Project 1.1

  1. Milner, Phillip J., et al. "A Diaminopropane-Appended Metal–Organic Framework Enabling Efficient CO2 Capture from Coal Flue Gas via a Mixed Adsorption Mechanism." Journal of the American Chemical Society 139.38 (2017): 13541-13553.

Project 1.2

  1. Andreades, Charalampos, and Per Peterson. "Nuclear Air Brayton Combined Cycle and Mark 1 Pebble Bed Fluoride-Salt-Cooled High-Temperature Reactor economic performance in a regulated electricity market." Nuclear Engineering and Design (2016).
  2. Andreades, C., Greenop, A., Gallagher, S., Choi, J. K., & Peterson, P. Coiled Tube Air Heater Test Loop Design. In 2017 25th International Conference on Nuclear Engineering (pp. V009T15A053-V009T15A053). American Society of Mechanical Engineers, (2017, July).

Project 1.3

  1. Rosner, F., Chen, Q., Rao, A., Samuelsen, S., Jayaraman, A., & Alptekin, G. (2019). Thermo-economic analyses of IGCC power plants employing warm gas CO2 separation technology. Energy185, 541-553.

Project 1.6

  1. Mizerak, Jordan P., and Van P. Carey. "Modeling of Transport During Droplet Deposition and Spreading on Smooth and Microstructured Superhydrophilic Surfaces." ASME 2016 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2016.
  2. Kunkle, C.M. and Carey, V.P., Metrics For Quantifying Surface Wetting Effects On Boiling And Evaporation At Nanostructured Hydrophilic Surfaces, paper PRTEC-15163, Proceedings of the First Pacific Rim Thermal Engineering Conference, PRTEC, March 13-17, 2016, Hawaii's Big Island, USA. 
  3. Kunkle, Claire M., and Van P. Carey. "Metrics for Quantifying Surface Wetting Effects on Vaporization Processes at Nanostructured Hydrophilic Surfaces." ASME 2016 Heat Transfer Summer Conference collocated with the ASME 2016 Fluids Engineering Division Summer Meeting and the ASME 2016 14th International Conference on Nanochannels, Microchannels, and Minichannels. American Society of Mechanical Engineers, 2016.
  4. LaBrie, Russell J., Jorge Padilla Jr, and Van P. Carey. "Experimental Study of Aqueous Binary Mixture Droplet Vaporization on Nanostructured Surfaces." Heat Transfer Engineering 38.14-15 (2017): 1260-1273.
  5. Kunkle, Claire M., Jordan P. Mizerak, and Van P. Carey. "The Effects of Wettability and Surface Morphology on Heat Transfer for Zinc Oxide Nanostructured Aluminum Surfaces." ASME 2017 Heat Transfer Summer Conference. American Society of Mechanical Engineers, 2017.
  6. Wemp, C.K. and Carey, V.P., Tuning Superhydrophilic Nanostructured Surfaces To Maximize Water Droplet Evaporation Heat Transfer Performance, paper IMECE2017-72679, presented and included in the Proceedings of the ASME 2017 International Mechanical Engineering Congress and Exposition, November 3-9, 2017, Tampa, Florida, USA.
  7. Wemp, Claire K., and Van P. Carey. "Water Wicking and Droplet Spreading on Randomly Structured Thin Nanoporous Layers." Langmuir 33.50 (2017): 14513-14525.
  8. Wemp, Claire K., and Van P. Carey. "Tuning Superhydrophilic Nanostructured Surfaces to Maximize Water Droplet Evaporation Heat Transfer Performance." Journal of Heat Transfer140.10 (2018): 102401.
  9. Carey, V. P., Wemp, C. K., McClure, E. R., & Cabrera, S. (2018, November). Mechanism Interaction During Droplet Evaporation on Nanostructured Hydrophilic Surfaces. In ASME 2018 International Mechanical Engineering Congress and Exposition (pp. V08AT10A029-V08AT10A029). American Society of Mechanical Engineers.
  10. Wemp, C. K., & Carey, V. P. (2019). Heat transport for evaporating droplets on superhydrophilic, thin, nanoporous layers. International Journal of Heat and Mass Transfer, 132, 34-51.

Project 2.2

  1. Wei, Y.; Wang, J.; Li, H.; Zhao, M.; Zhang, H.; Guan, Y.; Huang, H.; Mi, B.; Zhang, Y., “Partially reduced graphene oxide and chitosan nanohybrid membranes for selective retention of divalent cations,” RSC Advances 2018, 8, 13656-13663.
  2. Kang, Y., Zheng, S., Finnerty, C., Lee, M. J., & Mi, B.. "Regenerable Polyelectrolyte Membrane for Ultimate Fouling Control in Forward Osmosis." Environmental Science & Technology, (51(6), 2017) 3242-3249.
  3. Zheng, S., Tu, Q., Urban, J., Li, S., and Mi, B. (2017). "Swelling of graphene oxide membranes in aqueous solution: Characterization of interlayer spacing and insight into water transport mechanisms." ACS Nano, 11(6), pp. 6440-6450, DOI: 10.1021/acsnano.7b02999.
  4. Wang, Z., and Mi, B. (2017). "Environmental applications of 2D molybdenum disulfide (MoS2) nanosheets."Environmental Science and Technology, accpted, DOI: 10.1021/acsnano.7b02999.
  5. Oh, Y., Armstrong, D. L., Finnerty, C., Zheng, S., Hu, M., Torrents, A., & Mi, B. (2017). "Understanding the pH-responsive behavior of graphene oxide membrane in removing ions and organic micropollulants." Journal of Membrane Science, (2017) 541, 235-243.
  6. Jin, L., Wang, Z., Zheng, S., & Mi, B. "Polyamide-crosslinked graphene oxide membrane for forward osmosis." Journal of Membrane Science (2018), 545, 11-18.
  7. Finnerty, C., Zhang, L., Sedlak, D. L., Nelson, K. L., & Mi, B. "Synthetic Graphene Oxide Leaf for Solar Desalination with Zero Liquid Discharge." Environmental science & technology, (2017) 51(20), 11701-11709.

Project 2.4

  1. Scholes R.C., Prasse C. and Sedlak, D.L. The Role of Reactive Nitrogen Species in Sensitized Photolysis of Wastewater-Derived Trace Organic Contaminants.  Environ. Sci. Technol. 2019, 53:116483-6491.

Project 2.6

  1. Stringfellow, William T., et al. "Identifying chemicals of concern in hydraulic fracturing fluids used for oil production." Environmental Pollution 220 (2017): 413-420.
  2. Camarillo, M. K., J. K. Domen, W. T. Stringfellow. 2016. "Physical-chemical evaluation of hydraulic fracturing chemicals in the context of produced water treatment." Journal of Environ. Management 183: 164 - 174.
  3. Camarillo, M. K., W. T. Stringfellow. 2018. Biological treatment of oil and gas produced water: a review and meta-analysis. Clean Technologies and Environmental Policy, 20: 1127-1146.

Project 2.7

  1. Rao, E., McVerry, B., Borenstein, A., Anderson, M., Jordan, R. S., & Kaner, R. B. (2018). Roll-to-Roll Functionalization of Polyolefin Separators for High-Performance Lithium-Ion Batteries. ACS Applied Energy Materials, 1(7), 3292-3300.
  2. McVerry, B., Anderson, M., He, N., Kweon, H., Ji, C., Xue, S., ... & Jun, D. (2019). Next-Generation Asymmetric Membranes Using Thin-Film Liftoff. Nano letters19(8), 5036-5043.

Project 3.1

  1. Viers, Joshua H., and Daniel M. Nover. "Too Big To Fail: Limiting Public Risk in Hydropower Licensing." Hastings Envt'l LJ 24 (2018): 143.
  2. Viers, Joshua H. "Meeting ecosystem needs while satisfying human demands." Environmental Research Letters 12.6 (2017): 061001.

Project 3.2

  1. Yang, T., Asanjan, A.A., Faridzad, M., Hayatbini, N., Gao, X. and Sorooshian, S."An Enhanced Artificial Neural Network with A Shuffled Complex Evolutionary Global Optimization with Principal Component Analysis. Information Sciences." Information Sciences418, (2017)  302-316.
  2. Yang, T., Xiaogang, G., Sorooshian, S. (2016). "Simulating California reservoir operation using the classification and regression-tree algorithm combined with a shuffled cross-validation scheme." Water Resources Research. 52 (3). pp. 1626 - 1651. DOI 10.1002/2015WR017394
  3. Yang, T., Gao, X., Sellars, S. L., & Sorooshian, S. "Improving the multi-objective evolutionary optimization algorithm for hydropower reservoir operations in the California Oroville–Thermalito complex." Environmental Modelling & Software, 69, (2015) 262-279.
  4. Liu, X., Luo, Y., Yang, T., Liang, K., Zhang, M., & Liu, C. "Investigation of the probability of concurrent drought events between the water source and destination regions of China's water diversion project." Geophysical Research Letters, (2015). 42(20), 8424-8431.
  5. Thorstensen, A., Nguyen, P., Hsu, K., & Sorooshian, S. "Using densely distributed soil moisture observations for calibration of a hydrologic model." Journal of Hydrometeorology, (2016). 17(2), 571-590.
  6. Naeini, M. R., Yang, T., Sadegh, M., AghaKouchak, A., Hsu, K. L., Sorooshian, S., ... & Lei, X. (2018). Shuffled Complex-Self Adaptive Hybrid EvoLution (SC-SAHEL) optimization framework. Environmental Modelling & Software,  215-235.
  7. Chen, K., Guo, S., Wang, J., Qin, P., He, S., Sun, S., & Naeini, M. R. (2019). Evaluation of GloFAS-Seasonal Forecasts for Cascade Reservoir Impoundment Operation in the Upper Yangtze River. Water, 11(12), 2539.

Project 3.3

  1. Yang, Tiantian, et al. "Multi-criterion model ensemble of CMIP5 surface air temperature over China." Theoretical and Applied Climatology (2017): 1-16.
  2. Yang, T., Asanjan, A. A., Welles, E., Gao, X., Sorooshian, S., & Liu, X. "Developing reservoir monthly inflow forecasts using artificial intelligence and climate phenomenon information." Water Resources Research, (2017) 53(4), 2786-2812.
  3. Tao, Y., Gao, X., Hsu, K., Sorooshian, S., & Ihler, A. "A deep neural network modeling framework to reduce bias in satellite precipitation products." Journal of Hydrometeorology, 17(3), (2016) 931-945.
  4. Sarachi, Sepideh, Kuo-lin Hsu, and Soroosh Sorooshian. "A statistical model for the uncertainty analysis of satellite precipitation products." Journal of Hydrometeorology 16.5 (2015): 2101-2117.
  5. Nasrollahi, N., AghaKouchak, A., Cheng, L., Damberg, L., Phillips, T. J., Miao, C., ... & Sorooshian, S. "How well do CMIP5 climate simulations replicate historical trends and patterns of meteorological droughts?." Water Resources Research (2015) 51(4), 2847-2864.
  6. Yang, Z., Hsu, K., Sorooshian, S., Xu, X., Braithwaite, D., & Verbist, K. M. "Bias adjustment of satellite‐based precipitation estimation using gauge observations: A case study in Chile." Journal of Geophysical Research: Atmospheres, 121(8) (2016), 3790-3806.
  7. Ata Akbari Akbari Asanjan, A., Yang, T., Hsu, K., Sorooshian, S., Lin, J., & Peng, Q. (2018). Short‐Term Precipitation Forecast Based on the PERSIANN System and LSTM Recurrent Neural Networks. Journal of Geophysical Research: Atmospheres, 123(22), 12-543.
  8. Miao, Q., Pan, B., Wang, H., Hsu, K., & Sorooshian, S. (2019). Improving Monsoon Precipitation Prediction Using Combined Convolutional and Long Short Term Memory Neural Network. Water11(5), 977.
  9. Sadeghi, M., Asanjan, A. A., Faridzad, M., Nguyen, P., Hsu, K., Sorooshian, S., & Braithwaite, D. (2019). PERSIANN-CNN: Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks-Convolutional Neural Networks. Journal of Hydrometeorology, (2019).
  10. Sadeghi, M., Akbari Asanjan, A., Faridzad, M., Afzali Gorooh, V., Nguyen, P., Hsu, K., ... & Braithwaite, D. (2019). Evaluation of PERSIANN-CDR Constructed Using GPCP V2. 2 and V2.3 and A Comparison with TRMM 3B42 V7 and CPC Unified Gauge-Based Analysis in Global Scale. Remote Sensing, 11(23), 2755.

Project 3.4

  1. Hardin, E., AghaKouchak, A., Qomi, M. J. A., Madani, K., Tarroja, B., Zhou, Y., ... & Samuelsen, S. California drought increases CO 2 footprint of energy. Sustainable cities and society, 28 (2017): 450-452.
  2. Tarroja, Brian, Amir AghaKouchak, and Scott Samuelsen. "Quantifying climate change impacts on hydropower generation and implications on electric grid greenhouse gas emissions and operation." Energy 111 (2016): 295-305.
  3. Forrest, K., Tarroja, B., Chiang, F., AghaKouchak, A., and Samuelsen, S., “Assessing Climate Change Impacts on California Hydropower Generation and Ancillary Services Provision”, Climatic Change, 2018. 151: p.395-412.

Project 4.3

  1. Li, J., Hsu, K., AghaKouchak, A., Sorooshian, S. "Object-Based Assessment of Satellite Precipitation Products." Remote Sens. (2016). 8, 547.
  2. Nguyen, P., Shearer, E. J., Tran, H., Ombadi, M., Hayatbini, N., Palacios, T., ... & Kuligowski, B. (2019). The CHRS Data Portal, an easily accessible public repository for PERSIANN global satellite precipitation data. Scientific data, 6, 180296.
  3. Hayatbini, N., Hsu, K. L., Sorooshian, S., Zhang, Y., & Zhang, F. (2019). Effective Cloud Detection and Segmentation using a Gradient-Based Algorithm for Satellite Imagery; Application to improve PERSIANN-CCS. Journal of Hydrometeorology, (2019).
  4. Hayatbini, N., Kong, B., Hsu, K. L., Nguyen, P., Sorooshian, S., Stephens, G., ... & Ganguly, S. (2019). Conditional Generative Adversarial Networks (cGANs) for Near Real-Time Precipitation Estimation from Multispectral GOES-16 Satellite Imageries—PERSIANN-cGAN. Remote Sensing11(19), 2193.
  5. Nguyen, P., Shearer, E. J., Ombadi, M., Gorooh, V. A., Hsu, K., Sorooshian, S., ... & Ralph, M.(2019). PERSIANN Dynamic Infrared-Rain rate model (PDIR) for high-resolution, real-time satellite precipitation estimation. Bulletin of the American Meteorological Society, (2019).

Project 4.4

  1. Tao, Y., Yang, T., Faridzad, M., Jiang, L., He, X. and Zhang, X. (2017), "Non-stationary bias correction of monthly CMIP5 temperature projections over China using a residual-based bagging tree model." Int. J. Climatol. doi:10.1002/joc.5188
  2. Liu, X., Yang, T., Hsu, K., Liu, C., & Sorooshian, S. "Evaluating the streamflow simulation capability of PERSIANN-CDR daily rainfall products in two river basins on the Tibetan Plateau." Hydrology and Earth System Sciences, (2017) 21(1), 169.
  3. Katiraie-Boroujerdy, P. S., Ashouri, H., Hsu, K. L., & Sorooshian, S. "Trends of precipitation extreme indices over a subtropical semi-arid area using PERSIANN-CDR." Theoretical and Applied Climatology(2017) 130(1-2), 249-260.
  4. Nguyen P, Sorooshian S, Thorstensen A, Tran H, Huynh P, Pham T, Braithwaite D, Hsu K, AghaKouchak A, Ashouri H. Exploring trends through “RainSphere”: Research data transformed into public knowledge. Bulletin of the American Meteorological Society. 2016 Nov 2(2016).
  5. Ashouri, H., Nguyen, P., Thorstensen, A., Hsu, K. L., Sorooshian, S., & Braithwaite, D. (2016). Assessing the efficacy of high-resolution satellite-based PERSIANN-CDR precipitation product in simulating streamflow. Journal of Hydrometeorology, 17(7), 2061-2076.
  6. Faridzad, Mohammad, et al. "Rainfall Frequency Analysis for Ungauged Regions using Remotely Sensed Precipitation Information." Journal of Hydrology(2018).
  7. Ombadi, Mohammed, et al. "Developing Intensity‐Duration‐Frequency (IDF) Curves From Satellite‐Based Precipitation: Methodology and Evaluation." Water Resources Research(2018).

Project 4.5

  1. Guo, Z., Fogg, G. E., & Henri, C. V. (2019). Upscaling of regional scale transport under transient conditions: evaluation of the Multi‐rate Mass Transfer model. Water Resources Research.

Project 4.6

  1. Li, D., Lettenmaier, D. P., Margulis, S. A., & Andreadis, K. (2019). The value of accurate high-resolution and spatially continuous snow information to streamflow forecasts. Journal of Hydrometeorology, (2019).
  2. Margulis, S. A., Fang, Y., Li, D., Lettenmaier, D. P., & Andreadis, K. (2019). The Utility of Infrequent Snow Depth Images for Deriving Continuous Space‐Time Estimates of Seasonal Snow Water Equivalent. Geophysical Research Letters46(10), 5331-5340.
  3. Li, D., Lettenmaier, D. P., Margulis, S. A., & Andreadis, K. (2019). The role of rain‐on‐snow in flooding over the conterminous United States. Water Resources Research.

Project 5.3

  1. Li, Xi, et al. "Energy for water utilization in China and policy implications for integrated planning." International Journal of Water Resources Development 32.3 (2016): 477-494.

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