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At least 1,495 records · Page 83

Self-Propagating Frontal Polymerization in Water at Ambient Pressure

Advances in polymer chemistry have led to the development of monomers and initiation agents that enable propagating free-radical polymerization fronts to exist. These fronts are driven by the exothermicity of the polymerization reaction and the transport of heat from the polymerized product to the reactant monomer/solvent/initiator solution. The thermal energy transported to the reactant solution causes the initiator to decompose, yielding free radicals, which start the free radical polymerization process as discussed in recent reviews. The use of polymerization processes based on propagating fronts has numerous applications. Perhaps the most important of these is that it enables rapid curing of polymers without external heating since the polymerization process itself provides the high temperatures necessary to initiate and sustain polymerization. This process also enables more uniform curing of arbitrarily thick samples since it does not rely on heat transfer from an external source, which will necessarily cause the temperature history of the sample to vary with distance from the surface according to a diffusion-like process. Frontal polymerization also enables filling and sealing of structures having cavities of arbitrary shape without having to externally heat the structure. Water at atmospheric pressure is most convenient solvent to employ and the most important for practical applications (because of the cost and environmental issues associated with DMSO and other solvents). Nevertheless, to our knowledge, steady, self-propagating polymerization fronts have not been reported in water at atmospheric pressure. Currently, polymerization fronts require a high boiling point solvent (either water at high pressures or an alternative solvent such as dimethyl sulfoxide (DMSO) (boiling point 189 C at atmospheric pressure.) Early work on frontal polymerization, employed pressures up to 5000 atm in order to avoid boiling of the monomer/solvent/initiator solution. High boiling point solutions are needed because in order to produce a propagating front, a high front temperature is needed to produce sufficiently rapid decomposition of the free radical initiator and subsequent free radical polymerization and heat release at a rate faster than heat losses remove thermal energy from the system. (While the conduction heat loss rate increases linearly with temperature, the free radical initiator decomposition is a high activation energy process whose rate increases much more rapidly than linearly with temperature, thus as the temperature decreases, the ratio of heat loss to heat generation increases, eventually leading to extinction of the front if the temperature is too low.) In order to obtain atmospheric pressure frontal polymerization in water, it is necessary to identify a monomer/initiator combination that is water soluble and will not extinguish even when the peak temperature (T*) is less than 100 C. In this work acrylic acid (AA) was chosen as the monomer because is it one of the most reactive monomers and can polymerize readily at low temperatures even without initiators. Ammonium persulfate (AP) was chosen as the initiator because it decomposes readily at low temperatures, produces relatively few bubbles and is commercially available. The propagation rates and extinction conditions of the fronts are studied for a range of AA and AP concentrations. Small amounts of fumed silica powder (Cab-o-sil, Cabot Corporation) were added to the solutions to inhibit buoyancy induced convection in the solutions; future studies will investigate the effects of buoyant convection within the solutions.

Olten, Nesrin↗

Performance Prediction of Big Data Transfer Through Experimental Analysis and Machine Learning

Big data transfer in next-generation scientific applications is now commonly carried out over connections with guaranteed bandwidth provisioned in High-performance Networks (HPNs) through advance bandwidth reservation. To use HPN resources efficiently, provisioning agents need to carefully schedule data transfer requests and allocate appropriate bandwidths. Such reserved bandwidths, if not fully utilized by the requesting user, could be simply wasted or cause extra overhead and complexity in management due to exclusive access. This calls for the capability of performance prediction to reserve bandwidth resources that match actual needs. Towards this goal, we employ machine learning algorithms to predict big data transfer performance based on extensive performance measurements, which are collected over a span of several years from a large number of data transfer tests using different protocols and toolkits between various end sites on several real-life physical or emulated HPN testbeds. We first identify a comprehensive list of attributes involved in a typical big data transfer process, including end host system configurations, network connection properties, and control parameters of data transfer methods. We then conduct an in-depth exploratory analysis of their impacts on application-level throughput, which provides insights into big data transfer performance and motivates the use of machine learning. We also investigate the applicability of machine learning algorithms and derive their general performance bounds for performance prediction of big data transfer in HPNs. Experimental results show that, with appropriate data preprocessing, the proposed machine learning-based approach achieves 95% or higher prediction accuracy in up to 90% of the cases with very noisy real-life performance measurements.

Yun, Daqing↗

Binder systems and methods for tape casting lithium garnet electrolytes

Slurry compositions, tape casting binder systems and fabrication methods for the fabrication of lithium-garnet electrolyte scaffolds for use in solid state batteries and other devices are provided. Slurry compositions may be optimized mixtures of LLZO powder, a dispersant, a lithium salt, a wetting agent a binder, a plasticizer and at least one solvent. The optimized ceramic slurry compositions may include MgO as a sintering additive to improve density and ionic conductivity of the doped-LLZO sheets and produce a fine-grained microstructure. Sintering protocols for cast slurries of commercially available doped LLZO powders eliminate the requirement of mother-powder coverings or externally applied pressure. An environmentally friendly water-based system using methylcellulose as a binder is also provided producing green tape and final properties comparable to those obtained with organic solvent-based systems.

Jonson, Robert↗

Structure, Hydration, and Interactions of Native and Hydrophobically Modified Phytoglycogen Nanoparticles

Phytoglycogen is a highly branched polymer of glucose produced as soft, compact nanoparticles by sweet corn. Properties such as softness, porosity, and mechanical integrity, combined with nontoxicity and biodegradability, make phytoglycogen nanoparticles ideal for applications involving the human body, ranging from skin moisturizing and rejuvenation agents in personal care formulations to functional therapeutics in biomedicine. To further broaden the range of applications, phytoglycogen nanoparticles can be chemically modified with hydrophobic species such as octenyl succinic anhydride (OSA). In this study, we present a self-consistent model of the particle structure, water content, and degree of chemical modification of the particles, as well as the emergence of well-defined interparticle spacings in concentrated dispersions, based on small-angle neutron scattering (SANS) measurements of aqueous dispersions of native phytoglycogen nanoparticles and particles that were hydrophobically modified using octenyl succinic anhydride (OSA) in both its protiated (pOSA) and deuterated (dOSA) forms. Measurements on native particles with reduced polydispersity have allowed us to refine the particle morphology, which is well described by a hairy particle (core-chain) geometry with short chains decorating the surface of the particles. The isotopic variants of OSA-modified particles enhanced the scattering contrast for neutrons, revealing lightly modified hairy chains for small degrees of substitution (DS) of OSA, and a raspberry particle geometry for the largest DS value, where the OSA-modified hairy chains collapse to form small seeds on the surface of the particles. This refined model of native and OSA-modified phytoglycogen nanoparticles establishes a quantitative basis for the development of new applications of this promising sustainable nanotechnology.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Adsorptive behavior of poly (vinylidene fluoride) membranes for the recovery of lignin-derived hydrophobic deep eutectic solvents

Abstract Recently, membrane technology has gained significant traction as an energy-efficient alternative to traditional thermal processes for solvent recovery. Deep eutectic solvents (DESs) have emerged as sustainable alternatives to conventional organic solvents, yet a systematic methodology for selecting compatible membrane materials for their recovery remains underdeveloped. This study established a predictive framework for membrane material selection in hydrophobic DES applications using Hansen Solubility Parameters (HSP) with inverted criteria targeting materials with relative energy difference (RED) values greater than 1.0. Flat sheet membranes were fabricated via the non-solvent induced phase separation (NIPS) technique. Four NIPS fabricated polymer membranes were evaluated: polysulfone, cellulose acetate, polyvinylidene fluoride (PVDF) fabricated with polyethylene glycol (PEG) as a pore-forming agent, and polybenzimidazole (PBI). The HSP approach successfully predicted membrane-solvent compatibility, with polysulfone (RED = 0.6) and cellulose acetate (RED = 0.9) dissolving completely within 24 h, while PVDF (RED = 1.9) and PBI (RED = 1.1) maintained structural integrity throughout a 7-day exposure period. Furthermore, PVDF demonstrated superior performance with minimal weight gain (3.0%), hydrophobic surface characteristics (122° water contact angle), and enhanced mechanical properties following DES exposure. Comprehensive chemical and morphological characterization confirmed PVDF’s chemical stability and revealed a surface-selective interaction mechanism involving simultaneous PEG (pore-forming agent) extraction and DES component adsorption. Adsorption kinetics followed pseudo-first-order behavior with reversible characteristics, best described by the Temkin isotherm model (R² = 0.9987). PVDF membranes-maintained separation functionality with average lignin rejection (75.2 ± 7.69%) and demonstrated filtration permeability of 2.0 ± 0.34 LMH/bar. This methodology provides a rational approach for membrane selection in emerging solvent systems, contributing to the advancement of sustainable separation technologies for DES-based biomass processing applications.

Science & Technology - Other Topics↗

Computational and Systems Biology Advances to Enable Bioagent Agnostic Signatures

Enumerated threat agent lists have long driven biodefense priorities. The global SARS-CoV-2 pandemic demonstrated the limitations of searching for known threat agents as compared to a more agnostic approach. Recent technological advances are enabling agent-agnostic biodefense, especially through the integration of multi-modal observations of host-pathogen interactions directed by a human immunological model. Although well-developed technical assays exist for many aspects of human-pathogen interaction, the analytic methods and pipelines to combine and holistically interpret the results of such assays are immature and require further investments to exploit new technologies. In this manuscript, we discuss potential immunologically based bioagent-agnostic approaches and the computational tool gaps the community should prioritize filling.

59 BASIC BIOLOGICAL SCIENCES↗

Curriculum-based Reinforcement Learning for Distribution System Critical Load Restoration

This paper focuses on the critical load restoration problem in distribution systems following major outages. To provide fast online response and optimal sequential decision-making support, a reinforcement learning (RL) based approach is proposed to optimize the restoration. Due to the complexities stemming from the large policy search space, renewable uncertainty, and nonlinearity in a complex grid control problem, directly applying RL algorithms to train a satisfactory policy requires extensive tuning to be successful. To address this challenge, this paper leverages the curriculum learning (CL) technique to design a training curriculum involving a simpler steppingstone problem that guides the RL agent to learn to solve the original hard problem in a progressive and more effective manner. We demonstrate that compared with direct learning, CL facilitates controller training to achieve better performance. To study realistic scenarios where renewable forecasts used for decision-making are in general imperfect, the experiments compare the trained RL controllers against two model predictive controllers (MPCs) using renewable forecasts with different error levels and observe how these controllers can hedge against the uncertainty. Results show that RL controllers are less susceptible to forecast errors than the baseline MPCs and can provide a more reliable restoration process.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Surface Treatment

A simple surface treatment process is provided which offers a high performance surface for a variety of applications at low cost. This novel surface treatment, which is particularly useful for Ti-6Al-4V alloys, is achieved by forming oxides on the surface with a two-step chemical process and without mechanical abrasion. First, after solvent degreasing, sulfuric acid is used to generate a fresh titanium surface. Next, an alkaline perborate solution is used to form an oxide on the surface. This acid-followed-by-base treatment is cost effective and relatively safe to use in commercial applications. In addition, it is chromium-free, and has been successfully used with a sol-gel coating to afford a strong adhesive bond that exhibits excellent durability after the bonded specimens have been subjected to a harsh 72 hour water boil immersion. Phenylethynyl containing adhesives were used to evaluate this surface treatment with a novel coupling agent containing both trialkoxysilane and phenylethynyl groups. 8 Claims, 16 Drawing Sheets

Park, Cheol↗

Improving Search Algorithms by Using Intelligent Coordinates

We consider algorithms that maximize a global function G in a distributed manner, using a different adaptive computational agent to set each variable of the underlying space. Each agent eta is self-interested; it sets its variable to maximize its own function g (sub eta). Three factors govern such a distributed algorithm's performance, related to exploration/exploitation, game theory, and machine learning. We demonstrate how to exploit alI three factors by modifying a search algorithm's exploration stage: rather than random exploration, each coordinate of the search space is now controlled by a separate machine-learning-based player engaged in a noncooperative game. Experiments demonstrate that this modification improves simulated annealing (SA) by up to an order of magnitude for bin packing and for a model of an economic process run over an underlying network. These experiments also reveal interesting small-world phenomena.

Wolpert, David H.↗

Fc-receptor-mediated phagocytosis is regulated by mechanical properties of the target

Phagocytosis is an actin-based process used by macrophages to clear particles greater than 0.5 microm in diameter. In addition to its role in immunological responses, phagocytosis is also necessary for tissue remodeling and repair. To prevent catastrophic autoimmune reactions, phagocytosis must be tightly regulated. It is commonly assumed that the recognition/selection of phagocytic targets is based solely upon receptor-ligand binding. Here we report an important new criterion, that mechanical parameters of the target can dramatically affect the efficiency of phagocytosis. When presented with particles of identical chemical properties but different rigidity, macrophages showed a strong preference to engulf rigid objects. Furthermore, phagocytosis of soft particles can be stimulated with the microinjection of constitutively active Rac1 but not RhoA, and with lysophosphatidic acid, an agent known to activate the small GTP-binding proteins of the Rho family. These data suggest a Rac1-dependent mechanosensory mechanism for phagocytosis, which probably plays an important role in a number of physiological and pathological processes from embryonic development to autoimmune diseases.

NASA Program Fundamental Space Biology↗

Mobile Router Developed and Tested

The NASA Glenn Research Center, under a NASA Space Act Agreement with Cisco Systems, has been performing joint networking research to apply Internet-based technologies and protocols to space-based communications. As a result of this research, NASA performed stringent performance testing of the mobile router, including the interaction of routing and the transport-level protocol. In addition, Cisco Systems developed the mobile router for both commercial and Government markets. The code has become part of the Cisco Systems Internetworking Operating System (IOS) as of release 12.2 (4) T--which will make this capability available to the community at large. The mobile router is software code that resides in a network router and enables entire networks to roam while maintaining connectivity to the Internet. This router code is pertinent to a myriad of applications for both Government and commercial sectors, including the "wireless battlefield." NASA and the Department of Defense will utilize this technology for near-planetary observation and sensing spacecraft. It is also a key enabling technology for aviation-based information applications. Mobile routing will make it possible for information such as weather, air traffic control, voice, and video to be transmitted to aircraft using Internet-based protocols. This technology shows great promise in reducing congested airways and mitigating aviation disasters due to bad weather. The mobile router can also be incorporated into emergency vehicles (such as ambulances and life-flight aircraft) to provide real-time connectivity back to the hospital and health-care experts, enabling the timely application of emergency care. Commercial applications include entertainment services, Internet protocol (IP) telephone, and Internet connectivity for cruise ships, commercial shipping, tour buses, aircraft, and eventually cars. A mobile router, which is based on mobile IP, allows hosts (mobile nodes) to seamlessly "roam" among various IP subnetworks. This is essential in many wireless networks. A mobile router, unlike a mobile IP node, allows entire networks to roam. Hence, a device connected to the mobile router does not need to be a mobile node because the mobile router provides the roaming capabilities. There are three basic elements in the mobile IP: the home agent, the foreign agent, and the mobile node. The home agent is a router on a mobile node's home network that tunnels datagrams for delivery to the mobile node when it is away from home. The foreign agent is a router on a remote network that provides routing services to a registered mobile node. The mobile node is a host or router that changes its point of attachment from one network or subnetwork to another. In mobile routing, virtual communications are maintained by the home agent, which forwards all packets for the mobile networks to the foreign agent. The foreign agent passes the packets to the mobile router, which then forwards the packets to the devices on its networks. As the mobile router moves, it will register with its home agent on its whereabouts via the foreign agent to assure continuous connectivity.

Ivancic, William D.↗

A Quantum Dot Nanobiosensor for Rapid Detection of Botulinum Neurotoxin Serotype E

Botulinum neurotoxins (BoNTs) are potent toxins produced by Clostridium bacteria that are responsible for the illness botulism and are listed as bioterrorism agents. BoNT serotype E (BoNT/E) is one of four BoNT serotypes that cause human botulism and is the second most frequent cause of foodborne botulism. Rapid detection and discrimination of BoNT serotypes implicated in human disease are critical for ensuring timely treatment of patients and identifying sources of toxins, but there have been few reported detection methods for BoNT/E and even fewer methods usable for BoNT serotyping. We report a nanobiosensor based on Forster resonance energy transfer (FRET) between semiconductor nanocrystals (quantum dots, QDs) and dark quencher-labeled peptide probes to detect biologically active BoNT/E in aqueous media. The peptide probes contain a specific cleavage site for active BoNT/E. QD photoluminescence, which changes intensity due to FRET when the peptide probe is cleaved, was used to indicate toxin presence and quantity. The detection of a BoNT/E light chain (LcE) and holotoxin was observed within 3 h. The limits of detection were 0.02 and 2 ng/mL for LcE and holotoxin, respectively. The nanobiosensor shows good specificity toward the target in tests with nontarget BoNT serotypes. The high sensitivity, simple operation, short detection time, and ability to be used in parallel with probes developed for other BoNT serotypes indicate that the nanobiosensor will be useful for rapid BoNT/E detection and serotype discrimination in food analysis.

botulinum neurotoxin E↗

Bioinformatic and Mechanistic Analysis of the Palmerolide PKS-NRPS Biosynthetic Pathway From the Microbiome of an Antarctic Ascidian

Complex interactions exist between microbiomes and their hosts. Increasingly, defensive metabolites that have been attributed to host biosynthetic capability are now being recognized as products of host-associated microbes. These unique metabolites often have bioactivity targets in human disease and can be purposed as pharmaceuticals. Polyketides are a complex family of natural products that often serve as defensive metabolites for competitive or pro-survival purposes for the producing organism, while demonstrating bioactivity in human diseases as cholesterol lowering agents, anti-infectives, and anti-tumor agents. Marine invertebrates and microbes are a rich source of polyketides. Palmerolide A, a polyketide isolated from the Antarctic ascidian Synoicum adareanum, is a vacuolar-ATPase inhibitor with potent bioactivity against melanoma cell lines. The biosynthetic gene clusters (BGCs) responsible for production of secondary metabolites are encoded in the genomes of the producers as discrete genomic elements. A candidate palmerolide BGC was identified from a S. adareanum microbiome-metagenome based on a high degree of congruence with a chemical structure-based retrobiosynthetic prediction. Protein family homology analysis, conserved domain searches, active site and motif identification were used to identify and propose the function of the ~75 kbp trans-acyltransferase (AT) polyketide synthase-non-ribosomal synthase (PKS-NRPS) domains responsible for the stepwise synthesis of palmerolide A. Though PKS systems often act in a predictable co-linear sequence, this BGC includes multiple trans-acting enzymatic domains, a non-canonical condensation termination domain, a bacterial luciferase-like monooxygenase (LLM), and is found in multiple copies within the metagenome-assembled genome (MAG). Detailed inspection of the five highly similar pal BGC copies suggests the potential for biosynthesis of other members of the palmerolide chemical family. This is the first delineation of a biosynthetic gene cluster from an Antarctic microbial species, recently proposed as Candidatus Synoicihabitans palmerolidicus. These findings have relevance for fundamental knowledge of PKS combinatorial biosynthesis and could enhance drug development efforts of palmerolide A through heterologous gene expression.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Performance Prediction of Big Data Transfer Through Experimental Analysis and Machine Learning

Big data transfer in next-generation scientific ap- plications is now commonly carried out over connections with guaranteed bandwidth provisioned in High-performance Net- works (HPNs) through advance bandwidth reservation. To use HPN resources efficiently, provisioning agents need to carefully schedule data transfer requests and allocate appropriate band- widths. Such reserved bandwidths, if not fully utilized by the requesting user, could be simply wasted or cause extra overhead and complexity in management due to exclusive access. This calls for the capability of performance prediction to reserve bandwidth resources that match actual needs. Towards this goal, we employ machine learning algorithms to predict big data transfer perfor- mance based on extensive performance measurements, which are collected over a span of several years from a large number of data transfer tests using different protocols and toolkits between various end sites on several real-life physical or emulated HPN testbeds. We first identify a comprehensive list of attributes involved in a typical big data transfer process, including end host system configurations, network connection properties, and control parameters of data transfer methods. We then conduct an in-depth exploratory analysis of their impacts on application- level throughput, which provides insights into big data transfer performance and motivates the use of machine learning. We also investigate the applicability of machine learning algorithms and derive their general performance bounds for performance prediction of big data transfer in HPNs. Experimental results show that, with appropriate data preprocessing, the proposed machine learning-based approach achieves 95% or higher pre- diction accuracy in up to 90% of the cases with very noisy real-life performance measurements.

Yun, Daqing↗

Agentic Diagrammatica: Towards Autonomous Symbolic Computation in High Energy Physics

We present Diagrammatica, a symbolic computation extension to the HEPTAPOD agentic framework, which enables LLM agents to plan and execute multi-step theoretical calculations. Symbolic computation poses a distinctive reliability challenge for LLM agents, as correctness is governed by implicit mathematical conventions that are not encoded in a form that can be easily checked in the computational backend. We identify two complementary remedies, tool-constrained computation and targeted knowledge grounding, and pursue the first as the primary architecture. Concretely, we concentrate the agent's action distribution onto tool calls with convention-fixing semantics, in which the agent specifies a compact, human-auditable diagram specification and a trusted backend performs the symbolic or numerical manipulations exactly. The toolkit provides two complementary calculation paths consuming a shared diagram specification: Naive Dimensional Analysis (NDA) for order-of-magnitude rate estimates and Exact Diagrammatic Analysis (EDA) for tree-level symbolic calculations via automatic FeynCalc code generation, both supplemented by automatic Feynman diagram enumeration and a navigable theory knowledge base. The architecture is validated on two benchmarks: (1) an exhaustive catalog of all tree-level, single-vertex $1\to 2$ partial decay widths across scalar, fermion, and vector parents, with complete massless and threshold limits and Standard Model validation; and (2) an NDA sensitivity study of the muon decay multiplicity $μ^+ \to ν_μ\barν_e + n(e^+e^-) + e^-$, determining the maximum observable $n$ at current and planned muon experiments.

Menzo, Tony [Alabama U.; Fermilab] (ORCID:00000002↗

A layered abduction model of perception: Integrating bottom-up and top-down processing in a multi-sense agent

A layered-abduction model of perception is presented which unifies bottom-up and top-down processing in a single logical and information-processing framework. The process of interpreting the input from each sense is broken down into discrete layers of interpretation, where at each layer a best explanation hypothesis is formed of the data presented by the layer or layers below, with the help of information available laterally and from above. The formation of this hypothesis is treated as a problem of abductive inference, similar to diagnosis and theory formation. Thus this model brings a knowledge-based problem-solving approach to the analysis of perception, treating perception as a kind of compiled cognition. The bottom-up passing of information from layer to layer defines channels of information flow, which separate and converge in a specific way for any specific sense modality. Multi-modal perception occurs where channels converge from more than one sense. This model has not yet been implemented, though it is based on systems which have been successful in medical and mechanical diagnosis and medical test interpretation.

Josephson, John R.↗

Multi-objective goal-directed optimization of de novo stable organic radicals for aqueous redox flow batteries

Abstract Advances in the field of goal-directed molecular optimization offer the promise of finding feasible candidates for even the most challenging molecular design applications. One example of a fundamental design challenge is the search for novel stable radical scaffolds for an aqueous redox flow battery that simultaneously satisfy redox requirements at the anode and cathode, as relatively few stable organic radicals are known to exist. To meet this challenge, we develop a new open-source molecular optimization framework based on AlphaZero coupled with a fast, machine-learning-derived surrogate objective trained with nearly 100,000 quantum chemistry simulations. The objective function comprises two graph neural networks: one that predicts adiabatic oxidation and reduction potentials and a second that predicts electron density and local three-dimensional environment, previously shown to be correlated with radical persistence and stability. With no hard-coded knowledge of organic chemistry, the reinforcement learning agent finds molecule candidates that satisfy a precise combination of redox, stability and synthesizability requirements defined at the quantum chemistry level, many of which have reasonable predicted retrosynthetic pathways. The optimized molecules show that alternative stable radical scaffolds may offer a unique profile of stability and redox potentials to enable low-cost symmetric aqueous redox flow batteries.

25 ENERGY STORAGE↗

Production of oxygen from lunar ilmenite

The following subjects are addressed: (1) the mechanism and kinetics of carbothermal reduction of simulated lunar ilmenite using carbon and, particularly, CO as reducing agents; (2) the determination of the rate-limiting steps; (3) the investigation of the effect of impurities, particularly magnesium; (4) the search for catalysts suitable for enhancement of the rate-limiting step; (5) the comparison of the kinetics of carbothermal reduction with those of hydrogen reduction; (6) the study of the combined use of CO and hydrogen as products of gasification of carbonaceous solids; (7) the development of reduction methods based on the use of waste carbonaceous compounds for the process; (8) the development of a carbothermal reaction path that utilizes gasification of carbonaceous solids to reducing gaseous species (hydrocarbons and carbon monoxide) to facilitate the reduction reaction kinetics and make the process more flexible in using various forms of carbonaceous feeds; (9) the development of advanced gas separation techniques, including the use of high-temperature ceramic membranes; (10) the development of an optimum process flow sheet for carbothermal reduction, and comparison of this process with the hydrogen reduction scheme, as well as a general comparison with other leading oxygen production schemes; and (11) the use of new and advanced material processing and separation techniques.

Zhao, Y.↗