamic Light Scattering (DLS) are given in
Table 2. FBNPs fabricated at higher pyrolysis temperatures had lower Dh . Still, wood-based functionalized biochar
nanoparticles(FNWBCs) were generally smaller than those
based on corn residues (FNCBCs), presumably due to theoxidation of lignin in their structure, leading to the breakdown in aggregation (Zhang et al., 2020). The trend in Dh
of FBNPs was consistent with the FE-SEM images. The yieldof FBNPs followed an order: FNCBC400 > FNCBC700 >FNWBC400 > FNWBC700 (Table 2), implying that FBNP
formation is closely dependent on the pyrolysis temperatureand feedstock type. Biochars produced at lower pyrolysis
temperatures and containing less lignin, such as corn residuesbased biochars, are thus prone to be physically disturbed,
creating more nanoparticles. This finding agrees with earlier
studies that observed a decline in the yield of biochar nanoparticles derived from peanut shells when pyrolysis temperatures
were raised from 300˚C to 600˚C (Liu et al., 2018).
The ash content in FNCBCs was higher than in FNWBCs(Table 2), indicating a higher content of minerals in corn

residues than in wood feedstock (Zhao et al., 2017). Moreover, the ash content increased with the pyrolysis temperature,
confirming the minerals’ separation from their original form
during the extraction phase. pH was higher in FBNPs obtained
at 700˚C than at 400˚C, regardless of the acidic pH in all
FBNPs as a consequence of acid treatment in the synthesis
stage (Table 2). This result was supported by another study
by Xu et al. (2020) that reported a pH reduction in cornstraw biochar from 8.86 to 4.15 after using sulfuric acid.
FBNPs produced at 700˚C had higher CEC than those produced at 400˚C, indicating the existence of alkali cations
such as K+ , Ca2+ , Mg2+ , and Na+ in their structure. Similar to the pH, FNCBC700 had the highest CEC among all
nanoparticles.
The SSA of the FBNPs produced at 700˚C (compared
to 400˚C) increased, while their Dp decreased, implying
a more developed pore structure (Table 2). Theoretically,
the hemicellulose content regulates SSA. Moreover, biomass
containing a high lignin content has been shown to havelower SSA. Thus, adopting a modifying method could assist
in increasing FBNPs’ SSA (Ramanayaka et al., 2020),
which was used in the current research. Moreover, thetype H4 of hysteresis loops in N2 adsorption–desorption
isotherms of FBNPs (Figure 2) represented pores with narrow slit-like shapes with an internal meso and micro size
(Sing, 1985).
According to the elemental composition of FBNPs(Table 2), by raising the pyrolysis temperature, the C content was increased, while the O content was correspondingly

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BEHNAM ET AL.

Abbreviations: CEC, cation exchange capacity; FNCBC400, functionalized biochar nanoparticles derived from corn residues at 400˚C; FNCBC700, functionalized biochar nanoparticles derived from corn residues at 700˚C;
FNWBC400, functionalized biochar nanoparticles derived from wood at 400˚C; FNWBC700, functionalized biochar nanoparticles derived from wood at 700˚C; SSA, specific surface area.

−32.3

−36.2
114.4

89.71
1.14

2.13
2.15

1.08
46.15

53.06
15.26

12.55
6.39

7.94

30.35

2.74

18.62

2.73
0.1149167.73

95.91
8.16

11.38
2.85

2.71

0.365

1.77
0.456
FNWBC400FNWBC700

1.82

0.1340

3.94

3.35

−30.5

−33.4
152.9

133.6
0.75

1.76
0.88

0.76
36.58

56.79
5.08

7.81
5.65

5.07

36.27

2.30

24.20

3.39
0.0972
84.71

51.21
6.18

13.39
3.07
0.458

2.38
7.07
0.589
FNCBC400

FNCBC700

11.39

0.0800

6.25

1.79

Zeta
potential
Hydrodynamic
diameter
O/C
H/C
(–)
O
(%)
S
(%)
N
(%)
H
(%)
C
(%)
Dp
(nm)
Vt
(cm3 g−1 )SSA(m2 g−1 )CEC(cmolc kg−1 )
pH
(–)
Ash
(%)
Yield
(%)
Properties

Physicochemical properties of functionalized biochar nanoparticles (FBNPs).
TA B L E 2lowered. The elemental analysis of FBNPs displayed noclear trends in the H, N, and S contents, while the O/C
and H/C ratios were reduced at higher charring temperatures. FNWBCs exhibited higher H/C and O/C than FNCBCs,
demonstrating less aromaticity and more polarity features(Chen et al., 2008). The highest value of O/C found in
FNWBC400 suggested the highest polarity, which raises
the possibility of pollutant adsorption. The zeta potential of
FBNPs declined for higher charring temperatures (Table 2).
FNWBC400 and FNCBC700 had the most and least negative
zeta potentials, respectively, in line with having the highest
and lowest O/C. This result is similar to the findings of Honget al. (2019), who reported that the zeta potential and O/C ratioof rice straw biochar decreased after increasing the pyrolysis
temperature.
The functional groups of FBNPs were evaluated using
FTIR spectra (Figure 3). Broad peaks were observed at 3200–
3500 cm−1 as –OH stretching and 1600–1700 cm−1 as C = O
stretching of carboxylic acid (Jin et al., 2020). Moreover, thepeaks in the 1400–1600 cm−1 region accounted for aromatic
C = C. The bands at 1060–1200 cm−1 , representing C–O,
were sharper in FNWBCs than in FNCBCs. The peaks in the
600–8900 cm−1 interval were attributed to out-of-plane C–H
bending vibrations. The C–H stretching band at 2924.56 cm−1
appeared in FNCBCs, while it was not detected in FNWBCs(Wang et al., 2020). Finally, FNWBCs exposed the presenceof two peaks for –OH, showing a distinctive characteristiccompared to FNCBCs.

3.2

Adsorption experiments

3.2.1

Impact of initial pH

The impact of different pH values on the removal efficiency
and Pb2+ adsorption capacity is illustrated in Figure 4a.
Both removal efficiency and adsorption of Pb2+ markedly
increased up to pH 5. Therefore, pH 5 was used as the optimum initial pH for the remaining experiments. The sorption
capacity at pH = 2 was 12, 18.63, 13.46, and 19.74 mg g−1
for FNCBC700, FNCBC400, FNWBC700, and FNWBC400,
respectively. The sorption capacity increased to 24.54, 25,
24.68, and 25 mg g−1 at pH = 5, respectively. Furthermore,FNWBC400 and FNCBC400 demonstrated their full potential in terms of removal efficiency, while FNWBC700 and
FNCBC700 achieved rates of 98.75% and 98.19% at pH = 5,
respectively. The adsorption capacity and removal efficiencyof Pb2+ remained steady at pH = 6, although they slightly
decreased at pH = 7. A similar trend was reported by Danish
et al. (2011), who showed that Pb2+ adsorption by date beadcarbon activated with ZnCl2 rose within the pH range of 2–
6.5, while precipitation of Pb2+ ions as Pb(OH)2 was evident
at pH levels exceeding 7.

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6

7

F I G U R E 2 N2 adsorption-desorption isotherms at standard temperature and pressure (STP) for four types of functionalized biochar
nanoparticles (FBNPs).

F I G U R E 3 Fourier transform infrared spectroscopy (FTIR) spectra of functionalized biochar nanoparticles derived from corn residues at 700˚C(FNCBC700) (a), functionalized biochar nanoparticles derived from corn residues at 400˚C (FNCBC400) (b), functionalized biochar nanoparticles
derived from wood at 700˚C (FNWBC700) (c), and functionalized biochar nanoparticles derived from wood at 400˚C (FNWBC400) (d).

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BEHNAM ET AL.

F I G U R E 4 The effects of initial pH (a), contact time (b), and initial Pb2+ concentration (c) on removal efficiency (●) and Pb2+ adsorption (■)
using functionalized biochar nanoparticles derived from corn residues at 400˚C (FNCBC400), functionalized biochar nanoparticles derived fromcorn residues at 700˚C (FNCBC700), functionalized biochar nanoparticles derived from wood at 400˚C (FNWBC400), and functionalized biochar
nanoparticles derived from wood at 700˚C (FNWBC700).

Considering the inadequate removal efficiency and adsorption capacity at low pH values (pH ˂ 5), protonation of
pH-dependent charges on the adsorbent could initiate theelectrostatic repulsion of Pb2+ . Moreover, the competition
between Pb2+ and H+ ions may reduce the Pb2+ adsorption
capacity of FBNPs. When increasing the pH value, the surfacefunctional groups on FBNPs may deprotonate, creating moreavailable sites for Pb2+ . However, the adsorption capacity for
Pb2+ could decrease when pH exceeds 6 due to the development of hydroxide complexes, such as Pb(OH)2 (Ding et al.,2016; Mireles et al., 2019).

and FNCBC700, the upward trend continued until the dosageof 0.6 g L−1 when it leveled off, potentially because theactive sites became saturated with the pollutant (Jasim
et al., 2022). Therefore, the concentration of 0.6 g L−1 waschosen for the following experiments. The more the adsorbent dosage increased, the lower adsorption capacity wasobserved. Reducing adsorption sites for Pb2+ due to aggregation or overlapping may have caused this tendency (Das et al.,2013). Similarly, Wang et al. (2021) observed that an increase(up to 3.5 g L−1 ) in the application of biochar derived from
cotton straw resulted in the removal of nearly 100% of Pb2+ .

3.2.2

3.2.3

Impact of adsorbent dosage

The impact of the FBNPs dosage on the removal efficiency
and adsorption capacity of Pb2+ is depicted in Figure 5. At
an adsorbent dosage of 0.2 g L−1 , FNWBC400, FNWBC700,FNCBC400, and FNCBC700 exhibited removal efficienciesof 99.95%, 97.71%, 99.34%, and 96.90%, respectively. A
higher dosage resulted in higher Pb2+ removal efficiency,
up to the point when it reached the highest value for
FNWBC400 and FNCBC400. By contrast, for FNWBC700

Impact of contact time

The removal efficiency and adsorption capacity of Pb2+
sharply increased with time (Figure 4b). In 10 min,FNWBC400, FNWBC700, FNCBC400, and FNCBC700
achieved removal efficiencies of 97.46%, 96.22%, 96.61%,
and 95.79%, respectively. However, the equilibrium time for
FBNPs was around 30 min, corresponding to a Pb2+ adsorption capacity of 16.65, 16.60, 16.63, and 16.49 mg g−1
for FNWBC400, FNWBC700, FNCBC400, and FNCBC700,

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BEHNAM ET AL.

8

9F I G U R E 5 The effect of adsorbent dosage on removal efficiency (●) and Pb2+ adsorption (■) using functionalized biochar nanoparticles
derived from corn residues at 400˚C (FNCBC400), functionalized biochar nanoparticles derived from corn residues at 700˚C (FNCBC700),
functionalized biochar nanoparticles derived from wood at 400˚C (FNWBC400), and functionalized biochar nanoparticles derived from wood at
700˚C (FNWBC700).

respectively. Available active sites on FBNPs, which weresaturated faster by Pb2+ , may be the main reason for rapid
adsorption (Chakravarty et al., 2010). Consequently, theremoval efficiency and adsorption capacity remained steady,owing to a decrease in the availability of adsorption sites(Ghasemi et al., 2014). Pellera et al. (2012) argued that
two-stage adsorption of heavy metals with biochar-based
adsorbents is usual, indicating the existence of abundantactive sites. Moreover, a fast equilibrium time (<60 min)for carboxylated multi-walled CNTs was demonstrated byresearch conducted by Egbosiuba et al. (2020). Kumar et al.
(2021) observed that the peak removal of arsenic (As)occurred for biochar derived from wheat straw (83.7 ± 0.06%)
and waste-derived biochar (76.7 ± 0.16%) produced at 500˚Cafter 60 min of contact time. The authors suggest increased
As removal may be linked to increased active binding sites
within biochars over time.

3.2.4

Impact of initial Pb2+ concentration

Since Pb2+ concentrations could profoundly affect thequality of aqueous solution, the removal efficiency

and adsorption capacity of Pb2+ for different adsorbate
concentrations were examined. As the initial concentrationof Pb2+ rose from 10 to 50 mg L−1 , the removal efficiencies
of FNWBC400, FNCBC400, FNWBC700, and FNCBC700
declined to 96.89%, 95.19%, 90.20%, and 84.56%, respectively (Figure 4c). Hypothetically, the higher removal of
Pb2+ at its lower concentrations resulted from the presenceof more active sites on the adsorbents (Amarasinghe &Williams, 2007). Increasing the initial Pb2+ concentration
to 50 mg L−1 resulted in a corresponding increase in the
Pb2+ adsorption capacity of FNWBC400, FNCBC400,FNWBC700, and FNCBC700 to 80.74, 79.33, 75.17, and70.47 mg g−1 , respectively. This improvement could result
from driving force enhancement between FBNPs and Pb2+
and, consequently, a higher chance of colliding (Hou et al.,2016). Moreover, Pb2+ adsorption occurred rapidly for
all adsorbents, facilitated by seamless interactions with aconsistent amount of adsorbent material (Kumar et al., 2021).
The adsorption capacities of FBNPs in the current study
were similar to other adsorbents reported in other studies(Table 3).
Surface complexation with O-containing functionalgroups, metal exchange, and precipitation are the three main

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BEHNAM ET AL.

9.87
67.90

350–550

400

350

300

Paddy husk biochar

Biochar-supported hydrated
manganese oxide
nanoparticles

Watermelon seeds-based
biochar modified with
H2 O2Micro-nano bone biochar

70.90

84.7
80.0

500

249.85249.85

3D MnO2 modified ricehusk biochar

Chitosan-nano ironsulfide@ walnut shellbiochar
Starch-nano iron sulfide@walnut shell biochar

203.23

Micro-nano bone biochar

FeS nanoparticles formed bymixing FeSO4 and Na2 S, agedfor 24 h. Adsorbent prepared bywalnut shell biochar and FeS
nanoparticles with a mass of 4:1.

MnO2 modified biochar (MBC)
produced by mixing Rice husk
biochar (BC) with KMnO4,
adding Mn(II) acetate, andheating.

Mixed bone biochar, agate spheres,and deionized water (100:1
ratio). Ball milled at 300 rpm for
12 h, with rotation changes
every 3 h. Centrifuged, dried at
80˚C for 12 h, ground, andstored.

Watermelon seed biochar (BC)modified with hydrogen
peroxide (HP-BC). Washed,dried, and stored for analysis.

Peanut shell biochar used in thehydrated manganese oxide(HMO)-BC synthesis with
Mn(II) and NaClO-NaOH for
impregnation, oxidation,
precipitation, and rinsing.

Biochar produced from paddy husk
and saw dust via slow pyrolysis

Production method

LangmuirLangmuirFreundlich

Langmuir

Pseudo-second-order
Pseudo-second-order

Pseudo-second-order

Pseudo-second-order

Pseudo-second-order

Pseudo-second-order

LangmuirLangmuir

Pseudo-second-order

Pseudo-second-orderFreundlich

Langmuir

Pseudo-second-order

Pseudo-second-order

Kinetic model

LangmuirLangmuir

Isotherm model(Liu et al., 2022)(Liu et al., 2022)(Wu et al., 2020)(Xiao et al., 2020)(Xiao et al., 2020)(Xiao et al., 2020)(Ahmed et al., 2014)(Wan et al., 2018)(Wijeyawardana et al.,2022)

(Wijeyawardana et al.,2022)

References

BEHNAM ET AL.

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225.26450600

Micro-nano bone biochar

182.89

43.62

9.79

450–550

Saw dust biocharqe(mg g−1 )Type of adsorbent

Comparative studies on the Pb2+ adsorption capacity for various adsorbents.

Pyrolysis
temperature(˚C)

TA B L E 3

10

11

F I G U R E 6 Nonlinear kinetic models fitted to Pb2+ adsorbed to functionalized biochar nanoparticles derived from corn residues at 400˚C(FNCBC400) (a), functionalized biochar nanoparticles derived from corn residues at 700˚C (FNCBC700) (b), functionalized biochar nanoparticles
derived from wood at 400˚C (FNWBC400) (c), and functionalized biochar nanoparticles derived from wood at 700˚C (FNWBC700) (d) as a functionof time.

mechanisms governing Pb2+ adsorption to the adsorbents(Lu et al., 2012). As illustrated before, the removal efficiency of Pb2+ by FBNPs decreased when the pyrolysis
temperature increased. The adsorption ability decreased fromFNWBC400 to FNCBC400, FNWBC700, and FNCBC700.
The lower CEC of FBNPs produced at 400˚C and thehigher pH of FBNPs produced at 700˚C indicated that metal
exchange and precipitation are not dominant mechanisms
involved in the Pb2+ removal. To clarify the point, CEC
reflects the ability of four main cations (Na+ , K+ , Ca2+ , andMg2+ ) to exchange with Pb2+ (Cao et al., 2019). The higher
the pyrolysis temperature, the higher CEC was observed.
Therefore, this trend was inconsistent with the results ofhigher adsorption by FBNPs produced at lower charring
temperatures.
Furthermore, with increasing pH, precipitation of Pb2+
with mineral anions like carbonates and sulfates is probable, causing more adsorption capacity (Xu et al., 2013), whilein the current study, this trend was reversed. The coordination of hydroxyl (-OH) and carboxyl (-COOH) groups with
Pb2+ could be the main adsorption mechanism (Tan et al.,2015). This claim was confirmed by higher adsorption of Pb2+
using FBNPs produced at 400˚C, containing higher O/C andmore negative zeta potential (Table 2). It should be noted that

SSA may be an additional factor involved in improving Pb2+
removal (Li et al., 2017). However, in this study, the more thepyrolysis temperature was increased, the higher the SSA wasdetected, being exactly the opposite of the ability of FBNPs
produced at 700˚C to adsorb Pb2+ . Thus, the SSA might not
be a dominant factor affecting Pb2+ adsorption.

3.2.5

Adsorption kinetics

Experiments were conducted to evaluate the kinetics of Pb2+
adsorption to FBNPs. Kinetic models could shed light on therate of pollutant uptake by adsorbents. Adsorption kinetics of
Pb2+ onto FBNPs were assessed and interpreted using threestandard models fitted in both a nonlinear form (to untransformed data) and a linear form (to transformed data): thepseudo-first-order, pseudo-second-order, and intra-particle
diffusion models. Figures 6 and 7 show the nonlinear andlinear kinetic plots of Pb2+ adsorption onto FBNPs, respectively. Low values of R2 and NSE and high values of SEE
and RMSE for both linear and nonlinear fits of the pseudofirst-order kinetic model indicate a poor fit of the model to theexperimental data (Table 4), which confirms that the adsorption capacity may not be an influential factor affecting the14350661, 0, Downloaded from https://acsess.onlinelibrary.wiley.com/doi/10.1002/saj2.20669 by University Of California, Wiley Online Library on [07/04/2024]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License

BEHNAM ET AL.

F I G U R E 7 Linear pseudo-first-order (a), pseudo-second-order (b), and intra-particle diffusion (c) models fitted to Pb2+ adsorbed tofunctionalized biochar nanoparticles (FBNPs) as a function of time. FNCBC400, functionalized biochar nanoparticles derived from corn residues at
400˚C; FNCBC700, functionalized biochar nanoparticles derived from corn residues at 700˚C; FNWBC400, functionalized biochar nanoparticles
derived from wood at 400˚C; FNWBC700, functionalized biochar nanoparticles derived from wood at 700˚C.

adsorption mechanism of Pb2+ onto FBNPs (Jung, 2014). Furthermore, the adsorption capacities predicted by the linear
model were considerably smaller than those obtained directlyfrom the experiment. On the other hand, the adsorption capacities predicted by the nonlinear form of the model were morecomparable with the observed data.
R2 and NSE values for both linear and nonlinear forms of
the pseudo-second-order model were highly satisfying, especially for the linear form. Additionally, low SEE and RMSE
values confirmed the efficiency of the model. Fitted and measured values of qe in the linear form were indistinguishable
for all FBNPs. The nonlinear form of the pseudo-secondorder kinetic model provided an excellent fit to the Pb2+
adsorption data. FNWBC400 had the highest k2 value inboth linear and nonlinear regression approaches, implying it
had the fastest adsorption kinetic among all samples (Shenet al., 2019). This model has been applied in the literature
to depict the chemisorption process, including complexation, metal exchange, and precipitation (Wijeyawardana et al.,2022), indicating that these mechanisms may control Pb2+
adsorption to FBNPs. The study conducted by Naghdi et al.
(2017) also showed that the pseudo-second-order model was

the most representative of the kinetic removal process of
carbamazepine using biochar nanoparticles.
The intra-particle diffusion model was employed to assess
the rate of the controlling stage, which primarily depends on
the surface or pore diffusion. The model in linear and nonlinear forms resulted in low R2 and NSE and high SEE and
RMSE (Table 4). At the same time, fits in both linear andnonlinear forms resulted in almost identical error functions,demonstrating the similarity of linear and nonlinear regression methods. Therefore, this model was not considered to bea suitable model to represent kinetic data.

3.2.6

Adsorption isotherms

The adsorption isotherms were applied to interpret the interactions between pollutant and adsorbent at equilibrium.
Thereby, the Langmuir, Freundlich, and Temkin isotherm
models were employed (using linear and nonlinear regression) to evaluate the process of Pb2+ adsorption on FBNPs(Figures 8 and 9). The fitted parameters of all models are listed
in Table 5.

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BEHNAM ET AL.

12

13

F I G U R E 8 Nonlinear isotherm models fitted to the adsorption data of Pb2+ to functionalized biochar nanoparticles derived from corn residues
at 400˚C (FNCBC400) (a), functionalized biochar nanoparticles derived from corn residues at 700˚C (FNCBC700) (b), , functionalized biochar
nanoparticles derived from wood at 400˚C (FNWBC400) (c), and functionalized biochar nanoparticles derived from wood at 700˚C (FNWBC700)(d).

The Freundlich model (fitted using linear and nonlinear
regression) proved to be better, with the highest R2 values,
than the Langmuir and Temkin isotherm models. The Freundlich model assumes that adsorption is a heterogeneous,
multilayer, and nonideal process. Shi et al. (2019) evaluatedbiochars originating from rice husks produced at 300, 500,
and 700˚C for the Pb2+ removal. Their results showed that
the Freundlich isotherm model best fitted the observed data.
Moreover, their qm values (in the Langmuir isotherm) were
14.1, 21.7, and 26.7 mg g−1 , respectively, much smaller thanthose obtained in this study.
The Langmuir isotherm, which implies single-layer adsorption, fitted in its linear form, provided a better fit of theobserved data than its nonlinear form (Table 5). The fitted qm value was similar to the experimental values. The
Temkin model, describing a heterogeneous chemisorption
process of adsorption (Wang et al., 2015), provided worse
fits than the other models based on R2 (Table 5). The linear and nonlinear fits of the Temkin model produced identicalerror measures, likely due to the similitude between the two
equations (Benmessaoud et al., 2020).
The R2 , NSE, RMSE, and SEE values were used to assess
various adsorption isotherm models and their linear andnonlinear fits. The nonlinear Freundlich model had the highest
R2 value and the lowest RMSE and SEE values among all nonlinear isotherm models. The order of models from the highest
to lowest R2 values was as follows: nonlinear Freundlich >linear Freundlich > linear Langmuir > nonlinear Langmuir >linear Temkin = nonlinear Temkin. The Langmuir isotherm
fitted in its linear form provided the best fit with the highest
NSE values and the lowest RMSE and SEE values comparedto the other linear fits of isotherm models (Table 5). However,
applying linear fitting of models is improper because the distribution of errors may change due to the various linearization
techniques. Therefore, using a model in its standard nonlinear form is more reliable in evaluating isotherm parameters(Yadav & Singh, 2017).
In this research, the feedstock type and pyrolysis temperature were the key elements affecting the Pb2+ adsorption
capacity. FNWBCs had a higher Pb2+ adsorption capacity
than FNCBCs produced at the same pyrolysis temperature.
FNWBC400 had the highest value of qm , even higher thanFNCBC400 (Table 5). By contrast, the yield of FNCBC400
was 1.3 times higher than that of FNWBC400 (Table 2).
Hence, because of a lower yield and more time required
for the FNWBC400 production, using FNCBC400 for the
Pb2+ removal in the aqueous solution would be more
efficient.

4

CONCLUSIONSThis investigation evaluated the Pb2+ adsorption capacity andremoval efficiency using functionalized biochar nanoparti-

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BEHNAM ET AL.

BEHNAM ET AL.

F I G U R E 9 Linear fits of the Langmuir (a), Freundlich (b), and Temkin (c) models to the transformed adsorption data of Pb2+ to functionalizedbiochar nanoparticles (FBNPs). FNCBC400, functionalized biochar nanoparticles derived from corn residues at 400˚C; FNCBC700, functionalizedbiochar nanoparticles derived from corn residues at 700˚C; FNWBC400, functionalized biochar nanoparticles derived from wood at 400˚C;
FNWBC700, functionalized biochar nanoparticles derived from wood at 700˚C.

cles. The results showed that a pH of 5 and an adsorbent
concentration of 0.6 g L−1 provided the highest Pb2+ removal
efficiency, and the equilibrium time was about 30 min.
Kinetic data were successfully fitted using linear and nonlinear pseudo-second-order models. The nonlinear Freundlich
isotherm provided the best description of the Pb2+ adsorption capacity. The highest qm values of 85.39 mg g−1 (fora linear model) and 90.87 mg g−1 (for a nonlinear model)
were obtained for the functionalized biochar nanoparticles
produced from wood at 400˚C. Regarding the adsorbent
yield, producing functionalized biochar nanoparticles fromwood at 400˚C consumes more time and energy than producing them from corn residues at 400˚C. Therefore, selecting
corn residues–based functionalized biochar nanoparticles
produced at 400˚C as a suitable adsorbent to remove Pb2+
would be more time effective and cost effective. Furthermore, based on a comparison between linear and nonlinear
regression techniques, a nonlinear form was more accurate in
obtaining the parameters of adsorption isotherm and kinetic
models.
It is crucial to recognize the global implications of thisresearch in a broader context. The effectiveness of FBNPs inremoving Pb2+ from aqueous systems suggests a promising

approach to tackling water pollution, particularly groundwater. Given the severe global water quality challenges, applying
such sustainable and efficient adsorbents becomes critical in
addressing environmental concerns.
However, it is essential to acknowledge the limitationsof this study. This study focused solely on Pb2+ adsorption, neglecting interactions and competition with other ions,which would be common in real-world water systems. Future
research should explore multi-contaminant systems to better
assess more realistic and complex environmental conditions.
The potential for reusability and regeneration of the adsorbent was also not investigated. Future research should assess
FBNPs viability over multiple cycles for enhanced sustainability and economic feasibility in water purification. Such
exploration is crucial for understanding practical implications
and limitations.
While lab-scale tests showed effective Pb2+ removal by
FBNPs, real-world implementation remains unknown. Factors such as organic matter and varying pH in natural waters
may affect adsorption, leading to the need for further studies in natural conditions. Batch-mode adsorption experiments
may only partially reflect continuous-flow conditions in water
treatment. Additional studies using columns are essential to14350661, 0, Downloaded from https://acsess.onlinelibrary.wiley.com/doi/10.1002/saj2.20669 by University Of California, Wiley Online Library on [07/04/2024]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License

14

0.05

16.55

Nonlinear

16.62

Nonlinear

Linear

0.02

16.43

LinearNonlinear

0.343

0.701

0.857
0.392

−0.453 × 10−5

−1.656 × 10−5

0.375

0.669
0.060

0.351

0.782
0.222

−3.633 × 10−50.315

−0.329 × 10−5

0.347

R2

K1 (min−1 )

0.119

1.808

0.054

2.086

0.020

2.175

0.092

1.555

RMSE

0.701

0.392

0.858

0.061

0.669

0.223

0.783

0.315

NSE

0.140

2.139

0.064

2.468

0.024

2.574

0.109

1.840

SEE

16.671

16.62516.70316.65816.55016.50716.70616.655

0.152

0.267

0.228

0.546

0.163

0.285

0.165

0.323

0.883

1

0.902

1

0.869

1

0.908

1

R2

0.074

0.008

0.045

0.005

0.073

0.008

0.059

0.007

RMSE

0.883

1

0.902

1

0.869

1

0.909

1

NSE

0.088

0.010

0.053

0.006

0.087

0.010

0.070

0.009

SEE

16.21

16.21

16.40

16.40

16.12

16.12

16.29

16.29

C (mg
g−1 )

0.028

0.028

0.017

0.017

0.026

0.026

0.025

0.025

0.443

0.443

0.377

0.377

0.441

0.441

0.427

0.427

Kp (mg
g−1 min−1/2 ) R2

Intra-particle diffusion

0.162

0.162

0.114

0.114

0.152

0.152

0.150

0.150

0.444

0.444

0.378

0.378

0.442

0.442

0.427

0.427

RMSE NSE

SEE

0.192

0.192

0.135

0.135

0.180

0.180

0.177

0.177

81.16

89.41

LinearNonlinear

85.39

90.87

LinearNonlinear

75.17

0.836

1.412

3.393

5.396

0.744

0.887

2.134

3.370

KL (L
mg−1 )

0.892

0.956

0.886

0.962

0.875

0.952

0.887

0.962

R2

6.782

0.004

7.648

0.001

6.670

0.008

7.434

0.002

RMSE

0.893

0.956

0.886

0.962

0.876

0.953

0.888

0.962

NSE

8.755

0.005

9.874

0.001

8.611

0.010

9.598

0.002

SEE

0.35

0.29

0.33

0.27

0.36

0.32

0.33

0.28

1/n

41.48242.95867.97565.10832.32233.62357.81157.025Kf (Lmg−1 )

Freundlich

0.985

0.976

0.985

0.975

0.985

0.991

0.987

0.979

R2

2.485

0.035

2.756

0.037

2.302

0.020

2.477

0.034

RMSE

0.986

0.976

0.985

0.976

0.985

0.991

0.988

0.979

NSE

3.208

0.046

3.558

0.048

2.972

0.026

3.197

0.044

SEE

81.39181.391

542.418542.418

27.99227.992268.404

268.404

A (L g−1 )Temkin

10.95

10.95

10.33

10.33

11.57

11.57

10.67

10.67

B (J
mol−1 )

0.874

0.874

0.864

0.864

0.911

0.911

0.874

0.874

R2

7.346

7.346

8.344

8.344

5.626

5.626

7.867

7.867

RMSE

0.874

0.874

0.864

0.864

0.912

0.912

0.874

0.874

NSE

9.483

9.483

10.77310.773

7.263

7.263

10.157

10.157SEE

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Abbreviations: FNCBC400, functionalized biochar nanoparticles derived from corn residues at 400˚C; FNCBC700, functionalized biochar nanoparticles derived from corn residues at 700˚C; FNWBC400, functionalized biochar nanoparticles
derived from wood at 400˚C; FNWBC700, functionalized biochar nanoparticles derived from wood at 700˚C; NSE, Nash–Sutcliffe efficiency coefficient; RMSE, root mean square error; SEE, standard error of estimate.

FNWBC700FNWBC400

74.68

Nonlinear

Nonlinear

Linear

90.02

LinearFNCBC400

FNCBC700

84.53

Model

qm (mgg−1 )LangmuirParameters of the Langmuir, Freundlich, and Temkin isotherms for Pb2+ adsorption by functionalized biochar nanoparticles (FBNPs).

Adsorbents

TA B L E 5

Abbreviations: FNCBC400, functionalized biochar nanoparticles derived from corn residues at 400˚C; FNCBC700, functionalized biochar nanoparticles derived from corn residues at 700˚C; FNWBC400, functionalized biochar nanoparticles
derived from wood at 400˚C; FNWBC700, functionalized biochar nanoparticles derived from wood at 700˚C; NSE, Nash–Sutcliffe efficiency coefficient; RMSE, root mean square error; SEE, standard error of estimate.

FNWBC700FNWBC400

0.03

16.59

Nonlinear

LinearLinearFNCBC400

FNCBC700

0.06

Model

qe (mg
g−1 )

K2 (g(mgmin)−1 )Pseudo-second-order

Pseudo-first-orderqe (mgg−1 )Parameters of the pseudo-first-order, pseudo-second-order, and intra-particle diffusion kinetic models for Pb2+ adsorption by functionalized biochar nanoparticles (FBNPs).

Adsorbents

TA B L E 4

BEHNAM ET AL.

simulate more realistic situations and evaluate the practicalapplicability of FBNPs.
Lastly, while this study provides helpful insights into
removing Pb2+ using FBNPs, future research should tackle
broader environmental challenges, recognize study limitations, and explore innovative ways for advancing water
remediation technologies.
AU T H O R C O N T R I B U T I O N S
Hedieh Behnam: Conceptualization; data curation; formal
analysis; investigation; methodology; software; visualization; writing—original draft. Ahmad Farrokhian Firouzi:
Conceptualization; funding acquisition; methodology; project
administration; resources; supervision; validation; writing—review and editing. Jiří Šimůnek: Conceptualization; validation; writing—review and editing.
C O N F L I C T O F I N T E R E ST STAT E M E N T
The authors declare no conflicts of interest.
ORCID
Hedieh Behnam https://orcid.org/0000-0002-3101-2261
Ahmad Farrokhian Firouzi https://orcid.org/0000-00024955-1845Jiří Šimůnek https://orcid.org/0000-0002-0166-6563
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How to cite this article: Behnam, H., Firouzi, A. F.,& Šimůnek, J. (2024). Pb2+ adsorption on
functionalized biochar nanoparticles: Insights from
nanoparticle characterization and kinetic-isotherm
analysis. Soil Science Society of America Journal,
1–18. https://doi.org/10.1002/saj2.20669

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