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Assessing Composite Structure in Metal–Organic Framework-Polymer Mixed-Matrix Membranes
Metal-organic frameworks (MOFs) are renowned for their tunable structure, porosity, and internal chemistry, with demonstrated applications in molecular separations, storage, and conversion. While they are widely usable, the powdery characteristics of MOF materials can be limiting for large-scale processing and implementation in devices. Incorporating MOF particles into polymer supports affords engineering solutions to overcome these issues, yet the nature of the resulting composites is difficult to assess. Here, in this work, we present spectroscopic and calorimetric methods that we believe help establish a holistic physicochemical picture of the composite structure using a series of Zr MOFs with different pore sizes as a testbed. Power law decays are observed in X-ray scattering profiles in low q-space ranging between 2.4 and 3.3, which we interpret as changes in scattering due to polymer infiltrating MOF particles. This interpretation is supported by solid-state nuclear magnetic resonance spectroscopy and differential scanning calorimetry measurements that identify populations of the MOF-associated polymer. Additionally, positron annihilation lifetime spectroscopy measurements collected on a series of composites with different MOF-polymer ratios show multiple decay constants, each correlated to a different free volume elements. In combination with the spectroscopic, calorimetric, and scattering results, we utilize the trends in decay constants as a function of polymer mass fraction to hypothesize a polymer infiltration mechanism whereby large pores are preferentially filled, followed by small pores and, later still, interstitial spaces between particles. Even with vigorous investigation of polymer, MOF, and interface characteristics, the complex and heterogeneous nature of the composites makes absolute structural assertions difficult. We envision that the approaches demonstrated here will be a useful foundation to assess and ultimately guide the design of future MOF-polymer composites.
Perovskites with a Twist: Strong In[superscript 1+] Off-Centering in the Mixed-Valent CsInX[subscript 3] (X = Cl, Br)
Abstract not provided
Effects of Chlorine Mixing on Optoelectronics, Ion Migration, and Gamma-Ray Detection in Bromide Perovskites
Not Available
Molecular Design Strategies toward Improvement of Charge Injection and Ionic Conduction in Organic Mixed Ionic–Electronic Conductors for Organic Electrochemical Transistors
Not Available
Biodiesel Ethers: Fatty Acid-Derived Alkyl Ether Fuels as Improved Bioblendstocks for Mixing-Controlled Compression Ignition Engines
In the last 20 years, biodiesel consumption in the United States has swiftly increased to ~2 billion gallons per year as a renewable supplement to fossil fuel. However, further expansion of biodiesel use is currently limited in part by poor cold weather performance, which prevents year-round blending and necessitates blend walls ≤5% v/v. In order to provide a diesel fuel blendstock with improved cold weather performance (cloud point, pour point, and cold filter plug point), while at the same time maintaining other required fuel performance specifications, several biodiesel redox analogues were synthesized and tested. The best performing candidate fuels from this class showed improvement in the derived cetane number (29.3% shorter ignition delay), lower heating value (+4.7 MJ/kg), relative sooting tendency (–7.4 YSI/MJ), and cloud point (15 °C lower) when compared to a B100 biodiesel composed of an identical fatty acid profile. It was observed as a general trend that the reduced form of biodiesel, fatty alkyl ethers (FAEs), shows performance improvements in all fuel property metrics. The suite of improved properties provided by FAEs gives biodiesel producers the opportunity to diversify their portfolio of products derived from lipid and alcohol feedstocks to include long-chain alkyl ethers, a biodiesel alternative with particular applicability for winter weather conditions across the US.
Aerosol Physicochemical Mixing State and Cloud Nucleation Potential during Tracking Aerosol Convection Interactions Experiment (TRACER) Campaign
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Matching Analysis of Mixed Matrix Membranes for Organic Solvent Reverse Osmosis
Not provided.
Spatial Moment Analysis of Convective Mixing in Three-Dimensional Porous Media Using X-ray CT Images
Not Available
Mixed-Integer Linear Programming Formulation with Embedded Machine Learning Surrogates for the Design of Chemical Process Families
In previous work, we introduced process family design. The main idea is to design a platform of common elements, and, allowing us to capture additional cost savings, simultaneously design a family of processes, and reducing both engineering and deployment timelines. We formulate this as an optimization problem, specifically a nonlinear generalized disjunctive program (GDP). We have proposed two approaches for reformulating and solving this problem: one based on full-discretization of the design space and one that uses Machine Learning (ML) surrogates to replace the nonlinear process models. Using ML surrogates to predict required system costs and performance indicators allows us to reformulate the nonlinearities in the GDP generate an efficient MILP formulation. In this work, we apply the ML surrogate approach to two case studies. One case study involves designing a family of carbon capture systems to cover a set of different flue gas flow rates and inlet CO 2 concentrations, where we consider the absorber and stripper as common unit module types. The second case study focuses on a water-desalination process, where we design a family of these processes for a variety of salt concentrations and flow rates. In both of these case studies, we demonstrate a scalable optimization approach that enables the design of multiple processes simultaneously, reducing the time-to-market and overall costs by maximizing the cost savings due to both economies of scale and economies of numbers.
High-Voltage, Intermediate-Temperature, Fe- and Al-Mixed Metal Halide Molten Salt for Molten Sodium Battery Energy Storage
An inorganic Fe and Al halide-based, low-temperature molten salt catholyte is described, which, when paired with a molten sodium anode, has high operating potentials rivaling those of Li ion batteries. The newly developed catholyte consists of metal halides FeCl 3 /FeCl 2 –AlCl 3 –NaCl and is intended to cycle between Fe 3+ /Fe 2+ redox couples in the molten salt. The multicomponent molten salt was initially evaluated for phase behavior and basic electrochemical behavior before full battery testing. The assembled battery, utilizing a 20:35:45 (FeCl 3 :AlCl 3 :NaCl) composition, with a 50.83 Ah/kg theoretical gravimetric capacity and a specific energy of 176.95 Wh/kg, was cycled at variable depths of discharge (DoD) and current densities to determine its cycling efficiencies and limitations. In conclusion, preliminary cycling tests showed two operational potential regimes, with higher potential, 3.91 V (vs Na/Na + ), at low DoD and lower potential, 3.39 V (vs Na/Na + ), at high DoD with excellent energy efficiencies and cycling behavior under both regimes.
Design and Precursor-based Solid-State Synthesis of Mixed-Linker Zr-MIL-140A
Abstract not provided