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Biochar-Based Contaminant Removal: A Tutorial on Analytical
Quality Assurance and Best Practices in Batch Sorption
Tellulah A. Fernando , Dinushi N. Fernando ,
Sameera R. Gunatilake , Chanaka Navarathna , Xuefeng Zhang
PII:
DOI:
Reference:
S2772-3917(25)00017-9
https://doi.org/10.1016/j.jcoa.2025.100219
JCO 100219
To appear in:
Journal of Chromatography Open
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Revised date:
Accepted date:
4 March 2025
11 April 2025
11 April 2025
Please cite this article as: Tellulah A. Fernando , Dinushi N. Fernando , Sameera R. Gunatilake ,
Chanaka Navarathna , Xuefeng Zhang , Biochar-Based Contaminant Removal: A Tutorial on Analytical Quality Assurance and Best Practices in Batch Sorption, Journal of Chromatography Open (2025),
doi: https://doi.org/10.1016/j.jcoa.2025.100219
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Highlights
● Detailed steps for conducting a comprehensive batch sorption study are provided.
● Reliable quantification methods for adsorbate measurement are outlined.
● Practical considerations for performing batch sorption experiments are discussed.
● Key parameters and conditions for batch sorption studies are thoroughly described.
1
Biochar-Based Contaminant Removal: A Tutorial on Analytical Quality
Assurance and Best Practices in Batch Sorption
Tellulah A. Fernando1, Dinushi N. Fernando1, Sameera R. Gunatilake1*, Chanaka Navarathna2,
Xuefeng Zhang3
1
College of Chemical Sciences, Institute of Chemistry Ceylon, Rajagiriya, CO 10107, Sri Lanka
2
Department of Chemistry, Mississippi State University, MS 39762, USA
3
Advanced Structures and Composites Center, University of Maine, Orono, ME 04469, USA
*Corresponding Author: ranmal@ichemc.edu.lk
Abstract
Biochar, a biomass-derived, ubiquitous, porous, carbonaceous material that is rich in surface
functional groups, is a research frontier in water remediation due to its sustainability and
efficacy. The accuracy and the reliability of laboratory scale biochar-based batch sorption
experiments are essential for a successful transition to pilot scale applications. The accuracy in
preparation of solutions, proper storage conditions, and selection of the most appropriate
instrumental method and its corresponding sensitivity, selectivity, detection limits and
repeatability should be assessed to ensure analytical quality assurance (QA) during the
quantification of analytes. A comprehensive batch sorption study involves optimizing sorption
parameters and conducting kinetic studies, isotherm fitting, regeneration studies, competitive
sorption experiments, and evaluating remediation in a real water matrix. Moreover, sorption
parameters such as the pH, and contact time, should be optimized through iterative adjustments
utilizing standard procedures to avoid erroneous assessments. Biochar-based sorption studies are
conducted by scientists from various disciplines; thus, there is a propensity to overlook the
above-mentioned factors which can impact the credibility of results. This tutorial aims to bridge
this gap by expounding analytical QA and best practices in batch sorption experiments, ensuring
accuracy to obtain reliable results and conclusions.
2
Keywords
Biochar, Analytical QA, Optimization, Quantification, Remediation, Adsorption
1. Introduction
The exponential increase in anthropogenic activities due to widespread industrialization and
urbanization has deteriorated natural water bodies [1]. Therefore, extensive research continues
into sustainable water remediation, with adsorption being the most common area of investigation
due to its cost-effectiveness and simplicity of operation [2].
Various carbon-based materials such as activated carbon, carbon nanotubes, graphene, carbon
black, carbon fibers, fullerenes, and biochar have been used in batch sorption studies across
numerous applications [3, 4]. These materials exhibit significant differences in adsorption
capacity and selectivity, primarily due to variations in their structure, surface chemistry, and
methods of production. Traditional carbon materials typically possess a more established and
consistent structure, making them less adaptable. Among them, biochar stands out for its highly
tunable properties. Biochar is a porous carbonaceous material, primarily obtained via pyrolysis
of biomass [5]. It is a critical area of research for remediation in aquatic systems due to its
economic feasibility and strong adsorption capabilities that enables it to adsorb a wide spectrum
of pollutants [6, 7].
Its capacity and selectivity are closely tied to its physicochemical characteristics, which are
mainly influenced by three factors: the type of feedstock used, the pyrolysis method and
conditions, and any value-addition processes [8, 9]. This allows biochar to be engineered for
specific applications, offering a broad spectrum of functional possibilities.
The adsorption efficiency of biochar is largely determined by the nature of its surface functional
groups and their interactions with the target adsorbate. Oxygenated functional groups are
frequently discussed as they contribute to various interactions, including π–π donor–acceptor
interactions, hydrogen bonding, electrostatic interactions, and cation–π bonding. The extent and
nature of these interactions depend on whether the adsorbate is ionic or non-ionic, as well as on
its electron-donating or electron-accepting properties. In high-temperature biochars, where
surface functional groups are significantly reduced, adsorption is primarily driven by the
3
electron-rich, graphene-like surfaces. In such cases, π–π donor–acceptor and cation–π
interactions become dominant due to the inherent electron-donating nature of the carbon
structure [10].
The standard research approach of a biochar-based adsorption study involves producing and
characterizing the adsorbent material, followed by evaluating its remediation effectiveness [11,
12]. Batch sorption is a widely used method for laboratory-scale assessments of the remediation
characteristics of biochar for adsorbates of interest in a target environment [13, 14]. The process
involves equilibrating the adsorbate with the adsorbent, followed by separation of the adsorbate
and quantification.
Extrapolation into large-scale operations heavily relies on insights gained from laboratory-scale
experiments. As a result, ensuring their reliability is critical to prevent drawing incorrect
conclusions. Given the versatile applications of biochar, research has been conducted by
specialists across various fields, including chemists, engineers, material and environmental
scientists, and forestry specialists [15]. However, scientists may inherently favor their
disciplines, potentially overlooking important interdisciplinary aspects that could result in
inaccurate interpretations.
Optimization of sorption parameters such as the pH, contact time, and biochar dosage, and
conducting kinetic studies and isotherm fitting, require strict adherence to established
procedures to ensure credibility of the results. Additionally, an ideal biochar-based study should
include adequate characterization and consistent reporting of biochar production parameters,
and experimental designs with proper controls, sufficient replicates, and appropriate analytical
methods. Furthermore, the study should employ realistic operational conditions, account for
competitive adsorption phenomena occurring in natural water matrices and evaluate critical
parameters such as long-term stability and regeneration capacity. All these factors contribute to
the reproducibility and reliability of the reported data, enabling the translation of promising
laboratory outcomes into effective field-scale applications. This tutorial seeks to offer a
comprehensive guide on best practices for conducting batch sorption studies and analytical
quality assurance (QA) during the quantification process.
2. Quantification of Adsorbates: Analytical QA
4
2.1 Common Analytical Techniques in Batch Sorption Studies
The remediation of a wide spectrum of aqueous pollutants including heavy metals, oxyanions,
halides, pharmaceuticals, endocrine disruptors, persistent organic pollutants, etc. has been
reported in literature, and various instrumental methods are available for their quantification [1618]. A graphical representation of the most widely used instrumental methods in the 100 most
recently reported studies on biochar-based metallic contaminant removal is shown in Figure 1
(a). For metallic and metalloid adsorbates, atomic absorption spectrometry (AAS) and optical
emission spectrometry (OES) are commonly used. Additionally, a considerable number of
studies also reported the use of UV-Vis spectrometry (UV-Vis) in terms of direct and indirect
colorimetry.
OES has the advantage of simultaneous multi-element analysis, which is important when
studying the competitive sorption of multiple adsorbates [19]. In OES, inductively coupled
plasma (ICP) techniques are more commonly used than microwave plasma instruments and
flame photometers, likely due to their superior sensitivities and detection limits for most metallic
analytes. However, when studying a single element and when the applied concentrations are
within quantifiable limits, atomic absorption techniques are reliable and cost-effective.
Inductively coupled plasma mass spectrometry (ICP-MS) techniques provide the ability to
distinguish between isotopes of the same element, improving selectivity and achieving lower
detection limits [20]. Therefore, unless such capabilities are required, this technique can be
considered excessively expensive. It is also important to note that, in many laboratory settings,
the available reliable instruments are used for quantification despite their higher running costs.
This might explain why some studies focus on single elements at higher concentrations using
expensive ICP techniques.
In the case of molecular species, UV-Vis is used in the majority of recently reported studies as
shown in Figure 1 (b). This technique is ideal for quantifying analytes with sufficient nonbonding or π-bonding electrons, which can undergo electronic transitions to their lowest
unoccupied π orbitals when exposed to ultraviolet or visible radiation [21]. In most studies where
a single adsorbate is analyzed and no separation is required, UV-Vis provides reliable
5
quantification. Molecular fluorescence spectroscopy is also a sensitive and reliable technique;
however, its quantification is limited to analytes with fluorophores [20].
High-performance liquid chromatography (HPLC) is commonly reported in studies involving
multiple adsorbates. On the other hand, liquid chromatography hyphenated mass spectrometry
(LC-MS), offers greater selectivity and allows the quantification of analytes without
chromophores [22]. Although chromatographic separation effectively eliminates matrix
interferences, its use in single-adsorbate adsorption studies is minimal, likely due to high running
costs. It is also important to note that emission spectroscopic techniques cannot be used for
molecular species due to high operating temperatures [20].
Although calibration curve-based quantification is commonly practiced, absorption, emission,
and fluorescence are fundamentally different techniques based on distinct principles. Student
researchers without a strong background in analytical chemistry may sometimes focus primarily
on material science and sorption chemistry, without fully considering the analytical technique
and QA in adsorbate quantification. Understanding the principles, limitations, and key
considerations of the chosen quantification method before starting experiments is essential for
obtaining reliable results.
2.2 Ensuring Accuracy in Analytical Methodology
Upon selecting the most appropriate instrumental method to quantify the adsorbate, it is
important to ensure the analytical QA of quantification by assessing key parameters such as the
linear range, coefficient of determination (R2), sensitivity, and selectivity. A structured overview
of such analytical method development is illustrated in Figure 2.
2.2.1. Linear Range
The linear range of a calibration curve is the concentration range where the response remains
directly proportional to the analyte concentration [20]. Thus, all calibration points should lie
within this range. While it is not necessary to utilize the entire linear range of the instrument, the
selected calibration range must be contained within it and should be optimized to ensure the
adsorbate concentration falls within the curve's mid-range, minimizing uncertainties in the
measurement of unknowns [23, 24].
6
Ideally, the calibration standards used should cover the range of concentrations encountered in
the test samples to avoid excessive dilutions in multiple samples [23, 24]. Extrapolating beyond
the established concentration range of the calibration curve for direct quantification is not
recommended, as it can lead to significant inaccuracies and compromise the reliability of the
results.
Although uncommon in batch sorption, method detection limits can differ from instrument
detection limits if the analytical method involves sample preparation [20]. It is also important to
note that a near-zero reading indicates that the analyte is present at a concentration below the
instrument's detection limit, rather than being absent. Moreover, the likely concentrations of the
pollutant to be remediated in the target environment should be considered in the sorption
experimental design, and quantification methods capable of detecting such concentrations should
be selected.
Even though a calibration curve can theoretically be generated with three standards, a minimum
of five calibration standards is typically employed for greater accuracy and reliability. It is best
practice to generate standards in triplicates and include error bars in calibration curves to
represent measurement variability [24].
2.2.2 Coefficient of determination
R2 is a statistical measure that indicates the proportion of variance in the dependent variable that
is accounted for by the independent variable [25]. It is commonly used in linear regression as a
key indicator of the degree to which a constructed model is well fitted [23]. The value of R2
ranges from 0 to 1, with a value closer to 1 indicating a higher proportion of variance in the
dependent variable being explained by the model, thus suggesting a stronger model fit of the data
[25]. Although R2 is commonly used as a measure of linearity, it can easily be misinterpreted,
and several factors should be considered when evaluating the obtained R2 value.
A data point that significantly deviates from the overall trend, known as an outlier, has a
response (y-value) that falls well outside the predicted range, either much higher or lower than
the rest of the dataset [26]. It has the propensity to significantly affect statistical measures such
as the average and standard deviation, and so the R², making the dataset appear more skewed
7
than it is. On the other hand, a point of leverage generally has extreme predictor values (xvalues) and is less relevant in calibration curves since concentration is predetermined and
controlled [23]. However, extreme x-values can influence regression, making their identification
and removal crucial to prevent incorrect conclusions. There are several methods of identification
for outliers and leverage points [26]. In addition, many studies state that a preliminary visual
inspection of the plot is helpful to identify outliers or points of leverage [27].
Experimental design is an important factor when considering linearity. Uneven data distribution
is one of the principal causes of linearity failing since some points will have high leverage [27].
Therefore, it is essential to select appropriate standard concentrations, which are ideally
equidistant across the calibration range. If the concentration levels increase exponentially across
the x-axis, the latter calibration points will have a high leverage on R2, potentially skewing the
overall model accuracy [27]. It is also important to note that if the slope of linear regression is
considerably small, even though it is uncommon in atomic and molecular absorption
spectroscopy, the interpretation of R2 becomes invalid.
2.2.3 Sensitivity
The sensitivity of a calibration curve can be defined as the unit change in response per unit
change in concentration or the slope of the curve. Sensitivity depends on the analytical
technique, instrument condition, analyte of interest, and the matrix [20].
Solution pH is a key factor affecting sensitivity and can be evaluated by constructing calibration
curves with standards prepared at different pH levels. If instrument sensitivity varies
significantly with pH, it is crucial to match the pH of standards and unknowns to ensure accurate
quantification. For accurate results, it’s best to maintain the pH that provides the highest
instrument sensitivity. In batch sorption studies, a specific pH is usually maintained throughout
the experiment. However, if this pH differs from the one needed for analysis, it should be
adjusted beforehand. For example, heavy metal sorption is more effective at higher pH, but AAS
standards are prepared at lower pH for better metal solubility, requiring adjustment. It is
important to account for any volume changes during pH adjustment, as they may alter analyte
concentration.
8
In UV-Vis analysis, it is routine to determine the wavelength of maximum absorption (λmax) of an
analyte through a wavelength scan to ensure maximum sensitivity of the method of analysis.
However, the λmax can be influenced by pH and matrix components. For example, pH changes
that occur in the media during the experiment. This can be addressed by constructing calibration
curves using standards at the same pH as the sample after batch sorption, or by adjusting the pH
of unknowns before analysis to match that of the calibration curve. Another approach is to use
the isosbestic point which is defined as the wavelength, wavenumber, or frequency where the
total absorbance of a sample remains constant during a chemical or physical change [28]. In
some cases, an isosbestic point can be used for analysis instead of λmax, bypassing the pHdependent shifts in λmax. However, this may reduce the sensitivity of the method.
Another factor that can affect the sensitivity of an instrument is a matrix mismatch between the
sample and the standards used to prepare the calibration curve known as the matrix effect (M%)
as shown in equation (a). M% can be evaluated by comparing the sensitivities of the calibration
curves prepared in a neat solvent and sample matrix using the equation (a), where Mm is the
slope of the calibration curve in the matrix and Mp is the slope of the calibration curve in the neat
solvent [29].
[
(
)
]
(a)
A positive M% indicates matrix suppression where the curve is less sensitive, whereas a negative
value indicates matrix enhancement where the curve is more sensitive [29]. In either case,
significant differences in the sensitivity of the two curves can result in the underestimation or
overestimation of the analyte concentration producing erroneous results. Therefore, careful
evaluation of the M% is required to ensure the accuracy of the results obtained.
2.2.4 Selectivity
Selectivity refers to the ability of an analytical method to determine analyte(s) of interest in a
complex mixture without interference from other components [20, 30]. Specificity represents the
highest level of selectivity and is defined as the ability of a method to measure only the intended
analyte, without any contribution from other substances [30]. The selectivity of a method is a
9
critical parameter, as it determines its capacity to differentiate the analyte from the matrix and
produce accurate results.
The experimental design of biochar-based studies requires optimizing the remediation of the
target analyte in a neat solvent prior to conducting studies in real matrices. The selectivity of the
analytical method is usually not a cause for concern in such cases due to the absence of
interfering agents in the matrix. However, when conducting competitive sorption studies and
evaluating the effect of competing species in the matrix, the selectivity of the analytical method
should be investigated.
Figure 3 illustrates the absorption spectra of compounds A, B, and C, where compounds A and B
experience spectral overlap while compound C does not. Therefore, in a competitive study
involving all three compounds, A and B will require further evaluation using a method free from
interference. A theoretical possibility is to derivatize the species to shift the λmax. However, this
can be practically infeasible due to two reasons: (1) finding a derivatization technique that is
simple and efficient can be difficult; (2) the derivatization of a large number of samples usually
required during a batch sorption study can be both time-consuming and costly. Therefore, the
most common method to avoid spectral overlaps although it can compromise the sensitivity of
the measurement, is to select a wavelength of analysis that is different from the λmax with
negligible interference from competing species present [8]. It is also important to note that, in
general, solutions analyzed without separation will exhibit a broad range of absorption, thus it
can be challenging to find a wavelength devoid of interferences.
In the case of atomic spectroscopy, matrix interference due to overlapping of spectral lines is
highly probable. For instance, the Cd line at 228.802 nm can be interfered by the As line at
228.812 nm [20, 22]. Therefore, in such cases, it is essential to select a wavelength with
negligible overlap with interfering species although it could compromise the sensitivity of the
measurement.
2.3 Practical Aspects
10
In addition to selecting an appropriate analytical method with sufficient sensitivity and
selectivity, several other key factors must be considered to ensure the accurate and reliable
application of these techniques.
2.3.1 Quantification Process
UV-Vis can be performed using either single or dual-beam systems. In single-beam instruments,
the reference is measured first, followed by the sample. In contrast, dual-beam systems measure
both the sample and reference simultaneously, which compensates for fluctuations in light
intensity and changes in line voltage to the detector, ensuring more accurate and stable results.
While dual-beam systems are recommended for enhanced accuracy, if a single-beam system is
used, accuracy can be ensured by measuring the reference at regular intervals or frequently
recalibrating the instrument to account for any potential fluctuations or drift [22].
Moreover, when analyzing a large number of samples by both atomic and molecular
spectrometry, especially with a single-beam instrument, running of standards at intervals
between unknowns is encouraged to monitor instrument drift and ensure accurate results [31].
A zeroing solution, typically composed of the same matrix as the sample but without the analyte
at measurable concentrations, is often used to set the spectrometer's null signal [21]. As a
precaution, the zeroing solution can be prepared by shaking the matrix with biochar. This helps
account for any potential color changes or interactions between the biochar and the matrix,
ensuring that the spectrometer only detects changes in the analyte, and is sufficiently zeroed.
Additionally, in general the quality of data should be evaluated, avoiding noisy, limited-range
datasets with significant outliers.
2.3.2 Other Considerations
The choice of cuvette material is crucial for accurate absorbance measurements, as transparency
varies with wavelength. Borosilicate glass is suitable for the visible range but absorbs and
scatters UV radiation, making it inappropriate for UV measurements. For the UV region, quartz
or fused silica cuvettes are preferred due to their superior transparency, minimizing light
attenuation and ensuring reliable results [22].
11
Furthermore, cuvette mismatches should be avoided in UV-Vis when conducting analysis using
a reference or measuring sequential samples. Even using cuvettes made from the same material
(e.g., quartz), differences in wall thickness, surface quality, and manufacturing tolerances across
brands or production batches can introduce errors. Thus, using cuvettes from the same
manufacturer and stock helps maintain consistency and reproducibility [22].
Disposable plastic cuvettes are commonly used for routine, low-accuracy applications where cost
and convenience are prioritized over precision [21]. However, their slight path length variations
and low chemical resistance make them less suitable for more precise studies such as biochar
batch sorption research, where accurate and consistent measurements are critical. Therefore, it is
recommended to use glass or quartz cuvettes to ensure reliable results and maintain chemical
compatibility.
When taking measurements using the AAS, the viscosity of the sample should closely match that
of the standard as the sample solution is self-aspirated to the solution. Disparities in viscosity can
affect the aspiration rate and thus lead to inaccurate estimations of the analyte concentration [32].
Aspiration rates are less affected in OES as the aspiration rates are regulated by peristaltic
pumps. It is important to note that all solutions should be free of particles for instrumental
analysis and properly filtered beforehand. Sample filtering is discussed in Section 3.1.7.
If chromatographic analysis is required, the selection of liquid chromatography (LC) vials is
important [21]. When analyzing photoactive species, amber-colored vials are recommended to
minimize light exposure triggered degradation. For per- and polyfluoroalkyl substances (PFAS)
analysis, polypropylene or High Density Polyethylene (HDPE) vials are typically preferred due
to the known adsorption of PFAS onto glass surfaces [33].
3. Best Practices for Conducting Batch Sorption Studies
3.1 Practical Considerations
3.1.1 Storage of biochar
A single batch of biochar is recommended for systematic sorption studies. Therefore, the mass
required for all sorption experiments and characterizations should be carefully assessed prior to
12
biochar production. An excess amount should be prepared to account for losses and potential
experiment repetitions. If the available production facilities are insufficient to produce the
required amount, multiple batches can be produced within the shortest possible time and should
be thoroughly mixed to form a single lot. Materials produced in different batches are typically
used only in inter-batch studies to evaluate sorption consistency.
Moreover, to avoid alterations to the surface properties of biochar, particularly functionalized
biochar, experiments should be performed within the shortest possible timeframe. For
laboratory-scale studies, biochar should be stored in sealed containers under inert conditions,
protected from moisture, to minimize surface oxidation and passivation [34, 35]. Additionally,
storing biochar in multiple small containers, rather than one large container, helps minimize air
exposure during retrieval.
3.1.2 Particle Size
Maintaining a uniform particle size is critical for reliable sorption tests and can be achieved by
sieving to obtain the desired size range [36, 37]. While smaller particles offer a higher surface
area, they present challenges: in column studies, excessively small particles may require high
pressure to maintain flow, whereas in batch sorption experiments they can float, potentially
affecting the adsorbent's effective surface area [38].
The modification of biochar can alter the particle size, thus, to ensure consistency of the particle
size, it is advisable to initially functionalize a broader range of particle sizes; followed by sieving
to a desired particle size. Moreover, grinding or crushing functionalized biochar can change its
surface properties and should be avoided [39, 40].
It should also be noted that the adsorption capacities reported in literature are often compared as
an initial assessment when selecting feedstock, production conditions, and modifications.
However, variations in biochar particle size are often overlooked, despite its potential impact on
adsorption performance.
3.1.3 Weighing Biochar
13
Biochar dosages typically range from 10 to 100 mg, thus requiring the use of a properly
calibrated analytical balance [41, 42]. Measurements should either be taken at a fixed, precise
value or weighed and recorded with deviations not exceeding 0.5 mg, with the exact measured
mass used for calculations.
When weighing biochar, the weighing vessel, such as a weighing boat or weighing paper, should
be selected to prevent particles from adhering to their surface. This ensures that the measured
biochar can be easily and completely transferred to the container without loss. Alternatively, the
biochar can be weighed directly into the experimental tube to avoid any losses during transfer,
though this may reduce accuracy and make handling more challenging.
3.1.4 Preparation of Standard Absorbate Solutions
For studying sorption properties in the absence of competing species, quality-assured de-ionized
or distilled water should be used to prevent interference from ions or molecules that could
compete. Additionally, it is crucial to ensure the water is neutral, as variations in pH across
different water types may necessitate different amounts of adjusting agents. Moreover, the
presence of atmospheric and dissolved carbon dioxide can reduce the pH of water, leading to
slight acidity. To mitigate this effect, the test water should be purged with nitrogen before pH
adjustment in sorption tests, ensuring the removal of dissolved atmospheric carbon dioxide [43].
The selection of solutions for pH adjustment should depend on the adsorbate being studied. For
example, in an oxyanion adsorption study, pH adjustments to acidic conditions should be made
using HCl, whereas adjustments to basic conditions should utilize NaOH [44]. Using alternative
reagents such as HNO3 or Na2CO3 may introduce competing anions, potentially affecting the
adsorption process.
For batch sorption experiments, a single stock solution should be prepared, with the volume
estimated to provide a sufficient working solution for the planned experiments within a
reasonable timeframe, ensuring that a slight excess is included to account for any potential losses
during the preparation of working solutions. This approach minimizes variability and prevents
inaccuracies that may arise from repeated weighing and solution preparation, as sorption
experiments usually require a large number of samples. Additionally, serial dilutions are
14
preferred over direct dilution of small volumes to large volumes, as the latter introduces higher
errors, compromising the reliability of the results [21, 22].
Proper storage is essential to prevent photochemical degradation, thermal degradation, and
microbial contamination [45, 46]. Moreover, to avoid adsorption or reactions with the storage
vessel the compatibility of the solution storage containers must be carefully considered. Metal
ions typically adsorb onto glass containers over time, while PFAS can form micelles on glass
surfaces, leading to solution inhomogeneity and reduced analyte concentrations [33].
In batch sorption studies that span extended periods, the shelf life of the stock solution becomes
an important consideration, as it can vary depending on the nature of the analyte [21, 22]. The
concentration of the stock solution should be verified each time it is used, especially when the
solution is utilized on different days. While using a single stock solution is preferred, multiple
preparations may be necessary.
To ensure the accuracy of stock solutions prepared on different days, a relative analysis using a
calibration curve under identical instrument settings is essential where the concentrations of both
the new and old stock solutions are verified using a fresh calibration curve prepared using the
new stock solution. Any discrepancies may indicate errors in preparation, degradation, or
instrument variability. Additionally, if the stock solution contains buffers or salts, a control
sample should be analyzed to confirm that matrix effects remain consistent across different
batches. To account for instrument performance, a stable quality control standard should be
measured regularly to detect drift or inconsistencies [20].
It is also important to note that the transfer volume for each batch sorption experiment should be
accurately measured. Properly calibrated pipettes should be used to achieve this accuracy, as
measurements with measuring cylinders are not recommended due to potential inaccuracies [21,
47]. Additionally, the transfer volume should be kept consistent across all experiments to ensure
reliable and reproducible results.
3.1.5 Impact of Tube Material on Adsorption Efficiency
The selection of tube material for biochar batch sorption experiments significantly impacts
accuracy and reproducibility. Glass containers are used due to their chemical inertness, ease of
15
cleaning, and minimal interaction with organic compounds, though adsorption of metal ions and
fragility remain concerns [48, 49]. Polypropylene tubes offer affordability and resistance to weak
chemicals but may adsorb hydrophobic contaminants and leach plasticizers [41]. Teflon tubes
provide superior chemical resistance and minimal adsorption but are costly and less rigid [45].
While glass is generally preferred for its balance of inertness and reusability, Teflon is ideal for
organic sorption studies, whereas polypropylene serves as a cost-effective alternative with
certain limitations.
3.1.6 Batch Sorption Process
Continuous agitation of the adsorbate-containing solution with biochar is required to ensure
proper mixing and contact. Allowing the biochar to rest in the solution or relying on occasional
shaking is not recommended.
Choosing the most appropriate method for achieving equilibrium in biochar adsorption studies
requires careful consideration of factors such as the adsorbate's nature, biochar characteristics,
experimental setup, and research objectives. Vortexing is ideal for small-scale, rapid mixing but
may not suit delicate particles due to high shear forces [50]. Shaking is better for gentler,
prolonged mixing, making it suitable for kinetic studies [51]. Sonication excels in dispersing
agglomerated particles, enhancing surface contact, and is particularly useful for nano-sized
biochar or challenging adsorbates [49]. Stirring, whether magnetic or mechanical, offers
versatility for various volumes and controllable shear rates, ensuring homogenous mixing
without excessive agitation [52].
Each method’s speed or intensity should be tailored to optimize adsorption efficiency while
maintaining sample integrity. Preliminary testing is recommended to fine-tune these parameters
for specific experimental conditions. It is important to note that the method of equilibration
should be the same throughout experimentation; most studies have used shaking as the technique
of mixing.
The direction of shaking is also important, with horizontal (180°) agitation being ideal. To
ensure effective mixing, only about two-thirds of the tube should be filled with the adsorbate
solution, as an overfilled tube can restrict proper movement and hinder mixing.
16
The shaking speed in reported biochar batch sorption experiments typically ranges from 100 to
200 rpm [53-55]. This moderate shaking speed ensures adequate contact between the biochar and
the solution, facilitating efficient sorption while preventing excessive turbulence. The optimal
shaking speed should be determined experimentally, as a speed that is too high could cause
excessive mixing, leading to loss of sorbed compounds or breakage of the biochar, while a speed
that is too low may result in insufficient contact and slower equilibrium times. It is important to
maintain a constant shaking speed across all experiments.
It is also important to assess the adsorption of the adsorbate onto the container walls, using a
blank sample of the adsorbate without biochar [56, 57]. Additionally, if physically or chemically
modified biochar is being studied, the adsorption capacity of pristine biochar can be analyzed as
a control [58]. Furthermore, biomass could be shaken under the same conditions to compare its
adsorption capacity with that of biochar.
Thermodynamics associated with adsorption-desorption equilibria are temperature-dependent.
However, in most of the literature, the temperature in sorption studies has not been optimized, as
environmental remediation processes typically occur at room or ambient temperature, ranging
from 25 to 30 °C [45, 59, 60]. If room temperature is used for sorption tests, it should be
carefully controlled and maintained consistently throughout the experiment. The optimal
temperature should, however, align with the conditions of the intended commercial or real-world
application. Excessively high temperatures, though, may be impractical and cost ineffective.
3.1.7 Sample Preparation Considerations for Instrumental Analysis Following Adsorption
The presence of unfilterable suspended particles in real waters can affect the accuracy of
quantitative measurements by causing light scattering in UV-Vis and interferences in atomic
analysis [20]. Additionally, these particles can block the atomizer capillary in AAS, further
impacting the analysis.
Gravity filtration is a commonly used technique to separate the solution from biochar. However,
it may introduce errors with certain adsorbates, such as dyes, as they can adsorb onto the filter
paper during filtration [57]. To minimize this potential error, alternative techniques, such as
centrifugation or pre-saturating the filter paper with a small aliquot of the adsorbate can be
17
employed, where the aliquot is discarded before collecting the remaining filtrate for analysis
[61]. If the pore size of standard filter papers is inadequate, suction filtration with microfilter
papers is recommended [62, 63]. Depending on the instrumental analysis, syringe filtration of the
sample through a filter with the appropriate pore size may also be necessary [64].
3.2 Structured Approach to Batch Sorption Experiment Design
A batch sorption experiment is a laboratory technique used to study the adsorption of a fixed
volume of adsorbate onto a known amount of adsorbent under controlled conditions in a closed
system. The mixture is stirred or shaken for a specific period to reach equilibrium and the
concentration of the adsorbate in the solution is measured before and after the experiment to
determine the amount adsorbed onto the adsorbent [65].
A comprehensive batch sorption study is required to accurately assess the capability of biochar
as an adsorbent for specific analytes. The adsorption capacity of biochar is influenced by several
key parameters, including contact time, medium pH, initial adsorbate concentration, and biochar
dosage [66].
Batch sorption experiments can generally be conducted using two main approaches: the
commonly used one-factor-at-a-time method or the more systematic Design of Experiments
(DOE) approach [67, 68]. While DOE allows for the simultaneous study of multiple factors and
their interactions, this tutorial primarily focuses on the former approach since it is more
frequently reported in literature possibly due to its simplicity and ease of application in
fundamental studies [69, 70].
Figure 4 illustrates the usual flow in which experiments in an ideal biochar-based batch sorption
study. The first parameter is typically optimized by keeping the remaining parameters constant,
based on literature data or prior assumptions. The subsequent parameters are then optimized
under previously established conditions. For instance, when optimizing pH as the initial
parameter, a contact time derived from literature may be used [71]. Subsequently, when
optimizing contact time, the previously determined pH is applied. However, this sequential
approach assumes that parameters act independently, which may not always be valid. For
18
instance, the optimized pH may only be valid for that specific contact time, as adsorption
kinetics can depend on both pH and contact time.
Adsorption efficiency can be influenced by interactions between variables, and a more
comprehensive approach, such as response surface methodology, may be required for precise
optimization. Although evaluating each parameter across the full range of conditions presents
practical challenges, a more scientifically rigorous approach would involve assessing the
interdependence of adsorption parameters to ensure more accurate and reliable results. This
section discusses the methodological approach, key considerations, and QA in batch sorption
experiment design.
3.2.1 The Percent Removal and the Adsorption Capacity
The percent removal (R%), equation (b), and adsorption capacity (qe, mg/g), equation (c), are
widely used parameters for assessing the efficiency of biochar as an adsorbent [72].
(b)
(c)
R% in an adsorption process is calculated using equation (b), where C₀ and Cₑ represent the
initial and equilibrium analyte concentrations, respectively. These concentrations are expressed
in mass concentration units (typically milligrams of adsorbate per liter, mg/L) or molar
concentration units (typically moles of adsorbate per liter, mmol/L). R% is a unitless parameter
that represents the proportion of analyte removed relative to its initial concentration.
For a reliable sorption study, maintaining R% within the range of approximately 20%–80% is
recommended to minimize misinterpretations. A removal efficiency exceeding 90% does not
necessarily indicate complete saturation of adsorption sites, while lower R% values may result
from higher initial adsorbate concentration. Values below 10% do not necessarily imply biochar
inefficacy but may instead be attributed to an excessively high initial adsorbate concentration.
Moreover, extremely low R% values can lead to inaccuracies in interpretation, as the difference
19
between initial adsorbate concentration and equilibrium adsorbate concentration may become
negligible.
The term qe is defined as the maximum amount of adsorbate retained per unit mass of biochar
under specific conditions, as expressed in equation (c), where V represents the volume of the
analyte solution (L), and m denotes the mass of the biochar used (g). It can be expressed in mass
concentration units (typically milligrams of adsorbate per gram of adsorbent, mg/g) or molar
concentration units (typically moles of adsorbate per gram of adsorbent, mmol/g). Since qe
accounts for both the quantity of adsorbate removed and the mass of the adsorbent, it provides a
more representative measure of adsorption efficiency.
Relying solely on R% can be misleading, as it does not account for the amount of adsorbent
used. Conversely, while qe provides insight into the adsorbent’s capacity, it does not directly
indicate the proportion of analyte removed from the solution. Therefore, a comprehensive
evaluation of adsorption performance should incorporate both parameters. To ensure a reliable
assessment, qe should ideally be determined while maintaining R% within the recommended
range.
However, there may be cases where two biochars are compared, and the efficacy of one biochar
is notably higher than that of pristine biochar. In such instances, maintaining an R% within the
20%–80% range for all materials can be challenging. Although it is generally recommended to
use the same initial concentration for all materials in a given experiment to ensure consistency,
using different concentrations can be justified when evaluating biochars with substantially
different adsorption capacities.
3.2.2 Optimization of Biochar Dosage
The optimal adsorbent dosage, typically ranging from (50-200) mg, is determined by varying the
adsorbent mass at a constant adsorbate concentration and identifying the intersection of the
corresponding adsorption capacities and removal efficiencies, curves [51, 73-75]. Conversely,
batch sorption condition optimization using a predetermined biochar dosage is also commonly
reported in the literature [51, 55, 75].
20
Sorbent mass is a quantitative measurement that must be conducted with a minimum precision of
0.1 mg to ensure accuracy.
While using smaller amounts of biochar can lead to greater
percentage errors in weighing, larger amounts require a greater supply of material, which may
not always be feasible for the study.
3.2.3 Selection of initial adsorbate concentration
The usual practice is to use relatively high initial adsorbate concentration to evaluate adsorption
capacities and other adsorptive characteristics of biochar. However, it is crucial to also conduct
sorption experiments where conditions such as the pollutant concentration, and presence of
competing species align with the realistic environment. This ensures a comprehensive
understanding of the material's performance in practical applications.
For example, Rodrigo et al. utilized Fe3O4/Douglas fir biochar for the removal of PFAS
compounds. Although the authors initially conducted their adsorption isotherm experiments at
elevated initial concentrations of perfluorooctanoic acid (PFOA) and perfluorooctane sulfonate
(PFOS) to determine the adsorbent’s maximum capacity, they subsequently demonstrated
realistic adsorption performance by using a 1 parts per billion (ppb) solution. This approach
successfully illustrated the material’s practical capability to achieve the former Environmental
Protection Agency (EPA) advisory level of 70 parts per trillion (ppt) for PFAS in drinking water
[69].
In addition, high adsorbate concentration may induce various phenomena, such as iron leaching
in iron-incorporated biochar, or cause a shift in the adsorption mechanism from chemisorption to
stoichiometric precipitation. For certain adsorbates, elevated concentrations could also result in
micelle formation, as observed with surfactants like PFAS, or aggregation phenomena, as seen
with organic molecules such as dyes [76].
3.2.4 Determination of Optimum pH
The optimal pH for sorption is determined by preparing a series of adsorbate solutions, adjusting
the pH across the required range, and then maintaining a constant desired concentration [77, 78].
After performing batch sorption with biochar for the desired duration, Ce and R% are calculated.
21
This allows for a comparative assessment of the biochar’s removal efficiency at different pH
levels.
It is considered best practice to conduct the sorption study immediately after preparing the
solution to minimize changes in pH, which may result from the dissolution of atmospheric
carbon dioxide in the solution. Additionally, measuring the final pH of the solution immediately
after the batch sorption process is essential, as it provides valuable insights into the binding
interactions and helps interpret the adsorption behavior of the biochar [79].
The pH-dependent speciation of an adsorbate is a critical factor to consider during pH studies.
Certain adsorbates may precipitate at basic pH levels such as Pb2+ and Cd2+ while others can
become protonated under acidic conditions, forming weak acids (e.g., PO43-, AsO43-) [80].
Additionally, organic species can exist in various charged states depending on pH [43].
Consequently, assuming the adsorbate remains unchanged across a broad pH range constitutes an
oversimplification of the adsorption system and can lead to inaccurate interpretation.
3.2.5 Determination of Optimum Contact Time and Kinetic Modelling
Two common approaches are used to optimize contact time. The first involves preparing separate
tubes for each time interval, where solutions are withdrawn, and the adsorption capacities are
measured. This method ensures accurate, independent samples and minimizes contamination
risks, though it requires more material and labor. The second approach uses a single tube, where
the solution is withdrawn at different intervals, conserving material and time. However, this
method introduces potential risks, including sample contamination, volume changes, and
disturbance of the biochar, which could affect the adsorption process. While both methods are
employed, using separate tubes is generally preferred for its reliability and precision in
determining the adsorption kinetics.
In addition to determining the equilibrium time, defined as the point at which the adsorption
capacity remains constant or shows a negligible decrease with further contact time, adsorption
kinetic models are used to describe the rate and mechanism of sorption. Reaction-controlled
kinetic models, such as the pseudo-first order (PFO) and pseudo-second order (PSO) models, are
commonly applied to determine rate constants, providing quantitative insights into adsorption
22
kinetics [81, 82]. These models also aid in interpreting biochar-sorbate interactions. Literature
suggests that a well-fitted PSO model is often associated with chemisorption, whereas a wellfitted PFO model typically indicates physisorption [81, 82]. However, care must be taken to
ensure that reaction-controlled kinetic models are generated only using data points up to the
equilibrium time, as the inclusion of post-equilibrium data may lead to erroneous interpretations.
As illustrated in Figure 5, incorporating data beyond equilibrium may result in an
underestimation of PFO parameters and an overestimation of PSO parameters.
Figure 5 depicts the time optimization of a biochar-based sorption experiment where the study
spans 240 minutes and the equilibrium contact time is established as 90 minutes as illustrated in
Figure 5(a). In the case of PFO kinetic modeling, when extending the model from 90 min to 240
min the value of R2 has decreased from 0.970 to 0.645 as shown in Figures 5(b) and (c)
respectively. In contrast, PSO kinetic modeling displayed an increase in the value of R2 from
0.934 to 0.983 as displayed in Figures 5(d) and (e) respectively.
Additionally, diffusion-controlled kinetic models are used to determine the rate-limiting step in
adsorption based on the assumption that adsorption kinetics are influenced by bulk diffusion,
film diffusion, intraparticle diffusion, and equilibrium [18]. The Weber-Morris model is widely
applied to assess intraparticle diffusion, while the Boyd model is used to differentiate between
film and pore diffusion as potential rate-limiting steps [83].
It is crucial to collect data until the system reaches equilibrium, which is when the adsorption
capacity stabilizes. To optimize equilibrium time, a minimum of five time intervals should be
considered before equilibrium is assumed to have been reached. Rapid stabilization of adsorption
capacity may lead to inaccurate results, as it may not reflect the true equilibrium state of the
system.
In the case of excessively short contact times, the time taken for the transfer of solutions may
become significant. To compensate for any error associated, solutions should be transferred at
the midpoint of the pre-assessed transfer time. However, filling all vials simultaneously can be
practically challenging when kinetic experiments involve batch adsorption using multiple vials.
It is advisable to start filling the vials designated for longer time intervals (e.g., 1, 5, or 12 hours)
first, as minor delays will not significantly affect these data points. However, shorter intervals,
23
such as a few seconds or minutes, should be handled carefully. In such cases, it may be
beneficial to have multiple people assisting to ensure precise timing and minimize errors.
The selection of pollutant concentrations is crucial in kinetic studies. Low concentrations can
lead to very slow adsorption kinetics, potentially causing misinterpretation of the time needed to
reach equilibrium and overly generalized conclusions. If these equilibrium times are used in
isotherm studies, incomplete adsorption may occur at lower concentrations, as equilibrium might
not be fully achieved within the allotted time. This can result in errors that are not easily
detected.
Typically, kinetic model fittings assume a constant adsorbate concentration throughout the
experiment. However, if the adsorption process is significant, the remaining concentration after
each time interval can vary substantially. One alternative method to address this is to use a very
high initial concentration, conduct the kinetic study, and subsequently digest the adsorbent to
determine the adsorbed concentration, as the solution concentration would remain nearly
constant. Although this approach is cumbersome due to potential losses and contamination
during sample preparation and digestion, it is considered the gold standard for accurately
determining adsorption kinetics. This method is commonly employed in nanoparticle research
involving gold and silver [84].
3.2.6 Isotherm Modelling
An adsorption isotherm is generated by varying the initial adsorbate concentration while
maintaining a constant biochar dosage. At equilibrium, Cₑ and qe are determined, enabling the
evaluation of adsorption behavior through isotherm models. Two- and three-parameter isotherm
models are widely used to characterize biochar adsorption. Two parameter models describe
equilibrium interactions, assuming surface homogeneity or heterogeneity. Three-parameter
models, with an additional fitting parameter, enhance accuracy in systems exhibiting complex
adsorption behavior. These models are well documented in literature [85].
It is beneficial to only apply the appropriate models to the system under study. In some instances,
frequently used models can be inappropriate such as using a simple Langmuir model for
heterogeneous adsorption. Simplistic models may fail to capture multiple simultaneous
24
adsorption mechanisms, while excessively complex models that perfectly match experimental
data yet lack predictive power, can reduce the reliability of the findings. Moreover, factors such
as biochar's inherent heterogeneity and the complexity of adsorption processes must be explicitly
considered when selecting the appropriate models [86].
Linear isotherm models are simple and easy to apply but may introduce bias when transforming
data, leading to inaccuracies. Non-linear models, although more computationally demanding,
provide a better fit by directly matching data to the isotherm equations, offering greater accuracy.
Non-linear models are especially useful for systems with complex adsorption behaviors,
providing more precise insights into adsorption characteristics. A comparative analysis of models
using goodness-of-fit criteria, such as Root Mean Square Error (RMSE), Akaike information
criterion (AIC), and chi-square, should be considered alongside R² when selecting the most
appropriate model for data analysis [85].
Moreover, avoiding force-fitting errors in the analysis of adsorption isotherms and kinetic data in
biochar studies can prevent misleading conclusions and flawed adsorption system designs.
Additionally, the physical plausibility of fitted parameters should be considered to avoid
unrealistic estimations such as adsorption capacities exceeding theoretical monolayer coverage.
It is important to ensure that data is collected until the adsorption capacity reaches a stable value,
indicating equilibrium. At least five data points should be recorded before equilibrium is
reached, as datasets that show rapid equilibrium may not provide sufficient variation for accurate
isotherm analysis.
The algorithm used to evaluate data when conducting model fitting for isotherm studies should
be consistent. For example, either the Orthogonal Distance Algorithm (ODA) or the LevenbergMarquardt Algorithm (LMA) can be used. However, using the two interchangeably in the same
study to compare adsorption capacities is erroneous and should be avoided.
3.2.7 Evaluation of Thermodynamic Parameters of Adsorption
Batch adsorption experiments are performed at different temperatures to determine equilibrium
adsorption capacity and concentration. The distribution coefficient is calculated for each
temperature by dividing the adsorption capacity by the equilibrium concentration. A Van’t Hoff
25
plot is constructed, and from the slope and intercept, thermodynamic parameters such as
enthalpy, entropy, and Gibbs free energy (ΔG) are determined [82].
If an adsorption experiment is conducted at a specific temperature, it is essential to ensure the
analyte solution and biochar have reached this temperature before initiating the equilibration.
It is important to note that although Van’t Hoff plots are valuable for evaluating thermodynamic
properties in adsorption systems; their application to biochar adsorption studies involves several
significant limitations [87]. Biochar adsorption processes often include physical and chemical
transformations of both biochar and adsorbates, particularly at varying temperatures. Changes
such as alterations in the pore structure of biochar or decomposition of adsorbate molecules can
occur, and these factors are not captured by the simplistic assumptions underlying Van’t Hoff
analysis. Additionally, Van’t Hoff plots assume ideal solution behavior, which rarely applies in
real adsorption systems, especially at higher adsorbate concentrations where interactions
between solute molecules significantly affect activity coefficients. Due to these non-ideal
interactions and structural transformations, thermodynamic parameters (e.g., adsorption
enthalpy) derived from Van’t Hoff plots may not accurately reflect the true energetics of biochar
adsorption.
Therefore, it is advisable to complement Van’t Hoff analysis with additional analytical
techniques and consider the inherent heterogeneity of biochar, the potential for multiple
adsorption mechanisms, and non-ideal solution behaviors. This integrative approach ensures
more accurate and meaningful thermodynamic insights into biochar-based adsorption processes.
To ensure the correct calculation of thermodynamic parameters in adsorption studies, particularly
ΔG, the Langmuir constant (KL) must be made dimensionless before being used in the equation
ΔG = -RT ln K. A common mistake is using KL directly, even though it is often expressed in
units such as L/mol or L/g. Since ΔG must have units of J/mol and the term inside the logarithm
must be dimensionless, KL must be adjusted accordingly. For adsorption in aqueous solutions,
where water is the solvent, KL (in L/mol) should be multiplied by 55.5, representing the molarity
of water in pure water (55.5 mol/L), making the equilibrium constant dimensionless. Similarly, if
KL is given in L/g, it should be multiplied by 1000 to account for the solution density (assuming
~1 g/mL). The corrected equations for ΔG calculation then become ΔG = -RT ln (55.5 KL) for
26
KL in L/mol or ΔG = -RT ln (1000 KL) for KL in L/g. By applying these unit corrections,
adsorption thermodynamics can be accurately interpreted, preventing miscalculations that may
lead to incorrect conclusions about the spontaneity of the process.
3.2.8 Regeneration and Reusability
Investigating the regeneration and reusability of biochar is crucial for the economic feasibility
and sustainability of remediation, as an adsorbent capable of multiple regeneration cycles can
significantly reduce the operational costs of wastewater treatment.
Regeneration experiments must be conducted with great care, as they involve analysis of both
the liquid and solid phases, as well as the desorption (stripping) of adsorbates from the adsorbent
material. Regeneration techniques of biochar have been critically reviewed [88-90]. Generally,
these processes can be broadly categorized into desorption and decomposition, with their
application dependent on: (1) whether the adsorbent is pristine or modified, (2) adsorbent
properties, and (3) the type of adsorbate. Decomposition techniques focus on mineralizing the
adsorbate or transforming it into less toxic byproducts whereas desorption techniques focus on
breaking bonds between the adsorbate and adsorbent. Selecting the correct regeneration
technique is critical to ensure the performance of biochar is retained [89].
Chemical regeneration, due to ease of operation and efficiency, is the most commonly reported
technique and involves using solvents or chemical reagents to desorb both organic and inorganic
adsorbates. The choice of chemicals depends on the nature of both the adsorbate and the
adsorbent. In the case of heavy metal adsorption, mineral acids and bases, and chelating agents
have been commonly used to regenerate biochar. In contrast, for organic substances, organic
solvents such as ethanol, acetonitrile, and acetic acid are used in addition to the above chemicals
for regeneration [90]. The selection of chemicals should be made carefully to avoid causing
significant changes to the biochar [89].
Consider the regeneration of phosphate-laden iron oxide biochar. Several regeneration solvent
options exist, such as using anions that can competitively replace phosphate ions or employing
acidic solutions that protonate phosphate ions, converting them into hydrogen phosphate species.
However, if an acidic environment is utilized, it may lead to iron dissolution and leaching,
27
causing deterioration of the iron oxide structure and subsequent material degradation.
Alternatively, the use of a strong base, such as NaOH, could result in the precipitation of iron
oxide as iron hydroxides, subsequently stripping these particles from the biochar surface.
Consequently, the regenerated material after the first regeneration cycle may significantly differ
from the initial, pristine biochar.[76] Additionally, extensive washing after filtration during
regeneration cycles is essential, as residual phosphate ions can remain on the material's surface,
potentially affecting subsequent analyses and measurements of the stripping solution.
Most studies report both desorption efficiencies and variation of adsorption capacities after each
cycle. While desorption efficiency is an important parameter, the latter is more comprehensive as
it includes the performance of the biochar in each cycle [89].
It is crucial to monitor surface alterations of the biochar, including specific surface area, pore
size, surface functionality, and the point of zero charge, ensuring these parameters remain
consistent throughout each regeneration cycle. Regeneration of functionalized biochar, such as
surface-coated, nanoparticle-deposited, or nutrient-incorporated biochar, should involve a
process that minimizes damage to these modifications. However, practically this may pose
certain challenges, and in most cases surface alterations are inevitable.
Moreover, material losses on the filtration medium (e.g., filter paper) are inevitable and must
therefore be carefully measured. The resulting mass loss should be accounted for, and corrected
adsorption capacities should be reported accordingly. In addition, thorough drying of the
regenerated biochar is vital to prevent errors arising from residual water content in subsequent
mass or capacity measurements.
The regeneration process is significantly affected by surface interactions between biochar and the
adsorbate. According to literature, biochar that forms chemisorptive interactions with the
adsorbate is harder to regenerate than those that interact via physisorption since adsorbates can
form irreversible complexes or precipitate on the active sites of biochar [89]. In the case of heavy
metal ions, generally, the dominant adsorptive mechanism is chemisorption where interactions
are established by complexing with oxygenated functional groups and forming precipitates. On
the other hand, organic species primarily interact via physisorption by forming π- π- electron
donor accepter interactions, electrostatic interactions, and hydrogen bonds [10]. Therefore, the
28
regenerated biochar upon desorption of an organic analyte generally exhibits better stability and
removal efficiency compared to an inorganic analyte.
Regeneration
by
decomposition
is
typically
conducted
via
ultrasound,
oxidation,
microbiological, and electrochemical techniques. These techniques, although less commonly
reported, can be used for regeneration purposes depending on the adsorbate and adsorbent
properties [90].
3.2.9 Remediation in Real Water
Environmental conditions, including pH, temperature, humic and fulvic composition, and matrix
components such as common ions, significantly influence adsorption efficiency and should be
carefully considered during experimentation. For instance, industrial effluents can contain
several known competing ions at significantly high concentrations and exhibit extreme pH
conditions, natural waters may have a high humic composition, and domestic waters typically
contain elevated levels of organic matter [91-93]. These conditions can severely affect the
adsorption procedure since the binding affinity of the dominant chemical species of the adsorbate
with biochar functional groups is pH-dependent, competing ions may occupy binding sites meant
for the target adsorbate, while humic substances can block pores [94]. Therefore, evaluating the
performance of biochar for its suitability in in-field conditions is routinely conducted in the latter
stages of experimentation.
There are three common methods of investigation: (1) sorption in the presence of competing
species, (2) using humic and fulvic substances to simulate a natural water sample, and (3) using a
water sample from the water source to be remediated. However, several practical concerns are
associated with the above methods.
The real or simulated water samples should be properly filtered before sorption experiments due
to the concerns discussed previously in 3.1.7. Moreover, absorbance caused by humic substances
can interfere with the UV-Vis analysis of a colored substance, and measured concentrations can
be overestimated as humic acid does not exhibit a specific λmax but instead shows a broad
absorption spectrum [95]. Therefore, employing chromatographic separation or matrix effect
29
correction techniques, such as standard addition, is essential for obtaining accurate
measurements.
In the case of AAS, standard addition is commonly used to counteract the significant alteration in
the sensitivity of the measurements due to matrix components [20]. To ensure quantification
accuracy, a known amount of standard can be spiked into the real water sample, and the
measured concentration, after accounting for naturally occurring analyte, can be used to evaluate
the method's accuracy [22].
3.2.10 Competitive Sorption
Competitive sorption experiments are conducted to assess (1) the impact of unwanted competing
ions in the sample matrix and (2) the simultaneous adsorption of multiple compounds targeted
for remediation [96, 97]. In the first case, where only one compound is studied, quantification
can be adequately performed using UV-Vis or AAS with proper matrix matching. In contrast, the
second case requires the quantification of multiple compounds. Since AAS analyzes one element
at a time, OES is more suitable for simultaneous multi-element analysis. Additionally, for
multiple molecular species, chromatographic separation can be employed not only for
simultaneous analysis but also to minimize potential matrix effects from other compounds. The
individual sorption of the adsorbates should be initially assessed before conducting simultaneous
batch sorption studies for better understanding of the adsorption phenomena [98].
3.2.11 Mass Balance of the adsorbate
On most occasions, only the filtrates from the batch sorption experiments are analyzed to
evaluate the removal efficacy of biochar and it is assumed that only adsorption onto the biochar
surface takes place. Conducting a mass balance on the biochar dosage could provide deeper
insight into its sorption capacity.
While the practical difficulty is well-acknowledged, it’s
advisable to conduct a mass balance for sorption tests conducted at optimized conditions,
targeting relevant samples and conditions can still provide valuable insights into the sorption
capacity.
Theoretically, the sum of the adsorbed and the remaining adsorbate in the solution should be
equal to the total amount of the adsorbate used in the sorption study, provided the sole
30
mechanism of remediation is adsorption. For example, Alchouron et al. reported a bamboo
biochar activated with KOH and modified with Fe3O4 achieving an exceptionally high As(V)
removal capacity of 800 mg/g at 40 °C. To substantiate this extraordinary result and address
potential fitting anomalies, they performed extensive mass balance analyses and iron-leaching
tests. Without such compelling evidence, unusually high adsorption capacities derived from
anomalous fits might appear questionable or unrealistic [99].
3.2.12 Design of Experiments
DOE is a powerful and structured statistical tool that facilitates the planning, execution, and
analysis of controlled experiments. It enables systematic investigation of multiple input variables
and their interactions, offering a more comprehensive understanding of their effects on one or
more output responses. DOE allows for the simultaneous study of several variables, thereby
enhancing experimental efficiency, reducing resource consumption, and uncovering complex
interdependencies [100].
In the context of adsorption studies, DOE can help identify the most influential factors affecting
adsorption capacity and efficiency, allowing researchers to focus on optimizing these key
parameters. The typical DOE process involves defining clear experimental objectives, selecting
relevant factors and response variables, choosing an appropriate design (e.g., full factorial,
fractional factorial, or response surface methodology), conducting experiments accordingly, and
analyzing the results using statistical tools such as ANOVA or regression analysis [101].
4. Conclusions
The experimental design and analytical QA of adsorbate quantification in laboratory-scale batch
sorption studies are crucial to ensure the reliability of the reported results. Analytical QA
involves (1) assessing the accuracy of the prepared stock and working solution, and suitability of
sample storage conditions and containers, (2) choosing the most appropriate instrumental method
and related accessories, (3) assessing the effect variations in pH and changes in the matrix, neat
vs sample matrix, can have on the sensitivity of the quantification method, and (4) evaluating the
detection limits and repeatability of the quantification method.
31
A comprehensive batch sorption should be conducted, involving the optimization of parameters
in a sequential manner. This process is done by optimizing the first parameter while keeping the
others constant, based on literature data or initial assumptions. Afterward, subsequent parameters
are optimized under the conditions already established. The optimization of each parameter
should be conducted in accordance with the best practices outlined in the text to avoid inaccurate
interpretation of results. It is essential to evaluate the performance of the produced biochar in infield conditions to assess applicability in real-world environments. The quality in which
laboratory-scale experiments are conducted along with the interpretations derived from the study
are crucial for the extrapolation to large-scale applications. As a future direction, the
incorporation of DOE approach into batch sorption studies is recommended to systematically
investigate multiple input variables and their interactions, thereby gaining a more comprehensive
understanding of their influence on one or more output responses.
Acknowledgements
The authors acknowledge the financial support of the Institute of Chemistry Ceylon.
Figure Captions
Figure 1: Most widely used instrumental methods in the 100 most recently reported studies on
(a) biochar-based metallic contaminant and (b) molecular species removal
32
Figure 2: Schematic overview of the analytical workflow for method development
Figure 3: UV-Vis spectra of three compounds, where compounds A and B exhibit spectral
overlap while compound C does not.
33
Figure 4: Structured overview of the biochar-based batch sorption experimental design
Figure 5: PFO and PSO kinetic modeling of a biochar batch sorption process: (a) time
optimization, (b) PFO model using data up to equilibrium, (c) PFO model
incorporating data beyond equilibrium, (d) PSO model using data up to equilibrium,
(e) PSO model incorporating data beyond equilibrium.
34
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Author biography
Dinushi Nayanthara Fernando graduated from the Institute of Chemistry Ceylon in 2024 and
currently serves as a Graduate Teaching Assistant at the same institute. Her research explores
the application of carbon materials, batch sorption processes and the underlying mechanisms of
advanced oxidation techniques for environmental remediation.
Dr. Chanaka Navarathna graduated from the Institute of Chemistry Ceylon and obtained an
MPhil from the University of Kelaniya. He earned his PhD in Analytical Chemistry from
Mississippi State University and completed his postdoctoral research at Mississippi State
University, Rice University and Wayne State University. He is currently a postdoctoral associate
at the University of Utah. His research interests include engineered carbon materials for
environmental applications and the determination and remediation of poly- and perfluoroalkyl
substances.
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Dr. Sameera Ranmal Gunatilake graduated from the Institute of Chemistry Ceylon and earned
his PhD in Analytical Chemistry from Mississippi State University. He currently serves as a
Senior Lecturer at the Institute of Chemistry Ceylon. His research interests include
chromatographic method development for environmental residue analysis, the application of
innovative biochars for environmental remediation and soil enhancement, and mechanistic
investigations of advanced oxidation process-based pollutant degradation.
Dr Xuefeng Zhang obtained his B.S.E. and M.S.E in Wood Science & Engineering from the
Nanjing Forestry University, China. He received his PhD in Forest Resources, from Mississippi
State University and stayed on as a Postdoctoral Research Associate, and Assistant Research
Professor. He is currently an Associate Professor at the Advanced Structures and Composites
Center (ASCC), at The University of Maine. His research interests include integrating biobased
materials into additive manufacturing, developing biomass-derived carbon (nano) materials for
environmental & engineering applications, developing lignin-based thermosets, for adhesives
49
and coatings, and cellulose (nano) materials for packaging and environmental remediation, and
investigating the property enhancement of wood products.
Tellulah Alpana Fernando graduated from the Institute of Chemistry Ceylon in 2024 and is
currently employed as a Graduate Teaching Assistant at the same institute. Her research
focuses on the development of carbon-based materials, batch sorption studies, and mechanistic
investigations of advanced oxidation processes for environmental remediation.
Credit author statement
Tellulah A. Fernando: Writing - original draft. Dinushi N. Fernando: Writing - original draft.
Sameera R. Gunatilake: Writing - original draft, Writing - review & editing, Conceptualization,
Supervision. Chanaka Navarathna: Writing - review & editing. Xuefeng Zhang: Writing review & editing.
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Graphical abstract
Declaration of competing interest
Authors declare no conflict of interest.
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