Capturing Lithium from Wastewater Using a Fixed Bed Packed with 3

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Capturing Lithium from Wastewater Using A Fixed Bed Packed with 3-D MnO2 Ion Cages Xu-Biao Luo, Kai Zhang, Jinming Luo, Shenglian Luo, and John Crittenden Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.6b02247 • Publication Date (Web): 24 Oct 2016 Downloaded from http://pubs.acs.org on October 24, 2016

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Environmental Science & Technology

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Capturing Lithium from Wastewater Using A Fixed Bed Packed with

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3-D MnO2 Ion Cages Xubiao Luoa, Kai Zhanga, Jinming Luob,c, Shenglian Luoa*, John Crittendenc*

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a

Key Laboratory of Jiangxi Province for Persistent Pollutants Control and Resources Recycle,

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Nanchang Hangkong University, Nanchang 330063, PR China

b

Key Laboratory of Drinking Water Science and Technology, Research Center for

Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China

c

Brook Byer Institute for Sustainable Systems and School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA

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*Corresponding author

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Tel: +86 73183953371

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Fax: +86 73183953373

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E-mail address: [email protected], [email protected]

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ACS Paragon Plus Environment


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Visual Abstract /TOC

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ABSTRACT

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3-D MnO2 ion cages (CMO) were fabricated and shown to have a high capacity for

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lithium removal from wastewater. CMO had a maximum Li(I) adsorption capacity of

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56.87 mg/g, which is 1.38 times greater than the highest reported value (41.36 mg/g).

43

X-ray photoelectron spectroscopy indicated that the stability of the -Mn-O-Mn-O-

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skeleton played an essential role in Li adsorption. The lattice clearance had a high

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charge density, forming a strong electrostatic field. The Dubinin-Ashtakhov (DA) site

46

energy distribution model based on Polanyi theory described the linear increase of Li

47

adsorption capacity (Q0) with increasing temperature ( Q0 = k3 × Em + d3 = k3 ×(a ×T ) + d3 ).

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Furthermore, the pore diffusion model (PDM) accurately predicted the lithium

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breakthrough (R2 ≈ 0.99). The maximum number of bed volumes (BVs) treated were

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1,374, 1972 and 2493 for 200 µg/L at 20, 30 and 40 °C, respectively. Higher

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temperatures increased the number of BVs that may be treated, which implies that

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CMO will be useful in treating industrial Li(I) wastewater in regions with different

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climates (e.g., Northern or Southern China).

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INTRODUCTION

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Lithium (Li) is a critical material for energy-related technologies1. Accordingly,

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there has been increasing demand for lithium and Li compounds, particularly in the

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rapid expansion of rechargeable lithium ion batteries (LIBs).2,3 We are now facing a

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contraction the of the Li supply and increasing demand. Currently, approximately

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200-500 MT of spent LIBs are produced each year.4 The Li content in spent LIBs is

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2-7 wt.%, which is an important resource of lithium. Effective and efficient recovery

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of Li could become a very important because it not only can relieve Li shortages, but

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also can resolve environmental pollution problem caused by spent LIBs. Additionally,

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research on lithium recovery from LIBs have attracted considerable interest in recent

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years.5-7 Conventional technological approaches, including nanofiltration and

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electrolysis, have been used to remove lithium from wastewater.8 According to

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Tsuruta, the biological recovery of Li(I) can also be achieved using various

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microorganisms.9 However, precipitation is the most common technology used for

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lithium recovery. However, precipitation is difficult to use on lithium wastewater with

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high Mg/Li contents because Li2CO3 is highly soluble (approximately 1.3 g/100 mL

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H2O).10 Adsorption is one of the most promising methods for lithium recovery from

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lithium wastewater because it is one of the most cost-effective and environmentally

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friendly methods.11

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The adsorbent plays a vital role in determining the performance of packed bed

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adsorption systems; however, the majority of adsorbents have a lower adsorption

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capacity for smaller cations, such as Li.12 Moreover, many adsorbents have been used


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to remove Li(I) but with low capacities. Abe et al.13 synthesized an adsorbent, called

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the LiSbO3 precursor, and found that the adsorption capacity is only 1.0-1.4 mg/g.

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Baumant et al.14 prepared a LiCl·2Al(OH)3·nH2O-type adsorbent, which exhibited a

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Li(I) adsorption capacity of 2-3 mg/g. Song et al.15 investigated the Li(I) adsorption

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capacity of D751 resin and found that the maximum adsorption capacity was also

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only 20.82 mg/g and that the Li selectivity was poor as compared to other cations.

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The waste streams for Li recovery contains large amounts of interfering metal ions

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such as K, Na, Ca, Mg, Zn, Co, Cr, Cu, Al, Ni, Pb, and Cd, and the process stream is

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usually alkaline.16 Therefore, high-capacity and selective adsorbents must be

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developed to selectively separate Li(I) from wastewater that contains a variety of

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cations.

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Ionic-cage17,18 adsorbents possess a “memory effect” and that makes them ideal

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for extracting target ions with an extremely high selectivity. The specific adsorption

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sites for CMO were prepared by removing target ions from a stable inorganic solid

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that contain Li in the lattice. The ionic-cage adsorbents have the advantages of low

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toxicity and high chemical stability,19,20 but they have the disadvantage of slow

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adsorption kinetics. 3-D MnO2 ionic cages (CMO) have: (1) high selectively for Li(I)

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in the presence of competing ions, (2) high adsorption capacity and (3) a fast

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adsorption kinetics. These attributes are the result of 8a-16d-8a channels in a

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three-dimensional interstitial space provided by the -Mn-O-Mn-O- framework in the

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Li4Mn5O12 spine.



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The adsorption maximum capacity (Q0) increases because CMO is endothermic

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adsorbent. Previous studies have not examined the relationship between Q0 and T. We

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explored the relationship between Q0 and T using the Dubinin-Ashtakhov (DA) Site

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Energy Distribution model. The increase of the adsorption capacity with temperature

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adsorption is important for Li(I) removal, because wastewater from southern China

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can be considerably warmer than wastewater from northern China.

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We investigated the performance of a CMO adsorbent for Li(I) removal. To

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examine the practical applications of the CMO, fixed bed adsorption experiments and

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mathematical model were developed.21 Some of the classical models include as those

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developed by Thomas,22 Adams–Bohart,23 Clark,24 and Yoon–Nelson.25 These models

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typically have simple mathematical solutions. However, they are empirical models,

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which limits their ability to predict the performance of full-scale adsorption treatment

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systems. Accordingly, we used the pore diffusion model (PDM)26 to predict and

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validate small column breakthrough data. Thus, we can use the model to predict

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lithium breakthrough in full-scale adsorption systems. In brief, our goal is to: (1)

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explore the specific relationship between the Li adsorption capacity (Q0) and

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temperature (T), (2) verify a model that can predict the fixed bed performance for

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removal Li, and (3) provide specific guidance for engineering application.

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MATERIALS AND METHODS

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Chemicals and materials

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The sources and grades of the chemicals used in this study are provided in


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Section S1 of the Supporting Information, and the characterization methods are

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provided in Section S2. Synthetic Li solutions of different concentration were

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prepared in DI water for adsorption isotherm, kinetic studies, and shorter column

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fixed-bed experiments. The solutions with the concentration of 200 µg/L of Li, Na, K,

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Ca, Mg, pH = 10, TOC = 48.6 mg/g were used for competitive adsorption

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experiments and IX model because Na, K, Ca, and Mg ions have the similar ionic

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radius. The concentration of all ions was adjusted to 200 µg/L in order to better study

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selective adsorption experiments.

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Synthesis of 3-D MnO2 Ion Cages

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The process for preparing the 3-D MnO2 ion cages is described in detail in Section

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S3 of the Supporting Information. In brief, the 3-D MnO2 ion cages (CMO) were

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prepared using a two-step process (Figure 1). First, Li4Mn5O12 (LMO) precursor

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particles were prepared by hydrothermal synthesis via a low-temperature solid-phase

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reaction. The LMO particles were then washed with aqueous solutions of HCl to

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remove the Li(I) ions to prepare a selective absorbent with Li(I) transport channels.

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These channels consist of interconnected 3-D MnO2 ion cages within the 3-D lattice

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of the Li4Mn5O12 spinels within each absorbent particle.

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Adsorption Isotherm Procedure

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Adsorption isotherms were obtained by shaking a mixture containing 20 mg of

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CMO and 20 mL of the Li(I) solution at pH 10.1 using an incubator shaker at 160 rpm.

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The adsorption isotherms of Li(I) were obtained at temperatures of 20, 30 and 40 °C.



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The initial concentrations of Li(I) ranged from 20 to 500 mg/L. After being shaken for

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12 h, the resulting mixture was centrifuged at 12,000 rpm (which resulted in 22,550 G)

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to remove the adsorbent. The concentrations of Li(I) in the supernatant before and

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after adsorption were measured using atomic absorption spectrometry (AAS). The

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adsorption capacity of the Li(I) ion was calculated using Eq. 1:

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qe =

150

where qe (mg/g) denotes the equilibrium adsorption capacity, C0 and Ce are the initial

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and equilibrium concentrations (mg/L) of Li(I), respectively, V is the volume (mL) of

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adsorption solution, and m is the mass (mg) of the adsorbent.

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Adsorption Selectivity

C0 − C e ⋅V m

(1)

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The selectivity of Li+, Na+, K+, Mg2+, and Ca2+ ions with respect to H+ ions was

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determined. Fifty milligrams of CMO was mixed with a 50-mL water solution

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containing competing ions with a concentration of approximately 200 µg/L and pH 10

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at 20 °C. The adsorption equilibrium was reached after 12 h of shaking, and the ion

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concentrations were determined using AAS. In a multicomponent system, separation

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factors can be converted to different reference ions by using Eq. 2-3:

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α ki = α ij ⋅ α kj

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xi =

Ci

,

m

∑C k =1

( α ij =

k

xj =

qi C j

=

Ci q j

yi x j xi y j

)

Cj m

∑C

k

k =1


(2)

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yi =

xi n

∑ (x α ) i

k =i

k i

,

yj =

xj n

∑ (x α ) j

(3)

k j

k= j

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where α ki is separation factor of counter ion i with respect to ion k, unitless; q and C

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represent the equivalent solid phase and liquid phase concentration (mg/L) for ions i

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and j, respectively; x and y represent the liquid phase and the solid phase equivalent

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fraction or mole fraction of ions i and j, unitless; i denotes the counter ion and j

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represents the presaturant ion.

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Continuous Flow Fixed-Bed Column Adsorption Experiments

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A short bed adsorber (SBA) test was conducted to validate the calculated kf and

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Dp values.27,28 In the SBA test, a column was packed with a CMO. It has a bed depth

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of 0.9 cm (a mass of 0.65 g) and a diameter of 1.3 cm. The bed had an empty bed

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approach velocity of 0.09 m/hr and had an influent lithium concentration, C0, of 200

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µg/L. The exhausted Li+ on the CMO column was desorbed using 0.1 mol/L HCl

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solution and a flow rate of 1.0 mL/min.

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Determining the Parameters for Pore Surface Diffusion Modeling

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The PSDM assumes intraparticle mass flux is described by surface and pore

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diffusion and adsorption equilibrium of individual compounds can be represented by

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the Freundlich isotherm equation.

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Based on the Gnielinski correlation,29 the external mass transport coefficient (kf )

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was estimated using Eqs. S1-3 in Section S4 of the Supporting Information. The pore

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diffusion coefficient (Dp) was estimated using Eq. 4.29



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Dp =

ε p × D1 τ

(4)

The variable τ was estimated by Mackie and Meares (Eq. 5) for electrolyte

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solutions using the following expression.29

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τ=

186

where τ is the toruosity factor and εp is the particle porosity (εp ≈ 0.741). This equation

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yielded a τ of 2.14 with the estimated Dp ≈ 1.603 × 10-10 m2/s. We assumed that the

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surface diffusion coefficient was equal to zero because the Li ions could not be

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transported down the surface of the CMO and were transported into the ion channels

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for removal. So in actuality we used the pore diffusion model (PDM) to predict the

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lithium breakthrough curve.26,30,31 PDM simulations were conducted using the

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AdDesignS software (Michigan Technological University).32

(2 − ε )

2

p

εp

(5)

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The wall effect on mass transfer can be neglected for dcolumn/dp ratios > 20.33,34

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The relative importance of the internal (pore) and external (film) mass transport

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resistances was evaluated using the pore Biot number (Bip) (Eq. 6) according to

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Sontheimer et al. as follows:29

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Bip =

198 199

200

kf ×dp 2 × Dp

(6)

The calculated value of Bip was 29.8. Accordingly, intraparticle diffusion controls the overall mass transport of the system because Bip ≥ 20.29 The maximum number of bed volumes (BVMAX) (Eq. 7) could be estimated


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according to Sontheimer et al. as follows:26

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BVMAX =

203

where ρBED is the bed density of the media in the packed bed (g/cm3), and q0 is the

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adsorption capacity calculated at C0.

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RESULTS AND DISCUSSION

q0 × ρBED C0

(7)

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As discussed below, we evaluated CMO adsorbent performance for different

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concentrations, different temperatures, and pH values using isotherm tests, kinetic

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studies, and fixed bed experiments. With respect to adsorption thermodynamics, we

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determined the Li site energy distribution using various adsorption isotherm models

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and data. We verified the PDM by comparing it to batch kinetic and fixed bed

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experiments. We used the verified model to predict full scale performance and the

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impact of competing ions on the recovery of Li ion. In addition, we determined the

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recovery of Li using fixed bed adsorption by conducting adsorption and regeneration

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experiments.

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Adsorbent Characterization

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The pore size distribution is shown in Figure S1. The specific surface area (BET)

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of CMO was approximately 54.1 m2/g (see the figure inset). The XRD patterns of the

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MO, LMO and CMO particles are shown in Figure 2. The crystal phases of MO,

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LMO and CMO are pure tetragonal phase (T) β-MnO2 (according to JCPDS 24-0735),

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pure cubic phase (C) c-Li4Mn5O12 (according to JCPDS 46-0810) and λ-MnO2



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(according to JCPDS 44-0992), respectively. The substitution of H for Li in the lattice

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causes the crystal cell to shrink, whereas the crystal skeletons of LMO and CMO

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remain unchanged. As a result, the -Mn-O-Mn-O- skeleton will be extremely stable

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during adsorption because the lattice clearance contains vacancies with a high charge

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density, forming a strong electrostatic field and are the perfect size for Li ions. Thus,

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the MnO2 ion cages can adsorb the lithium ions. Figure 3 shows the SEM

227

micrographs of the obtained samples. Figures 3a and 3b illustrate that the MO

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nanorods are delicate, smooth and uniform before calcination. In Figure 3c, the

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resulting LMO from the calcined MO are still nanorods. Figure 3d clearly illustrates

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that CMO maintains a similar morphology as LMO. However, the magnified view

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shows that the CMO nanorods are partially cracked and the surface is coarse. The

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particle surface appears to be partially dissolved during the acid wash that removes Li

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ions (pickling process). This phenomenon was confirmed by the results of the XRD

234

analysis, shown in Figure 2.

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As shown in Figures 3 and S1, the LMO and CMO have a high degree of porosity

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and large pores between the nanorods, which increases the intraparticle pore diffusion

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and reduces the impact of counter diffusion. The lack of pore connectivity (nanorods

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contacting one another as opposed to a solid support that contains pores) and strong

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electrostatic interaction between ions and the surface eliminates surface diffusion to

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the intraparticle transport flux. As shown below, liquid-phase pore diffusion is rapid

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and produces a steep breakthrough curve.

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XPS Analyses. The chemical composition of the CMO before and after Li adsorption


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was measured using X-ray photoelectron spectroscopy (XPS). As shown in Figure 4a

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and 4b, the peaks corresponding to Mn 3p, Li 1s, O 1s and Mn 2p are distinct. The

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XPS spectrum of Mn 2p is plotted in Figure 4c and 4d. Two peaks located at 642.1

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and 653.8 eV correspond to Mn 2p3/2 and Mn 2p1/2, respectively, with a spin energy

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separation of 11.7 eV, which is consistent with the well-characterized MnO2 XPS

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spectra in the literature.35 The ratio of the Mn/O atomic concentration (Mn: 25.89%, O:

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51.72%) is 1 : 2, indicating the dominance of Mn4+ in the CMO. There was no change

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in the proportion of Oad/Olatt before and after Li adsorption (Figure 4e and 4f), which

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was approximately 48.2% on the CMO, after Li adsorption, indicating that the

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chemical environment is stable and unaffected by the adsorbed Li(I). Figure 4g and 4h

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were XPS spectrum of Li 1s. In Figure 4h, the appearance of the Li 1s peak at the

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binding energy of 54.9 eV indicates that Li(I) is adsorbed on the CMO. This finding

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indicates that stability of the Mn-O bond, which plays an indispensable role in

256

forming Li cages and it has a high charge density. The high charge density forms a

257

strong electrostatic field, which aids in the adsorption of Li(I). Researchers believe

258

that there are three mechanisms that explain the adsorption behavior of Li in ion cages:

259

(i) redox reactions36; (ii) ion exchange37; and (iii) a complexation mechanism from a

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redox reaction during ion exchange38. Our results demonstrate that the Mn valence

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cannot be reduced when CMO adsorbs Li(I), which was confirmed by XPS (Figures

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4c and 4d). Consequently, the adsorption of Li on the CMO is an ion exchange

263

reaction, which is in good agreement with the results of the analysis using the D-R

264

isotherm model.



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Effect of Initial Concentration and Temperature. Figure 5 shows the adsorption

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isotherms of Li(I) on CMO for three temperatures, i.e., 20, 30 and 40 °C. The

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adsorption capacity of Li(I) on the CMO increases with increasing concentration and

268

temperature.

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approximately 61.32 mg/g at equilibrium concentration about 300 mg/L at 40 °C,

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which is higher than those reported for other lithium ion-cage adsorbents. For

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example, the adsorption capacity is 20.82 mg/g for D751 resins,15 17.0 mg/g for

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H2.0Li0.1Mn4.0O7.8·0.22H2O,39 31.23 mg/g for LiAlMnO440 and 41.36 mg/g on

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Li4Mn0.5Ti0.5O4.41 Hence, the CMO exhibits a significantly higher adsorption capacity

274

than these other adsorbents. Additionally, the maximum Li(I) adsorption capacity on

275

CMO is 15 times greater than on Fe3O4@SiO2-IIP (4.1 mg/g).16 In addition, the

276

synthesis cost of the CMO adsorbent is much less than Fe3O4@SiO2-IIP, which makes

277

industrial lithium recycling more feasible. The adsorption capacity also increased with

278

the increasing temperature, suggesting that the adsorption of lithium is endothermic.

279

Effect of pH. pH plays an important role in determining the performance of an

280

adsorbent. Figure S2 shows that the adsorption capacity of Li(I) increases with

281

increasing pH. When the pH is below pH 4.0, the adsorption capacity is low due to

282

the protonation of the -OH group, when the pH is higher than pH 4.0, high adsorption

283

capacity of Li+ is observed. The maximum adsorption capacity for Li(I) was obtained

284

at a pH of 12.95. The CMO ion exchange/adsorption site is an -OH group and can

285

exchange the H+ for Li(I). At high pH, the -OH group ionizes and adsorbs Li(I).42

286

Adsorption Selectivity. Competitive adsorption experiments of Li+, Na+, K+, Mg2+

Moreover,

the

maximum

equilibrium


adsorption

capacity

is

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and Ca2+ were conducted to determine the selectivity of these ions on CMO. The

288

selectivities are summarized in Table 1. As can be seen in Table 1, the CMO exhibit a

289

much higher selectivity separation factor ( α ki ) and affinity of Li(I) as compared with

290

other competitive ions. Hence, the CMO exhibits a higher selectivity toward Li(I)

291

than other ions that may be found in wastewater. At approximately 0.7 Å, the

292

three-dimensional (1 × 3) tunnels in the spinel lattice are a suitable size for adsorbing

293

lithium ions in the CMO nanocrystal cubic phase.43 In other words, lithium ions can

294

be adsorbed in three-dimensional (1 × 3) tunnels, which have a strong electrostatic

295

field, whereas other larger metallic ions can only be adsorbed to a lesser extent on the

296

surface sites. The ion cages had a higher selectivity for Li+ than for Mg2+, despite their

297

similar ionic sizes and different valence.

298

In generally, multivalent ions tend to have higher selectivity than monovalent

299

ions. Because the hydration energy of Mg2+ is approximately 4 times larger than that

300

of Li+ (Mg(II)hydration energy = - 455 kcal/mol vs. Li(I)hydration energy = - 122 kcal/mol),44 a

301

higher energy would be required for dehydration to enter the cavity of a lithium ion

302

cages. Accordingly, it is easier to remove the water of hydration from Li(I), which

303

results in a higher selectivity for Li(I) ions. Therefore, there are 2 criteria that must be

304

met for adsorption to occur in the three-dimensional tunnels: (1) the ion must fit, and

305

(2) the adsorption free energy must be greater than dehydration energy which is

306

required to removal the water of hydration. The dehydration energy causes Li

307

adsorption to be endothermic.

308

Adsorbent Regeneration in a Batch Reactor. A good adsorbent should have



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superior desorption and regeneration performance. The regeneration of Li(I) was

310

desorbed using 0.1 mol/L HCl solution, the adsorbent was separated and washed with

311

deionized water, then dried. After that, regenerated CMO was placed into a lithium

312

adsorption solution (the experiment is similar to isotherm procedure). To assess the

313

CMO’s performance, the CMO adsorbent was subjected to six repetitive

314

adsorption-desorption cycles for the removal of Li(I) ions from aqueous solutions. As

315

shown in Figure 6, the adsorption capacity for Li(I) decreased slightly after each

316

regeneration,

317

adsorption-desorption cycle for Li(I) was only 7.6% less than that for the first cycle.

318

During the adsorbent regeneration, the lost Mn can destroy the 3-D tunnel in the

319

spinel structure, which can cause a decrease in the adsorption capacity. These results

320

demonstrate that the CMO can be reused for a large number of adsorption-desorption

321

cycles.

322

Adsorption Isotherm Models. The Langmuir and Freundlich isotherm models,

323

shown here, respectively, were used to fit the CMO isotherm data.

324

qe =

325

qe = k F Cen

326

where qe (mg/g) and Ce (mg/L) are the amount of lithium adsorbed at equilibrium and

327

the equilibrium concentration, respectively. kL (L/mg) is the equilibrium constant and

328

and Q0 the maximum adsorption capacity. kF and 1/n are the capacity factor and

329

intensity for the Freundlich isotherm equation.

Q0 k L C e 1 + k LCe

and

the

adsorption

capacity

of

the

CMO

for

the

sixth

(8)

1

(9)


Page 17 of 45


330

The isotherm parameters that were determined by fitting the data in Figure 5 are

331

summarized in Table 2. A comparison of the correlation coefficients (R2) illustrates

332

that the Freundlich equation described the data slightly better than the Langmuir

333

equation, suggesting that the adsorption of Li(I) on CMO occurs on sites that have a

334

distribution of site energies (i.e., heterogeneous site energies). In contrast, the

335

Langmuir equation assumes that all of the site energies are identical.

336

Adsorption Site Energy Thermodynamics. We used the Dubinin-Radushkevich

337

(D-R) isotherm45 to determine whether the adsorption process originated from

338

physical (Van der Walls) or chemical (surface reaction) forces. The D-R isotherm is

339

given by Eq. 10:

340

ln qe = ln Q0 − βε 2

341

where qe is the amount of metal ions adsorbed on the adsorbent per unit weight

342

(mol/g), Q0 is the maximum adsorption capacity (mol/g), βDR is the activity coefficient

343

related to the mean free energy of adsorption (mol2/J2) and ε (J/mol) is the Polanyi

344

potential ( ε = RT ln (1 + 1/Ce ) ). The D-R isotherm model fit the equilibrium data well

345

(R2 > 0.99, Figure S3 and Table S1). The intercept of the plots for 20, 30 and 40 °C

346

yield qm values of 5.81, 6.21 and 6.91 mol/g, respectively. The mean free energy of

347

adsorption, E (kJ/mol), is determined using Eq. 11:

348

E = (2 β DR )

349

The mean free adsorption energy was approximately 8.6 kJ/mol, which is in the

350

energy range of an ion-exchange reaction (i.e., 8–16 kJ/mol).46

-1 / 2

(10)

(11)



351

Thermodynamic calculations were performed to further explain the endothermic

352

nature of the process. The thermodynamic parameters (namely, the Gibbs free energy

353

∆G0 (kJ/mol), enthalpy ∆H0 (kJ/mol) and entropy ∆S0 (kJ/mol K) were estimated by

354

Eqs. 12 and 13, respectively:47

355

∆G 0 = − RT ln K

356

ln K =

357

where R is the universal gas constant (8.314 J/mol K) and T is the temperature (K). K

358

is the thermodynamic equilibrium constant in the adsorption process, which was

359

determined using the method of Khan and Singh48 by plotting ln(qe/Ce) versus qe and

360

extrapolating to zero qe.

∆S 0 ∆H 0 1 − × R R T

(12)

(13)

361

Based on Eq. 13, The thermodynamic parameters of ∆H0 and ∆S0 can be

362

determined from the slope and the intercept, respectively, as shown in Figure S4 and

363

Table S2. The positive value of ∆H0 (11.34 kJ/mol) proves that the adsorption process

364

is endothermic between 20 and 40 °C. The positive value of ∆S0 (0.08 kJ/mol K)

365

suggests that the degree of randomness increases during Li(I) adsorption. This may be

366

due to the loss of the water of hydration from the Li. The ∆G0 was calculated to be -

367

12.20, - 13.05 and - 13.77 kJ/mol for 20, 30 and 40 °C, respectively, indicating that

368

Li(I) adsorption is thermodynamically favored. A smaller negative ∆G0 indicates that

369

adsorption is less favored thermodynamically. Moreover, ∆G0 became more negative

370

(the absolute value of ∆G0 increased) as temperature increased at the same adsorbate

371

loading, suggesting that the driving force of the adsorption increased with increasing


Page 18 of 45

Page 19 of 45


372

temperature, which consistent the adsorption capacity increasing with increasing

373

temperature. This conclusion is in good agreement with the results obtained from the

374

D-R isotherm model.

375

Dubinin-Ashtakhov (DA) Model-Based Site Energy Distribution. The Polanyi

376

theory49 assumes that the adsorption potential is independent of temperature and

377

application of the theory describes the adsorption process. The nonlinear DA model

378

based on Polanyi theory was used to fit the experimental isotherm data according to

379

Eq. 14:

380

log Qe = log Q0 − (ε sw /E d )

381

where Qe (mg/kg) is the amount of adsorbate compared to adsorbent, Q0 (mg/kg) is the

382

maximum adsorption capacity, εsw = RTln(Cs/Ce) (J/mol) is the effective adsorption

383

potential, Cs (mg/L) is the water solubility of the adsorbate, Ce (mg/L) is the

384

equilibrium concentration of the adsorbate in the liquid phase, Ed (J/mol) is the

385

“normalized constant”, and b is the empirical isotherm fitting parameter.

b

(14)

386

The adsorption capacity results from the adsorption onto adsorption sites that

387

have a distribution of energies F(E*) (see section S5 in the Supporting Information)

388

and is given by Eqs. S5-8. Because the resulting site energy distributions are not

389

normalized, the area under the distribution is equal to the maximum adsorption

390

capacity Q0.

391

Figure S5a and Table S3 illustrate that the experimental data fit the DA model

392

well (R2 > 0. 99). The site energy distributions were all unimodal distributions at



393

different temperatures, as shown in Figure S5b. With increasing site energy (E*), the

394

frequency function F(E*) increased until it reached the F(E*) peak apex, after which it

395

decreased to approach zero. Theoretically, the area under the peak represents the

396

number of available adsorption sites in a specific energy range. Increasing the

397

temperature from 20 °C to 40 °C increased the area under the peak. The affinity of Li

398

for the CMO surface was related to the position of the energy distribution mean on the

399

energy axis.50 A larger value of the mean energy resulted in a higher adsorption

400

affinity and thus a higher adsorption capacity. The ∆G0 analysis was consistent with

401

the energy analysis, confirming that the affinity of CMO increased with increasing

402

temperature.

403

DA Model-Based Average Site Energy and Adsorption Site Heterogeneity.

404

The DA Model-based average site energy distribution was employed to determine

405

the interaction forces between adsorbent and adsorbate, and its width describes the

406

surface energy heterogeneity of the adsorbents. The mean site energy (Em) and

407

adsorbent site energy heterogeneity index (σe*) were calculated (see section S6 in the

408

Supporting Information) from Eq. S10. The adsorbent site energy heterogeneity can

409

be described by determining the standard deviation (σe*) using Eqs. S11 and S12.

410

Figure S6 describes the site energy distributions with variations in Ed and b,

411

demonstrating that both the mean and shape of the distribution changed as either Ed or

412

b were varied. This result implies that b could not represent the site energy

413

heterogeneity. Em increased significantly with increasing temperature (Figure S7 and


Page 20 of 45

Page 21 of 45


414

Table S4), which can be ascribed to the three-dimensional tunnels structure in the

415

CMO, forming additional adsorption sites. The term σe* decreased with increasing

416

temperature, indicating that the stronger adsorption affinity for Li on the CMO.

417

The mean site energy (Em) was examined to further understand the relationship

418

between the maximum adsorption capacity (Q0) and adsorption temperature (T). In Eq.

419

15, Em is considered to be a function of T only. In Eq. 16, Q0 is determined by Em by

420

plugging Eq. 15 into Eq. 16 and then incorporating the result into in Eq. 17. The

421

adsorption capacity (Q0) was found to be linearly related to the adsorption

422

temperature (T) based on Eqs. 15-17:

423

Em = k1 × T + d1

(15)

424

Q0 = k2 × Em + d 2

(16)

425

Q0 = k3 × Em + d 3 = k3 × (a × T ) + d 3

(17)

426

where k1 (1/K), k2 and k3 (µmol/J) are regression coefficients and d1, d2 and d3 (mg/kg)

427

are fitting parameters. The terms k1, k2 and k3 are dimensionless and represent the

428

contribution fractions of T to Em, Em to Q0 and T to Q0, respectively.

429

The regression results revealed the contribution of the average site energy (Table

430

S5). The maximum adsorption capacity (Q0) was found to be linearly related to the

431

adsorption temperature. Furthermore, Em increased and Q0 significantly increased

432

with increasing temperature, indicating that the number of adsorption sites also

433

increases with increasing temperature. For endothermic reactions with increasing

434

adsorption temperature, the positive value of (k1 × T) increased. In contrast, an



435

increasing negative value of (k1 × T) was observed with increasing temperature for

436

exothermic reactions. The increases in the positive value of (k1 × T) with increasing

437

temperature are in good agreement with our thermodynamic study.

438

Using the Pore Diffusion Model (PDM) and the Homogeneous Surface Diffusion

439

Model to Describe Adsorption Kinetics

440

In the next few sections, we first use batch kinetic tests to determine intraparticle

441

diffusivities. Then we show that the models can predict Li breakthrough in short fixed

442

bed column tests. Next we will use the validated models to predict full scale

443

performance and we evaluated the impact of multiple ions (that are found in a typical

444

waste water from LIB recovery operations) on Li breakthrough. We also determine

445

the Li recovery regenerating the fixed bed using acid.

446

Batch Kinetic Studies. Adsorption kinetics are important in determining the fixed

447

bed performance. A kinetic experiment was performed with CMO. The initial

448

concentration of Li was 300 mg/L, and the CMO dose was 1 g/L. Figure 7 shows that

449

the Li(I) adsorption capacity grew rapidly for the first 50 min. Then, the Li(I) uptake

450

gradually reached equilibrium at about 120 min. In addition, the equilibrium

451

adsorption capacity increased with the adsorption temperature, further supporting the

452

notion that the adsorption of Li(I) on CMO is an endothermic process.

453

To describe the adsorption kinetic data, a simplified version of the homogeneous

454

surface diffusion model (HSDM)51 was used to determine the surface diffusion

455

coefficient (See section S7 in the Supporting Information). The appropriate


Page 22 of 45

Page 23 of 45


456

Freundlich isotherm model parameters for a given temperature were used in the

457

HSDM. The 2 h data obtained from the kinetics test are given in the first two columns

458

of Table S7. The equilibrium concentration was 250 mg/L and Ce/C0 = 0.8. The

459

experimental data and solution to the HSDM were used to fit the dimensionless model

460

concentrations, as illustrated in Figure S8. The best fit values are shown in Table S7.

461

The most appropriate simplified HSDM equation is given in Table S6, and Ds was

462

found to be 4.63×10-10 m2/s at 20 °C. We used the simplified HSDM because other

463

researchers could use the simplified HSDM solution that we have provided in the

464

literature.51 In addition, we have provided a simplified HSDM for fixed bed

465

calculations that also could be used for fitting short fixed bed experiments and full

466

scale predictions when competing ions are not present.

467

The most appropriate model to use to fit the kinetic data is the PDM with a Ds

468

value of zero; because, as discussed elsewhere, surface diffusion is not possible for

469

CMO. We calculated the Dp using Eq. 4 and we can calculate a Ds value that would

470

give the same result as the PDM using the surface to pore diffusion flux ratio (SPDFR)

471

equal to 1.0. This gives a Ds value of 2.63× 10-10 m2/s. However, when we fit the data

472

with the simplified HSDM, we found that the SPDFR was 2. (See Section S8 of the

473

Supporting Information.) When we ran the PDM with a Dp that was calculated from

474

Eq. 4, we had an excellent prediction SBA data as discussed below.

475

Hence, when competing ions are not present, we can use either the HSDM or the

476

PDM because they give similar results. And the two model could be used for

477

preliminary design if there are no competing ions.52



478

Short Bed Adsorber (SBA) Test and Pore Diffusion Model (PDM). We used the

479

simplified batch HSDM to fit the adsorption kinetic data and determined the surface

480

diffusivity. Next, we used the HSDM to predict SBA data using that surface

481

diffusivity (see Figure S9). The HSDM did not predict the data well (R2 ≈ 0.86),

482

because the St for the SBA is smaller than the Stmin (required to establish constant

483

pattern). Consequently, the HSDM prediction has an earlier breakthrough. (However,

484

the simplified HSDM could be used for St values (or EBCT values) above those that

485

correspond to Stmin.) Consequently, we used the PDM to predict Li breakthrough

486

curve and the Dp value that was calculated from Eq. 4. As shown in Figure 8a, we

487

predicted the SBA data using the PDM and the breakthrough curves for 20, 30 and

488

40 °C show increasing column capacity with increasing temperature. The Freundlich

489

isotherm values came from fitting the equilibrium data. The pore diffusion coefficient,

490

film transfer coefficient, and sphericity were calculated from Eq. 4，Eq. S1 and Eq. S4

491

(Dp = 1.60 × 10-10 m2/s, 1.64 × 10-10 m2/s, 1.68 × 10-10 m2/s, kf = 4.79 × 10-5 m/s, 4.91×

492

10-5 m/s, 5.03× 10-5 m/s, and Φ = 0.62 for 20, 30 and 40 °C, respectively). The PDM

493

predictions for the SBA were excellent (R2 ≈ 0. 99). As shown in Figure 8a, in fact,

494

the integrated capacities (areas above the curves as a function of BVs fed) were 1,374,

495

1972 and 2493 BVs, respectively. These are the maximum number of BVs that can be

496

treated, if we had an infinite mass transfer rate (i.e., the breakthrough curve would be

497

a vertical line that occurs when the bed is exhausted).

498

As shown in Figure 8b, the approximately 98.7% of the Li+ was extracted after

499

approximately 66.41 BVs using a 0.1 mol/L HCl solution at 20 °C. Additional, we


Page 24 of 45

Page 25 of 45


500

predict desorption curve using PDM. The PDM can fit the data well at 1 min EBCT

501

and predict 10 min EBCT data. We calculated the enrichment factor using the BVmax,

502

which the 10 minute EBCT approaches and the number of bed volumes (using 0.1

503

mol/L HCl acid) that are required to desorb Li. The number desorption bed volumes

504

were assumed to be at the point where the Li effluent concentration, C/Co, was less

505

than 0.05. This corresponds to 66.42 and 82.41 desorption bed volumes for 1 and 10

506

minutes of EBCT, respectively. Using these values, we determined that the

507

enrichment factors were 20 and 15, respectively, by dividing BVmax by these

508

desorption volumes. This means that the regenerated solution has a very high

509

concentration of Li and it is easy for recovery Li using evaporation.

510

The above results confirm that a higher temperature is beneficial for increasing

511

the treated number of BVs. Furthermore, reasonable breakthrough and regeneration

512

curves for Li can be predicted using the PDM. As a result, the model can be used to

513

predict full-scale performance, thus reducing the time and cost required as compared

514

to pilot studies.

515

Predicting Full-Scale Performance Using the Validated PDM. The performance of

516

full-scale fixed bed systems was simulated using PDM with the same operating

517

parameters as the SBA tests. The full-scale operating conditions were as follows: (1)

518

the superficial velocity was the same as in the SBA tests, (2) the empty bed contact

519

times (EBCTs) were 2.5, 5 and 10 min; and (3) the influent concentrations were 20,

520

50 and 200 µg/L, which are realistic values for LIB waste water. The treatment

521

objective of 10 µg/L Li(I). The input data used for the PDM are shown in Table S8.



522

Figure 9a shows the full-scale breakthrough curves for EBCTs of 2.5, 5 and 10

523

min and an influent lithium concentration of 200 µg/L. For a treatment objective of 10

524

µg/L Li(I), the number of BVs that can be treated increased with increasing EBCT.

525

The PDM predicted that approximately 740 BVs can be treated at an EBCT of 2.5

526

min. When EBCT was increased to 5 and 10 min, the number of treated BVs

527

increased to 1,051 and 1,237, respectively, approaching the BVMAX of 1,374 BVs.

528

Figure 9b illustrates the same lithium breakthrough predictions described according to

529

the liters of water treated per gram of dry media. When the EBCT was 2.5, 5 and 10

530

min, approximately 2.2, 2.9 and 3.2 L of water can be treated when the treatment

531

objective of 10 µg/L is reached.

532

For an EBCT of 10 min, the lithium breakthroughs for influent lithium

533

concentrations C0 of 20, 50 and 200 µg/L were calculated and are presented in Figure

534

9c. When C0 was increased from 20 to 50 and 200 µg/L, the number of BVs that could

535

be treated until reaching the 10 µg/L gradually decreased from 1,681 to 1,414 and

536

1237 BVs, respectively. Figure 9d presents the same lithium breakthrough predictions

537

based on the liters of treated water per gram dry media at different C0 values. When

538

C0 was 20, 50 and 200 µg/L, approximately 6.3, 4.5 and 3.2 L of water could be

539

treated per gram of dry media, respectively, until the treatment objective of 10 µg/L

540

was reached.

541

PDM Predictions for Multiple Ions Breakthrough. The PDM that describes the fate

542

of ions in a fixed bed is also was called the IX model when pore diffusion is the only

543

intraparticle transport mechanism and multiple ions are present.52 We used the


Page 26 of 45

Page 27 of 45


544

selectivity values that were measured and the multicomponent Langmuir equation to

545

description IX equilibria. The PDM can be used for preliminary design and predicts

546

the breakthrough of all competing ions. Figure 10 shows the predictions for an

547

influent containing Li+, Na+, K+, Mg2+ and Ca2+ all at 200 µg/L and pH 10. The other

548

competing ions broke through earlier than Li+ which is a result of a high selectivity

549

for Li+ over the other ions. The CMO can treat 420 BVs at EBCT 10 min BVs for a Li

550

treatment objective of 10 µg/L, which is much larger than other competing ions. The

551

BVs treated when Li was by itself 1,237 for 10 minutes of EBCT and a treatment

552

objective of 10 µg/L. This is 150, 93, 34, 4.6 times greater (for Na, K, Mg, Ca,

553

respectively) than when the other ions are present at concentrations that are typical in

554

LIB waste water.

555

Practical Significance

556

The maximum adsorption capacity (Q0) increased with increasing temperature,

557

and this relationship was described by using the DA model and its site energy

558

distribution (F(E*)). This increase in the adsorption capacity with temperature is

559

useful because the technology can be used in warm and cold climates. The PDM can

560

be used to predict the lithium ion breakthrough for full-scale processes. The IX model

561

can predict the lithium ion breakthrough when the wastewater containing multiple

562

ions. The predictive power of this model will allow engineers to evaluate the

563

performance of a fixed bed containing CMO when multiple ions are present.

564

ACKNOWLEDGEMENTS



565

This study was financially supported by the National Science Foundation of

566

China (51238002, 51272099), the National Science Fund for Excellent Young

567

Scholars (51422807), the Key Project of Science and Technology Department of

568

Jiangxi Province (20143ACG70006) and the Cultivating Project for Academic and

569

Technical Leader of Key Discipline of Jiangxi Province (20153BCB22005). The

570

authors appreciate support from the Brook Byers Institute for Sustainable Systems

571

(BBISS), Hightower Chair and Georgia Research Alliance at Georgia Institute of

572

Technology.

573

574

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Figure and Table

Figure 1. Preparation of the lithium ion cages (CMO)



Figure 2. XRD patterns of MnO2 oxide (MO), Li4Mn5O12 tri-oxide precursor (LMO) and MnO2 ion cages (CMO).


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Figure 3. (a) and (b) shows the SEM image of MO, (c) and (d) shows SEM images of LMO and CMO, respectively.



Figure 4. XPS characterization of CMO before and after Li(I) adsorption. (a) and (b) XPS survey spectra of the samples, high-resolution XPS spectra of (c) and (d) Mn 2p, (e) and (f) O 1s (Olatt (lattice oxygen), Oad (adsorb oxygen) and OH2O (chemisorbed oxygen species)) and (g) and (h) Li 1s before and after Li(I) adsorption, respectively.


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Figure 5. Adsorption isotherm at different temperatures for Li(I) adsorption on CMO. The initial Li(I) concentration was ranged in 20-450 mg/L. Adsorbent dose: 1g/L, Total solution volume: 20 mL, pH: 10.1, Temperature: 20, 30 and 40 °C.



Figure 6. Regeneration cycles of CMO. Adsorbents mass: 200 mg, The initial Li(I) concentration: 300 mg/L, pH: 10.1, Temperature: 30 °C


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Figure 7. Adsorption kinetics for Li(I) adsorption on CMO at different temperatures.



Figure 8. (a) The experimental data and the predictions from PDM for Li(I) breakthrough in SBA tests. The initial Li(I) concentration: 200 μg/L, pH: 10.1, Temperature: 20, 30 and 40 °C. (b) Fixed bed column regeneration test using 0.1 mol/L HCl solution at EBCT of 1 min, and PDM prediction for the desorption at EBCTs of 1 min and 10 min at 20 °C, respectively.


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Figure 9. (a) The lithium breakthroughs for Li(I) with the initial concentration of 200 μg/L, by using the validated pore surface diffusion model with empty bed contact times (EBCTs) at 2.5, 5 and 10 min, respectively. (b) The same lithium breakthrough predictions in Figure a, but with liters of treated water per gram dry media at different EBCTs at 20 °C. (c) The lithium breakthroughs for Li(I) with the initial concentration of 20, 50 and 200 μg/L, respectively, by using the validated pore surface diffusion model with a fixed EBCT at 10 min. (d) The same lithium breakthrough predictions in Figure c, but expressed as liters of treated water per gram dry media at different initial Li concentration at 20 °C.



Figure 10. The mathematical model consists of ion exchange (IX) models for describing the selectivity behavior of the competition ion on CMO bed. The competition for adsorption of Li+, Na+, K+, Mg2+, and Ca2+ ions with respect to H+ ions was determined. The initial Li(I) concentration: 200 μg/L, The empty bed contact times (EBCT): 10 min, Temperature: 20 °C


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Table 1. Selectivity of various ions for CMO

Metal ions

Ionic radius (pm)

Li(I)

76

118.48

Na(I)

102

1.68

K(I)

138

2.35

Mg(II)

72

28.21

Ca(II)

100

49.09

α ki

Table 2. Langmuir and Freundlich parameters for Li(I) adsorption on CMO

Equations

Langmuir model

Freundlich model

T

Q0

kl

(oC)

(mg/g)

(L/mg)

20

56.05

0.013

0.9756

0.45

2.854

0.9837

30

71.22

0.011

0.9737

0.51

3.216

0.9879

40

76.80

0.012

0.9798

0.58

3.777

0.9902

R2

1/n

kF

R2

(mg1-(1/n)L1/n/g)


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