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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,693 records · Page 94
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Membrane Adsorbents Comprising Self-Assembled Inorganic Nanocages (SINCs) for Super-fast Direct Air Capture Enabled by Passive Cooling

The objectives of the proposed project were to develop highly porous membrane adsorbents comprising CO 2 -philic polymers and self-assembled inorganic nanocages (SINCs) for rapid temperature swing adsorption using electricity-free solar heating and radiative cooling, enabling an economically viable approach for direct air capture (DAC). Our core technical activities combine three key innovations. (1) Highly porous flat-sheet membrane adsorbents contain CO 2 -philic amines that can be easily produced using a phase inversion method. (2) CO 2 -philic SINCs can be easily dispersed in the polymers with great stability (compared with the metal-organic frameworks or MOFs). (3) The adsorption and desorption are integrated with solar heating and radiative cooling for rapid continuous operation, in contrast to traditional long-cycle separate operation. The membrane adsorbents containing amines, polymers, and SINCs were produced using a one-step industrial process. The porous membranes coupled with porous SINCs offer low resistance for gas flow and fast CO 2 sorption/desorption cycles, while the incorporation of the additional amine groups provides high CO 2 sorption capacity. The key achievements are summarized below. (1) Membrane adsorbents with high PEI loading (>40%), high porosity of >80%, and low gasflow resistance were prepared in one step using commercially available, low-cost materials. (2) Membrane adsorbents based on Solupor and PEI show CO 2 sorption capacity of >1.5 mmol/g using air containing 400 ppm at a relative humidity of 15%. (3) Effect of the adsorbent compositions (such as PEI type, PEI content, SINC content, porosity) on the CO 2 sorption was systematically investigated. (4) Effect of the processing conditions (such as CO 2 content, temperature, and relative humidity) on the CO 2 sorption was systematically investigated; (5) The stability of the membrane adsorption against many cycles of sorption and desorption was studied. The higher molecular weight of PEI (PEI25k) shows better stability than PEI800. (6) Advanced materials with radiative cooling were developed, which can decrease the temperature by 5-7 °C compared to the ambient temperature. (7) Preliminary techno-economic analysis shows that our process may achieve a capture cost of $1,343/tonne CO 2 with a total OPEX cost of $1,112/tonne CO 2 . The adsorbent replacement cost accounted for 52% of the total OPEX cost. Membrane adsorbents with lower costs and longer operation life can significantly decrease the cost. The proposed project directly addresses the requirement of DE-FOA-0002188, i.e., novel materials with CO 2 adsorption capacity for direct air capture with integrated solar heating and radiative cooling to reduce the cost of the DAC. Our future work will focus on the development of low-cost adsorbents that can be stable at the sorption and desorption conditions for long term.

14 SOLAR ENERGY↗
DOE OSTI2023

Bayesian Cloud Property Retrievals from ARM Active and Passive Measurements

The optimum use of the continuous measurements of thermodynamics, radiation, aerosols, clouds and precipitation from the DOE Atmospheric Radiation Measurement (ARM) program is key to achieve the DOE Atmospheric System Research (ASR)’s objectives. One of the key mission requirements is to retrieve cloud and precipitation properties, as well as vertical motion parameters, along the vertical cross- section defined by the profiling active sensors. Such retrievals are challenging to perform continuously in the entire spectrum of cloud and precipitation conditions due to the large natural microphysical and dynamical variability, the often-limited information content in the measurements, and the lack of proper characterization of measurement quality and uncertainty. Today, the acquisition of new remote and in-situ sensors by the ARM program creates opportunities to address the microphysical retrieval problem by exploiting new, more robust retrieval techniques and integrating various scattered advancements in both sensor techniques and retrieval algorithms. During this project, we constructed a robust Bayesian Markov chain Monte Carlo (MCMC) cloud property retrieval algorithm that includes a state of the art radar forward model. Our MCMC-based retrieval produces both the best estimate of height-resolved cloud and precipitation properties in the radar profile, as well as an estimate of the in-cloud vertical motion and turbulence. In addition, the MCMC algorithm automatically produces robust and flexible estimates of retrieval uncertainty. We tested the algorithm on several synthetic cloud profiles obtained from large eddy simulation (LES) models with bin-resolved microphysics.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

233 U oxide Measurement Campaign Data: Passive and Active Neutron Multiplicity Measurements of Uranium Oxide Samples at Oak Ridge National Laboratory

During FY2023, three measurement campaigns were conducted at Oak Ridge National Laboratory. The goal was to quantify the neutron signatures of samples of uranium oxide containing uranium 233 and uranium-235. This report presents the neutron multiplicity data obtained using the large volume active well coincidence counter (LV-AWCC).

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗