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At least 235 records · Page 13

The Lack of Evidence on the Madden–Julian Oscillation to Drive Its Relationship With the Quasi–Biennial Oscillation Through Modulation of Stratospheric Wave Activity

Previous studies have found that Madden-Julian Oscillation (MJO) amplitude depends on the Quasi-Biennial Oscillation (QBO) during boreal winter. This MJO-QBO relationship is important to realizing subseasonal-to-seasonal prediction skills, but the underlying mechanism remains unclear. It is often thought that this relationship arises through the modulation of the upper-troposphere and lower-stratosphere lapse rate by the QBO, but this mechanism assumes the one-way impact of the QBO onto the MJO. Alternatively, the MJO can be hypothesized to influence the QBO by modulating stratospheric wave activity that is known to be critical to QBO dynamics. Therefore, using satellite and reanalysis data, this study examines whether MJO monthly activity can impact stratospheric wave activity and QBO downward propagation speed. The results depicted a lack of such impacts, suggesting this observed MJO-QBO relationship cannot be driven by the MJO modulation of stratospheric wave forcing.

54 ENVIRONMENTAL SCIENCES↗

Effects of Cofeeding Hydrogen on Propane Dehydrogenation Catalyzed by Isolated Iron Sites Incorporated into Dealuminated BEA

Iron sites dispersed on nonacidic siliceous supports have been reported to be catalytically active for propane dehydrogenation (PDH), yet the precise relationship between site structure and catalytic activity remains elusive. Here, this study provides a comprehensive understanding of the catalytic performance of iron supported on dealuminated BEA (DeAlBEA) zeolites for PDH. Using XAS, UV-vis, and IR spectroscopy of adsorbed pyridine and deuterated acetonitrile, it was found that, at an Fe/Al 0 of 0.04, isolated Fe sites form. These isolated sites exhibit a forward rate of PDH of 213 mol propene/mol Fe·h at 823 K and a feed containing 15 kPa propane. When 15 kPa of H 2 is added to the feed, the forward rate of PDH rises to 391 mol of propene/mol of Fe·h. In both cases, the propene selectivity is over 99%. IR spectroscopy of d 3 -acetonitrile suggests that the open Lewis acid site ((-Si-O-) 2 Fe 3+ -OH) serves as the active site responsible for PDH, while Brønsted acid sites (≡Fe 3+ -O(H)-Si≡) contribute to propane cracking with increasing Fe/Al 0 ratios. Kinetic analysis of the effects of H 2 addition to the propane feed on PDH kinetics shows that H 2 enhances the activity of 0.04FeDeAlBEA primarily by enhancing the strength of the propane adsorption.

Alghannam, Afnan [University of California, Berkel↗

Correlation between oxygen evolution reaction activity and surface compositional evolution in epitaxial La 0.5 Sr 0.5 Ni 1– x Fe x O 3– δ thin films

Water electrolysis can use renewable electricity to produce green hydrogen, a portable fuel and sustainable chemical precursor. Improving electrolyzer efficiency hinges on the activity of the oxygen evolution reaction (OER) catalyst. Earth-abundant, ABO 3 -type perovskite oxides offer great compositional, structural, and electronic tunability, with previous studies showing compositional substitution can increase the OER activity drastically. However, the relationship between the tailored bulk composition and that of the surface, where OER occurs, remains unclear. Here, we study the effects of electrochemical cycling on the OER activity of La 0.5 Sr 0.5 Ni 1– x Fe x O 3– δ ( x = 0–0.5) epitaxial films grown by oxide molecular beam epitaxy as a model Sr-containing perovskite oxide. In this work, electrochemical testing and surface-sensitive spectroscopic analyses show Ni segregation, which is affected by electrochemical history, along with surface amorphization, coupled with changes in OER activity. Our findings highlight the importance of surface composition and electrochemical cycling conditions in understanding OER performance, suggesting common motifs of the active surface with high surface area systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Graphene-supported single atom catalysts for high performance lithium-oxygen batteries

The optimal choice of d-block metals in single atom catalysts (SACs) is crucial for designing efficient electrocatalysts for activating the Oxygen reduction reaction (ORR)/ Oxygen evolution reaction (OER) in lithiumoxygen batteries (LOBs). Herein, we used the Quantum Mechanics methods to understand the origin of reactivity for a series of 16 d-block metals supported on nitrogen-doped graphene as SACs for ORR and OER in LOBs. Based on the Gibbs free energy calculations, we found that among the 16 SACs investigated, Zn-SAC exhibits the highest electrochemical activity with the lowest overpotential of 0.17 V. Here we then used machine learning (ML) to develop an intrinsic descriptor, phi, that correlates the catalytic activity with electronic and chemical properties of the catalytic centers at the M-N 4 active site on graphene surface. We established a linear relationship between phi and the catalytic activity that provides guidance for designing efficient SACs for electrocatalysis in LOBs. To validate these predictions, we report electrochemical measurements showing that Zn-SAC exhibits an ultra-stable cyclability with reduced overpotentials over Mo-SAC and nitrogen-doped graphene (NG), confirming our theoretical prediction. This fundamental work provides a deep understanding on the rational design of efficient SACs for OER/ ORR in LOBs.

25 ENERGY STORAGE↗

Evaluation of E3SM Simulated Aerosols and Aerosol‐Cloud Interactions Across GCM and Convection‐Permitting Scales

This paper introduces an Earth system modeling testbed for predicting aerosols and aerosol‐cloud interactions (ACIs) at convection‐permitting scales. Using the Energy Exascale Earth System Model (E3SM) version 2 with a four‐mode Modal Aerosol Module, we conduct simulations at 3.25 km resolution on a regionally refined mesh (RRM) across four regions with distinct aerosol and cloud regimes. Results are compared with the standard 100 km E3SM configuration and evaluated against satellite, aircraft, and ground‐based observations. We find that increasing model resolution improves heavy precipitation simulation but amplifies positive bias in light drizzle at coarse resolution. These resolution‐induced changes affect cloud and aerosol properties to varying degrees across regions. Generally, cloud cover and liquid water path (LWP) show better agreement with satellite retrievals at 3.25 km, though surface‐based comparisons suggest otherwise. Aerosol composition remains poorly represented at both resolutions. The RRM increases Aitken mode aerosol number concentrations via enhanced new particle formation. However, accumulation mode aerosols are decreased at higher resolution as aerosol removals become more efficient. This partially contributes to fewer cloud condensation nuclei (CCN) and lower cloud droplet number concentrations (N d ), which produces larger model biases in some scenarios. These findings suggest that solely increasing horizontal resolution to kilometer scales is insufficient to broadly improve aerosol and cloud predictions without concurrent advancements in physical and chemical process representations. Nonetheless, the RRM moderately improves key ACI relationships such as CCN‐N d correlation, reflecting enhanced aerosol activation representation. The LWP‐N d relationship is also better captured by RRM, suggesting a better characterization of LWP adjustment.

Huang, Meng [Pacific Northwest National Laboratory↗

Industrial Assessment Center

Established in 1990, San Diego State University’s (SDSU) Industrial Assessment Center (IAC) is proud of its years of service. During this period, it has served over 620 small and medium-sized manufacturing plants in Southern California. SDSU/IAC’s efforts to transfer state-of-the-art technologies to industry have increased revenues, cultivated creativity, improved energy efficiencies, and benefited the environment. The Center has contributed to the region's economic growth and stability by assisting small and medium size companies to better compete in the global market. It has helped mitigate climate change by reducing greenhouse gas emissions. IAC activities have fostered productive relationships between the University and local industry, assisted industrial sectors to improve their energy efficiency and enhance their manufacturing productivity, in turn impacting the material and working conditions of their employees. In addition to financial savings and environmental benefits, we have trained tens of students who became energy specialists in various companies. Thus, a substantial benefit of the IAC has been the ongoing training of engineering faculty and students. All IAC graduates were offered jobs before or within weeks of their graduation. The activities of the SDSU/IAC have expanded the institutional expertise of the College and improved the knowledge base of the faculties involved leading to several related publications, master’s theses, and senior student projects. Significant number of peer-reviewed publications of the IAC director at SDSU have greatly benefitted from the experience of the Center. As a result of this extensive exposure to manufacturing processes, the SDSU/IAC has grown to be an integral component of SDSU’s engineering research and training. We have successfully built upon these established achievements and academic excellence. IAC service to industry is particularly vital in Southern California, a region with one of the highest manufacturing concentrations in the country. SDSU/IAC has understood and implemented the overall objectives of DOE’s IAC program and guidelines except for the pandemic years when the country’s manufacturing sector was put in dire stress. In addition to student training and service to industry, IAC’s contribution to state and local governments as well as utility companies to assess energy policies and design rebate and incentive strategies cannot be undermined.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Correlating Experimentally Determined Hydrogen Binding Energy with Hydrogen Evolution Activity over Metal Monolayers on Molybdenum Nitride

It is well established that hydrogen binding energy (HBE) is a key descriptor for hydrogen evolution reaction (HER) activity, and such a relationship is a useful tool for searching efficient and cost-effective HER catalysts. However, in almost all cases, the HBE values are obtained from density functional theory (DFT) calculations. Here in this study, temperature programmed desorption (TPD) was used to experimentally determine the HBE values of metal monolayers supported on molybdenum nitride (Mo 2 N), and electrochemical measurements were performed on the same surfaces. Combined DFT and kinetic Monte Carlo (kMC) simulations were used to validate the trend observed with TPD and the electrochemical HER activity. Depositing one monolayer of Pt on Mo 2 N led to similar HBE values seen for bulk Pt, and electrochemical measurements showed that monolayer Pt on Mo 2 N had similar HER activity to that of bulk Pt. Similar studies were also performed for monolayer Pd on Mo 2 N to confirm the correlation. This work also demonstrates that Mo 2 N is a promising support to reduce precious metal loading in HER catalysts.

08 HYDROGEN↗

Descriptors for Cu facets for CO2 reduction reaction activity

Computation screening is crucial for designing efficient electrochemical catalysts for carbon dioxide (CO2R) reduction to valuable hydrocarbons and oxygenates. Herein, leveraging density functional theory calculations of the CO adsorption energy ΔE_CO on seventeen Cu terminations, we discover a strong linear correlation between ΔE_CO and the recently experimentally measured CO2R electrochemical currents (ACS Catal. 2022, 12, 11, 6578–6588). Examining the ab initio thermodynamics of the early critical intermediates CO*, COH*, and CHO*, we find that CO* → CHO* is the thermodynamically preferred step, and notably shows a volcano trend with the experimental currents where the maximum CO2R current corresponds to the moderate CHO* formation energy. Importantly, we show that increasing the step and kink density of the Cu termination not only enhances CO adsorption strength but also modulates the CO* → CHO* pathway, as respectively exemplified in the (941) and (741) facets. We also explain why (741) is exceptional with high CO2R activity as measured experimentally due to its relatively low activity toward the hydrogen evolution reaction compared with the other Cu surfaces. Beyond the general CO adsorption energy that only shows a linear trend with CO2R activity, we show that the reaction CO* → CHO* free energy is a descriptor that displays a volcano relationship with the overall CO2R activity on Cu facets.

machine learning↗

Assessing residential activity in a home plumbing system simulator: monitoring the occurrence and relationship of major opportunistic pathogens and phagocytic amoebas

Opportunistic premise plumbing pathogens (OPPPs) have been detected in buildings’ plumbing systems causing waterborne disease outbreaks in the United States. In this study, we monitored the occurrence of OPPPs along with free-living amoeba (FLA) and investigated the effects of residential activities in a simulated home plumbing system (HPS). Water samples were collected from various locations in the HPS and analyzed for three major OPPPs: Legionella pneumophila, nontuberculous mycobacterial species (e.g., Mycobacterium avium, M. intracellulare, and M. abscessus), and Pseudomonas aeruginosa along with two groups of amoebas (Acanthamoeba and Vermamoeba vermiformis). A metagenomic approach was also used to further characterize the microbial communities. Results show that the microbial community is highly diverse with evidence of spatial and temporal structuring influenced by environmental conditions. L. pneumophila was the most prevalent pathogen (86% of samples), followed by M. intracellulare (66%) and P. aeruginosa (21%). Interestingly, M. avium and M. abscessus were not detected in any samples. The data revealed a relatively low prevalence of Acanthamoeba spp. (4%), while V. vermiformis was widely detected (81%) across all the sampling locations within the HPS. Locations with a high concentration of L. pneumophila and M. intracellulare coincided with the highest detection of V. vermiformis, suggesting the potential growth of both populations within FLA and additional protection in drinking water. After a period of stagnation lasting at least 2-weeks, the concentrations of OPPPs and amoeba immediately increased and then decreased gradually back to the baseline. Furthermore, monitoring the microbial population after drainage of the hot water tank and partial drainage of the entire HPS demonstrated no significant mitigation of the selected OPPPs. This study demonstrates that these organisms can adjust to their environment during such events and may survive in biofilms and/or grow within FLA, protecting them from stressors in the supplied water.

59 BASIC BIOLOGICAL SCIENCES↗

Automated Experiment in 4D-STEM: Exploring Emergent Physics and Structural Behaviors

Automated experiments in 4D scanning transmission electron microscopy (STEM) are implemented for rapid discovery of local structures, symmetry-breaking distortions, and internal electric and magnetic fields in complex materials. Deep kernel learning enables active learning of the relationship between local structure and 4D-STEM-based descriptors. With this, efficient and “intelligent” probing of dissimilar structural elements to discover desired physical functionality is made possible. This approach allows effective navigation of the sample in an automated fashion guided by either a predetermined physical phenomenon, such as strongest electric field magnitude, or in an exploratory fashion. Therefore, we verify the approach first on preacquired 4D-STEM data and further implement it experimentally on an operational STEM. The experimental discovery workflow is demonstrated using graphene and subsequently extended toward a lesser-known layered 2D van der Waals material, MnPS 3 . This approach establishes a pathway for physics-driven automated 4D-STEM experiments that enable probing the physics of strongly correlated systems and quantum materials and devices, as well as exploration of beam-sensitive materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Cation-doped ZnS catalysts for polysulfide conversion in lithium–sulfur batteries

Catalytic conversion of polysulfides is regarded as a crucial approach to enhancing kinetics and suppressing the shuttle effect in lithium-sulfur (Li-S) batteries. However, the activity prediction of Li-S catalysts remains elusive owing to the lack of mechanistic understanding of activity descriptors. Here, we report a volcano-shaped relationship between polysulfide adsorption ability and catalytic activity. In conjunction with theoretical analysis, we distinguish catalytic and anchoring effects to delineate the role of adsorption and emphasize the passivation of catalysts. These findings enable us to develop a composite catalyst, Co0.125Zn0.875S, which shows higher performance than simple binary compounds. Such a fundamental understanding of the intrinsic link between polysulfide adsorption and catalytic activity offers a rational viewpoint for designing Li-S catalysts and tuning their activities.

Shen, Zihan↗

Enhancing the connection between computation and experiments in electrocatalysis

Combining computational and experimental methods is a powerful approach to understand the variables that govern catalyst performance and ultimately design improved materials. However, the effectiveness of this approach rests on the strength of the relationships between calculated parameters and experimental measurements. These relationships are complicated by the intricacy and dynamic behaviour of catalytic active sites, and by the non-trivial relationship between calculated reaction energetics and observed rates. In this Perspective, we highlight opportunities to enhance the connection between computation and experiment in electrocatalysis. These include measuring the intrinsic kinetic behaviour of catalysts, creating precise models for the active site and its environment, and forming clear relationships between calculated reaction energetics and observed rates. As experimental and computational methods continue to become more powerful, clear connections between the two will maximize their utility to guide the design of efficient and selective electrocatalysts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Suppressed Lone Pair Electrons Explain Unconventional Rise of Lattice Thermal Conductivity in Defective Crystalline Solids

Manipulating thermal properties of materials can be interpreted as the control of how vibrations of atoms (known as phonons) scatter in a crystal lattice. Compared to a perfect crystal, crystalline solids with defects are expected to have shorter phonon mean free paths caused by point defect scattering, leading to lower lattice thermal conductivities than those without defects. While this is true in many cases, alloying can increase the phonon mean free path in the Cd-doped AgSnSbSe 3 system to increase the lattice thermal conductivity from 0.65 to 1.05 W m –1 K –1 by replacing 18% of the Sb sites with Cd. It is found that the presence of lone pair electrons leads to the off-centering of cations from the centrosymmetric position of a cubic lattice. X-ray pair distribution function analysis reveals that this structural distortion is relieved when the electronic configuration of the dopant element cannot produce lone pair electrons. Furthermore, a decrease in the Grüneisen parameter with doping is experimentally confirmed, establishing a relationship between the stereochemical activity of lone pair electrons and the lattice anharmonicity. The observed “harmonic” behavior with doping suggests that lone pair electrons must be preserved to effectively suppress phonon transport in these systems.

36 MATERIALS SCIENCE↗

Substrate-Driven Catalyst Reducibility for Oxygen Evolution and Its Effect on the Operation of Proton Exchange Membrane Water Electrolyzers

To increase the efficiency of hydrogen production by proton exchange membrane water electrolyzers (PEMWEs), the relationships between the specific activity and stability of the membrane–electrode assembly (MEA) must be clarified. Ir oxide electrodeposited on Ti substrate is used as an oxygen electrode, and its electronic properties and electrochemical behavior in PEMWE operation are observed. The electrode, fabricated through a facile strategy based on annealing in the air atmosphere, enhances the specific oxygen evolution reaction (OER) activity and stability in PEMWE operation. Furthermore, the electronic catalyst–substrate interactions associated with different reducibilities in the heteroatom system are studied. The morphology, electronic properties, and chemical state of the oxygen electrode are investigated through X-ray spectroscopy, microscopy techniques, and computational analyses. Thermal treatment of the catalyst-coated substrate decreases the bulk oxidation state of Ir and increases the surface oxidation state. According to the electrochemical and physical behavior analyses of PEMWEs, the oxygen content in the Ir oxide structure, which defines the OER activity and its stability, is influenced by the crystalline structure and formation of a stable interface between the catalyst and substrate. The outcomes can facilitate the development of strategies for enhancing the performance of PEMWEs and designing rational MEAs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Grain growth kinetics of the gamma phase metallic uranium

We report metallic uranium is a leading fuel form for sodium cooled fast reactors as an enabling technology of future nuclear energy systems. Mechanistic understanding of fuel behaviors and kinetics under thermodynamic equilibrium and highly non-equilibrium conditions are essential for evaluating fuel performance. It is important to understand and predict the grain and pore evolutions of metallic fuels under thermal and irradiation conditions. However, very limited data are available on the grain growth kinetics and mechanisms of pure gamma phase uranium. In this paper, the pure gamma uranium pellets with different grain structures were fabricated by combining high-energy ball milling and spark plasma sintering. Isothermal annealing tests were performed to investigate the grain growth behavior of the pure gamma phase uranium with different initial grain sizes. A parabolic relationship in grain growth with time was identified for the submicron-sized (374 nm) sample. In contrast, for the nano-sized (137 nm) sample, the grain growth shows a linear relationship with time. The activation energies of grain growth were determined as 199.5 KJ/mol and 80.6 KJ/mol for nano-sized and submicron-sized grain structures, respectively. For the nano-sized sample, the rate-control step of grain growth is dominated by the triple-junction migration, in which the grain boundary triple junction drags the grain growth, leading to a higher activation energy than the bulk diffusion. The dominating mechanism for the submicron-sized sample is grain boundary diffusion. The mechanistic understanding and critical data obtained on the kinetics of pure uranium phases will be useful to evaluate fuel behavior under thermodynamic equilibrium conditions and develop a high fidelity model to predict fuel performance.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Analysis of tumor-immune functional responses in a mathematical model of neoantigen cancer vaccines

Cancer neoantigen vaccines have emerged as a promising approach to stimulating the immune system to fight cancer. We propose a simple model including key elements of cancer-immune interactions and conduct a phase plane analysis to understand the immunological mechanisms of cancer neoantigen vaccines. Analytical results are obtained for two widely used functional forms that represent the killing rate of tumor cells by immune cells: the law of mass action (LMA) and the dePillis-Radunskaya Law (LPR). Using the LMA, our results reveal that a slowly growing tumor can escape the immune surveillance and that there is a unique periodic solution. The LPR offers richer dynamics, in which tumor elimination and uncontrolled tumor growth are both present. We show that tumor elimination requires sufficient number of initial activated T cells in relationship to the malignant cells, which lends support to using the neoantigen cancer vaccine as an adjuvant therapy after the primary tumor is surgically removed or treated using radiotherapy. We also derive a sufficient condition for uncontrolled tumor growth under the assumption of the LPR. Here, the juxtaposition of analyses with these two different choices for the killing rate function highlights their importance on model behavior and biological implications, by which we hope to spur further theoretical and experimental work to understand mechanisms underlying different functional forms for the killing rate.

60 APPLIED LIFE SCIENCES↗

Exploring the Relationship of Microstructure and Conductivity in Metal Halide Perovskites via Active Learning-Driven Automated Scanning Probe Microscopy

Electronic transport and hysteresis in metal halide perovskites (MHPs) are key to the applications in photovoltaics, light emitting devices, and light and chemical sensors. These phenomena are strongly affected by the materials microstructure including grain boundaries, ferroic domain walls, and secondary phase inclusions. Here, we demonstrate an active machine learning framework for “driving” an automated scanning probe microscope (SPM) to discover the microstructures responsible for specific aspects of transport behavior in MHPs. In our setup, the microscope can discover the microstructural elements that maximize the onset of conduction, hysteresis, or any other characteristic that can be derived from a set of current–voltage spectra. Furthermore, this approach opens new opportunities for exploring the origins of materials functionality in complex materials by SPM and can be integrated with other characterization techniques either before (prior knowledge) or after (identification of locations of interest for detail studies) functional probing.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗