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Aluru NR, Aydin F, Bazant MZ, Blankschtein D, Brozena AH, de Souza JP, Elimelech M, Faucher S, Fourkas JT, Koman VB, Kuehne M, Kulik HJ, Li HK, Li Y, Li Z, Majumdar A, Martis J, Misra RP, Noy A, Pham TA, Qu H, Rayabharam A, Reed MA, Ritt CL, Schwegler E, Siwy Z, Strano MS, Wang Y, Yao YC, Zhan C, Zhang Z. Fluids and Electrolytes under Confinement in Single-Digit Nanopores. Chem Rev 2023; 123:2737-2831. [PMID: 36898130 PMCID: PMC10037271 DOI: 10.1021/acs.chemrev.2c00155] [Citation(s) in RCA: 28] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/12/2023]
Abstract
Confined fluids and electrolyte solutions in nanopores exhibit rich and surprising physics and chemistry that impact the mass transport and energy efficiency in many important natural systems and industrial applications. Existing theories often fail to predict the exotic effects observed in the narrowest of such pores, called single-digit nanopores (SDNs), which have diameters or conduit widths of less than 10 nm, and have only recently become accessible for experimental measurements. What SDNs reveal has been surprising, including a rapidly increasing number of examples such as extraordinarily fast water transport, distorted fluid-phase boundaries, strong ion-correlation and quantum effects, and dielectric anomalies that are not observed in larger pores. Exploiting these effects presents myriad opportunities in both basic and applied research that stand to impact a host of new technologies at the water-energy nexus, from new membranes for precise separations and water purification to new gas permeable materials for water electrolyzers and energy-storage devices. SDNs also present unique opportunities to achieve ultrasensitive and selective chemical sensing at the single-ion and single-molecule limit. In this review article, we summarize the progress on nanofluidics of SDNs, with a focus on the confinement effects that arise in these extremely narrow nanopores. The recent development of precision model systems, transformative experimental tools, and multiscale theories that have played enabling roles in advancing this frontier are reviewed. We also identify new knowledge gaps in our understanding of nanofluidic transport and provide an outlook for the future challenges and opportunities at this rapidly advancing frontier.
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Affiliation(s)
- Narayana R Aluru
- Oden Institute for Computational Engineering and Sciences, Walker Department of Mechanical Engineering, The University of Texas at Austin, Austin, 78712TexasUnited States
| | - Fikret Aydin
- Materials Science Division, Physical and Life Science Directorate, Lawrence Livermore National Laboratory, Livermore, California94550, United States
| | - Martin Z Bazant
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts02139, United States
- Department of Mathematics, Massachusetts Institute of Technology, Cambridge, Massachusetts02139, United States
| | - Daniel Blankschtein
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts02139, United States
| | - Alexandra H Brozena
- Department of Chemistry and Biochemistry, University of Maryland, College Park, Maryland20742, United States
| | - J Pedro de Souza
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts02139, United States
| | - Menachem Elimelech
- Department of Chemical and Environmental Engineering, Yale University, New Haven, Connecticut06520-8286, United States
| | - Samuel Faucher
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts02139, United States
| | - John T Fourkas
- Department of Chemistry and Biochemistry, University of Maryland, College Park, Maryland20742, United States
- Institute for Physical Science and Technology, University of Maryland, College Park, Maryland20742, United States
- Maryland NanoCenter, University of Maryland, College Park, Maryland20742, United States
| | - Volodymyr B Koman
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts02139, United States
| | - Matthias Kuehne
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts02139, United States
| | - Heather J Kulik
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts02139, United States
| | - Hao-Kun Li
- Department of Mechanical Engineering, Stanford University, Stanford, California94305, United States
| | - Yuhao Li
- Materials Science Division, Physical and Life Science Directorate, Lawrence Livermore National Laboratory, Livermore, California94550, United States
| | - Zhongwu Li
- Materials Science Division, Physical and Life Science Directorate, Lawrence Livermore National Laboratory, Livermore, California94550, United States
| | - Arun Majumdar
- Department of Mechanical Engineering, Stanford University, Stanford, California94305, United States
| | - Joel Martis
- Department of Mechanical Engineering, Stanford University, Stanford, California94305, United States
| | - Rahul Prasanna Misra
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts02139, United States
| | - Aleksandr Noy
- Materials Science Division, Physical and Life Science Directorate, Lawrence Livermore National Laboratory, Livermore, California94550, United States
- School of Natural Sciences, University of California Merced, Merced, California95344, United States
| | - Tuan Anh Pham
- Materials Science Division, Physical and Life Science Directorate, Lawrence Livermore National Laboratory, Livermore, California94550, United States
| | - Haoran Qu
- Department of Chemistry and Biochemistry, University of Maryland, College Park, Maryland20742, United States
| | - Archith Rayabharam
- Oden Institute for Computational Engineering and Sciences, Walker Department of Mechanical Engineering, The University of Texas at Austin, Austin, 78712TexasUnited States
| | - Mark A Reed
- Department of Electrical Engineering, Yale University, 15 Prospect Street, New Haven, Connecticut06520, United States
| | - Cody L Ritt
- Department of Chemical and Environmental Engineering, Yale University, New Haven, Connecticut06520-8286, United States
| | - Eric Schwegler
- Materials Science Division, Physical and Life Science Directorate, Lawrence Livermore National Laboratory, Livermore, California94550, United States
| | - Zuzanna Siwy
- Department of Physics and Astronomy, Department of Chemistry, Department of Biomedical Engineering, University of California, Irvine, Irvine92697, United States
| | - Michael S Strano
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts02139, United States
| | - YuHuang Wang
- Department of Chemistry and Biochemistry, University of Maryland, College Park, Maryland20742, United States
- Maryland NanoCenter, University of Maryland, College Park, Maryland20742, United States
| | - Yun-Chiao Yao
- Materials Science Division, Physical and Life Science Directorate, Lawrence Livermore National Laboratory, Livermore, California94550, United States
- School of Natural Sciences, University of California Merced, Merced, California95344, United States
| | - Cheng Zhan
- Materials Science Division, Physical and Life Science Directorate, Lawrence Livermore National Laboratory, Livermore, California94550, United States
| | - Ze Zhang
- Department of Mechanical Engineering, Stanford University, Stanford, California94305, United States
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Ritt CL, Liu M, Pham TA, Epsztein R, Kulik HJ, Elimelech M. Machine learning reveals key ion selectivity mechanisms in polymeric membranes with subnanometer pores. SCIENCE ADVANCES 2022; 8:eabl5771. [PMID: 35030018 PMCID: PMC8759746 DOI: 10.1126/sciadv.abl5771] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/12/2023]
Abstract
Designing single-species selective membranes for high-precision separations requires a fundamental understanding of the molecular interactions governing solute transport. Here, we comprehensively assess molecular-level features that influence the separation of 18 different anions by nanoporous cellulose acetate membranes. Our analysis identifies the limitations of bulk solvation characteristics to explain ion transport, highlighted by the poor correlation between hydration energy and the measured permselectivity (R2 = 0.37). Entropy-enthalpy compensation, spanning 40 kilojoules per mole, leads to a free-energy barrier (∆G‡) variation of only ~8 kilojoules per mole across all anions. We apply machine learning to elucidate descriptors for energetic barriers from a set of 126 collected features. Notably, electrostatic features account for 75% of the overall features used to describe ∆G‡, despite the relatively uncharged state of cellulose acetate. Our work presents an approach for studying ion transport across nanoporous membranes that could enable the design of ion-selective membranes.
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Affiliation(s)
- Cody L. Ritt
- Department of Chemical and Environmental Engineering, Yale University, New Haven, CT 06520-8286, USA
| | - Mingjie Liu
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Tuan Anh Pham
- Quantum Simulations Group, Lawrence Livermore National Laboratory, Livermore, CA 94550, USA
| | - Razi Epsztein
- Faculty of Civil and Environmental Engineering, Technion–Israel Institute of Technology, Haifa 32000, Israel
| | - Heather J. Kulik
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Corresponding author. (M.E.); (H.J.K.)
| | - Menachem Elimelech
- Department of Chemical and Environmental Engineering, Yale University, New Haven, CT 06520-8286, USA
- Corresponding author. (M.E.); (H.J.K.)
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