Journal Pre-proof 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 Received date: 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 This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2025 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) 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. 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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. 48 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. 50 Graphical abstract Declaration of competing interest Authors declare no conflict of interest. 51
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