A Flow Adsorption Microcalorimetry-Logistic Modeling Approach for

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A Flow Adsorption Microcalorimetry-Logistic Modeling Approach for assessing heterogeneity of Bronstedtype surfaces: Application to Pyrogenic Organic Materials Omar R. Harvey, Burke C Leonce, and Bruce E Herbert Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.8b00104 • Publication Date (Web): 02 May 2018 Downloaded from http://pubs.acs.org on May 5, 2018

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Environmental Science & Technology

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A Flow Adsorption Microcalorimetry-Logistic Modeling Approach for assessing

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heterogeneity of Bronsted-type surfaces: Application to Pyrogenic Organic Materials

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Omar R. Harvey1,*, Burke C. Leonce1 and Bruce E. Herbert2 1

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School of Geology, Energy and the Environment, Texas Christian University, Fort Worth, TX

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76129 2

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Office of Scholarly Communications, Texas A&M University, College Station, TX 77843

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*Corresponding author:

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Email: [email protected]

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Tel: +1817-257-4272

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1

ACS Paragon Plus Environment


ABSTRACT

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Biogeochemical functioning of oxides and pyrogenic organic matter (pyOM) are greatly

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influenced by surface and deprotonation characteristics. We present an energetics-based,

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logistic modeling approach for quantifying surface homogeneity (𝜙!"#$ ) and surface

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acidity (pKa, surf) for Bronsted-type surfaces. The 𝜙!"#$ , pKa, surf and associated

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deprotonation behavior of pyOM were quantified across feedstock (honey mesquite-HM,

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pine – PI, cord grass - CG) and heat-treatment-temperatures (HTT; 200-650 oC). At

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HTT200, lower 𝜙!"#$ [HM (0.86)>PI (0.61)>CG (0.42)] and higher pKa, surf [CG (4.4)>PI

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(4.2) >HM (4.1)] for CG indicated higher heterogeneity and lower acidity for bronsted-

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type surface moeities on grass versus wood pyOM. Surface acidity of CG increased at

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HTT550/650 oC with no effect on 𝜙!"#$ ; while the surface heterogeneity of both wood

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pyOMs increased, the acidity of HM increased and that of PI decreased. Despite different

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HTT-induced 𝜙!"#$ and pKa, surf trajectories, the deprotonation range for all pyOM was

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pH = pK a, surf ±

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lower pH, over a wider range and (for similar pK a, surf and cation exchange capacity) are

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better cation/metal binding surfaces at pH< pK a, surf . The approach also facilitates the

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evaluation of surface and deprotonation characteristics for mixtures and more complex

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surfaces.

2 ϕsurf

. Therefore, higher heterogeneity pyOMs deprotonate more readily at

INTRODUCTION

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Inorganic and organic materials with bronsted-type acidic surfaces (i.e. surfaces

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that donate H+ over a specific pH range to become negatively charged) of hydroxyl,

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carboxyl and phenol moieties are ubiquitous in natural and engineered systems, where

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they often control biogeochemical cycling of nutrients and contaminants - via effects on

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sorption/desorption, dissolution/precipitation and redox reactions. Particulate pyrogenic

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organic matter (pyOM)- the carbon-rich, solid byproduct of natural or engineered

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biomass burning/pyrolysis – is one such bronsted-type surface. There is widespread

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consensus that pyOM is physicochemically heterogeneous and therefore their surface

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composition and deprotonation behavior, and subsequent roles in soil and aqueous

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systems will vary.1-7 A significant amount of recent research has focused on the interaction between

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deprotonatable sites, on the surfaces of pyOM, and dissolved species in solution.8-13

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These studies suggest different interaction/sorption mechanisms dictated by the nature of

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the dissolved species, solution chemistry and, the charge and charge distribution on the

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pyOM surface. Uchimaya et al.14 found that Pb2+ and Cu2+ interaction at pH 4.5 with

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cotton-seed-hull- and flax-shive-derived pyOM occurred primarily on deprotonated

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carboxylic groups via electrostatic attraction. Studies by Harvey et al.13 (on grass- and

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wood-derived pyOM) and Jia and Thomas9 (on activated charcoal) also found evidence of

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electrostatic Cd2+ interaction with deprotonated carboxyls on the pyOM surface but both

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noted the irreversibility of such sorption ruling out simple ion exchange as the dominant

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sorption mechanism for Cd2+. Harvey et al.13 further noted that the reversible sorption

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expected for simple ion exchange on pyOM surface was observed only with hard “lewis

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acid” cations (e.g. K+ and Na+) while for Cd2+ (a soft cation) the dominant mechanism

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varied between electrostatic interactions and cation-π bonding – depending on charge and

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charge distribution of the pyOM surface.

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As with many other physicochemical properties of pyOM, surface heterogeneity

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is a function of feedstock/biomass chemistry, pyrolysis conditions and the environmental

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conditions under which the pyOM exists (or existed).12, 15-17 Spectroscopic and back-

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titration methods are the most common approaches used to probe pyOM surface

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heterogeneity as it pertains to bronsted type surface sites.6, 18-23 Spectroscopic methods,

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such as infrared and NMR spectroscopy, provide insights into bulk and/or surface

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characteristics of pyOM in well defined analytical windows for deprotonatable moieties

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(e.g. carboxyls and phenols) - allowing for the comparison of the relative abundance of

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surface acid groups across different types of pyOM. Back-titration methods, such as the

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widely used Boehm titration method, provide insights into the surface heterogeneity of

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pyOM – via operationally defined analytical windows for total surface acidity as well as

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strong, moderate and weak organic acid fractions. A major limitation of current spectroscopic and back-titration methods, as it

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pertains to surface heterogeneity, is that they lack the resolving power for differentiating

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in-situ deprotonation behavior of the identified surface sites. For instance, the strong

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organic acid fraction in the Boehm titration method is determined via equilibration of

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NaHCO3 (pH 8-8.3) with a suspension of the respective pyOM, and the subsequent back-

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titration of the residual base against a strong acid. The fraction of sites captured by this

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approach is therefore comprised of all functional groups that deprotonate at pH ≤ 8.3 with

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potential contributions from surface structures with acid dissociation constants (pKa) as

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high as 10.3 (assuming the standard pKa±2 deprotonation window). A single analytical

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window of 8.3 (or 10.3) pH units is far too wide to resolve 1) effects of pH in natural

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systems - typically pH 4 to 10 and 2) most importantly, surface heterogeneity contributed

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by the cinnamic and benzoic acid derivatives that dominate lignocellulose-derived

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pyOM.24-27 It is the chemical structure and relative proportions of these cinnamic and

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benzoic acid derivatives - arising from differential thermal alteration of lignocellulose -

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that would dictate surface heterogeneity and deprotonation behavior/reactivity of

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respective pyOM. In cases of high-ash containing pyOM and that ash is comprised mainly

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of insoluble Si, Al and Fe-hydroxides, it is also plausible that these metal-OH moieties

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may also contribute to overall deprotonation behavior of the pyOM. Therefore, although

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back-titration methods provide bulk estimates on total deprotonable sites they lack the

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resolving power needed for detailed assessments of surface heterogeneity. A similar

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scenario exists with current spectroscopic approaches where direct insights into the total

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number of deprotonatable groups are easily obtained but insights on the actual

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deprotonating behavior of these groups are lacking. Electrophoretic and potentiometric techniques have also been extensively used to

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assess surface or surface-related characteristics of pyOM. Among electrophoretic

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techniques, zeta-/electrokinetic potentials derived for the electrophoretic mobility of a

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suspension of pyOM at different solution pH are most widely used - with 1) an

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increasingly negative zeta-potential (with increasing solution pH) being indicative of

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increasing surface deprotonation and 2) the pH at which zeta-potential is zero being

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indicative of the of the isoelectric point (IEP) or point of zero net charge (PZNC) of the

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surface.18, 28-32 Among potentiometric techniques, acid-base titrimetry/potentiometric

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titration involving the incremental addition of known volumes of an acid (and/or a base)

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to a suspension of the pyOM and the simultaneous monitoring of the associated pH or

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mV change is most common. The resulting pH-volume or mV-volume curves are then

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transformed to charge-pH curves and fitted with a model to estimate acid-base surface

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properties including acid dissociation constants (pKa) and point of zero charge (pHpzc).18,

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33-37

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potential techniques vary widely. Additionally, both techniques are highly sensitive to

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variations in temperature and solution composition - leading to difficulty in interpreting

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resulting data as purely from surface influence and/or being comparable across

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methods.18, 30, 36, 37 For example, ash-induced buffering and the presence of dissolved

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organic components would obscure the true deprotonation behavior of the surface and

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therefore would introduce artefacts/errors that must be accounted for in the determination

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of pyOM surface properties. To our knowledge, there are currently no well-defined

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experimental protocols to correct for (or eliminate) artefacts/errors associated with

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dissolved organic matter or ash-induced buffering in zeta-potential or potentiometric

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titrations in pyOM systems. Also, we are unaware of any fitting models or routines that

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allows for derivation of surface heterogeneity for a given pyOM.

The complexity of data fitting models and routines for both potentiometric and zeta-

Characterization of the true/innate surface properties of pyOM- devoid of any

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artefacts - is central to understanding interactions and dynamics of natural pyOM in the

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environment as well as the designing and testing of engineered pyOM for soil

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enhancement, carbon sequestration and contaminant removal in soil or aqueous systems.

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It is the innate pyOM surface properties (e.g. pKa - a measure of functional group type –

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and heterogeneity – a measure of functional group distribution) that would dictate

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interactions with soils and soil minerals, native and exogenous dissolved organic matter,

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nutrients, microbiota and water/solutions these systems. We present a unique approach

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for studying and quantifying the innate heterogeneity, acidity and deprotonation behavior

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of bronsted type surfaces, in general, but using pyOM surfaces as an example. The

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approach combines energetics (specifically enthalpy-based) data from ion-probe flow

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adsorption microcalorimetry (ip-FAMC) and logistic modeling to examine pH-dependent

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surface response and the subsequent use of this information to assess surface properties.

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In this particular study, we used energetics data from the pH-dependent K+ for Na+ ion

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exchange to probe the deprotonation behavior of different pyOM surfaces; then

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combining this information with a logistic/S-curve modeling approach to quantify pyOM

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surface homogeneity/heterogeneity, surface acidity and deprotonation window across

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different feedstock and heat treatment conditions. For comparison purposes, we also

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directly measured and modeled K+ ion exchange onto the same Na+-saturated pyOM used

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in ip-FAMC experiments. The applicability of the knowledge on heterogeneity and

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acidity was also extended to include more complex surfaces and mixtures. Since the

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enthalpy of a reaction is temperature-insensitive then derived pyOM surface properties

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from the proposed approach will also be temperature-insensitive. The nature of ip-FAMC

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also enables the elimination of artefacts associated with ash-induced buffering or

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dissolved organic matter and hence results should be representative of the innate surface.

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Other advantages of the ip-FAMC technique over batch techniques are well documented

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and include: simultaneous, direct measurements of heat of reactions; temporal resolution

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on the order of seconds; and the ability to perform multiple adsorption/desorption cycles

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on a single sample facilitating the differentiation of reversible/irreversible processes.13, 38-

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sample size (PI>CG) at HTT550/650 can also be

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tied to differences in the chemical composition of lignin. Lignin in non-woody tissue is

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dominated by cinnamic acid derivatives, while that of woody tissues are dominated by

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benzoic acid derivatives such as vanillic and syringic acids. Data from Kuo et al.15

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indicated that cinnamyl:vanillyl ratios in the uncharred CG, HM and PI feedstocks (used

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to make the pyOM in our study) were 1.13, 0.06 and 0.01 respectively. Vanillic acid

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concentrations in wood-derived pyOM were also shown to be 2-4 times that in grass-

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derived pyOM, with the cinnamic acid derivative – ferulic and p-coumaric acid- being

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present in appreciable amounts only in grass pyOM. The innately higher proportion of

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cinnamic acids in CG pyOM would explain their more acidic (lower pKa, surf) surfaces. In

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contrast to benzoic acids (which have their carboxyls directly attached to the phenyl

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ring), carboxylic groups in cinnamic acids are attached to the phenyl ring via a bridging

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ethylenic group. The presence of the bridging ethylenic group in cinnamic acid

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derivatives dictates that the electron-donating (and consequently destabilizing) effect of

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the phenyl ring on the carboxylate anion will be lowered compared to benzoic acid. The

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net effect is that the cinnamic carboxylate anion in cinnamic acid derivatives is more

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stable than in benzoic acid derivatives and therefore dissociation of the protonated

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carboxyl is more thermodynamically favored and reflected in the lower pKa of cinnamic-

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rich CG pyOM.

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Pyrolysis effects on surface heterogeneity in pyOM. Whereas pKa, surf describes the

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average tendency of carboxyls (-COOH) on the pyOM surface to deprotonate to form a

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more negatively-charged surface dominated by COO-, the surface homogeneity factor

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𝜙!"#$ describes how the distribution of carboxyls on the surface differs from the ideal

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homogenous Bronsted-type acid. That is, a surface with the carboxyl(s) attached to one

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type of R-group and therefore starts deprotonating at pH = pKa, surf -2 and becomes

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completely deprotonated at pH = pKa, surf +2. As mentioned earlier, for such an ideal

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homogeneous Bronsted-type acid, 𝜙!"#$ has a value 1 and the model is equivalent to the

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Henderson-Hasselbalch equation.52 For the pyOM we studied, 𝜙!"#$ ranged between 0.94

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and 0.14 indicative of fairly homogenous (𝜙!"#$ = 0.94) to very heterogeneous (𝜙!"#$ =

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0.14) surfaces. In general, surface homogeneity at a given HTT followed the trend

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HM>PI≥CG (Table 1). At HTT200, values for 𝜙!"#$ were 0.86, 0.61 and 0.42 for HM,

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PI and CG pyOM respectively; suggesting that the CG pyOM had 1.5 to 2 times the

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number of unique R-groups associated with surface carboxyls than their wood

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counterparts. Since little to no thermal degradation of lignocellulose structure occurs at

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HTT200, the higher heterogeneity (lower 𝜙!"#$ ) for CG pyOM at this HTT was most

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plausibly an innate (rather than thermally-induced) characteristic of the grass versus

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wood feedstocks.

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Increasing HTT from 200 to 350, and then 550/650 °C had distinctive trend-wise

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and magnitudinal effects on the surface homogeneity of CG, PI and HM pyOM. For CG

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pyOM, a slight decrease (0.42 to 0.35 at HTT350), then increase (0.35 to 0.49) in surface

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homogeneity was observed (Table 1). By comparison, a slight increase (0.86 to 0.94 at

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HTT350), then decrease (0.94 to 0.63 at HTT650) in surface homogeneity was observed

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in HM pyOM; while a consistent increase (0.61 to 0.38 to 0.14) in surface homogeneity

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was observed in PI pyOM with increasing HTT. As with differences in pKa, surf, surface

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heterogeneity trends across the CG, PI and HM pyOM were reflective of differences in

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feedstock chemistry and their respective susceptibility to thermal alteration. In the case of

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CG pyOM, trends in surface heterogeneity were reflective of cellulosic components in the

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soft grass tissues undergoing defragmentation around 350 °C - resulting in an

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approximate 21% increase in the diversity of R-groups attached to carboxyls. The

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chemical dehydration of these cellulosic fragments/derivatives, the disintegration of

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lignin components and subsequent condensation/aromatization of R-groups would

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account for the more homogeneous surface at HTT550.20 In HM pyOM, the more lignin-rich and thermally-resistant nitrogen-rich tissues

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showed little to no effect of the thermal degradation of cellulosic components on the

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diversity of R-groups at HTT350. However, the decrease in homogeneity (0.94 to 0.63) at

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HTT550 was reflective of the disintegration of lignin components and condensation of

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these derivatives resulting in an approximate 25% increase in the unique number of R-

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groups attached to carboxyls on the surface of the HM pyOM. For PI pyOM, the presence

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of oleoresins (e.g. monoterpenoids, sesquiterpenoids and diterpenes)51 would explain the

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29% higher innate diversity of R-groups at HTT200 compared to their HM counterpart. It

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is the thermal degradation of these oleoresins coupled to that of cellulosic components

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and later lignin components that are most probably responsible for the approximate 30%

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increase and more than doubling (0.44 to 0.14) of the types of carboxyl-associated R-

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groups in PI pyOM at HTT350 and HTT650 (compared to HTT200), respectively.

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Model applicability. By combining values of pKa, surf and ϕsurf with other routinely

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measured pyOM properties, equation 1 (or a modified model) can be used to predict pH-

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dependent deprotonation behavior, charge development and cation sorption

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characteristics. For pyOM deprotonation behavior, only equation 1 (with c =1) and values

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of pKa, surf and ϕsurf for each pyOM are needed with the deprotonation window for a given

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pyOM ranging from pH = pKa, surf -‐

2 ϕsurf

to pH = pKa, surf +

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2 ϕsurf

and the pH at which a

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given degree of deprotonation (fcoo-; where 0 pKa, surf with the largest effects occurring at pKa, surf ±

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(0.693-log ϕsurf2). 3) Lower heterogeneity and lower pKa, surf (e.g. addition of HM350 to PI350; Figure

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5C) promotes deprotonation in the mixture at

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!!"#$!

pK a, surf1 -‐ !

!!"#$!

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pH >

450

at pH <

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occurring around pH ~ pKa, surf2 ±

!!"#$! !!!"#$! !!"#$!

!!"#$! !!!"#$!

!"#$! !!!"#$!

pK a, surf1 -‐ !

pK a, surf2 but reduces deprotonation

!!"#$! !"#$! !!!"#$!

0.693 ϕsurf2

pK a, surf2 with the largest effects

.

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ENVIRONMENTAL SIGNIFICANCE

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With vegetation fires becoming increasingly important as a key modifier of

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natural landscapes, and “designer” pyOMs (e.g. biochars) continuing to emerge as valued

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components in soil management and water treatment strategies; the need to be able to

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model/predict pyOM behavior is paramount. Whilst the myriad of physicochemical

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properties (and consequently biogeochemical functions) observed in pyOMs can be

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qualitatively explained by variations in feedstock chemistry and pyrolysis conditions; the

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development of quantitative models of pyOM behavior based on feedstock and pyrolysis

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conditions is both unfeasible and impractical given that 1) in natural systems these

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parameters are most often, unknown and 2) for “designer” pyOM (e.g. biochars), the

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number of possible feedstock-pyrolysis-condition combinations and the variability in

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outcomes are unfathomably large. It is in this context (as a global modeling tool for

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pyOM surface behavior) and in scenarios requiring the assessment of innate surface

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properties that the methods discussed in this study is of particular relevance. In addition

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to providing estimates of surface acidity and heterogeneity for any pyOM, the data from

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ip-FAMC and the logistic model provides a simple and flexible option for describing,

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predicting and comparing deprotonation behavior, pH-dependent charge development or

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cation exchange onto surface of pyOM (or mixtures of pyOM) without the need to know

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feedstock source or pyrolysis condition.

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SUPPORTING INFORMATION

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Additional data on physicochemical properties of pyOM, modeled surface parameters and

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modeled deprotonation behavior of mixtures are presented in the Supporting Information.

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This material is available free of charge via the Internet at http://pubs.acs.org. ACKNOWLEDGMENT

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Financial support to support this manuscript was primarily from start-up funds provided

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to ORH by Texas Christian University. Partial financial support to BCL from the School

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of Geology, Energy and the Environment graduate research fund is also gratefully

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acknowledged. We are also grateful to Dr. Li-jung Kuo for providing the pyOM materials

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and Dr. Roy D. Rhue (retired) for valuable insights on building the micro-calorimeter as

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well as discussions on data.

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29



Page 30 of 36

644

LIST OF TABLES

645 646 647 648 649

Table 1. Summary of logistic model fit parameters to fractional deprotonation from energetics (fcoo-, E) obtained through ion-probe flow adsorption microcalorimetry of K+ for Na+ exchange on Honey mesquite (HM), Loblolly pine (PI) and Cordgrass (CG) pyOM produced at heat treatment temperatures of 200, 350 and 650 oC. Experiments were done at pH 3, 5, 7 and 9.

650

Honey mesquite pyOM HM200 HM350 HM650

Energy-based (E) model fits c Φsurf pK a, surf ------------------ value±SE -------------------0.975±0.026 0.861±0.115 4.06±0.138 0.964±0.037 0.939±0.192 4.07±0.21 0.983±0.02 0.628±0.062 3.8±0.098

0.998 0.995 0.998

Loblolly pine pyOM PI200 PI350 PI650

0.936±0.079 1.08±0.19 1.26±1.07

0.606±0.227 0.378±0.204 0.137±0.175

4.16±0.392 5.18±0.743 5.13±1.65

0.975 0.962 0.91

Cordgrass pyOM CG200 CG350 CG550

1.01±0.012 0.998±0.05 0.966±0.051

0.419±0.02 0.347±0.068 0.487±0.127

4.36±0.055 4±0.243 3.66±0.256

1 0.993 0.987

30


R2

Page 31 of 36


LIST OF FIGURES

651

Simulated fractional deprotonation

1.0

0.8 1st Derivative

0.6

0.4

0

2

4

6

8

10

12

14

pH

H1 (φsurf = 1, pKa, surf = 3.5) H2 (φsurf = 1, pKa, surf = 5.5)

0.2

H3 (φsurf = 0.25, pKa, surf = 5.5) H4 (φsurf = 0.5, pKa, surf = 5.5)

0.0 0

2

4

6

8 pH

10

12

14

16

655

Figure 1. Simulated logistic modeling of the deprotonating behavior for four hypothetical bronsted-type surfaces (H1, H2, H3 and H4) with different surface homogeneity (𝜙!"#$ ) and surface acidity (𝑝𝐾!,!"#$ ). The inset shows the first derivative of the deprotonation curves for H2-H4 and illustrates the effect of 𝜙!"#$ on “deprotonation envelope”

656

(𝑝𝐻 = 𝑝𝐾!,!"#$ ±

652 653 654

!

!!"#$

) for surface with similar 𝑝𝐾!,!"#$ .

31



1.0

Page 32 of 36

pH 9 pH 7 pH 5

Power (mW g-1)

0.8

pH 3 0.6

0.4

0.2

0.0 0 657 658 659 660

5

10 15 Time (minutes)

20

Figure 2. Representative ion-probe flow adsorption microcalorimetry thermograms obtained from progressively probing Na+-saturated HM350 pyOM with K+ at pH 9, 7, 5 and 3.

32


Page 33 of 36


Energy-based modeled parameters

6 5 4 pKa,surf

φsurf 1.0 0.5 0.0 0.0

663 664 665 666

1.0

4

5

6

Sorption-based modeled parameters

661 662

0.5

Figure 3. Comparison of surface homogeneity (𝜙!"#$ ; open symbols) and surface acidity (𝑝𝐾!,!"#$ ; filled symbols) determined using logistic modeling and deprotonation data (pH 3-9) from direct sorption (sorption-based) and flow adsorption microcalorimetry (energy-based) measurements of K+ probing of Na+-saturated honey mesquite (red symbols), loblolly pine (black symbols) and cord grass (blue symbols) pyOM.

33


0.6

0.4

Fraction of deprotonated surface sites, fcoo-, E

Fitted Parameters c = 0.975 φsurf = 0.861 pKa, surf = 4.06

0.2

0

1

2

3

4

5 pH

6

7

8

9

0.8

0.6

0.4 Fitted Parameters c = 1.00 φsurf = 0.419 pKa, surf = 4.36

0.2

0

1

2

3

4

5 pH

6

7

8

9

0.8

0.6


0.2

0

1

2

3

4

5 pH

6

7

8

9

0.8

0.6


0.2

0

1

2

3

4

5 pH

6

7

8

9

Figure 4. Logistic model fits (lines) to deprotonation data (fcoo-,E) obtained from flow adsorption microcalorimetry measurements at pH 9, 7, 5 and 3 for K+ probing of Na+saturated (A, B) honey mesquite and (C, D) cord grass pyOM produced at (A, C) 200, (B) 650 or (D) 550 oC, respectively. Error bars are standard deviations for triplicate calorimetric measurements at a given pH.

34

10

D. CG550 pyOM

1.0

0.0

10

Page 34 of 36

B. HM650 pyOM

1.0

0.0

10

C. CG200 pyOM

1.0

0.0

668 669 670 671 672


0.8

0.0

667

A. HM200 pyOM

1.0





10

Page 35 of 36


1.0

0.8 0.7 0

1

2

0.5

3

4

5 pH

6

7

8

9

10

0.4

Mixtures 100CG 75CG: 25PI 50CG: 50PI 25CG:75PI 100PI

0.3 0.2 0.1 0.0

673

A.

0.9

0.6

0

1

2

3

4

5 pH

6

7

Fraction of deprotonated surface sites, fcoo-

1.0

675 676 677 678 679 680 681

8

9

0.8 0.7 0.6

0

1

2

3

4

5 pH

6

8

9

10

Mixtures 100CG 75CG: 25HM 50CG: 50HM 25CG:75HM 100HM

0.4 0.3 0.2 0.1 0

1

2

3

4

5 pH

6

7

8

9

10

C.

0.8 0.7 0.6

0

1

2

3

4

5 pH

6

7

8

9

10

0.5 0.4

Mixtures 100PI 75PI: 25HM 50PI: 50HM 25PI:75HM 100HM

0.3 0.2 0.1 0

1

2

3

4

5 pH

6

7

8

9

10

Figure 5. Predicted pH-dependent deprotonation behavior for bronsted type sites on the surface of mixtures comprising of different proportions of two pyOM with (A) similar surface heterogeneity (𝜙!"#$ ) but different pKa,surf - CG350 and PI350, (B) similar pKa,surf but different 𝜙!"#$ − CG350 and HM350 and (C) different pKa,surf and 𝜙!"#$ - PI350 and HM350 pyOM. Proportions are in % and are indicated by numbers preceding feedstock identifier. e.g. 25CG: 75PI indicate a mixture comprising 25% of CG350 and 75% PI350. Inset shows derivative of respective deprotonation curves.

682 683 684

7

0.5

0.0

10

B.

0.9

Derivative

0.9

0.0

674

Derivative

Derivative



1.0

35


1.0

Homogenous Heterogenous Environmental Science & Technology Page 36 ofpyOM 36 pyOM surface

fcoo-

surface φsurf = 1

0 < φsurf < 1 Deprotonation window pKa, surf ± 2/φsurf

0.5

More heterogenous pyOM = broader window Homogenous pyOM narrower window

ACS Paragon Plus Environment 0.0

pH

A Flow Adsorption Microcalorimetry-Logistic Modeling Approach for

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