ME Research Publications

Permanent URI for this collection

Browse

Recent Submissions

Now showing 1 - 5 of 546
  • Item
    High-Performance Ammonia Electrosynthesis from Nitrate in a NaOH?KOH?H2O Ternary Electrolyte
    (John Wiley and Sons Inc, 2024) Shahid, Usman; Kwon, Yongjun; Yuan, Yuan; Gu, Shuang; Shao, Minhua
    A glut of dinitrogen-derived ammonia (NH3) over the past century has resulted in a heavily imbalanced nitrogen cycle and consequently, the large-scale accumulation of reactive nitrogen such as nitrates in our ecosystems has led to detrimental environmental issues. Electrocatalytic upcycling of waste nitrogen back into NH3 holds promise in mitigating these environmental impacts and reducing reliance on the energy-intensive Haber�Bosch process. Herein, we report a high-performance electrolyzer using an ultrahigh alkalinity electrolyte, NaOH?KOH?H2O, for low-cost NH3 electrosynthesis. At 3,000 mA/cm2, the device with a Fe?Cu?Ni ternary catalyst achieves an unprecedented faradaic efficiency (FE) of 92.5�1.5 % under a low cell voltage of 3.83 V; whereas at 1,000 mA/cm2, an FE of 96.5�4.8 % under a cell voltage of only 2.40 V was achieved. Techno-economic analysis revealed that our device cuts the levelized cost of ammonia electrosynthesis by ~40 % ($30.68 for Fe?Cu?Ni vs. $48.53 for Ni foam per kmol-NH3). The NaOH?KOH?H2O electrolyte together with the Fe?Cu?Ni ternary catalyst can enable the high-throughput nitrate-to-ammonia applications for affordable and scalable real-world wastewater treatments. � 2024 The Authors. Angewandte Chemie International Edition published by Wiley-VCH GmbH.
  • Item
    PAN-based fiber-reinforced carbon-carbon composites for improved fire retardancy and thermal and electrical conductivities for harsh environments
    (SAGE Publications Ltd, 2024) Murali, T.K.S.; Murad, Md. Shafinur; Bakir, M.; Asmatulu, Ramazan
    Carbon-carbon (C-C) fiber composites are a new class of materials that are used in various industries due to their exceptional chemical, thermal and electrical conductivities properties. In this study, carbon-carbon fiber composites were manufactured where polyacrylonitrile (PAN) powder was dissolved in dimethylformamide (DMF) solution, and carbon fibers with desired concentrations (20�80�wt%) were immersed into this solution as reinforcement through evaporation and solidification. The PAN-fiber systems were then stabilized at 250�270�C for 120�min in the air and subsequently carbonized at 650, 750, and 850�C for 60�min in the presence of argon gas to obtain the desired C-C fiber composites. Thermogravimetric analysis (TGA) results showed that the carbonized samples had a small weight loss of 2.5%, while actual and oxidized samples had more weight loss. Moreover, the carbonized sample surface was more hydrophobic compared to other samples due to the carbon presence and surface texture changes. Fourier-Transform Infrared (FTIR) spectroscopy peaks showed the presence of different functional groups of PAN before oxidation and carbonization, but those peaks disappeared after oxidation and carbonization. The developed carbon-carbon composite passed the UL94 vertical flame retardancy testing with a V-0 rating. Surface smoothness, proper matrix and reinforcements bonding were confirmed by scanning electron microscopy (SEM) results and the manufactured composite properties changes were validated by the confocal microscopy images. The carbon-carbon fiber composite achieved an electrical conductivity value up to 4.75 � 103�S/m after the carbonization process. The excellent thermal, chemical, and electrical properties of these composites can be useful for numerous industrial applications in different extreme environments. � The Author(s) 2024.
  • Item
    PAN-based fiber-reinforced carbon-carbon composites for improved fire retardancy and thermal and electrical conductivities for harsh environments
    (SAGE Publications Ltd, 2024) Murali, T.K.S.; Murad, Md. Shafinur; Bakir, M.; Asmatulu, Ramazan
    Carbon-carbon (C-C) fiber composites are a new class of materials that are used in various industries due to their exceptional chemical, thermal and electrical conductivities properties. In this study, carbon-carbon fiber composites were manufactured where polyacrylonitrile (PAN) powder was dissolved in dimethylformamide (DMF) solution, and carbon fibers with desired concentrations (20�80�wt%) were immersed into this solution as reinforcement through evaporation and solidification. The PAN-fiber systems were then stabilized at 250�270�C for 120�min in the air and subsequently carbonized at 650, 750, and 850�C for 60�min in the presence of argon gas to obtain the desired C-C fiber composites. Thermogravimetric analysis (TGA) results showed that the carbonized samples had a small weight loss of 2.5%, while actual and oxidized samples had more weight loss. Moreover, the carbonized sample surface was more hydrophobic compared to other samples due to the carbon presence and surface texture changes. Fourier-Transform Infrared (FTIR) spectroscopy peaks showed the presence of different functional groups of PAN before oxidation and carbonization, but those peaks disappeared after oxidation and carbonization. The developed carbon-carbon composite passed the UL94 vertical flame retardancy testing with a V-0 rating. Surface smoothness, proper matrix and reinforcements bonding were confirmed by scanning electron microscopy (SEM) results and the manufactured composite properties changes were validated by the confocal microscopy images. The carbon-carbon fiber composite achieved an electrical conductivity value up to 4.75 � 103�S/m after the carbonization process. The excellent thermal, chemical, and electrical properties of these composites can be useful for numerous industrial applications in different extreme environments. � The Author(s) 2024.
  • Item
    High-Performance Ammonia Electrosynthesis from Nitrate in a NaOH?KOH?H2O Ternary Electrolyte
    (John Wiley and Sons Inc, 2024) Shahid, Usman; Kwon, Yongjun; Yuan, Yuan; Gu, Shuang; Shao, Minhua
    A glut of dinitrogen-derived ammonia (NH3) over the past century has resulted in a heavily imbalanced nitrogen cycle and consequently, the large-scale accumulation of reactive nitrogen such as nitrates in our ecosystems has led to detrimental environmental issues. Electrocatalytic upcycling of waste nitrogen back into NH3 holds promise in mitigating these environmental impacts and reducing reliance on the energy-intensive Haber�Bosch process. Herein, we report a high-performance electrolyzer using an ultrahigh alkalinity electrolyte, NaOH?KOH?H2O, for low-cost NH3 electrosynthesis. At 3,000 mA/cm2, the device with a Fe?Cu?Ni ternary catalyst achieves an unprecedented faradaic efficiency (FE) of 92.5�1.5 % under a low cell voltage of 3.83 V; whereas at 1,000 mA/cm2, an FE of 96.5�4.8 % under a cell voltage of only 2.40 V was achieved. Techno-economic analysis revealed that our device cuts the levelized cost of ammonia electrosynthesis by ~40 % ($30.68 for Fe?Cu?Ni vs. $48.53 for Ni foam per kmol-NH3). The NaOH?KOH?H2O electrolyte together with the Fe?Cu?Ni ternary catalyst can enable the high-throughput nitrate-to-ammonia applications for affordable and scalable real-world wastewater treatments. � 2024 The Authors. Angewandte Chemie International Edition published by Wiley-VCH GmbH.
  • Item
    Accurate and robust predictions of pool boiling heat transfer with micro-structured surfaces using probabilistic machine learning models
    (Elsevier Ltd, 2024) Mehdi, Sadaf; Borumand, Mohammad; Hwang, Gisuk
    The accurate and reliable prediction of enhanced heat transfer performance of micro-structured surfaces is crucial to optimally design and operate pool boiling systems. However, the existing empirical models predict the enhanced pool boiling heat transfer with very large errors up to ±81 % even using the experimental data from the same study, mainly due to the complex nature of the pool boiling processes. More importantly, the existing models predict only limited coolant types, surface geometries, and operating conditions. To overcome these challenges, this study examines three deterministic and two probabilistic machine learning (ML) models for accurate and reliable enhanced pool boiling prediction, while using carefully selected key six dimensionless numbers. The models were trained and tested using 1,241 data from 20 experimental studies with 80/20 % of train/test ratio, and the pre-trained models were tested for additional 519 data from 6 studies to evaluate the models reliability. The predicted mean absolute percentage error (MAPE) shows that Bayesian, deep, and 1-D convolutional neural network (1D-CNN) models outperform the random forest and natural gradient (NG) boost models due to their extended hidden layers. The machine learning models improve the MAPE by up to 30 % compared to the existing correlations. Furthermore, a parameter sensitivity analysis is performed using explainable artificial intelligence showing that the boiling Reynolds number is the most critical parameter followed by the kinetic Reynolds and Bond numbers. The probabilistic ML models can also provide the uncertainties to improve prediction reliability compared to the deterministic ones. © 2024 Elsevier Ltd