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Open Access Publications from the University of California

Scholarly Works (3 results)

  • Article
  • Peer Reviewed
UC Berkeley Previously Published Works (2023)
Low carbon fuel and waste management policies at the federal and state levels have catalyzed the construction of Californias wet anaerobic digestion (AD) facilities. Wet ADs can digest food waste and dairy manure to produce compressed natural gas (CNG) for natural gas vehicles or electricity for electric vehicles (EVs). Carbon capture and sequestration (CCS) of CO2 generated from AD reduces the fuel carbon intensity by carbon removal in addition to avoided methane emissions. Using a combined lifecycle and techno-economic analysis, we determine the most cost-effective design under current and forthcoming federal and state low carbon fuel policies. Under many scenarios, designs that convert biogas to electricity for EVs (Biogas to EV) are favored; however, CCS is only cost-effective in these systems with policy incentives that exceed $200/tonne of CO2 captured. Adding CCS to CNG-producing systems (Biogas to CNG) only requires a single unit operation to prepare the CO2 for sequestration, with a sequestration cost of $34/tonne. When maximizing negative emissions is the goal, incentives are needed to either (1) fund CCS with Biogas to EV designs or (2) favor CNG over electricity production from wet AD facilities.
    Cover page: Policy Analysis of CO2 Capture and Sequestration with Anaerobic Digestion for Transportation Fuel Production.Creative Commons 'BY' version 4.0 license
    • Thesis
    • Peer Reviewed
    UC Berkeley Electronic Theses and Dissertations (2023)

    Discovering new materials underlies many innovations, and the traditional Edisonian ap- proach of brute-force screening through material databases is inefficient given the massive combinatorial search space of most material classes. Here, I target a particular class of materials that are porous and tunable, metal-organic frameworks (MOFs), where one could easily generate millions to billions of possible materials. I use various machine learning techniques such as Bayesian optimization trained on small quantities of data to identify promising candidates for methane storage as a proof-of-concept and air separations as a new material design problem. In both these studies, I show that Bayesian optimization only requires a small fraction (roughly 0.1%) of the dataset to identify candidate materials. In addition, the underlying surrogate model in Bayesian optimization, Gaussian processes, can be constructed to predict using multiple fidelities of information, thus ameliorating model inaccuracies due to a small quantity of data. Machine learning can also be used to directly learn energies and forces, which can be used to understand adsorption mechanisms in complex materials. I use this to identify new binding mechanisms of CO2 in known amine-appended MOFs and show that the same models can be used to propose new amine candidates suitable for direct air capture or improved carbon capture capacities. These two flavors of machine learning show how the materials discovery process can be accelerated dramatically by circumventing the poor computational scaling of most ab initio methods.

      • Article
      • Peer Reviewed
      UC Berkeley Previously Published Works (2022)
      Oxygen is a critical gas in numerous industries and is produced globally on a gigatonne scale, primarily through energy-intensive cryogenic distillation of air. The realization of large-scale adsorption-based air separations could enable a significant reduction in associated worldwide energy consumption and would constitute an important component of broader efforts to combat climate change. Certain small-scale air separations are carried out using N2-selective adsorbents, although the low capacities, poor selectivities, and high regeneration energies associated with these materials limit the extent of their usage. In contrast, the realization of O2-selective adsorbents may facilitate more widespread adoption of adsorptive air separations, which could enable the decentralization of O2 production and utilization and advance new uses for O2. Here, we present a detailed evaluation of the potential of metal-organic frameworks (MOFs) to serve as O2-selective adsorbents for air separations. Drawing insights from biological and molecular systems that selectively bind O2, we survey the field of O2-selective MOFs, highlighting progress and identifying promising areas for future exploration. As a guide for further research, the importance of moving beyond the traditional evaluation of O2 adsorption enthalpy, ΔH, is emphasized, and the free energy of O2 adsorption, ΔG, is discussed as the key metric for understanding and predicting MOF performance under practical conditions. Based on a proof-of-concept assessment of O2 binding carried out for eight different MOFs using experimentally derived capacities and thermodynamic parameters, we identify two existing materials and one proposed framework with nearly optimal ΔG values for operation under user-defined conditions. While enhancements are still needed in other material properties, the insights from the assessments herein serve as a guide for future materials design and evaluation. Computational approaches based on density functional theory with periodic boundary conditions are also discussed as complementary to experimental efforts, and new predictions enable identification of additional promising MOF systems for investigation.
        Cover page: Metal-organic frameworks as O2-selective adsorbents for air separationsCreative Commons 'BY' version 4.0 license
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