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All sources NASA NTRS DOE OSTI

DOE OSTI2017

Utica Shale Energy and Environment Laboratory Final Scientific/Technical Report

The Ohio State University (OSU) was awarded a contract on October 1, 2014, from the Department of Energy National Energy Technology Laboratory (DOE-NETL) to develop the Utica Shale Energy and Environment Laboratory (USEEL) in the Utica-Point Pleasant shale play of the Appalachian Basin. It was designed to be an environmental and technology development lab that would enable the academic, industry, government, and non-governmental organization (NGO) research communities to better understand unconventional oil and gas (UOG) engineering practices and technology to increase production and safety, and decrease environmental effects. Political and economic consequences necessitated changes in project site location and design, from the Ohio State Eastern Agricultural Research Station (EARS) and the Muskingum Watershed Conservancy District (MWCD) in east-central Ohio to a site located at an Energy Corporation of America (ECA) Marcellus drill pad in Greene County, Pennsylvania. Although the overall project progressed little beyond planning and administration before termination on September 18, 2017, significant research and MS or PhD investigations were completed or continue today. An experimental study design for site specific and regional baseline assessments was developed utilizing ecoregions, United States Geological Survey (USGS) Hydraulic Unit Code watersheds, and GIS technology and databases. This can be utilized to build a defensible and scalable management and research framework for UOG investigations that can be extrapolated for predictive and comparative analyses. The most commonly mentioned perceived risks of shale energy development identified in a socioeconomic analysis included impacts to the environment and water resources, traffic and road deterioration, and crime. Economic benefits, such as the windfall wealth to residents, job opportunities, and the demand for hotels and restaurants emerged, as the main positive impact to the community. Preliminary results of an analysis of the location and mechanisms for extremely high pore pressures of the deep Utica-Point Pleasant formations of southwestern Pennsylvania and their possible relationship to high initial production rates indicate that the overpressure is caused by a combination disequilibrium compaction and fluid expansion. Drilling fluids research designed a proof-of-concept device to evaluate desalination and solvent separation in hydraulic fracturing flowback water that could facilitate water recycling and potentially mitigate deep-well injection. A groundwater study was devised to characterize the subsurface aquifers in terms of the geological framework and groundwater chemistry, including the chemistry of dissolved gases and salts. Brines and gases associated with hydrocarbon migration in groundwater (including noble gases and hydrocarbons) were to be specifically fingerprinted for use as tracers of fluid migration. Trace element analysis of fish otoliths, using laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS), is being utilized to assess surface water quality. This methodology will be used to reconstruct the exposure histories of fish from watersheds and discriminate between the signatures associated with hydraulic fracturing and coal mining activities.

03 NATURAL GAS↗

DOE OSTI2013

Prediction of Peak Hydrogen Concentrations for Deep Sludge Retrieval in Tanks AN-101 and AN-106 from Historical Data of Spontaneous Gas Release Events

Radioactive and chemical wastes from nuclear fuel processing are stored in large underground storage tanks at the Hanford Site. The Tank Operations Contractor is continuing a program of moving solid wastes from single-shell tanks (SSTs) to double-shell tanks (DSTs) and preparing for waste feed delivery (WFD). A new mechanism for a large spontaneous gas release event (GRE) in deep sludge sediments has been postulated. The creation of this potential new GRE hazard, deep sludge gas release events (DSGREs), is the retrieval of sludge waste into a single DST that results in a sediment depth greater than operating experience has demonstrated is safe. The Tank Operations Contractor program of moving solid wastes from SSTs to DSTs and preparing for WFD is being negatively impacted by this sediment depth limit.

Deep sludge retrieval↗

DOE OSTI2024

Untapped Potential of Fluoride Ions in Maximizing the Electrochemical Stability of Deep Eutectic Solvents

Advancing the batteries is pivotal to propelling our society toward a sustainable, electrified future. The stability of electrolytes forms the backbone of energy storage systems. This is particularly the case for redox flow batteries (RFBs). Their deployability depends on their longevity and dependability. The presence of unstable electrolytes can trigger undesirable reactions, degrade performance, and lead to battery failure. Aqueous electrolytes, with limited electrochemical stability window (ESW), are prone to hydrogen and oxygen evolution. Conversely, non-aqueous electrolytes offer enhanced stability. In this study, we unveil the ESW of a non-aqueous eutectic solvent, comprising choline fluoride and ethylene glycol—a composition that has eluded experimental investigation until now. Our findings show that the stability window, reduction and oxidation potential limits of deep eutectic solvents, are sensitive to variations in the halide component of the ammonium salt. Furthermore, this work not only highlights the benefits of novel deep eutectic solvents but also sets the stage for their strategic use in future battery electrolytes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2018

Identification and Removal of Contaminant Sequences From Ribosomal Gene Databases: Lessons From the Census of Deep Life

Earth’s subsurface environment is one of the largest, yet least studied, biomes on Earth, and many questions remain regarding what microorganisms are indigenous to the subsurface. Through the activity of the Census of Deep Life (CoDL) and the Deep Carbon Observatory, an open access 16S ribosomal RNA gene sequence database from diverse subsurface environments has been compiled. However, due to low quantities of biomass in the deep subsurface, the potential for incorporation of contaminants from reagents used during sample collection, processing, and/or sequencing is high. Thus, to understand the ecology of subsurface microorganisms (i.e., the distribution, richness, or survival), it is necessary to minimize, identify, and remove contaminant sequences that will skew the relative abundances of all taxa in the sample. In this meta-analysis, we identify putative contaminants associated with the CoDL dataset, recommend best practices for removing contaminants from samples, and propose a series of best practices for subsurface microbiology sampling. The most abundant putative contaminant genera observed, independent of evenness across samples, were Propionibacterium, Aquabacterium, Ralstonia, and Acinetobacter. While the top five most frequently observed genera were Pseudomonas, Propionibacterium, Acinetobacter, Ralstonia, and Sphingomonas. The majority of the most frequently observed genera (high evenness) were associated with reagent or potential human contamination. Additionally, in DNA extraction blanks, we observed potential archaeal contaminants, including methanogens, which have not been discussed in previous contamination studies. Such contaminants would directly affect the interpretation of subsurface molecular studies, as methanogenesis is an important subsurface biogeochemical process. Utilizing previously identified contaminant genera, we found that ~27% of the total dataset were identified as contaminant sequences that likely originate from DNA extraction and DNA cleanup methods. Thus, controls must be taken at every step of the collection and processing procedure when working with low biomass environments such as, but not limited to, portions of Earth’s deep subsurface. Taken together, we stress that the CoDL dataset is an incredible resource for the broader research community interested in subsurface life, and steps to remove contamination derived sequences must be taken prior to using this dataset.

16S rRNA↗

DOE OSTI2015

Strong localization induced anomalous temperature dependence exciton emission above 300 K from SnO{sub 2} quantum dots

SnO{sub 2} quantum dots (QDs) are potential materials for deep ultraviolet (DUV) light emitting devices. In this study, we report the temperature and excitation power-dependent exciton luminescence from SnO{sub 2} QDs. The exciton emission exhibits anomalous blue shift, accompanied with band width reduction with increasing temperature and excitation power above 300 K. The anomalous temperature dependences of the peak energy and band width are well interpreted by the strongly localized carrier thermal hopping process and Gaussian shape of band tails states, respectively. The localized wells and band tails at conduction minimum are considered to be induced by the surface oxygen defects and local potential fluctuation in SnO{sub 2} QDs.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2020

Deep learning model to detect various synchrophasor data anomalies

High-density synchrophasors provide valuable information for power grid situational awareness, operation and control. Unfortunately, due to factors including communication instability and hardware failure, their data quality can be greatly deteriorated by anomalies. Since the anomalies can impact the performance of the synchrophasor applications, it is of paramount significance to propose a model to detect anomalies in synchrophasor. In this study, a convolutional neural network model is established to detect and classify the anomalies in the synchrophasor measurements. Additionally, four types of anomalies observed in actual synchrophasors including erroneous patterns, random spikes, missing points and high-frequency interferences are considered in this study. The proposed model is extensively evaluated via field-collected measurements from the synchrophasor network in Jiangsu grid, China. The superior performance of the proposed model indicates the great potential of using deep learning for the detection of abnormal synchrophasor measurements.

42 ENGINEERING↗

DOE OSTI2025

Deep learning forecasts the spatiotemporal evolution of fluid-induced microearthquakes

Microearthquakes generated by subsurface fluid injection record the evolving stress state and permeability of reservoirs. Forecasting their spatiotemporal evolution is therefore critical for applications such as enhanced geothermal systems, carbon dioxide sequestration and other geoengineering applications. Here we propose a transformer neural network model that ingests hydraulic stimulation history and prior microearthquake observations to forecast four key quantities: cumulative microearthquake count, cumulative logarithmic seismic moment, and the 50th- and 95th-percentile extents of the microearthquake cloud. Applied to the EGS Collab Experiment 1 dataset, the model achieves R2 > 0.98 for the 1-s forecast horizon and R2 > 0.88 for the 15-s forecast horizon across all targets, and supplies uncertainty estimates through a learned standard deviation term. These accurate, uncertainty-quantified forecasts enable real-time inference of fracture propagation and permeability evolution, demonstrating the strong potential of deep-learning approaches to improve seismic-risk assessment and guide mitigation strategies in future fluid-injection operations.

Chung, Jaehong↗

DOE OSTI2024

Improved deep learning prediction of antigen–antibody interactions

Identifying antibodies that neutralize specific antigens is crucial for developing effective immunotherapies, but this task remains challenging for many target antigens. The rise of deep learning–based computational approaches presents a promising avenue to address this challenge. Here, we assess the performance of a deep learning approach through two benchmark tests aimed at predicting antibodies for the receptor-binding domain of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein. Three different strategies for constructing input sequence alignments are employed for predicting structural models of antigen–antibody complexes. In our initial testing set, which comprises known experimental structures, these strategies collectively yield a significant top-ranked prediction for 61% of cases and a success rate of 47%. Notably, one strategy that utilizes the sequences of known antigen binders outperforms the other two, achieving a precision of 90% in a subsequent test set of ~1,000 antibodies, balanced between true and control antibodies for the antigen, albeit with a lower recall of 25%. Our results underscore the potential of integrating deep learning methods with single B cell sequencing techniques to enhance the prediction accuracy of antigen–antibody interactions.

Science & Technology - Other Topics↗

NASA NTRS2016

The Deep-Space Positioning System Concept: Automating Complex Navigation Operations Beyond the Earth

There is no equivalent of GPS in “deep space,” that is beyond the Earth Moon system (and use of GPS beyond LEO and MEO is already problematic.) NASA has been awarded a patent on the concept of the “Deep-space Positioning System” (DPS) which is a self-contained instrument concept that would use in-situ observations to provide an automated and autonomous means of determining its position in the Solar System, and at least as important, its position relative to a target, whether that target is a planet, asteroid, moon or comet. In addition, the DPS system computes trajectory change parameters, as necessary to keep a spacecraft on a planned trajectory. Implementation options include the use of a radio receiver to receive a frequency beacon from a non-in-situ source for additional navigation information over and above that which can be provided by the cameras that are the principal data source of DPS. This paper discusses the DPS instrument concept, and its potential use in deep space.

Guinn, Joseph R.↗

NASA NTRS2011

Large Aperture "Photon Bucket" Optical Receiver Performance in High Background Environments

The potential development of large aperture groundbased "photon bucket" optical receivers for deep space communications, with acceptable performance even when pointing close to the sun, is receiving considerable attention. Sunlight scattered by the atmosphere becomes significant at micron wavelengths when pointing to a few degrees from the sun, even with the narrowest bandwidth optical filters. In addition, high quality optical apertures in the 10-30 meter range are costly and difficult to build with accurate surfaces to ensure narrow fields-of-view (FOV). One approach currently under consideration is to polish the aluminum reflector panels of large 34-meter microwave antennas to high reflectance, and accept the relatively large FOV generated by state-of-the-art polished aluminum panels with rms surface accuracies on the order of a few microns, corresponding to several-hundred micro-radian FOV, hence generating centimeter-diameter focused spots at the Cassegrain focus of 34-meter antennas. Assuming pulse-position modulation (PPM) and Poisson-distributed photon-counting detection, a "polished panel" photon-bucket receiver with large FOV will collect hundreds of background photons per PPM slot, along with comparable signal photons due to its large aperture. It is demonstrated that communications performance in terms of PPM symbol-error probability in high-background high-signal environments depends more strongly on signal than on background photons, implying that large increases in background energy can be compensated by a disproportionally small increase in signal energy. This surprising result suggests that large optical apertures with relatively poor surface quality may nevertheless provide acceptable performance for deep-space optical communications, potentially enabling the construction of cost-effective hybrid RF/optical receivers in the future.

photon bucket detection↗

DOE OSTI2025

Improved guided-wave acoustic defect detection and localization in pipes under varying temperature conditions using deep learning

Early defect detection in pipelines is critical across industries, particularly in the oil and gas sector, where failures result in significant maintenance costs and operational disruptions. Acoustic guided-wave techniques are widely used for nondestructive evaluation of pipeline defects due to their long-distance propagation capability. However, environmental variations, sensitivity limitations, and complex signal interpretation challenges limit the effectiveness of traditional signal processing approaches with guided-wave signals. Recent advances in deep learning methods have demonstrated remarkable success in solving complex real-world problems in many fields. In particular, deep-learning-based signal processing holds substantial promise to overcome limitations and challenges of conventional signal processing. This study presents a deep learning framework for pipeline inspection using acoustic guided-wave signals under temperature varying environments. The proposed framework employs a dual-path one-dimensional convolutional autoencoder that combines defect detection, localization, and temperature prediction functions. The proposed system utilizes multi-mode and broadband acoustic waves with an optimized number of sensors that provide high accuracy while retaining practical simplicity. Experimental validation is performed on a carbon steel pipe. The results indicate exceptional defect detection accuracy and precise defect localization with a mean absolute error of 66 mm. The proposed technique also predicts the effective average temperature of the pipe with a mean absolute error of 0.2°C. Comparative analysis shows superior performance of the proposed method over a traditional method previously developed by the authors' team. These results highlight the potential of integrating deep learning methods into guided-wave pipeline inspection systems to improve reliability under varying environmental conditions.

42 ENGINEERING↗

DOE OSTI2024

Understanding the effect of density functional choice and van der Waals treatment on predicting the binding configuration, loading, and stability of amine-grafted metal organic frameworks

Metal organic frameworks (MOFs) are crystalline, three-dimensional structures with high surface areas and tunable porosities. Made from metal nodes connected by organic linkers, the exact properties of a given MOF are determined by node and linker choice. MOFs hold promise for numerous applications, including gas capture and storage. M2(4,4′-dioxidobiphenyl-3,3′-dicarboxylate)—henceforth simply M2(dobpdc), with M = Mg, Mn, Fe, Co, Ni, Cu, or Zn—is regarded as one of the most promising structures for CO2 capture applications. Further modification of the MOF with diamines or tetramines can significantly boost gas species selectivity, a necessity for the ultra-dilute CO2 concentrations in the direct-air capture of CO2. There are countless potential diamines and tetramines, paving the way for a vast number of potential sorbents to be probed for CO2 adsorption properties. The number of amines and their configuration in the MOF pore are key drivers of CO2 adsorption capacity and kinetics, and so a validation of computational prediction of these quantities is required to suitably use computational methods in the discovery and screening of amine-functionalized sorbents. In this work, we study the predictive accuracy of density functional theory and related calculations on amine loading and configuration for one diamine and two tetramines. In particular, we explore the Perdew–Burke–Ernzerhof (PBE) functional and its formulation for solids (PBEsol) with and without the Grimme-D2 and Grimme-D3 pairwise corrections (PBE+D2/3 and PBEsol+D2/3), two revised PBE functionals with the Grimme-D2 and Grimme-D3 pairwise corrections (RPBE+D2/3 and revPBE+D2/3), and the nonlocal van der Waals correlation (vdW-DF2) functional. We also investigate a universal graph deep learning interatomic potential’s (M3GNet) predictive accuracy for loading and configuration. These results allow us to identify a useful screening procedure for configuration prediction that has a coarse component for quick evaluation and a higher accuracy component for detailed analysis. Our general observation is that the neural network-based potential can be used as a high-level and rapid screening tool, whereas PBEsol+D3 gives a completely qualitatively predictive picture across all systems studied, and can thus be used for high accuracy motif predictions. We close by briefly exploring the predictions of relative thermal stability for the different functionals and dispersion corrections.

Chemistry↗

DOE OSTI2026

Deep Spectroscopy with DESI for Photometric Redshift Training and Calibration

Deep spectroscopic samples can improve photometric redshift (photo-z) estimates and reduce uncertainties on redshift distributions. Such improvements can increase the cosmological constraining power of large imaging-based experiments such as the Vera C. Rubin Observatory’s Legacy Survey of Space and Time (LSST) and mitigate what may be a limiting systematic effect. We present results from the “DESI-Deep pilot” program, which was designed to assess the capability of the Dark Energy Spectroscopic Instrument (DESI) on the 4m Mayall telescope to measure redshifts of galaxies as faint as expected lensing samples for early LSST data (m i ≤ 24.5). We find that DESI is remarkably efficient at this task, with redshift success rates comparable to the results of observations from 10 m class telescopes with only ∼2 × longer integration time (rather than ∼8 × longer as would be expected from aperture-area scaling), while simultaneously achieving ∼30 times larger multiplexing. We also find that the signal-to-noise ratio of the spectra scales as expected for background-limited observations even for the longest exposure times (∼7 hr) and faintest targets in the program. These results demonstrate that DESI could provide the definitive redshift sample for the early years of LSST with a modest investment of observing time. Based upon the results of this program, we provide updated predictions for the time required to collect benchmark samples for photo-z training and calibration using a variety of spectroscopic facilities. Finally, we describe a potential “DESI-Deep” survey designed to train and calibrate photo-z’s for imaging experiments, and provide forecasts of its impact on cosmological inference.

Dey, Biprateep [University of Toronto; University ↗

DOE OSTI2024

Bottom-up design of actinide materials from molecular clusters: Demonstration of a general-purpose simulation capability leveraging machine-learned atomic potentials

Actinide thin-film coatings such as uranium dioxide (UO 2 ) play an important role in nuclear reactors and other mission-relevant applications, but realization of their potential requires a deep fundamental understanding of the chemical vapor deposition (CVD) processes used for their growth. The slow experimental progress can be attributed, in part, to the standard safety guidelines associated with handling uranium byproducts, which are often corrosive, toxic, and radioactive. Accurate simulation techniques, when used in concert with experiment, can improve laboratory safety, material durability, and deliverable timeframes. However, state-of-the-art computational methods are either insufficiently accurate or intractably expensive. To remedy this situation, in this project we suggested a machine-learning (ML) accelerated workflow for simulating molecular clustering toward deposition. As a benchmark test case, we considered molecular clustering in steam and assessed independent components of our workflow by comparing with measured thermodynamic properties of water. After analyzing each component individually and finding no fundamental barrier to realization of the workflow, we attempted to integrate the ML component, a Sandia-developed tool called FitSNAP. As this was the first application of FitSNAP to atoms and molecules in the gas phase at Sandia, the method required more fitting data than was originally anticipated. Systematic improvements were made by including in the fit data diatomic potentials, molecular single-bond-breaking curves, and symmetry-constrained intermolecular potentials. We concluded that our strategy provides a feasible pathway toward modeling CVD and related processes, but that extensive training data must be generated before it can be of practical use.

36 MATERIALS SCIENCE↗

DOE OSTI2015

Effects of increased upward flux of dissolved salts caused by CO2 storage or other factors

Injection of CO2 in deep saline aquifers is being considered to reduce greenhouse gases in the atmosphere, and this process is expected to increase the pressure in these deep aquifers. One potential consequence of pressurization is an increase in the upward flux of saline water. Saline groundwater occurs naturally at shallow depths in many sedimentary basins, so an upward flux of solutes could degrade the quality of freshwater aquifers and threaten aquatic ecosystems. One problem could occur where saline water flowed upward along preferential paths, like faults or improperly abandoned wells. Diffuse upward flow through the natural stratigraphy could also occur in response to basin pressurization. This process would be slower, but diffuse upward flow could affect larger areas than flow through preferential paths, and this motivated us to evaluate this process. We analyzed idealized 2D and 3D geometries representing the essential details of a shallow, freshwater aquifer underlain by saline ground water in a sedimentary basin. The analysis was conducted in two stages, one that simulated the development of a freshwater aquifer by flushing out saline water, and another that simulated the effect of a pulse-like increase in the upward flux from the basin. The results showed that increasing the upward flux from a basin increased the salt concentration and mass loading of salt to streams, and decrease the depth to the fresh/salt transition. The magnitude of these effects varied widely, however, from a small, slow process that would be challenging to detect, to a large, rapid response that could be an environmental catastrophe. The magnitude of the increased flux, and the initial depth to the fresh/salt transition in groundwater controlled the severity of the response. We identified risk categories for salt concentration, mass loading, and freshwater aquifer thickness, and we used these categories to characterize the severity of the response. This showed that risks would likely be minor if the upward flux was smaller than a few tenths of the magnitude of recharge, according to the 2D analyses. The 3D analyses also show that upward flux could occur without a significant increase in the risk categories. The major contribution of this work is that it shows how a large increase in diffuse upward flux from a basin could cause significant problems, but a small increase in upward flux may occur without significantly affecting risks to the shallow freshwater flow system. This heightens the importance of understanding interactions between shallow and deep hydrologic systems when characterizing CO2 storage projects.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Effect of Ambient Organic Acids on the Water Structure at ${\rm TiO}_{2}$ Interfaces

A molecular-level understanding of the effects of ambient organic compounds on the wettability of titanium dioxide ( ${\rm TiO}_{2}$ ) surfaces is relevant to many of its energy-related and environmental applications. Herein, we focus on two common atmospheric carboxylic acids, formic and acetic acid, and characterize their adsorption/ desorption at the aqueous interfaces of anatase and rutile ${\rm TiO}_{2}$ using molecular dynamics with an ab initio deep neural network potential. Our simulations show that these acids prefer to be localized in the interfacial water layers close to the ${\rm TiO}_{2}$ surface where they are stabilized by the interaction/exchange of their acid proton with a surface oxygen, rather than chemisorb at the surface Ti sites by displacing the adsorbed water. Notably, these acids make the surface of anatase hydrophobic, whereas the larger fraction of adsorbed water dissociation can offset their effect on rutile. Furthermore, these results provide a picture where carboxylic acids control the wettability of ${\rm TiO}_{2}$ largely through acid-base chemistry at the interface rather than chemisorption on the oxide surface, a finding that can help improve the design of self-cleaning surfaces and photocatalytic devices.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Speciation and diffusive dynamics in hydrated grain boundaries of complex oxide Gd2Ti2O7

Abstract Grain boundaries in polycrystalline materials significantly affect their properties, such as ionic transport, corrosion, and chemical durability. The pyrochlore compound (Gd 2 Ti 2 O 7 ) is employed as a model for complex oxides and is known for its diverse applications, including nuclear waste immobilization. Density functional theory-based first-principles molecular dynamics simulations were performed at different temperatures on the hydrated grain boundary system. The results show extensive transformations within the grain boundaries among hydrous water species (OH − , H 2 O, and H 3 O + ). The temperature dependence of self-diffusion coefficients follows Arrhenius behavior, with an activation energy of 35.9 kJ/mol for hydrogen and 46.3 kJ/mol for oxygen. The lifetime of OH − is about three to four times longer than that of H 2 O at temperatures from 800 to 2100 K, suggesting the greater stability of OH − over H 2 O, a unique characteristic of the grain boundaries. The estimated lifetime of the hydrous species decreases as the temperature increases, with an activation energy of 9.9 kJ/mol for OH − and 13.4 kJ/mol for H 2 O. While Gd 3 + is more mobile than Ti 4+ , both the Gd 3 + and Ti 4+ cations are orders of magnitude less mobile than the water species. The results suggest that water species are much more mobile within grain boundaries than in the bulk crystal and have the potential to penetrate deep into polycrystalline materials through grain boundaries, leading to grain boundary degradation and dissolution. The different mobilities of cations in complex oxides can lead to leaching of certain cations and incongruent dissolution during the chemical weathering of Earth and industrial materials.

B. Ghosh, Dipta↗

DOE OSTI2023

Perovskite evolution on La modified Mn 1.5 Co 1.5 O 4 spinel through thermal ageing with enhanced oxidation activity: Is sintering always an issue?

Non-precious metal oxides have great potential in catalytic deep oxidation of emitted hydrocarbons to help address various environmental pollution concerns. However, sintering issue is a stumbling block in practical application upon long-term high-temperature operation or thermal shock experience. Herein, La was applied to modify Mn 1.5 Co 1.5 O 4 spinel to get a highly active oxidation catalyst with exceptional stability against high-temperature thermal ageing treatment. With thermal ageing at 750 °C for 100 h, the La modified Mn–Co composite reached 90 % conversion in toluene oxidation at 265 °C under the high WHSV of 120,000 mL g -1 h -1 , while this value increased to 312 °C over blank Mn–Co spinel. The addition of La not only inhibited the growth of spinel nanocrystals, but also brought about perovskite formation through sintering, generating perovskite-spinel interfaces, thus enhancing both activity and stability. Meanwhile, the La modified Mn–Co spinel exhibited superior performance in presence of water or sulfur dioxide after thermal ageing treatment. In conclusion, this work offers a versatile strategy to design anti-sintering and active oxidation nanocatalysts based on non-precious metal oxides for industrial applications.

36 MATERIALS SCIENCE↗