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Nanomaterial Adsorbent Design: From Bench Scale Tests to Engineering Design
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Jinming Luo* and John C. Crittenden Brook Byers Institute for Sustainable Systems and School of Civil and Environmental Engineering, Georgia Institute of Technology, 828 West Peachtree Street, Atlanta, Georgia 30332, United States dichalcogenides (TMD) (e.g., MoS2, WS2, MoSe2, etc.) contain a semiconducting phase (2H) and a metallic phase (1T), which have distinct properties. However, no studies have distinguished the adsorption behavior of these two phases, and the adsorption thermodynamics of these phases can be calculated using DFT. In addition to material selection, describing the adsorption isotherm and kinetics is indispensable for the engineering design of adsorption contactors (e.g., optimizing their size and operation). Many researchers are analyzing adsorption isotherm data using Langmuir, Freundlich, and Dubinin− Radushkevich models and determining the adsorption thermodynamics, including the isosteric, integral, and differential heats of adsorption. This thermodynamic analysis determines the adsorbent/adsorbate interactions and temperature impacts. The combined thermodynamic analysis using the experimental isotherm analysis and DFT calculations provides insights into the adsorption process and adsorbent design. For the kinetic data analysis, most researchers use pseudo kinetic models. However, the parameters (i.e., rate constants) obtained from the pseudo kinetic models depend on the he main purpose of developing nanomaterial adsorbents adsorbent dose and are not appropriate for engineering design. is for engineering applications. The adsorption research Generally, bench-scale experiments should be conducted and regarding nanomaterial adsorbents currently comprises four analyzed in a manner that can be used for engineering analysis parts: (i) material design, (ii) isotherm analysis, (iii) kinetic and design. A mass transfer model can help to elucidate the analysis, and (iv) engineering design. In recent decades, adsorption kinetic data collected at the bench scale in the thousands of research papers have been published on manner of engineering design.3 Mass transfer models include developing adsorbents to remove heavy metals, dyes, antithe external mass transfer coefficient and pore or surface biotics, etc. from water. However, it is impossible to fabricate diffusion coefficient, and these are called the pore diffusion and test the adsorption capacity and kinetics of all possible model (PDM) or homogeneous surface diffusion model materials and their combinations because there are numerous (HSDM). When both intraparticle diffusion coefficients are options. used, the model is called the pore surface diffusion model An understanding of the desirable functional phases/facets/ (PSDM). In detail, it is appropriate to apply the PDM for the functional groups on nanomaterial adsorbents is essential for gel-type (i.e., hydrogel) or highly porous adsorbents or ion exchange resins (because the adsorbate diffuses through waterwater treatment, and these aspects can be comprehensively filled pores). Meanwhile, the HSDM is suitable for solid determined via density functional theory (DFT) calculations. adsorbents that are less porous because the pore concentration Accordingly, the adsorption thermodynamics, such as the is much smaller than the adsorbate surface concentration. The adsorption energy and reaction potential, can be calculated by PSDM is most suitable for multicomponent simulations in DFT calculations, which can help researchers in this regard. solid adsorbents. Moreover, it is important to remember that Currently, many researchers use DFT calculations to explain we can calculate the Biot number (Bi) to determine whether experimental results, which is certainly a good approach.1,2 In the external or intraparticle mass transfer controls the mass addition, we can use DFT calculations to select promising transfer rate. For the HSDM, the external mass transfer adsorbents from many options (i.e., ∼100 000). Once selected, controls the rate when Bi < 1, and the intraparticle surface we can conduct experiments on the selected adsorbents for diffusion controls the rate when Bi > 30. removal of the target pollutant. This approach is “greener” and more thorough because it does not generate any waste reagents, saves time, and enables us to consider more Received: August 7, 2019 possibilities. For example, two-dimensional transition-metal
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© XXXX American Chemical Society
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DOI: 10.1021/acs.est.9b04371 Environ. Sci. Technol. XXXX, XXX, XXX−XXX
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Environmental Science & Technology
Figure 1. (A) Granular adsorbents (such as granular activated carbon (GAC)) in a fixed bed column; (B) Floc blanket reactor for application in powdered adsorbents (such as powdered activated carbon (PAC)) systems. The figures were initially adapted from MWH’s Water Treatment: Principles and Design, 3rd edition textbook, and some changes were made based on the original figures.
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To continue this discussion, we must distinguish two major applications: (I) fixed beds using granular adsorbents (Figure 1A) and (II) slurry (or floc blanket) reactors using powdered adsorbents (Figure 1B).4 For fixed beds, we must determine the external mass transfer coefficient (kf, m/s) and intraparticle diffusivities (i.e., pore diffusion coefficient and/or surface diffusion coefficient) for the engineering design. kf can be calculated using the mass transfer correlations that include the Reynolds number, Schmidt number and Sherwood number, or by fitting experimental data from a short fixed bed (this quantity can be calculated from an immediate breakthrough for C/C0 > 0.3). Moreover, the surface or pore diffusion coefficients can be estimated by fitting the breakthrough data from the short fixed bed when kf has been determined. The pore diffusion coefficient may also be calculated from the porosity and tortuosity of the adsorbent. The calculated intraparticle diffusivities and kf can be used in the PDM or HSDM for engineering design. For the engineering design for fixed beds, other operational parameters must be selected, including the empty-bed contact time (EBCT, volume of the bed occupied by the adsorbent divided by the flow rate), particle size and hardness. Depending on the water treatment objectives, the fixed bed column operation for engineering design include two modes: series or parallel operation.5 Similarly, the kinetics for powdered adsorbents can be quantified using the HSDM or PDM. Usually, kinetic experiments are conducted in batch reactors, but we must ensure that the mixing does not disrupt the adsorbents because smaller particles will have faster adsorption kinetics. Furthermore, if the external mass transfer controls the adsorption kinetics, then the models that only include the external mass transfer can be used to fit the data. However, kf cannot be used for engineering design because it depends on the mixing conditions in the slurry reactor, and it is unlikely that they will match the mixing in a batch reactor. However, if we collect data using bench-scale vessels with identical mixing to the full contactors, we can use kf for the engineering design. In many cases of powdered solid adsorbents, such as powdered activated carbon, the surface diffusion controls the adsorption process, and the HSDM can be used to fit the batch data and for engineering design. Overall, we offer these perspectives to improve the real engineering applications of nanomaterial adsorbents by (1) performing DFT calculations to consider more options for adsorbent selection, (2) determining the adsorption thermodynamics from DFT calculations, isotherm models and experiments, and (3) using mass transfer models to analyze the bench scale to determine the external mass transfer coefficient and intraparticle diffusivities.
AUTHOR INFORMATION
Corresponding Author
*E-mail:
[email protected]. ORCID
Jinming Luo: 0000-0001-8698-7624 John C. Crittenden: 0000-0002-9048-7208 Notes
The authors declare no competing financial interest.
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ACKNOWLEDGMENTS We appreciate the support from the Brook Byers Institute for Sustainable Systems, Hightower Chair, and the Georgia Research Alliance at Georgia Institute of Technology. The views and ideas expressed herein are solely those of the authors and do not represent the ideas of the funding agencies in any form.
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REFERENCES
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DOI: 10.1021/acs.est.9b04371 Environ. Sci. Technol. XXXX, XXX, XXX−XXX
