NAWI PUBLICATIONS

Data Modeling and Analysis
Cordoba, S., Das, A., Rao, A. K., Xie, Y., Alnajdi, S., Beni, A. N., Aboderin, J., Esquivel-Puentes, A., Castillo, L., Garcia, J. M., Tow, E. W., & Warsinger, D. M. (2026). Demonstration of double-acting piston batch reverse osmosis with seawater salinity. Journal of Membrane Science, 741, 125081. https://doi.org/10.1016/j.memsci.2025.125081
Project: 3.22 Reciprocating Piston Batch Reverse Osmosis: Pushing the limits of efficiency and fouling resistance
Data Modeling and Analysis
Lugo, A., Oddonetto, T. L., Ahmed, M. F., Senanayake, P. S., Stoll, Z., Moe, N., Barber, J., Wang, H., & Xu, P. (2026). Bipolar membrane electrodialysis for acid and base production in high salinity desalination brine – dynamic modeling and system design evaluation. Journal of Membrane Science, 742, 125113. https://doi.org/10.1016/j.memsci.2025.125113
Project: 3.15 Concentrate Treatment and Chemical Production Using Innovative Electrodialysis Processes for Near Zero-Waste Discharge
Materials and Manufacturing
Mohamed, M. S., Chaplin, B. P., & Abokifa, A. A. (2026). PFOA catalytic reaction mechanisms on zerovalent iron (Fe0): A DFT investigation. Journal of Catalysis, 454, 116604. https://doi.org/10.1016/j.jcat.2025.116604
Project: 6.17 Selective Electrocatalytic Destruction of PFAS using a Reactive Electrochemical Membrane System
Data Modeling and Analysis
Tucker, C. I., Amusat, O. O., Atia, A. A., Dudchenko, A. V., Bartholomew, T. V., & Mauter, M. S. (2026). Incorporating corrosion design constraints in desalination process optimization: A case study in mechanical vapor compression. Desalination, 621, 119698. https://doi.org/10.1016/j.desal.2025.119698
Project: 3.06 NAWI Water Treatment Model Development
Process Innovation and Intensification
Ahmadipouya, S., Sheikh, U. J., Ferguson, N., Hacifazlioglu, M. C., Ipekci, D., & McCutcheon, J. R. (2025). Reduction in solvent and chemical use for membrane manufacturing using electrospray. Current Opinion in Chemical Engineering, 49, 101173. https://doi.org/10.1016/j.coche.2025.101173
Project: 5.11 Additive Manufacturing for Customized Membranes
Data Modeling and Analysis
Alnajdi, S., Naderi Beni, A., Roldan-Carvajal, M., Aboderin, J., Rao, A. K., & Warsinger, D. M. (2025). Salinity gradient energy recovery with Batch Reverse Osmosis. Renewable Energy, 246, 122801. https://doi.org/10.1016/j.renene.2025.122801
Project: 3.22 Reciprocating Piston Batch Reverse Osmosis: Pushing the limits of efficiency and fouling resistance
Data Modeling and Analysis
Anwar, A., Saka, U. M., & Fierro, G. (2025). Poster Abstract: Graph Learning on Cyber-Physical Knowledge Graphs. Proceedings of the 23rd ACM Conference on Embedded Networked Sensor Systems, 644–645. https://doi.org/10.1145/3715014.3724049
Project: 3.02 Development, Deployment, and Refinement of the Water Technology Data and Analysis Management System (Water-DAMS)
Materials and Manufacturing
Bashir, S., Fariha, A., Miller, L., Quiroz, D., Jun, Y.-S., Yang, Y., & Kim, T. (2025). Antiscalant Effects on pH-Mediated Electrochemical Precipitation of Scaling Ions in Brackish Reverse Osmosis Concentrates. ACS ES&T Engineering, 5(12), 3249–3261. https://doi.org/10.1021/acsestengg.5c00534
Project: 6.21 Controlling The Effects of Antiscalants on the Nucleation of Highly Soluble Salts in Reverse Osmosis Concentrate
Materials and Manufacturing
Chen, F., Nnorom, N. C., Carby, T.-L., & Verduzco, R. (2025). Permselective Transport of Cu2+ through Polymer Networks Functionalized with Iminodiacetic Acid. Macromolecules, 58(8), 4300–4308. https://doi.org/10.1021/acs.macromol.4c02927
Project: 6.13 Copper Recovery from Mining Process Waters with Ion-Selective Electrodialysis
Process Innovation and Intensification
Chowdhury, D., Kuras, A., Weix, D., Cath, T., Melin, A., Polsky, Y., Hering, A. S., Cath, T., & Villez, K. (2025). Closing the loop: model-predictive control for a closed-circuit reverse osmosis system. Water Supply, 25(4), 727–748. https://doi.org/10.2166/ws.2025.045
Project: 5.08 Advanced Process Controls – Autonomous Control and Optimization
Cohen, Y., Khan, B. M., Soto, M., Marki, N., Jarma, Y., & Zhou, Y. (2025). Distributed water desalination and purification systems: perspective and future directions. Current Opinion in Chemical Engineering, 47, 101078. https://doi.org/10.1016/j.coche.2024.101078
Process Innovation and Intensification
Conway, K. M., & Mi, B. (2025). Sacrificial membranes in water purification: Concepts, current status, and outlook. Separation and Purification Technology, 362, 131748. https://doi.org/10.1016/j.seppur.2025.131748
Project: 5.18 Multifunctional Membrane for Oxyanion Removal
Process Innovation and Intensification
Du, X., Perera, H., Ahasan, T., Wang, Y., Shu, F., Wang, H., Yuan, K., Anovitz, L. M., Ben Ishai, P., & Xu, P. (2025). Mechanisms of electromagnetic field control on mineral scaling in brackish water reverse osmosis: Combined homogenous and heterogeneous nucleation. Separation and Purification Technology, 355, 129630. https://doi.org/10.1016/j.seppur.2024.129630
Project: 5.12 Electromagnetic Field for Membrane Scaling Control
Materials and Manufacturing
Einkauf, J. D., Zeng, Z., Shen, Z., Meyer, P. A., Stamberga, D., Willocks, J., Gilliland, M., Cagley, M., Yiacoumi, S., Tsouris, C., & Custelcean, R. (2025). DeSelenator: A Se-Removal Process for Environmental Decontamination of Wastewaters from Coal-Burning Power Plants. ACS Environmental Au, 6(1), 116–124. https://doi.org/10.1021/acsenvironau.5c00190
Project: 6.09 Selective Separation of Selenium Oxyanions by Chelating Hydrogen-Bonding Ligands
Process Innovation and Intensification
Hacıfazlıoğlu, M. C., Ahmadipouya, S., Ipekci, D., Li, Y., Kumar, M., Warner, J., Zhang, Y., & McCutcheon, J. R. (2025). Customized membranes: needs and opportunities for moving beyond conventional interfacial polymerization for desalination membranes. Current Opinion in Chemical Engineering, 49, 101151. https://doi.org/10.1016/j.coche.2025.101151
Project: 5.11 Additive Manufacturing for Customized Membranes
Process Innovation and Intensification
He, X., Flynn, E. D., Catalano, J. G., & Giammar, D. E. (2025). Selenium(VI) Removal by Continuous Flow-Through Iron Electrocoagulation: Effects of Operating Conditions and Stability of Selenium in Residual Solids. Environmental Science &Amp; Technology, 59(10), 5359–5369. https://doi.org/10.1021/acs.est.4c12305
Project: 5.13 Tailored Reductants for Selenium Removal in Iron Electrocoagulation
Process Innovation and Intensification
Jang, G. G., Keum, J. K., Dutta, S., Damron, J. T., Wiechert, A. I., Halbert, C. E., Browning, J. F., Hensley, D. K., Jassby, D., Hatzell, M. C., & Tsouris, C. (2025). Understanding the Dissolution and Passivation of an Aluminum Electrode during Electrocoagulation of Groundwater Using Neutron and X-ray Reflectometry. ACS Applied Materials &Amp; Interfaces, 17(17), 25996–26012. https://doi.org/10.1021/acsami.5c02215
Project: 5.14 Enabling Minimal Liquid Discharge through a Modular, Flexible, and Electrified Pretreatment System
Data Modeling and Analysis
Lugo, A., Ahmed, M. F., Oddonetto, T. L., Senanayake, P. S., Basnayake, I., Stoll, Z., Ehsani, M., Moe, N. E., Barber, J., Wang, H., & Xu, P. (2025). Operational optimization of bipolar membrane electrodialysis for acid and base production in zero liquid discharge of high-salinity brine. Desalination, 615, 119290. https://doi.org/10.1016/j.desal.2025.119290
Project: 3.15 Concentrate Treatment and Chemical Production Using Innovative Electrodialysis Processes for Near Zero-Waste Discharge
Process Innovation and Intensification
Lugo, A., Mejía-Saucedo, C., Senanayake, P. S., Stoll, Z., Sitterley, K., Wang, H., Kota, K., Kuravi, S., Fthenakis, V., Kurup, P., & Xu, P. (2025). Technical, Economic, Energetic, and Environmental Evaluation of Pretreatment Strategies for Scaling Control in Brackish Water Desalination Brine Treatment. Water, 17(5), 708. https://doi.org/10.3390/w17050708
Project: 5.12 Electromagnetic Field for Membrane Scaling Control
Process Innovation and Intensification
Mahajan, S., & Li, Y. (2025). Toward Molecular Simulation Guided Design of Next-Generation Membranes: Challenges and Opportunities. Langmuir, 41(20), 12388–12402. https://doi.org/10.1021/acs.langmuir.4c05181
Project: 5.11 Additive Manufacturing for Customized Membranes
Process Innovation and Intensification
McCutcheon, J. R., & Mauter, M. (2025). Editorial overview: Transforming water technologies in the United States: Insights from the National Alliance for Water Innovation. Current Opinion in Chemical Engineering, 50, 101180. https://doi.org/10.1016/j.coche.2025.101180
Project: 5.11 Additive Manufacturing for Customized Membranes
Data Modeling and Analysis
Mejía-Saucedo, C., Stoll, Z., Senanayake, P. S., Xu, P., & Wang, H. (2025). Evaluating Pretreatment Strategies with Modeling for Reducing Scaling Potential of Reverse Osmosis Concentrate: Insights from Ion Exchange and Activated Alumina. Water, 17(6), 828. https://doi.org/10.3390/w17060828
Project: 3.15 Concentrate Treatment and Chemical Production Using Innovative Electrodialysis Processes for Near Zero-Waste Discharge
Materials and Manufacturing
Mohamed, M. S., Chaplin, B. P., & Abokifa, A. A. (2025). Ab-initio computational methods for PFAS adsorption and degradation: A critical review. Journal of Environmental Chemical Engineering, 13(6), 119776. https://doi.org/10.1016/j.jece.2025.119776
Project: 6.17 Selective Electrocatalytic Destruction of PFAS using a Reactive Electrochemical Membrane System
Materials and Manufacturing
Mohamed, M. S., Chaplin, B. P., & Abokifa, A. A. (2025). Screening of transition metals for PFAS adsorption: A comparative DFT investigation. Chemical Engineering Science, 307, 121363. https://doi.org/10.1016/j.ces.2025.121363
Project: 6.17 Selective Electrocatalytic Destruction of PFAS using a Reactive Electrochemical Membrane System
Data Modeling and Analysis
Senanayake, P. S., Ahmed, M. F., Lugo, A., Mejía-Saucedo, C., Edirisooriya, E. M. N. T., Basnayake, I., Stoll, Z., Xu, P., & Wang, H. (2025). Techno-economic analysis of recycling reverse osmosis concentrate for tailored water production and zero liquid discharge using selective electrodialysis: Experimental and theoretical study. Chemical Engineering Journal, 519, 165382. https://doi.org/10.1016/j.cej.2025.165382
Project: 3.15 Concentrate Treatment and Chemical Production Using Innovative Electrodialysis Processes for Near Zero-Waste Discharge
Senanayake, P. S., Ahmed, M. F., Lugo, A., Stoll, Z., Moe, N. E., Ehsani, M., Barber, J., Xu, P., & Wang, H. (2025). Enhancing the efficiency of electrodialysis brine concentrator to reduce the levelized cost of salt production through dynamic model-based simulations. Desalination, 598, 118416. https://doi.org/10.1016/j.desal.2024.118416
Senanayake, P. S., Lugo, A., Ahmed, M. F., Stoll, Z., Moe, N. E., Barber, J., Shane Walker, W., Xu, P., & Wang, H. (2025). Electrodialysis modeling for desalination and resource recovery. Current Opinion in Chemical Engineering, 47, 101081. https://doi.org/10.1016/j.coche.2024.101081
Sharma, N., Apraku, E., Gong, M., & Tarpeh, W. A. (2025). Integrating adsorbents and electrochemistry to advance selective wastewater phosphate separations. Current Opinion in Chemical Engineering, 47, 101080. https://doi.org/10.1016/j.coche.2024.101080
Materials and Manufacturing
Sharma, N., Apraku, E., Holmes, H., Gong, M., Bustamante, D., Weker, J., Bone, S., & Tarpeh, W. (2025). Probing the Mechanism of Selective Phosphate Adsorption from Wastewater using Aqueous and Synchrotron X-ray Characterization. https://doi.org/10.26434/chemrxiv-2025-lg870
Project: 6.19 Hybrid Electrochemical-Ion Exchange Process for Selective Phosphate Recovery as Struvite
Data Modeling and Analysis
Tong, T., Xu, L., Horseman, T., Westerhoff, P., Xu, P., Yao, Y., Zhang, X., Alghanayem, R., & Lin, S. (2025). Brine management with zero and minimal liquid discharge. Nature Reviews Clean Technology, 1(3), 185–200. https://doi.org/10.1038/s44359-025-00036-2
Project: 3.15 Concentrate Treatment and Chemical Production Using Innovative Electrodialysis Processes for Near Zero-Waste Discharge
Materials and Manufacturing
Wang, Y., Zhu, Y., Zuo, X., He, L., & Jun, Y.-S. (2025). Soft Nanoconfinement Nucleates and Stabilizes Ultrasmall Amorphous Calcium Carbonate from Aggregation. Crystal Growth &Amp; Design, 25(20), 8325–8331. https://doi.org/10.1021/acs.cgd.4c01555
Project: 6.21 Controlling The Effects of Antiscalants on the Nucleation of Highly Soluble Salts in Reverse Osmosis Concentrate
Process Innovation and Intensification
Yao, C., Ben-Zvi, A. M., Xu, R., Ram, N., Stolov, M., Gupta, P., Coupin, M. J., Behera, H., Freger, V., Warner, J., Ramon, G. Z., Kumar, M., & Segal-Peretz, T. (2025). 3D Nanoscale Structures of Hydrated Polyamide Desalination Membranes Revealed by Cryogenic Transmission Electron Microscopy Tomography. ACS Nano, 19(17), 16718–16731. https://doi.org/10.1021/acsnano.5c01190
Project: 5.11 Additive Manufacturing for Customized Membranes
Data Modeling and Analysis
Yao, Y., Zhang, X., Lin, S., Wang, R., & Tong, T. (2025). Modeling-Based Comparison of Gypsum Scaling Potential between Reverse Osmosis and Electrodialysis for Brackish Water Desalination. ACS ES&T Engineering, 5(11), 3007–3017. https://doi.org/10.1021/acsestengg.5c00400
Project: 3.29 Towards Net Zero Water Manufacturing: Managing and Reusing Industrial Reverse-Osmosis Brines to Advance Water Sustainability for the Semiconductor Industry in Arizona
Process Innovation and Intensification
Yuan, Y., Mehrotra, M., He, X., Flynn, E. D., Catalano, J. G., & Giammar, D. E. (2025). Advancing Selenium(VI) Removal by Iron Electrocoagulation: Roles of Water Chemistry and Operating Conditions. ACS ES&T Engineering, 5(7), 1821–1830. https://doi.org/10.1021/acsestengg.5c00068
Project: 5.13 Tailored Reductants for Selenium Removal in Iron Electrocoagulation
Materials and Manufacturing
Zeng, Z., Shen, Z., Einkauf, J. D., Ladshaw, A. P., Custelcean, R., Tsouris, C., & Yiacoumi, S. (2025). Removal of Selenium Oxyanions from Aqueous Solutions by Ion Exchange: Equilibrium, Kinetics, and Mechanistic Modeling. ACS ES&T Water, 6(1), 438–454. https://doi.org/10.1021/acsestwater.5c01136
Project: 6.09 Selective Separation of Selenium Oxyanions by Chelating Hydrogen-Bonding Ligands
Data Modeling and Analysis
Amusat, O. O., Dudchenko, A. V., Atia, A. A., & Bartholomew, T. (2024). Cost-optimal Selection of pH Control for Mineral Scaling Prevention in High Recovery Reverse Osmosis Desalination. Proceedings of the 10th International Conference on Foundations of Computer-Aided Process Design FOCAPD 2024, 3, 253–260. https://doi.org/10.69997/sct.143335
Project: 3.06 NAWI Water Treatment Model Development
Materials and Manufacturing
Anvari, A., Wu, J., Edalat, A., Voutchkov, N., Al-Ahmoudi, A., Bhattacharjee, S., & Hoek, E. M. V. (2024). What will it take to get to 250,000 ppm brine concentration via ultra-high pressure reverse osmosis? And is it worth it? Desalination, 580, 117565. https://doi.org/10.1016/j.desal.2024.117565
Project: 6.11 UHPRO Membrane and Module Design & Optimization Innovations to Enable & Optimize Ultra-High Pressure Reverse Osmosis Membranes
Materials and Manufacturing
Clark, B., Sharma, N., Apraku, E., Dong, H., & Tarpeh, W. A. (2024). Ligand Exchange Adsorbents for Selective Phosphate and Total Ammonia Nitrogen Recovery from Wastewaters. Accounts of Materials Research, 5(4), 492–504. https://doi.org/10.1021/accountsmr.3c00290
Project: 6.19 Hybrid Electrochemical-Ion Exchange Process for Selective Phosphate Recovery as Struvite
Process Innovation and Intensification
Du, X., Jiang, W., Wang, Y., Shu, F., Wang, H., Vazquez, D., Yuan, K., Anovitz, L. M., Ishai, P. B., & Xu, P. (2024). Numerical modeling of electromagnetic field spatiotemporal evolution to evaluate the effects on calcium carbonate crystallization. Desalination, 592, 118128. https://doi.org/10.1016/j.desal.2024.118128
Project: 5.12 Electromagnetic Field for Membrane Scaling Control
Materials and Manufacturing
Duan, Y., & Sedlak, D. L. (2024). Electrochemical Hydrogen Peroxide Generation and Activation Using a Dual-Cathode Flow-Through Treatment System: Enhanced Selectivity for Contaminant Removal by Electrostatic Repulsion. Environmental Science &Amp; Technology, 58(31), 14042–14051. https://doi.org/10.1021/acs.est.4c05481
Project: 6.12 Electrochemical Advanced Oxidation
Data Modeling and Analysis
Dudchenko, A. V., & Amusat, O. O. (2024). Neural Networks for Prediction of Complex Chemistry in Water Treatment Process Optimization. Proceedings of the 10th International Conference on Foundations of Computer-Aided Process Design FOCAPD 2024, 3, 267–274. https://doi.org/10.69997/sct.107047
Project: 3.07 NAWI Analysis
Process Innovation and Intensification
Grimm, T. R., Branch, A., Thompson, K. A., Salveson, A., Zhao, J., Johnson, D., Hering, A. S., & Newhart, K. B. (2024). Long-Term Statistical Process Monitoring of an Ultrafiltration Water Treatment Process. ACS ES&T Engineering, 4(6), 1492–1506. https://doi.org/10.1021/acsestengg.4c00042
Project: 5.17 Data-Driven Fault Detection and Process Control for Potable Reuse with Reverse Osmosis
Process Innovation and Intensification
Jang, G. G., Castillo, C., Dutta, S., Su, Y.-F., Jun, J., Keum, J. K., Kim, K., Chellam, S., & Tsouris, C. (2024). Performance Restoration of Iron Electrodes by Polarity Reversal after Long-Term Surface Fouling in Wastewater Electrocoagulation. ACS ES&T Water, 4(6), 2390–2402. https://doi.org/10.1021/acsestwater.3c00741
Project: 5.15 Electrocoagulation/Electrooxidation to Accelerate Cost-Effective Potable Water Reuse
Khor, C. M., Liao, M. E., Iddya, A., Ma, S., Yang, F., Liu, Y.-H., Bootwala, Y. Z., Jang, G. G., Goorsky, M. S., Hoek, E. M. V., Tsouris, C., Mothersbaugh, J., Hatzell, M. C., & Jassby, D. (2024). Physical and Electrochemical Characterization of Aluminum Electrodes during Electrocoagulation. ACS ES&T Water, 4(1), 44–56. https://doi.org/10.1021/acsestwater.3c00263
Process Innovation and Intensification
Kim, K., Castillo, C., Jang, G. G., Zhang, Y., Tsouris, C., & Chellam, S. (2024). Porous Iron Electrodes Reduce Energy Consumption During Electrocoagulation of a Virus Surrogate: Insights into Performance Enhancements Using Three-Dimensional Neutron Computed Tomography. ACS ES&T Engineering, 4(10), 2573–2584. https://doi.org/10.1021/acsestengg.4c00317
Project: 5.15 Electrocoagulation/Electrooxidation to Accelerate Cost-Effective Potable Water Reuse
Materials and Manufacturing
King, J. F., & Chaplin, B. P. (2024). Electrochemical reduction of per- and polyfluorinated alkyl substances (PFAS): is it possible? Applying experimental and quantum mechanical insights from the reductive defluorination literature. Current Opinion in Chemical Engineering, 44, 101014. https://doi.org/10.1016/j.coche.2024.101014
Project: 6.17 Selective Electrocatalytic Destruction of PFAS using a Reactive Electrochemical Membrane System
Process Innovation and Intensification
Le, V. T. C., Nguyen, Q. P., Mai, H. D., Wang, B., & Bui, N. T. (2024). An Adsorptive Membrane Platform for Precision Ion Separation: Membrane Design and First‐Principles Studies. Small Structures, 5(8). Portico. https://doi.org/10.1002/sstr.202400067
Project: 5.16 Energy-efficient selective removal of metal ions from mining influenced waters(MIW) using H-bonded Organic-Inorganic Frameworks (HOIFs)
Li, M., & Li, J. (2024). Novel Variable Configuration Design for Ultrahigh Recovery Reverse Osmosis Desalination of Brackish Water. Industrial &Amp; Engineering Chemistry Research, 63(13), 5813–5822. https://doi.org/10.1021/acs.iecr.3c04406
Materials and Manufacturing
Mohamed, M. S., Chaplin, B. P., & Abokifa, A. A. (2024). Adsorption of per- and poly-fluoroalkyl substances (PFAS) on Ni: A DFT investigation. Chemosphere, 357, 141849. https://doi.org/10.1016/j.chemosphere.2024.141849
Project: 6.17 Selective Electrocatalytic Destruction of PFAS using a Reactive Electrochemical Membrane System
Data Modeling and Analysis
Oddonetto, T. L., Deemer, E., Lugo, A., Cappelle, M., Xu, P., Santiago, I., & Walker, W. S. (2024). Assessment of salt-free electrodialysis metathesis: A novel process for brine management in brackish water desalination using monovalent selective ion exchange membranes. Desalination, 592, 118160. https://doi.org/10.1016/j.desal.2024.118160
Project: 3.18 Salt-Free Electrodialysis Metathesis (EDM) For High Recovery Concentrate Management
Data Modeling and Analysis
Rao, A. K., Atia, A. A., Knueven, B., & Mauter, M. S. (2024). Optimizing Desalination Operations for Energy Flexibility. ACS Sustainable Chemistry &Amp; Engineering, 12(42), 15696–15704. https://doi.org/10.1021/acssuschemeng.4c06353
Project: 3.24 Open-Source Platform for Assessing the Cost and Carbon Benefits of Flexible Desalination
Data Modeling and Analysis
Rao, A. K., Bolorinos, J., Musabandesu, E., Chapin, F. T., & Mauter, M. S. (2024). Valuing energy flexibility from water systems. Nature Water, 2(10), 1028–1037. https://doi.org/10.1038/s44221-024-00316-4
Project: 3.24 Open-Source Platform for Assessing the Cost and Carbon Benefits of Flexible Desalination
Van Houghton, B. D., Rosenblum, J. S., Lampi, K., Beaudry, E., Herron, J. J., del Cerro, M., De Finnda, C. T. K., Elimelech, M., Gilron, J., & Cath, T. Y. (2024). Pilot Scale Demonstration of Low-Salt-Rejection Reverse Osmosis (LSRRO) Desalination of High Salinity Brines. ACS ES&T Water, 4(11), 5089–5104. https://doi.org/10.1021/acsestwater.4c00673
Process Innovation and Intensification
Wang, M., Song, Y.-J., Jiang, W., Fornasiero, F., Urban, J. J., & Mi, B. (2024). Layer-by-layer Assembly of Nanosheets with Matching Size and Shape for More Stable Membrane Structure than Nanosheet-Polymer Assembly. ACS Applied Materials &Amp; Interfaces, 16(20), 26568–26579. https://doi.org/10.1021/acsami.4c03891
Project: 5.18 Multifunctional Membrane for Oxyanion Removal
Process Innovation and Intensification
Wang, R., & Lin, S. (2024). Membrane Design Principles for Ion-Selective Electrodialysis: An Analysis for Li/Mg Separation. Environmental Science &Amp; Technology. https://doi.org/10.1021/acs.est.3c08956
Project: 5.06 A Novel Electro-dialytic Crystallizer (EDC) for Energy Efficient Zero-liquid Discharge
Materials and Manufacturing
Wang, Y., Zhu, Y., Gupta, P., Singamaneni, S., Lee, B., & Jun, Y.-S. (2024). The Roles of Oil–Water Interfaces in Forming Ultrasmall CaSO4 Nanoparticles. ACS Applied Materials &Amp; Interfaces, 16(22), 29390–29401. https://doi.org/10.1021/acsami.4c02256
Project: 6.21 Controlling The Effects of Antiscalants on the Nucleation of Highly Soluble Salts in Reverse Osmosis Concentrate
Process Innovation and Intensification
Wilson, A. D., Foo, Z. H., Jayasinghe, A. S., Stetson, C., Lee, H., Rollins, H. W., Deshmukh, A., & Lienhard, J. H. (2024). Modeling Henry’s law and phase separations of water–NaCl–organic mixtures with solvation and ion-pairing. Physical Chemistry Chemical Physics, 26(2), 749–759. https://doi.org/10.1039/d3cp02003g
Project: 5.07 Solvent-Driven Zero Liquid Discharge for Production of Synthetic Gypsum
Materials and Manufacturing
Xie, A., Martínez-Vargas, D. R., Yang, Z., & Zou, S. (2024). Efficient selenate removal from impaired waters with TiO2-assisted electrocatalysis. Water Research, 262, 122134. https://doi.org/10.1016/j.watres.2024.122134
Project: 6.08 Direct Electrochemical Reduction of Selenium to Achieve A-PRIME Water Treatment
Process Innovation and Intensification
Yao, Y., Zhang, X., Wang, R., Lin, S., & Tong, T. (2024). Mineral scaling and organic fouling in electrodialytic crystallization. Journal of Membrane Science, 707, 123024. https://doi.org/10.1016/j.memsci.2024.123024
Project: 5.06 A Novel Electro-dialytic Crystallizer (EDC) for Energy Efficient Zero-liquid Discharge
Data Modeling and Analysis
Zaniolo, M., Fletcher, S., & Mauter, M. (2024). FIND: A Synthetic weather generator to control drought Frequency, Intensity, and Duration. Environmental Modelling &Amp; Software, 172, 105927. https://doi.org/10.1016/j.envsoft.2023.105927
Project: 3.10 Robust Technology and Policy Pathways for Municipal Water Security
Zhao, J., Yang, Z., Wang, Q., Alderman, B., & Zou, S. (2024). Electrooxidation of aminomethyl phosphonic acid (AMPA) using a boron doped diamond anode. Journal of Environmental Chemical Engineering, 12(1), 111669. https://doi.org/10.1016/j.jece.2023.111669
Process Innovation and Intensification
Zhou, Y., Marki, N., Khan, B., Aguilar, C., Jarma, Y., & Cohen, Y. (2024). Machine learning models of intermittent operation of RO wellhead water treatment for salinity reduction and nitrate removal. Desalination, 588, 117951. https://doi.org/10.1016/j.desal.2024.117951
Project: 5.10 Analytics for Causal Analysis and Decision Support Models for Autonomous and Smart Water Treatment
Materials and Manufacturing
Zhu, Y., Wang, Y., Gao, Z., Gupta, P., Singamaneni, S., Zuo, X., & Jun, Y.-S. (2024). In Situ Monitoring the Nucleation and Growth of Nanoscale CaCO3 at the Oil–Water Interface. ACS Nano, 18(39), 26522–26531. https://doi.org/10.1021/acsnano.4c02490
Project: 6.21 Controlling The Effects of Antiscalants on the Nucleation of Highly Soluble Salts in Reverse Osmosis Concentrate
Data Modeling and Analysis
Alhussaini, M. A., Binger, Z. M., Souza-Chaves, B. M., Amusat, O. O., Park, J., Bartholomew, T. V., Gunter, D., & Achilli, A. (2023). Analysis of backwash settings to maximize net water production in an engineering-scale ultrafiltration system for water reuse. Journal of Water Process Engineering, 53, 103761. https://doi.org/10.1016/j.jwpe.2023.103761
Project: 4.02 PROTEUS: Integrated Computational Capability for Optimizing Advanced Water Treatment Systems
Data Modeling and Analysis
Atia, A. A., Allen, J., Young, E., Knueven, B., & Bartholomew, T. V. (2023). Cost optimization of low-salt-rejection reverse osmosis. Desalination, 551, 116407. https://doi.org/10.1016/j.desal.2023.116407
Project: 3.06 NAWI Water Treatment Model Development
Process Innovation and Intensification
Deemer, E. M., Xu, P., Verduzco, R., & Shane Walker, W. (2023). Challenges and opportunities for electro-driven desalination processes in sustainable applications. Current Opinion in Chemical Engineering, 42, 100972. https://doi.org/10.1016/j.coche.2023.100972
Project: 5.12 Electromagnetic Field for Membrane Scaling Control
Materials and Manufacturing
Duan, Y., Jiang, W., & Sedlak, D. L. (2023). Surface Processes Control the Fate of Reactive Oxidants Generated by Electrochemical Activation of Hydrogen Peroxide on Stainless-Steel Electrodes. Environmental Science &Amp; Technology, 57(47), 18680–18689. https://doi.org/10.1021/acs.est.2c08404
Project: 6.12 Electrochemical Advanced Oxidation
Materials and Manufacturing
DuChanois, R. M., Mazurowski, L., Fan, H., Verduzco, R., Nir, O., & Elimelech, M. (2023). Precise Cation Separations with Composite Cation-Exchange Membranes: Role of Base Layer Properties. Environmental Science &Amp; Technology, 57(15), 6331–6341. https://doi.org/10.1021/acs.est.3c00445
Project: 6.13 Copper Recovery from Mining Process Waters with Ion-Selective Electrodialysis
Materials and Manufacturing
Einkauf, J. D., Williams, N. J., Seipp, C. A., & Custelcean, R. (2023). Near Quantitative Removal of Selenate and Sulfate Anions from Wastewaters by Cocrystallization with Chelating Hydrogen-Bonding Guanidinium Ligands. JACS Au, 3(3), 879–888. https://doi.org/10.1021/jacsau.2c00673
Project: 6.09 Selective Separation of Selenium Oxyanions by Chelating Hydrogen-Bonding Ligands
Process Innovation and Intensification
Ferguson, N., Chowdhury, M. R., Fitzsimonds, C., Beauregard, N., Ostwal, M., Pemberton, M., Wazer, E., Cyr, C., Srivastava, R., & McCutcheon, J. R. (2023). Self-Regulating Behavior of Hybrid Membrane Systems as Demonstrated in an Element-Scale Forward Osmosis-Reverse Osmosis Hybrid System. https://doi.org/10.2139/ssrn.4528921
Project: 5.22 Printed Polyelectrolyte Complex (APEC) Membranes for Ultra-high Permeance Nanofiltration
Gabitto, J. F., & Tsouris, C. (2023). A review of transport models in charged porous electrodes. Frontiers in Chemical Engineering, 4. https://doi.org/10.3389/fceng.2022.1051594
Materials and Manufacturing
Gupta, S., Chismar, A., & Muhich, C. (2023). Understanding the Effect of Single Atom Cationic Defect Sites in an Al2O3 (012) Surface on Altering Selenate and Sulfate Adsorption: An Ab Initio Study. The Journal of Physical Chemistry C, 127(14), 6925–6937. https://doi.org/10.1021/acs.jpcc.3c00098
Project: 6.20 Precise Electrochemical Dehalogenation of Halogenated Compounds in Desalination Concentrates by High Entropy Catalysts
Materials and Manufacturing
He, J., Arbaugh, T., Nguyen, D., Xian, W., Hoek, E. M. V., McCutcheon, J. R., & Li, Y. (2023). Molecular mechanisms of thickness-dependent water desalination in polyamide reverse-osmosis membranes. Journal of Membrane Science, 674, 121498. https://doi.org/10.1016/j.memsci.2023.121498
Project: 6.11 UHPRO Membrane and Module Design & Optimization Innovations to Enable & Optimize Ultra-High Pressure Reverse Osmosis Membranes
Materials and Manufacturing
He, J., McCutcheon, J. R., & Li, Y. (2023). Effect of different manufacturing methods on polyamide reverse-osmosis membranes for desalination: Insights from molecular dynamics simulations. Desalination, 547, 116204. https://doi.org/10.1016/j.desal.2022.116204
Project: 6.04 Development of HPRO Membrane Platform for the Desalination of High Salinity Waters
Process Innovation and Intensification
He, J., Yue, T., McCutcheon, J. R., & Li, Y. (2023). Recent developments in 3D-printed membranes for water desalination. Journal of Physics: Energy, 6(1), 12002. https://doi.org/10.1088/2515-7655/ad0d01
Project: 5.22 Printed Polyelectrolyte Complex (APEC) Membranes for Ultra-high Permeance Nanofiltration
Iovino, P., Chianese, S., Fenti, A., Blotevogel, J., & Musmarra, D. (2023). An innovative approach for atrazine electrochemical oxidation modelling: Process parameter effect, intermediate formation and kinetic constant assessment. Chemical Engineering Journal, 474, 146022. https://doi.org/10.1016/j.cej.2023.146022
Johnston, J., Dischinger, S. M., Nassr, M., Lee, J. Y., Bigdelou, P., Freeman, B. D., Gleason, K. L., Martinand, D., Miller, D. J., Molins, S., Spycher, N., Stringfellow, W. T., & Tilton, N. (2023). A reduced-order model of concentration polarization in reverse osmosis systems with feed spacers. Journal of Membrane Science, 675, 121508. https://doi.org/10.1016/j.memsci.2023.121508
Materials and Manufacturing
Kim, K., Zagalskaya, A., Ng, J. L., Hong, J., Alexandrov, V., Pham, T. A., & Su, X. (2023). Coupling nitrate capture with ammonia production through bifunctional redox-electrodes. Nature Communications, 14(1). https://doi.org/10.1038/s41467-023-36318-1
Project: 6.16 Redox-Mediated Electrodes for Precision Separation of Nitrogen and Phosphorus Oxyanions
Process Innovation and Intensification
Klanderman, M. C., Lee, J., Villez, K., Cath, T. Y., & Hering, A. S. (2023). Adaptive Online Multivariate Signal Extraction With Locally Weighted Robust Polynomial Regression. Data Science in Science, 2(1). https://doi.org/10.1080/26941899.2023.2200856
Project: 5.08 Advanced Process Controls – Autonomous Control and Optimization
Process Innovation and Intensification
Liang, Y., Dudchenko, A. V., & Mauter, M. S. (2023). Novel method for accurately estimating membrane transport properties and mass transfer coefficients in reverse osmosis. Journal of Membrane Science, 679, 121686. https://doi.org/10.1016/j.memsci.2023.121686
Project: 5.05 CFD Modeling and Operando Measurements of Multiscale Heat and Mass Transfer for Membrane Module Customization
Process Innovation and Intensification
Liu, T., & Mauter, M. S. (2023). Comparative Infrared Microscopy for Measuring Membrane Thermal Conductivity and Validating Theoretical Heat Transport Models. ACS ES&T Engineering, 3(6), 874–882. https://doi.org/10.1021/acsestengg.2c00436
Project: 5.05 CFD Modeling and Operando Measurements of Multiscale Heat and Mass Transfer for Membrane Module Customization
Process Innovation and Intensification
McCutcheon, J. R., & Mauter, M. S. (2023). Fixing the desalination membrane pipeline. Science, 380(6642), 242–244. https://doi.org/10.1126/science.ade5313
Project: 5.11 Additive Manufacturing for Customized Membranes
Miller, D. M., Abels, K., Guo, J., Williams, K. S., Liu, M. J., & Tarpeh, W. A. (2023). Electrochemical Wastewater Refining: A Vision for Circular Chemical Manufacturing. Journal of the American Chemical Society, 145(36), 19422–19439. https://doi.org/10.1021/jacs.3c01142
Materials and Manufacturing
Patel, S. K., Pan, W., Shin, Y.-U., Kamcev, J., & Elimelech, M. (2023). Electrosorption Integrated with Bipolar Membrane Water Dissociation: A Coupled Approach to Chemical-free Boron Removal. Environmental Science &Amp; Technology, 57(11), 4578–4590. https://doi.org/10.1021/acs.est.3c00058
Project: 6.14 Novel Bipolar Membrane Assisted Electrosorption Process for the Selective Removal of Boron
Process Innovation and Intensification
Penteado de Almeida, J., Stoll, Z., & Xu, P. (2023). An Alternating, Current-Induced Electromagnetic Field for Membrane Fouling and Scaling Control during Desalination of Secondary Effluent from Municipal Wastewater. Water, 15(12), 2234. https://doi.org/10.3390/w15122234
Project: 5.12 Electromagnetic Field for Membrane Scaling Control
Process Innovation and Intensification
Qian, X., Anvari, A., Hoek, E. M. V., & McCutcheon, J. R. (2023). Advancements in conventional and 3D printed feed spacers in membrane modules. Desalination, 556, 116518. https://doi.org/10.1016/j.desal.2023.116518
Project: 5.11 Additive Manufacturing for Customized Membranes
Materials and Manufacturing
Qin, J., Ziemann, E., Bar-Zeev, E., Bone, S. E., Liang, Y., Mauter, M. S., Herzberg, M., & Bernstein, R. (2023). Microporous Polyethersulfone Membranes Grafted with Zwitterionic Polymer Brushes Showing Microfiltration Permeance and Ultrafiltration Bacteriophage Removal. ACS Applied Materials &Amp; Interfaces, 15(14), 18343–18353. https://doi.org/10.1021/acsami.3c01495
Project: 6.05 Kinetic Modeling and Experimental Validation of Mineralization in High Salinity Brine Solutions with and without Scale Inhibitors
Quon, H., & Jiang, S. (2023). Decision making for implementing non-traditional water sources: a review of challenges and potential solutions. Npj Clean Water, 6(1). https://doi.org/10.1038/s41545-023-00273-7
Process Innovation and Intensification
Seth, K., Busse, M., Jang, G., Joag, S., Kim, K., Pankratz, T., Sahu, D., Sharma, R., Stokes-Draut, J., Tsouris, C., & Chellam, S. (2023). Electrocoagulation of high-salinity produced water: lessons learned from its early applications in unconventional reservoir plays. Current Opinion in Chemical Engineering, 42, 100952. https://doi.org/10.1016/j.coche.2023.100952
Project: 5.15 Electrocoagulation/Electrooxidation to Accelerate Cost-Effective Potable Water Reuse
Data Modeling and Analysis
Spearing, L. A., Tariq, M., Safarpour, H., Abia, T., Mallory, M., Guild, J., Katz, L. E., & Faust, K. M. (2023). Moving beyond techno-economic considerations: incorporating organizational constraints into fit-for-use technologies. Current Opinion in Chemical Engineering, 42, 100950. https://doi.org/10.1016/j.coche.2023.100950
Project: 3.09 Assessing the Impact of APRIME on Industrial Sector Supply Portfolios: Chemical Industry and Data Center/Large Campus Case Studies
Materials and Manufacturing
Tao, L., He, J., Arbaugh, T., McCutcheon, J. R., & Li, Y. (2023). Machine learning prediction on the fractional free volume of polymer membranes. Journal of Membrane Science, 665, 121131. https://doi.org/10.1016/j.memsci.2022.121131
Project: 6.04 Development of HPRO Membrane Platform for the Desalination of High Salinity Waters
Materials and Manufacturing
Tauqir, W., Maduako, A. C., Yang, X., & Bollas, G. M. (2023). Modeling aqueous multi-electrolyte systems for the simulation and optimization of concentrated brine processes. 33rd European Symposium on Computer Aided Process Engineering, 733–738. https://doi.org/10.1016/b978-0-443-15274-0.50117-7
Project: 6.07 Waterpy: Open-Source Modeling & Optimization Library for Water Treatment Processes
Materials and Manufacturing
Villalobos, L. F., Pataroque, K. E., Pan, W., Cao, T., Kaneda, M., Violet, C., Ritt, C. L., Hoek, E. M. V., & Elimelech, M. (2023). Orientation matters: Measuring the correct surface of polyamide membranes with quartz crystal microbalance. Journal of Membrane Science Letters, 3(2), 100048. https://doi.org/10.1016/j.memlet.2023.100048
Project: 6.11 UHPRO Membrane and Module Design & Optimization Innovations to Enable & Optimize Ultra-High Pressure Reverse Osmosis Membranes
Materials and Manufacturing
Wang, L., He, J., Heiranian, M., Fan, H., Song, L., Li, Y., & Elimelech, M. (2023). Water transport in reverse osmosis membranes is governed by pore flow, not a solution-diffusion mechanism. Science Advances, 9(15). https://doi.org/10.1126/sciadv.adf8488
Project: 6.11 UHPRO Membrane and Module Design & Optimization Innovations to Enable & Optimize Ultra-High Pressure Reverse Osmosis Membranes
Process Innovation and Intensification
Wang, R., Duddu, R., & Lin, S. (2023). Extended Donnan-Manning theory for selective ion partition and transport in ion exchange membrane. Journal of Membrane Science, 681, 121782. https://doi.org/10.1016/j.memsci.2023.121782
Project: 5.06 A Novel Electro-dialytic Crystallizer (EDC) for Energy Efficient Zero-liquid Discharge
Process Innovation and Intensification
Wang, X., Rashid, I., Zhao, Z., Oladele, M., Xiang, W., Huang, Y., Wazer, E., McCutcheon, J., Bollas, G., Contreras, J., & Li, B. (2023). Machine Learning Algorithm Integrated with Real-Time In Situ Sensors and Physiochemical Principle-Driven Soft Sensors toward an Anaerobic Digestion-Data Fusion Framework. ACS ES&T Water, 4(3), 1061–1072. https://doi.org/10.1021/acsestwater.3c00236
Project: 5.22 Printed Polyelectrolyte Complex (APEC) Membranes for Ultra-high Permeance Nanofiltration
Materials and Manufacturing
Yang, Z., Zhao, J., Sullivan, E. G., & Zou, S. (2023). Cost-Effective Cathode Materials To Electrochemically Tackle Aquatic Selenite Pollution. ACS ES&T Engineering, 3(4), 458–466. https://doi.org/10.1021/acsestengg.2c00323
Project: 6.08 Direct Electrochemical Reduction of Selenium to Achieve A-PRIME Water Treatment
Data Modeling and Analysis
Yao, Y., Yu, S., & Battiato, I. (2023). Understanding flow dynamics in membrane distillation: Effects of reactor design on polarization. Separation and Purification Technology, 314, 123664. https://doi.org/10.1016/j.seppur.2023.123664
Project: 4.03 High-Fidelity Modeling
Data Modeling and Analysis
Zaniolo, M., Fletcher, S., & Mauter, M. S. (2023). Multi-scale planning model for robust urban drought response. Environmental Research Letters, 18(5), 54014. https://doi.org/10.1088/1748-9326/acceb5
Project: 3.10 Robust Technology and Policy Pathways for Municipal Water Security
Process Innovation and Intensification
Zhang, X., Yao, Y., Horseman, T., Wang, R., Yin, Y., Zhang, S., Tong, T., & Lin, S. (2023). Electrodialytic crystallization to enable zero liquid discharge. Nature Water, 1(6), 547–554. https://doi.org/10.1038/s44221-023-00095-4
Project: 5.06 A Novel Electro-dialytic Crystallizer (EDC) for Energy Efficient Zero-liquid Discharge
Data Modeling and Analysis
Zilberman, D., Huang, A., Goldberg, L., & Reardon, T. (2023). The evolution of symbiotic innovation, water, and agricultural supply chains. Applied Economic Perspectives and Policy, 45(3), 1592–1603. Portico. https://doi.org/10.1002/aepp.13342
Project: 3.08 Next-Gen Desal for Ag Drainage
Bae, Y., Crompton, N. M., Sharma, N., Yuan, Y., Catalano, J. G., & Giammar, D. E. (2022). Impact of dissolved oxygen and pH on the removal of selenium from water by iron electrocoagulation. Water Research, 213, 118159. https://doi.org/10.1016/j.watres.2022.118159
Process Innovation and Intensification
Cabrera, S. M., Winnubst, L., Richter, H., Voigt, I., McCutcheon, J., & Nijmeijer, A. (2022). Performance evaluation of an industrial ceramic nanofiltration unit for wastewater treatment in oil production. Water Research, 220, 118593. https://doi.org/10.1016/j.watres.2022.118593
Project: 5.22 Printed Polyelectrolyte Complex (APEC) Membranes for Ultra-high Permeance Nanofiltration
Das, S., O’Connell, M. G., Xu, H., Bernstein, R., Kim, J.-H., Sankhala, K., Segal-Peretz, T., Shevate, R., Zhang, W., Zhou, X., Darling, S. B., & Dunn, J. B. (2022). Assessing Advances in Anti-fouling Membranes to Improve Process Economics and Sustainability of Water Treatment. ACS ES&T Engineering, 2(11), 2159–2173. https://doi.org/10.1021/acsestengg.2c00184
Process Innovation and Intensification
Foo, Z. H., Stetson, C., Dach, E., Deshmukh, A., Lee, H., Menon, A. K., Prasher, R., Yip, N. Y., Lienhard, J. H., & Wilson, A. D. (2022). Solvent-driven aqueous separations for hypersaline brine concentration and resource recovery. Trends in Chemistry, 4(12), 1078–1093. https://doi.org/10.1016/j.trechm.2022.09.004
Project: 5.07 Solvent-Driven Zero Liquid Discharge for Production of Synthetic Gypsum
Materials and Manufacturing
He, J., Yang, J., McCutcheon, J. R., & Li, Y. (2022). Molecular insights into the structure-property relationships of 3D printed polyamide reverse-osmosis membrane for desalination. Journal of Membrane Science, 658, 120731. https://doi.org/10.1016/j.memsci.2022.120731
Project: 6.11 UHPRO Membrane and Module Design & Optimization Innovations to Enable & Optimize Ultra-High Pressure Reverse Osmosis Membranes
Process Innovation and Intensification
Horseman, T., & Lin, S. (2022). Exceptional Mineral Scaling Resistance from the Surface Gas Layer: Impacts of Surface Wetting Properties and the Gas Layer Charging Mechanism. ACS Environmental Au, 2(5), 418–427. https://doi.org/10.1021/acsenvironau.2c00011
Project: 5.06 A Novel Electro-dialytic Crystallizer (EDC) for Energy Efficient Zero-liquid Discharge
Jang, G. G., Zhang, Y., Keum, J. K., Bootwala, Y. Z., Hatzell, M. C., Jassby, D., & Tsouris, C. (2022). Neutron tomography of porous aluminum electrodes used in electrocoagulation of groundwater. Frontiers in Chemical Engineering, 4. https://doi.org/10.3389/fceng.2022.1046627
Data Modeling and Analysis
Liu, T., & Mauter, M. S. (2022). Heat transfer innovations and their application in thermal desalination processes. Joule, 6(6), 1199–1229. https://doi.org/10.1016/j.joule.2022.05.004
Project: 2.10 Not Found
Liu, Y.-H., Bootwala, Y. Z., Jang, G. G., Keum, J. K., Khor, C. M., Hoek, E. M. V., Jassby, D., Tsouris, C., Mothersbaugh, J., & Hatzell, M. C. (2022). Electroprecipitation Mechanism Enabling Silica and Hardness Removal through Aluminum-Based Electrocoagulation. ACS ES&T Engineering, 2(7), 1200–1210. https://doi.org/10.1021/acsestengg.1c00433
Data Modeling and Analysis
Liu, Y., & Mauter, M. S. (2022). High-Resolution Carbon Accounting Framework for Urban Water Supply Systems. Environmental Science &Amp; Technology, 56(19), 13920–13930. https://doi.org/10.1021/acs.est.2c04127
Project: 3.10 Robust Technology and Policy Pathways for Municipal Water Security
Data Modeling and Analysis
Mauter, M. S., Dionysiou, D. D., & Kim, J.-H. (2022). Technology Baselines and Innovation Priorities for Securing Water Supply. ACS ES&T Engineering, 2(3), 271–272. https://doi.org/10.1021/acsestengg.2c00014
Project: 3.10 Robust Technology and Policy Pathways for Municipal Water Security
Process Innovation and Intensification
Ostwal, M., Wazer, E., Pemberton, M., & McCutcheon, J. R. (2022). Scaling electrospray based additive manufacturing of polyamide membranes. Journal of Membrane Science Letters, 2(2), 100035. https://doi.org/10.1016/j.memlet.2022.100035
Project: 5.22 Printed Polyelectrolyte Complex (APEC) Membranes for Ultra-high Permeance Nanofiltration
Data Modeling and Analysis
Plata, S. L., Devenport, C. L., Miara, A., Sitterley, K. A., Evans, A., Talmadge, M., Van Allsburg, K. M., Kurup, P., Cox, J., Kerber, S., Howell, A., Breckenridge, R., Manygoats, C., Stokes-Draut, J. R., Macknick, J., & Childress, A. E. (2022). Zero Liquid Discharge and Water Reuse in Recirculating Cooling Towers at Power Facilities: Review and Case Study Analysis. ACS ES&T Engineering, 2(3), 508–525. https://doi.org/10.1021/acsestengg.1c00377
Project: 3.04 Roadmap to R&D Cycle: Technology Baselines, NAWI Performance Tracking, and Technology Roadmapping
Materials and Manufacturing
Qian, X., Ostwal, M., Asatekin, A., Geise, G. M., Smith, Z. P., Phillip, W. A., Lively, R. P., & McCutcheon, J. R. (2022). A critical review and commentary on recent progress of additive manufacturing and its impact on membrane technology. Journal of Membrane Science, 645, 120041. https://doi.org/10.1016/j.memsci.2021.120041
Project: 6.04 Development of HPRO Membrane Platform for the Desalination of High Salinity Waters
Data Modeling and Analysis
Ramanuj, V., Sankaran, R., Malenica, L., Cole, K., Day, M., & McCutcheon, J. (2022). Macrovoid resolved simulations of transport through HPRO relevant membrane geometries. Journal of Membrane Science, 662, 120958. https://doi.org/10.1016/j.memsci.2022.120958
Project: 4.03 High-Fidelity Modeling
Data Modeling and Analysis
Sitaraman, H., & Battiato, I. (2022). Impact of large-scale effects on mass transfer and concentration polarization in Reverse Osmosis membrane systems. Separation and Purification Technology, 303, 122121. https://doi.org/10.1016/j.seppur.2022.122121
Project: 4.03 High-Fidelity Modeling
Materials and Manufacturing
Tran, R., Wang, D., Kingsbury, R., Palizhati, A., Persson, K. A., Jain, A., & Ulissi, Z. W. (2022). Screening of bimetallic electrocatalysts for water purification with machine learning. The Journal of Chemical Physics, 157(7). https://doi.org/10.1063/5.0092948
Project: 6.02 Machine Learning Platform for Catalyst Design
Process Innovation and Intensification
Wang, R., & Lin, S. (2022). Thermodynamics and Energy Efficiency of Zero Liquid Discharge. ACS ES&T Engineering, 2(8), 1491–1503. https://doi.org/10.1021/acsestengg.2c00013
Project: 5.06 A Novel Electro-dialytic Crystallizer (EDC) for Energy Efficient Zero-liquid Discharge
Process Innovation and Intensification
Wilson, A. D., Lee, H., & Stetson, C. (2022). Local stress within a granular molecular solvent matrix, a mechanism for individual ion hydration. Journal of Molecular Liquids, 361, 119544. https://doi.org/10.1016/j.molliq.2022.119544
Project: 5.07 Solvent-Driven Zero Liquid Discharge for Production of Synthetic Gypsum
Winter, L. R., Cooper, N. J., Lee, B., Patel, S. K., Wang, L., & Elimelech, M. (2022). Mining Nontraditional Water Sources for a Distributed Hydrogen Economy. Environmental Science &Amp; Technology, 56(15), 10577–10585. https://doi.org/10.1021/acs.est.2c02439
Materials and Manufacturing
Wu, J., Jung, B., Anvari, A., Im, S., Anderson, M., Zheng, X., Jassby, D., Kaner, R. B., Dlamini, D., Edalat, A., & Hoek, E. M. V. (2022). Reverse osmosis membrane compaction and embossing at ultra-high pressure operation. Desalination, 537, 115875. https://doi.org/10.1016/j.desal.2022.115875
Project: 6.11 UHPRO Membrane and Module Design & Optimization Innovations to Enable & Optimize Ultra-High Pressure Reverse Osmosis Membranes
Data Modeling and Analysis
Xu, X., Ness, J. E., Miara, A., Sitterley, K. A., Talmadge, M., O’Neill, B., Coughlin, K., Akar, S., Edirisooriya, E. M. N. T., Kurup, P., Rao, N., Macknick, J., Stokes-Draut, J. R., & Xu, P. (2022). Analysis of Brackish Water Desalination for Municipal Uses: Case Studies on Challenges and Opportunities. ACS ES&T Engineering, 2(3), 306–322. https://doi.org/10.1021/acsestengg.1c00326
Project: 3.04 Roadmap to R&D Cycle: Technology Baselines, NAWI Performance Tracking, and Technology Roadmapping
Materials and Manufacturing
Yang, J., Tao, L., He, J., McCutcheon, J. R., & Li, Y. (2022). Machine learning enables interpretable discovery of innovative polymers for gas separation membranes. Science Advances, 8(29). https://doi.org/10.1126/sciadv.abn9545
Project: 6.04 Development of HPRO Membrane Platform for the Desalination of High Salinity Waters
Process Innovation and Intensification
Yin, Y., Li, T., Zuo, K., Liu, X., Lin, S., Yao, Y., & Tong, T. (2022). Which Surface Is More Scaling Resistant? A Closer Look at Nucleation Theories for Heterogeneous Gypsum Nucleation in Aqueous Solutions. Environmental Science &Amp; Technology, 56(22), 16315–16324. https://doi.org/10.1021/acs.est.2c06560
Project: 5.06 A Novel Electro-dialytic Crystallizer (EDC) for Energy Efficient Zero-liquid Discharge
Materials and Manufacturing
Zadeh, A. E., Touati, K., Mulligan, C. N., McCutcheon, J. R., & Rahaman, Md. S. (2022). Closed-loop pressure retarded osmosis draw solutions and their regeneration processes: A review. Renewable and Sustainable Energy Reviews, 159, 112191. https://doi.org/10.1016/j.rser.2022.112191
Project: 6.04 Development of HPRO Membrane Platform for the Desalination of High Salinity Waters
, Borch, T., Dionysiou, D. (., Katz, L., Xu, P., Breckenridge, R., Ellison, K., Fox, J., Macknick, J., Sedlak, D., & Stokes-Draut, J. (2021). National Alliance for Water Innovation (NAWI) Agriculture Sector Technology Roadmap 2021 (, Ed.). Office of Scientific and Technical Information (OSTI). https://doi.org/10.2172/1782447
Process Innovation and Intensification
Bell, E. M., De Guise, S., McCutcheon, J. R., Lei, Y., Levin, M., Li, B., Rusling, J. F., Lawrence, D. A., Cavallari, J. M., O’Connell, C., Javidi, B., Wang, X., & Ryu, H. (2021). Exposure, health effects, sensing, and remediation of the emerging PFAS contaminants – Scientific challenges and potential research directions. Science of the Total Environment, 780, 146399. https://doi.org/10.1016/j.scitotenv.2021.146399
Project: 5.22 Printed Polyelectrolyte Complex (APEC) Membranes for Ultra-high Permeance Nanofiltration
Data Modeling and Analysis
Cooper, C. M., McCall, J., Stokes, S. C., McKay, C., Bentley, M. J., Rosenblum, J. S., Blewett, T. A., Huang, Z., Miara, A., Talmadge, M., Evans, A., Sitterley, K. A., Kurup, P., Stokes-Draut, J. R., Macknick, J., Borch, T., Cath, T. Y., & Katz, L. E. (2021). Oil and Gas Produced Water Reuse: Opportunities, Treatment Needs, and Challenges. ACS ES&T Engineering, 2(3), 347–366. https://doi.org/10.1021/acsestengg.1c00248
Project: 3.04 Roadmap to R&D Cycle: Technology Baselines, NAWI Performance Tracking, and Technology Roadmapping
Data Modeling and Analysis
Dudchenko, A. V., Bartholomew, T. V., & Mauter, M. S. (2021). Cost optimization of multi-stage gap membrane distillation. Journal of Membrane Science, 627, 119228. https://doi.org/10.1016/j.memsci.2021.119228
Project: 4.02 PROTEUS: Integrated Computational Capability for Optimizing Advanced Water Treatment Systems
Data Modeling and Analysis
Dudchenko, A. V., Bartholomew, T. V., & Mauter, M. S. (2021). High-impact innovations for high-salinity membrane desalination. Proceedings of the National Academy of Sciences, 118(37). https://doi.org/10.1073/pnas.2022196118
Project: 4.02 PROTEUS: Integrated Computational Capability for Optimizing Advanced Water Treatment Systems
Data Modeling and Analysis
Giammar, D. E., Greene, D. M., Mishrra, A., Rao, N., Sperling, J. B., Talmadge, M., Miara, A., Sitterley, K. A., Wilson, A., Akar, S., Kurup, P., Stokes-Draut, J. R., & Coughlin, K. (2021). Cost and Energy Metrics for Municipal Water Reuse. ACS ES&T Engineering, 2(3), 489–507. https://doi.org/10.1021/acsestengg.1c00351
Project: 3.04 Roadmap to R&D Cycle: Technology Baselines, NAWI Performance Tracking, and Technology Roadmapping
Giammar, D., Jiang, S., Xu, P., Breckenridge, R., Edirisooriya, T., Jiang, W., Lin, L., Macknick, J., Rao, N., Sedlak, D., Stokes-Draut, J., & Xu, X. (2021). National Alliance for Water Innovation (NAWI) Municipal Sector Technology Roadmap 2021 (, Ed.). Office of Scientific and Technical Information (OSTI). https://doi.org/10.2172/1782448
Data Modeling and Analysis
Gingerich, D. B., Liu, J., & Mauter, M. S. (2021). Carbon Benefits of Drinking Water Treatment Electrification. ACS ES&T Engineering, 2(3), 367–376. https://doi.org/10.1021/acsestengg.1c00165
Project: 3.03 Develop the Water Technology Techno-Economic Assessment Pipe Parity Platform
Data Modeling and Analysis
Gunter, D., Amusat, O., Bartholomew, T., & Drouven, M. (2021). Santa Barbara Desalination Digital Twin Technical Report (, Ed.). Office of Scientific and Technical Information (OSTI). https://doi.org/10.2172/1831427
Project: 4.02 PROTEUS: Integrated Computational Capability for Optimizing Advanced Water Treatment Systems
Data Modeling and Analysis
Hejase, C. A., Weitzel, K. A., Stokes, S. C., Grauberger, B. M., Young, R. B., Arias-Paic, M. S., Kong, M., Chae, S., Bandhauer, T. M., Tong, T., Herber, D. R., Stout, S., Miara, A., Huang, Z., Evans, A., Kurup, P., Talmadge, M., Kandt, A., Stokes-Draut, J. R., et al. (2021). Opportunities for Treatment and Reuse of Agricultural Drainage in the United States. ACS ES&T Engineering, 2(3), 292–305. https://doi.org/10.1021/acsestengg.1c00277
Project: 3.04 Roadmap to R&D Cycle: Technology Baselines, NAWI Performance Tracking, and Technology Roadmapping
Data Modeling and Analysis
Ling, B., Xie, P., Ladner, D., & Battiato, I. (2021). Dynamic Modeling of Fouling in Reverse Osmosis Membranes. Membranes, 11(5), 349. https://doi.org/10.3390/membranes11050349
Project: 4.03 High-Fidelity Modeling
Data Modeling and Analysis
Liu, Y., & Mauter, M. S. (2021). Marginal energy intensity of water supply. Energy &Amp; Environmental Science, 14(8), 4533–4540. https://doi.org/10.1039/d1ee00925g
Project: 2.10 Not Found
Data Modeling and Analysis
Liu, Y., Sim, A., & Mauter, M. S. (2021). Energy-Optimal Siting of Decentralized Water Recycling Systems. Environmental Science &Amp; Technology, 55(22), 15343–15350. https://doi.org/10.1021/acs.est.1c04708
Project: 2.10 Not Found
Data Modeling and Analysis
Mauter, M. S. (2021). Build back wiser. Science, 373(6562), 1417. https://doi.org/10.1126/science.abm3438
Project: 2.10 Not Found
Materials and Manufacturing
McCutcheon, J. R. (2021). Unraveling the mysteries of the thin film composite reverse osmosis membrane. Joule, 5(3), 528–530. https://doi.org/10.1016/j.joule.2021.03.004
Project: 6.04 Development of HPRO Membrane Platform for the Desalination of High Salinity Waters
Meese, A. F., Kim, D. J., Wu, X., Le, L., Napier, C., Hernandez, M. T., Laroco, N., Linden, K. G., Cox, J., Kurup, P., McCall, J., Greene, D., Talmadge, M., Huang, Z., Macknick, J., Sitterley, K. A., Miara, A., Evans, A., Thirumaran, K., et al. (2021). Opportunities and Challenges for Industrial Water Treatment and Reuse. ACS ES&T Engineering, 2(3), 465–488. https://doi.org/10.1021/acsestengg.1c00282
Data Modeling and Analysis
Miller, K. D., Bentley, M. J., Ryan, J. N., Linden, K. G., Larison, C., Kienzle, B. A., Katz, L. E., Wilson, A. M., Cox, J. T., Kurup, P., Van Allsburg, K. M., McCall, J., Macknick, J. E., Talmadge, M. S., Miara, A., Sitterley, K. A., Evans, A., Thirumaran, K., Malhotra, M., et al. (2021). Mine Water Use, Treatment, and Reuse in the United States: A Look at Current Industry Practices and Select Case Studies. ACS ES&T Engineering, 2(3), 391–408. https://doi.org/10.1021/acsestengg.1c00244
Project: 3.04 Roadmap to R&D Cycle: Technology Baselines, NAWI Performance Tracking, and Technology Roadmapping
Materials and Manufacturing
Podar, M., May, A. L., Bai, W., Peyton, K., Klingeman, D. M., Swift, C. M., Linson, D. A. F., Mathieu, J., Siljeström, D., Beneyto, I., Stadler, L. B., Pinhas, Y., Löffler, F. E., Alvarez, P. J. J., & Kumar, M. (2021). Microbial diversity analysis of two full-scale seawater desalination treatment trains provides insights into detrimental biofilm formation. Journal of Membrane Science Letters, 1(1), 100001. https://doi.org/10.1016/j.memlet.2021.100001
Project: 6.02 Machine Learning Platform for Catalyst Design
Materials and Manufacturing
Qian, X., Ravindran, T., Lounder, S. J., Asatekin, A., & McCutcheon, J. R. (2021). Printing zwitterionic self-assembled thin film composite membranes: Tuning thickness leads to remarkable permeability for nanofiltration. Journal of Membrane Science, 635, 119428. https://doi.org/10.1016/j.memsci.2021.119428
Project: 6.04 Development of HPRO Membrane Platform for the Desalination of High Salinity Waters
Data Modeling and Analysis
Quay, A. N., Fiske, P. S., & Mauter, M. S. (2021). Recommendations for Advancing FAIR and Open Data Standards in the Water Treatment Community. ACS ES&T Engineering, 2(3), 337–346. https://doi.org/10.1021/acsestengg.1c00245
Project: 2.10 Not Found
Data Modeling and Analysis
Quon, H., Sperling, J., Coughline, K., Greene, D., Miara, A., Akar, S., Talmadge, M., Stokes-Draut, J. R., Macknick, J., & Jiang, S. (2021). Pipe Parity Analysis of Seawater Desalination in the United States: Exploring Costs, Energy, and Reliability via Case Studies and Scenarios of Emerging Technology. ACS ES&T Engineering, 2(3), 434–445. https://doi.org/10.1021/acsestengg.1c00270
Project: 3.04 Roadmap to R&D Cycle: Technology Baselines, NAWI Performance Tracking, and Technology Roadmapping
Data Modeling and Analysis
Sim, A., & Mauter, M. S. (2021). Cost and energy intensity of U.S. potable water reuse systems. Environmental Science: Water Research &Amp; Technology, 7(4), 748–761. https://doi.org/10.1039/d1ew00017a
Project: 3.04 Roadmap to R&D Cycle: Technology Baselines, NAWI Performance Tracking, and Technology Roadmapping
Process Innovation and Intensification
Wang, R., & Lin, S. (2021). Pore model for nanofiltration: History, theoretical framework, key predictions, limitations, and prospects. Journal of Membrane Science, 620, 118809. https://doi.org/10.1016/j.memsci.2020.118809
Project: 5.06 A Novel Electro-dialytic Crystallizer (EDC) for Energy Efficient Zero-liquid Discharge
Process Innovation and Intensification
Wilson, A. D., Lee, H., & Stetson, C. (2021). Mass action model of solution activity via speciation by solvation and ion pairing equilibria. Communications Chemistry, 4(1). https://doi.org/10.1038/s42004-021-00599-8
Project: 5.07 Solvent-Driven Zero Liquid Discharge for Production of Synthetic Gypsum
Materials and Manufacturing
Yang, J., Shen, Z., He, J., & Li, Y. (2021). Efficient separation of small organic contaminants in water using functionalized nanoporous graphene membranes: Insights from molecular dynamics simulations. Journal of Membrane Science, 630, 119331. https://doi.org/10.1016/j.memsci.2021.119331
Project: 6.04 Development of HPRO Membrane Platform for the Desalination of High Salinity Waters
Process Innovation and Intensification
Yang, J., Tao, L., He, J., McCutcheon, J., & Li, Y. (2021). Discovery of Innovative Polymers for Next-Generation Gas-Separation Membranes using Interpretable Machine Learning. https://doi.org/10.26434/chemrxiv-2021-p4g7z
Project: 5.22 Printed Polyelectrolyte Complex (APEC) Membranes for Ultra-high Permeance Nanofiltration
Process Innovation and Intensification
Zhou, Y., Khan, B., Gu, H., Christofides, P. D., & Cohen, Y. (2021). Modeling UF fouling and backwash in seawater RO feedwater treatment using neural networks with evolutionary algorithm and Bayesian binary classification. Desalination, 513, 115129. https://doi.org/10.1016/j.desal.2021.115129
Project: 5.10 Analytics for Causal Analysis and Decision Support Models for Autonomous and Smart Water Treatment
Data Modeling and Analysis
Mauter, M. S., & Fiske, P. S. (2020). Desalination for a circular water economy. Energy &Amp; Environmental Science, 13(10), 3180–3184. https://doi.org/10.1039/d0ee01653e
Project: 2.10 Not Found
Data Modeling and Analysis
Yao, Y., Yu, S., & Battiato, I. (2022). Understanding Flow Dynamics in Membrane Distillation: Effects of Reactor Design on Polarization. ArXiv. https://doi.org/10.48550/ARXIV.2209.04983
Project: 4.03 High-Fidelity Modeling