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NASA NTRS2006

Fast Erase Method and Apparatus For Digital Media

A non-contact fast erase method for erasing information stored on a magnetic or optical media. The magnetic media element includes a magnetic surface affixed to a toroidal conductor and stores information in a magnetic polarization pattern. The fast erase method includes applying an alternating current to a planar inductive element positioned near the toroidal conductor, inducing an alternating current in the toroidal conductor, and heating the magnetic surface to a temperature that exceeds the Curie-point so that information stored on the magnetic media element is permanently erased. The optical disc element stores information in a plurality of locations being defined by pits and lands in a toroidal conductive layer. The fast erase method includes similarly inducing a plurality of currents in the optical media element conductive layer and melting a predetermined portion of the conductive layer so that the information stored on the optical medium is destroyed.

Oakely, Ernest C.↗

NASA NTRS2006

Porous Media Approach for Modeling Closed Cell Foam

In order to minimize boil off of the liquid oxygen and liquid hydrogen and to prevent the formation of ice on its exterior surface, the Space Shuttle External Tank (ET) is insulated using various low-density, closed-cell polymeric foams. Improved analysis methods for these foam materials are needed to predict the foam structural response and to help identify the foam fracture behavior in order to help minimize foam shedding occurrences. This presentation describes a continuum based approach to modeling the foam thermo-mechanical behavior that accounts for the cellular nature of the material and explicitly addresses the effect of the internal cell gas pressure. A porous media approach is implemented in a finite element frame work to model the mechanical behavior of the closed cell foam. The ABAQUS general purpose finite element program is used to simulate the continuum behavior of the foam. The soil mechanics element is implemented to account for the cell internal pressure and its effect on the stress and strain fields. The pressure variation inside the closed cells is calculated using the ideal gas laws. The soil mechanics element is compatible with an orthotropic materials model to capture the different behavior between the rise and in-plane directions of the foam. The porous media approach is applied to model the foam thermal strain and calculate the foam effective coefficient of thermal expansion. The calculated foam coefficients of thermal expansion were able to simulate the measured thermal strain during heat up from cryogenic temperature to room temperature in vacuum. The porous media approach was applied to an insulated substrate with one inch foam and compared to a simple elastic solution without pore pressure. The porous media approach is also applied to model the foam mechanical behavior during subscale laboratory experiments. In this test, a foam layer sprayed on a metal substrate is subjected to a temperature variation while the metal substrate is stretched to simulate the structural response of the tank during operation. The thermal expansion mismatch between the foam and the metal substrate and the thermal gradient in the foam layer causes high tensile stresses near the metal/foam interface that can lead to delamination.

Ghosn, Louis J.↗

NASA NTRS2011

Numerically Exact Computer Simulations of Light Scattering by Densely Packed, Random Particulate Media

Direct computer simulations of electromagnetic scattering by discrete random media have become an active area of research. In this progress review, we summarize and analyze our main results obtained by means of numerically exact computer solutions of the macroscopic Maxwell equations. We consider finite scattering volumes with size parameters in the range, composed of varying numbers of randomly distributed particles with different refractive indices. The main objective of our analysis is to examine whether all backscattering effects predicted by the low-density theory of coherent backscattering (CB) also take place in the case of densely packed media. Based on our extensive numerical data we arrive at the following conclusions: (i) all backscattering effects predicted by the asymptotic theory of CB can also take place in the case of densely packed media; (ii) in the case of very large particle packing density, scattering characteristics of discrete random media can exhibit behavior not predicted by the low-density theories of CB and radiative transfer; (iii) increasing the absorptivity of the constituent particles can either enhance or suppress typical manifestations of CB depending on the particle packing density and the real part of the refractive index. Our numerical data strongly suggest that spectacular backscattering effects identified in laboratory experiments and observed for a class of high-albedo Solar System objects are caused by CB.

Dlugach, Janna M.↗

NASA NTRS2016

First-Principles Modeling Of Electromagnetic Scattering By Discrete and Discretely Heterogeneous Random Media

A discrete random medium is an object in the form of a finite volume of a vacuum or a homogeneous material medium filled with quasi-randomly and quasi-uniformly distributed discrete macroscopic impurities called small particles. Such objects are ubiquitous in natural and artificial environments. They are often characterized by analyzing theoretically the results of laboratory, in situ, or remote-sensing measurements of the scattering of light and other electromagnetic radiation. Electromagnetic scattering and absorption by particles can also affect the energy budget of a discrete random medium and hence various ambient physical and chemical processes. In either case electromagnetic scattering must be modeled in terms of appropriate optical observables, i.e., quadratic or bilinear forms in the field that quantify the reading of a relevant optical instrument or the electromagnetic energy budget. It is generally believed that time-harmonic Maxwell's equations can accurately describe elastic electromagnetic scattering by macroscopic particulate media that change in time much more slowly than the incident electromagnetic field. However, direct solutions of these equations for discrete random media had been impracticable until quite recently. This has led to a widespread use of various phenomenological approaches in situations when their very applicability can be questioned. Recently, however, a new branch of physical optics has emerged wherein electromagnetic scattering by discrete and discretely heterogeneous random media is modeled directly by using analytical or numerically exact computer solutions of the Maxwell equations. Therefore, the main objective of this Report is to formulate the general theoretical framework of electromagnetic scattering by discrete random media rooted in the Maxwell- Lorentz electromagnetics and discuss its immediate analytical and numerical consequences. Starting from the microscopic Maxwell-Lorentz equations, we trace the development of the first principles formalism enabling accurate calculations of monochromatic and quasi-monochromatic scattering by static and randomly varying multiparticle groups. We illustrate how this general framework can be coupled with state-of-the-art computer solvers of the Maxwell equations and applied to direct modeling of electromagnetic scattering by representative random multi-particle groups with arbitrary packing densities. This first-principles modeling yields general physical insights unavailable with phenomenological approaches. We discuss how the first-order-scattering approximation, the radiative transfer theory, and the theory of weak localization of electromagnetic waves can be derived as immediate corollaries of the Maxwell equations for very specific and well-defined kinds of particulate medium. These recent developments confirm the mesoscopic origin of the radiative transfer, weak localization, and effective-medium regimes and help evaluate the numerical accuracy of widely used approximate modeling methodologies.

Michael I Mishchenko↗

NASA NTRSDate not supplied

Pressure Gradient and Gas Hold-Up in Two-Phase Flows through Porous Media in Microgravity

Single and two-phase flow through porous media are encountered on ground and in space in numerous applications related to life support systems, fuel cells, chemical/materials processing and transporting nutrients to plants. These systems operate differently in the microgravity environment encountered in space travel because the density differences no longer cause the phases to separate or “drain” under the body force of gravity. In the absence of gravity, the interfacial or capillary forces play a more significant role in determining the operational variables such as phase distribution, liquid holdup, and pressure drop especially when the liquid inertia and viscous forces are at minimum. Lower liquid flow rates in air-water two-phase flows represent the case for most of the space processes that depend on two-phase flows in porous media. The Packed Bed Reactor Experiment (PBRE) was developed and flown on the International Space Station (ISS) by NASA to extend a series of fundamental studies of gas-liquid flows through porous media. The goal of the this series of flight experiments (PBRE-1 and PBRE-2) is to better understand the hydrodynamics of two phase flows in porous media in flow regimes dominated by inertia and viscous effects and understanding the role of the interfacial forces and their role in determining the flow dynamics. The flight experiments were preceded by a series of low gravity experiments performed on the low gravity aircraft. These experiments led to the development of a semi-empirical two-phase pressure drop and flow pattern transition in support of a number of reactor beds planned for water reclamation processes onboard of the international space station. The PBRE flight experiment was designed to deliver a wide range of tightly controlled gas (nitrogen) and liquid (water) flows to one of two interchangeable test sections differing only in the type of internal packing material. In this presentation, we describe the PBRE-2 experiment and present preliminary low gravity data on pressure drop and gas hold-up and compare with semi-empirical model pressure drop predictions.

Brian Motil↗

DOE OSTI2021

Prediction of social media postings as trusted news or as types of suspicious news

Disclosed are systems, techniques, and non-transitory storage media for predicting social media postings as being trusted news or a type of suspicious news. The systems, techniques, and non-transitory storage media are based on unique neural network architectures that learn from a combined representation including at least representations of social media posting content and a vector representation of communications among connected users.

Volkova, Svitlana↗

DOE OSTI2021

Lattice Boltzmann Modeling of Drying of Porous Media Considering Contact Angle Hysteresis

Drying of porous media is governed by a combination of evaporation and movement of the liquid phase within the porous structure. Contact angle hysteresis induced by surface roughness is shown to influence multi-phase flows, such as contact line motion of droplet, phase distribution during drainage and coffee ring formed after droplet drying in constant contact radius mode. However, the influence of contact angle hysteresis on liquid drying in porous media is still an unanswered question. Lattice Boltzmann model (LBM) is an advanced numerical approach increasingly used to study phase change problems including drying. In this paper, based on a geometric formulation scheme to prescribe contact angle, we implement a contact angle hysteresis model within the framework of a two-phase pseudopotential LBM. The capability and accuracy of prescribing and automatically measuring contact angles over a large range are tested and validated by simulating droplets sitting on flat and curved surfaces. Afterward, the proposed contact angle hysteresis model is validated by modeling droplet drying on flat and curved surfaces. Then, drying of two connected capillary tubes is studied, considering the influence of different contact angle hysteresis ranges on drying dynamics. Finally, the model is applied to study drying of a dual-porosity porous medium, where phase distribution and drying rate are compared with and without contact angle hysteresis. The proposed model is shown to be capable of dealing with different contact angle hysteresis ranges accurately and of capturing the physical mechanisms during drying in different porous media including flat and curved geometries.

36 MATERIALS SCIENCE↗

DOE OSTI2025

The rigorous upscaling of advection-dominated transport in heterogeneous porous media via the Method of Finite Averages

Systems involving advection-dominated transport through heterogeneous porous and fractured media are ubiquitous in subsurface engineering applications. However, upscaling such systems continues to challenge rigorous modeling efforts, particularly when advection is stronger than diffusion at fine spatial scales (i.e., when the Péclet number is greater than one at length scales that characterize a system’s unit-cells, representative elementary volumes, or averaging regions). Here, in this work, we propose and validate a strategy for extending the Method of Finite Averages (MoFA), a rigorous upscaling methodology for heterogeneous porous media, to upscale transport systems experiencing stronger advection than diffusion at fine scales (i.e., fine-scale Péclet numbers greater than one). We detail the strategy, the physical conditions under which it can be applied while retaining a priori modeling error guarantees, and implement the strategy to obtain a MoFA model for advective-diffusive transport that accommodates advective physics at fine spatial scales. We then perform two numerical experiments considering systems with system-scale Péclet numbers of 300 and 1000 — which correspond to fine-scale Péclet numbers of 30 and 100, respectively — to verify that the error guarantees are met under the strategy. After, we conduct a numerical study to demonstrate the strategy’s advantages over the original MoFA methodology. The results suggest that rigorously-upscaled transport models for heterogeneous porous media experiencing advective physics at finer spatial scales can be derived through MoFA and resolved orders of magnitude faster than their pore-scale counterparts. The results also suggest that the presented strategy is limited to modeling shallow concentration gradients when there are large differences between the time scales related to advection and a system’s temporally-varying boundary conditions. This limitation hinders the strategy’s practicality in modeling more advective systems, and as such, opportunity exists for developing additional strategies that accommodate rapidly-varying boundary conditions — and consequentially, steeper concentration gradients — while modeling advective systems with MoFA.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Prediction of local concentration fields in porous media with chemical reaction using a multi scale convolutional neural network

The study of solute transport in porous media is of interest in many chemical engineering systems. Some example applications include packed bed catalytic reactors, filtration devices, and batteries. The pore scale modeling of these systems is time consuming and may require large computing resources, for this reason computational fluid dynamics (CFD) simulations are not practical if a large number of simulations is required, like in multiscale modeling, where a model at a large scale calls for pore scale simulations. It has been shown that neural networks can be trained with a dataset of flow simulations and then predict fields orders of magnitude faster, and with less computational resources, in new domains. However, it is crucial to provide the neural network with an effective description of the domain and the undergoing operating conditions to be able to train models that generalize accurately in unseen samples. Therefore, research is needed to employ neural networks in new complex systems. The appropriate training of a network for predicting coupled flow and solute transport processes is an outstanding problem due to the complex interplay between geometry and operating conditions. In this work, we train a multi scale convolutional neural network (MSNet) with a diverse dataset of simulations of transport and chemical reaction in porous media to predict the local concentration fields in images of porous media. Our dataset contains a wide diversity of sphere pack arrangements under different operating conditions (Péclet and Reynolds numbers). Further, we train a robust model by employing different input descriptors that represent the medium and the different operating conditions of each system. Our trained model is able to provide nearly instantaneous predictions, compared to around twenty hours of the CFD workflow, with less than 3.5% error on new geometries and transport conditions. Thus the model could be easily integrated in a multiscale workflow where fast response is needed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

The Co-Transport of PFAS and Cr(VI) in porous media

PFAS and Cr are present at some sites as co-contaminants. Here, the objective of this research was to investigate the co-transport behavior of per- and polyfluoroalkyl substances (PFAS) and hexavalent chromium (Cr(VI)) in porous media. Miscible-displacement experiments were conducted using two soils and an aquifer sediment with different geochemical properties. Perfluorooctane sulfonic acid (PFOS) and perfluorooctanoic acid (PFOA) were employed as model PFAS. The retardation of PFOS was decreased in the presence of Cr(VI). Conversely, the transport and retardation of PFOA was not affected by the presence of Cr(VI). The reduction of PFOS retardation caused by Cr(VI) is likely due to sorption competition for both organic-carbon and inorganic (metal-oxides and clay minerals) domains. The relative contributions of the three soil constituents to PFOS sorption and the potential for competition between PFOS and Cr(VI) is a function of the geochemical composition of the porous media (i.e., organic carbon, metal-oxides and clay minerals). The PFAS had minimal impact on the retention and transport of Cr(VI). To our knowledge, the results presented herein represent the first reported data for PFOS and Cr(VI) co-transport in porous media. The results of this study indicate that the presence of Cr(VI) has the potential to increase the migration potential of PFOS in soil and groundwater, which should be considered when characterizing electroplating facilities, leather tanning facilities, and other co-contaminated sites.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

A gradient-based deep neural network model for simulating multiphase flow in porous media

We report simulation of multiphase flow in porous media is crucial for the effective management of subsurface energy and environment-related activities. The numerical simulators used for modeling such processes rely on spatial and temporal discretization of the governing mass and energy balance partial-differential equations (PDEs) into algebraic systems via finite-difference/volume/element methods. These simulators usually require dedicated software development and maintenance, and suffer low efficiency from a runtime and memory standpoint for problems with multi-scale heterogeneity, coupled-physics processes or fluids with complex phase behavior. Therefore, developing cost-effective, data-driven models can become a practical choice, and in this work, we choose deep learning approaches as they can handle high dimensional data and accurately predict state variables with strong nonlinearity. In this paper, we describe a gradient-based deep neural network (GDNN) constrained by the physics related to multiphase flow in porous media. We tackle the nonlinearity of flow in porous media induced by rock heterogeneity, fluid properties, and fluid-rock interactions by decomposing the nonlinear PDEs into a dictionary of elementary differential operators. We use a combination of operators to handle rock spatial heterogeneity and fluid flow by advection. Since the augmented differential operators are inherently related to the physics of fluid flow, we treat them as first principles prior knowledge to regularize the GDNN training. We use the example of pressure management at geologic CO 2 storage sites, where CO 2 is injected in saline aquifers and brine is produced, and apply GDNN to construct a predictive model that is trained with physics-based simulation data and emulates the physics process. We demonstrate that GDNN can effectively predict the nonlinear patterns of subsurface responses, including the temporal and spatial evolution of the pressure and saturation plumes. We also successfully extend the GDNN to convolutional neural network (CNN), namely gradient-based CNN (GCNN), and validate its capability to improve the prediction accuracy. GDNN has great potential to tackle challenging problems that are governed by highly nonlinear physics and enable the development of data-driven models with higher fidelity.

42 ENGINEERING↗

DOE OSTI2020

Electrochemical Synthesis and Investigation of Stoichiometric, Phase-Pure CoSb 2 O 6 and MnSb 2 O 6 Electrodes for the Oxygen Evolution Reaction in Acidic Media

The electrochemical oxidation of water to oxygen gas is the primary counter reaction to the formation of hydrogen gas via water splitting. In acidic media, the only well-established and active oxygen evolution catalysts are expensive noble metal oxides such as IrO x and RuO x , necessitating the development of practical oxygen evolution catalysts that are stable in acidic media. In this study, we prepared stoichiometric, phase-pure CoSb 2 O 6 and MnSb 2 O 6 electrodes using electrochemical synthesis and investigated their ability to oxidize water in 0.5 M H 2 SO 4 (pH 0.3). In addition, their stabilities during the oxygen evolution reaction (OER) were carefully examined by comparing their morphologies, crystallinities, compositions, and surface compositions before and after the OER. The chlorine evolution reaction on CoSb 2 O 6 and MnSb 2 O 6 in acidic media was also examined so that their performances can be compared with previously reported non-stoichiometric CoSb 2 O 6 and MnSb 2 O 6 electrodes. The electrochemical properties and stabilities of stoichiometric, phase-pure CoSb 2 O 6 and MnSb 2 O 6 reported in this study can provide useful insights into the development and understanding of acid-stable, non-noble metal oxide-based OER catalysts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Exploratory investigation on the use of low-cost alternative media for ultrafine grinding of coal

In this study, the energy efficiency of three alternative grinding media including, coarse silica beads, fine silica beads, and granular sand, were evaluated and compared against standard stainless steel. Stirred mill test results show that both silica beads and granular sand were more efficient than stainless steel in producing ultrafine particles following the order of coarse silica beads>granular sand>fine silica beads>stainless steel. The finest product size corresponding to a P 80 value of 2.33 μm was generated by coarse silica beads with a nominal size of 420×1190 μm, which also consumed the least amount of grinding energy. The specific energy input was reduced by almost 50% as opposed to that of stainless steel, while the operating work index was reduced by 87%. Overall, the test data indicate that grinding media density, media particle size and shape all play a role in determining the optimized grinding behavior of the coal material.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2022

Neural network–based pore flow field prediction in porous media using super resolution

Direct pore-scale simulations of fluid flow through porous media are computationally expensive to perform for realistic systems. Previous works have demonstrated using the geometry of the microstructure of porous media to predict the velocity fields therein based on neural networks. However, such trained neural networks do not perform well for unseen porous media with a large degree of heterogeneity. In this study we propose that incorporating a coarse velocity field in the input of neural networks is an effective way to improve the prediction performance. The coarse velocity field can be simulated with a low computational cost and provides global information to regularize the ill-posedness of the learning problem, which is usually caused by the use of local geometries due to the computational resource constraints. We show that incorporating the coarse-mesh velocity field significantly improves the prediction accuracy of the fine-mesh velocity field by comparison to the prediction that relies on geometric information alone, especially for the porous medium with a large interior vuggy pore space. We also show the flexibility of training the network in using coarse velocity fields with various resolutions. The results suggest that even using coarse velocity field with a very low resolution, the predictions are still enhanced and close to the ground truths. The feasibility of the method is further demonstrated by testing the trained network on real rocks. This study highlights the merits of incorporating a coarse-mesh velocity field into the input for neural networks, which provides global, physics-based information for the model, thereby improving the model's generalization capability.

42 ENGINEERING↗

DOE OSTI2020

Comparison of Social Media, Syndromic Surveillance, and Microbiologic Acute Respiratory Infection Data: Observational Study

Internet data can be used to improve infectious disease models. However, the representativeness and individual-level validity of internet-derived measures are largely unexplored as this requires ground truth data for study. This study sought to identify relationships between Web-based behaviors and/or conversation topics and health status using a ground truth, survey-based dataset. This study leveraged a unique dataset of self-reported surveys, microbiological laboratory tests, and social media data from the same individuals toward understanding the validity of individual-level constructs pertaining to influenza-like illness in social media data. Logistic regression models were used to identify illness in Twitter posts using user posting behaviors and topic model features extracted from users’ tweets. Of 396 original study participants, only 81 met the inclusion criteria for this study. Of these participants’ tweets, we identified only two instances that were related to health and occurred within 2 weeks (before or after) of a survey indicating symptoms. It was not possible to predict when participants reported symptoms using features derived from topic models (area under the curve [AUC]=0.51; P=.38), though it was possible using behavior features, albeit with a very small effect size (AUC=0.53; P≤.001). Individual symptoms were also generally not predictable either. The study sample and a random sample from Twitter are predictably different on held-out data (AUC=0.67; P≤.001), meaning that the content posted by people who participated in this study was predictably different from that posted by random Twitter users. Individuals in the random sample and the GoViral sample used Twitter with similar frequencies (similar @ mentions, number of tweets, and number of retweets; AUC=0.50; P=.19). To our knowledge, this is the first instance of an attempt to use a ground truth dataset to validate infectious disease observations in social media data. The lack of signal, the lack of predictability among behaviors or topics, and the demonstrated volunteer bias in the study population are important findings for the large and growing body of disease surveillance using internet-sourced data.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

Three-Dimensional Imaging and Quantification of Gas Storativity in Nanoporous Media via X-rays Computed Tomography

This study provides the engineering science underpinnings for improved characterization and quantification of the interplay of gases with kerogen and minerals in shale. Natural nanoporous media such as shale (i.e., mudstone) often present with low permeability and dual porosity, making them difficult to characterize given the complex structural and chemical features across multiple scales. These structures give nanoporous solids a large surface area for gas to sorb. In oil and gas applications, full understanding of these media and their sorption characteristics are critical for evaluating gas reserves, flow, and storage for enhanced recovery and CO 2 sequestration potential. Other applications include CO 2 capture from industrial plants, hydrogen storage on sorbent surfaces, and heterogeneous catalysis in ammonia synthesis. Therefore, high-resolution experimental procedures are demanded to better understand the gas–solid behavior. In this study, CT imaging was applied on the sub-millimeter scale to shale samples (Eagle Ford and Wolfcamp) to improve quantitative agreement between CT-derived and pulse decay (mass balance) derived results. Improved CT imaging formulations are presented that better match mass balance results, highlighting the significance of gas sorption in complex nanoporous media. The proposed CT routine implemented on the Eagle Ford sample demonstrated a 17% error reduction (22% to 5%) when compared to the conventional CT procedure. These observations are consistent in the Wolfcamp sample, emphasizing the reliability of this technique for broader implementation of digital adsorption studies in nanoporous geomaterials.

42 ENGINEERING↗

NASA NTRS2001

The fungicidal and phytotoxic properties of benomyl and PPM in supplemented agar media supporting transgenic arabidopsis plants for a Space Shuttle flight experiment

Fungal contamination is a significant problem in the use of sucrose-enriched agar-based media for plant culture, especially in closed habitats such as the Space Shuttle. While a variety of fungicides are commercially available, not all are equal in their effectiveness in inhibiting fungal contamination. In addition, fungicide effectiveness must be weighed against its phytotoxicity and in this case, its influence on transgene expression. In a series of experiments designed to optimize media composition for a recent shuttle mission, the fungicide benomyl and the biocide "Plant Preservative Mixture" (PPM) were evaluated for effectiveness in controlling three common fungal contaminants, as well as their impact on the growth and development of arabidopsis seedlings. Benomyl proved to be an effective inhibitor of all three contaminants in concentrations as low as 2 ppm (parts per million) within the agar medium, and no evidence of phytotoxicity was observed until concentrations exceeded 20 ppm. The biocide mix PPM was effective as a fungicide only at concentrations that had deleterious effects on arabidopsis seedlings. As a result of these findings, a concentration of 3 ppm benomyl was used in the media for experiment PGIM-01 which flew on shuttle Columbia mission STS-93 in July 1999.

Non-NASA Center↗

NASA NTRSDate not supplied

Graphene-Based Filtration Media for Spacecraft Potable Water Systems: An Early Investigation

Graphene-based materials have allowed fundamental advances in fields such as energy storage, electronics development, material science, optics, medicine, and water processing due to their unique two-dimensional structure, mechanical robustness, large surface, and high conductivity. However, little to no effort has been devoted to exploiting and studying these materials to develop new water technologies suited for spacecraft applications. One such application is the potential use of graphene-based materials as filtration media for reclaimed water. Therefore, studying the adsorptive performance of these new materials becomes crucial in identifying the opportunity to replace/upgrade state-of-the-art filtration media currently used in space vehicles with water recovery capability; especially if consumable requirements can be lessened as a result of extended filtration capacity. This early Life-Support-Systems investigation pioneers in graphene research by testing a number of graphene-based materials in comparative adsorption and antimicrobial experiments where contaminant removal efficiency, maximum adsorption capacity, and bacterial reduction are probed. This preliminary investigation informs on the practicality of using graphene-based materials as filtration media and provides a discussion on the scaling-up and optimization of this prospective filtration technology for spacecraft potable water systems.

graphene↗