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A Generalized Approach to Photon Avalanche Upconversion in Luminescent Nanocrystals
Photon avalanching nanoparticles (ANPs) exhibit extremely nonlinear upconverted emission valuable for subdiffraction imaging, nanoscale sensing, and optical computing. Avalanching has been demonstrated with Tm 3+ -, Pr 3+ -, or Nd 3+ -doped nanocrystals, but their emission is limited to a few wavelengths and materials. Here, we utilize Gd 3+ -assisted energy migration to tune the emission wavelengths of Tm 3+ -sensitized ANPs and generate highly nonlinear emission from Eu 3+ , Tb 3+ , Ho 3+ , and Er 3+ ions. The upconversion intensities of these spectrally discrete ANPs scale with nonlinearity factor s = 10-17 under 1064 nm excitation at power densities as low as 7 kW cm -2 . This strategy for imprinting avalanche behavior on remote emitters can be extended to fluorophores adjacent to ANPs, as here we demonstrate with CdS/CdSe/CdS core/shell/shell quantum dots. ANPs with rationally designed energy transfer networks provide the means to transform conventional linear emitters into a highly nonlinear ones, expanding the use of photon avalanching in biological, chemical, and photonic applications.
Capturing the Catalytic Proton of Dihydrofolate Reductase: Implications for General Acid–Base Catalysis
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General Synthesis of Large Inorganic Nanosheets via 2D Confined Assembly of Nanoparticles
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A General and Predictive Understanding of Thermal Transport from 1D- and 2D-Confined Nanostructures: Theory and Experiment
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General Descriptors for CO[subscript 2]-Assisted Selective C?H/C?C Bond Scission in Ethane
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Polyoxometalate-Assisted Crystallization: A General Strategy Enabling Structural Characterization of Molecular Radium Complexes
A fundamental understanding of radium (Ra) coordination chemistry has been hindered by the scarcity and radiological hazards of 226 Ra, leaving the structural characterization of molecular Ra complexes almost entirely unexplored. Here, we introduce a polyoxometalate (POM)-assisted crystallization strategy that enables single-crystal X-ray diffraction analysis of Ra–chelator complexes from microgram-scale samples. Employing the plenary Keggin anion [SiW 12 O 40 ] 4– , we isolated and structurally characterized Ra 2+ complexes of 18-crown-6 and the bis-picolinate macrocycle macropa, together with their Sr 2+ and Ba 2+ analogues. The resulting structures reveal systematic, size-dependent trends in coordination number and metal-donor distances across the alkaline earth series and provide the first direct experimental measurements of Ra–N and Ra–OCOO bond distances. Together, these results establish POM-assisted crystallization as a robust approach for obtaining solid-state structural information on Ra 2+ complexes of organic chelators, opening new opportunities to advance the coordination chemistry needed to fully realize radium’s potential in isotope production and targeted radiotherapy. More broadly, this approach expands the experimental toolkit available for studying scarce, highly radioactive elements accessible only in microgram quantities.
Ship‐Based Lidar Evaluation of Southern Ocean Low Clouds in the Storm‐Resolving General Circulation Model ICON and the ERA5 and MERRA‐2 Reanalyses
Global storm resolving models (GSRMs) represent the next generation of global climate models. One of them is a 5-km Icosahedral Nonhydrostatic Weather and Climate Model (ICON). Its high resolution means that parameterizations of convection and clouds, including subgrid-scale clouds, are omitted, relying on explicit simulation but necessarily utilizing microphysics and turbulence parameterizations. Standard-resolution (10–100 km) models, which use convection and cloud parameterizations, have substantial cloud biases over the Southern Ocean (SO), adversely affecting radiation and sea surface temperature. The SO is dominated by low clouds, which cannot be observed accurately from space due to overlapping clouds, attenuation, and ground clutter. We evaluated SO clouds in ICON and the ERA5 and MERRA-2 reanalyzes using approximately 2400 days of lidar observations and 2300 radiosonde profiles from 31 voyages and a Macquarie Island station during 2010–2021, compared to the model and reanalyzes using a ground-based lidar simulator. We found that ICON and the reanalyzes underestimate the total cloud fraction by about 10% and 20%, respectively. ICON and ERA5 overestimate the cloud occurrence peak at about 500 m, associated with underestimated lower tropospheric stability and overestimated lifting condensation level. The reanalyzes strongly underestimate fog and very low-level clouds, and MERRA-2 underestimates cloud occurrence at almost all heights. Outgoing shortwave radiation is overestimated in MERRA-2, implying a “too few, too bright” cloud problem. SO cloud and fog biases are a substantial issue in the analyzed model and reanalyzes and result in shortwave and longwave radiation biases.
A Decadal Hybrid GCM Simulation Using Deep‐Learning‐Based Cloud and Convection Parameterization Generalized to a Warm Climate
A critical challenge for machine‐learning (ML) parameterization in global climate models (GCMs) is to achieve stable, accurate simulations under climates not seen during training. Previous studies have demonstrated promising offline performance and year‐long online stability in aquaplanet simulations but have encountered difficulties in real geography and under climate warming. Here we report that a GCM with real geography configuration using neural‐network‐based cloud and convection parameterization, trained exclusively with present‐day climate data, successfully performs a stable, decade‐long simulation of a warm climate with +4 K sea surface temperature (SST). The neural network (NN) is based on Han et al. (2023, https://doi.org/10.1029/2022ms003508 ) with additional inputs. The simulation captures the global precipitation distribution, surface temperatures, vertical atmospheric structures, and extreme precipitation very well, closely matching simulations from both the superparameterized CAM (SPCAM) and the conventional CAM5 in the warm climate without accuracy degradation compared to those in the baseline climate. Moreover, it produces a climate response to +4 K SST in atmospheric thermodynamic states and circulations similar to those from SPCAM and CAM5. Prognostic ablation tests on NN input variables show that the NN without convective memory as input suffers from numerical instability, and the NN without considering radiative variables and land fraction as input, or with reduced training samples produce less accurate results. To our knowledge, this is the first time an ML parameterization successfully achieves online extrapolation to a warm climate without using additional warm‐climate data for training. It demonstrates the potential of ML‐driven parameterizations for credible long‐term climate projections.
A general strategy for preparing pyrrolic-N 4 type single-atom catalysts via pre-located isolated atoms
Single-atom catalysts (SACs) have been applied in many fields due to their superior catalytic performance. Because of the unique properties of the single-atom-site, using the single atoms as catalysts to synthesize SACs is promising. In this work, we have successfully achieved Co 1 SAC using Pt 1 atoms as catalysts. More importantly, this synthesis strategy can be extended to achieve Fe and Ni SACs as well. X-ray absorption spectroscopy (XAS) results demonstrate that the achieved Fe, Co, and Ni SACs are in a M1-pyrrolic N 4 (M= Fe, Co, and Ni) structure. Density functional theory (DFT) studies show that the Co(Cp) 2 dissociation is enhanced by Pt 1 atoms, thus leading to the formation of Co 1 atoms instead of nanoparticles. These SACs are also evaluated under hydrogen evolution reaction (HER) and oxygen evolution reaction (OER), and the nature of active sites under HER are unveiled by the operando XAS studies. These new findings extend the application fields of SACs to catalytic fabrication methodology, which is promising for the rational design of advanced SACs.
Global energy spectrum of the general oceanic circulation
Abstract Advent of satellite altimetry brought into focus the pervasiveness of mesoscale eddies $${{{{{{{\bf{{{{{{{{\mathcal{O}}}}}}}}}}}}}}}}({100})$$ O ( 100 ) km in size, which are the ocean’s analogue of weather systems and are often regarded as the spectral peak of kinetic energy (KE). Yet, understanding of the ocean’s spatial scales has been derived mostly from Fourier analysis in small "representative” regions that cannot capture the vast dynamic range at planetary scales. Here, we use a coarse-graining method to analyze scales much larger than what had been possible before. Spectra spanning over three decades of length-scales reveal the Antarctic Circumpolar Current as the spectral peak of the global extra-tropical circulation, at ≈ 10 4 km, and a previously unobserved power-law scaling over scales larger than 10 3 km. A smaller spectral peak exists at ≈ 300 km associated with mesoscales, which, due to their wider spread in wavenumber space, account for more than 50% of resolved surface KE globally. Seasonal cycles of length-scales exhibit a characteristic lag-time of ≈ 40 days per octave of length-scales such that in both hemispheres, KE at 10 2 km peaks in spring while KE at 10 3 km peaks in late summer. These results provide a new window for understanding the multiscale oceanic circulation within Earth’s climate system, including the largest planetary scales.
General synthesis of high-entropy single-atom nanocages for electrosynthesis of ammonia from nitrate
Given the growing emphasis on energy efficiency, environmental sustainability, and agricultural demand, there’s a pressing need for decentralized and scalable ammonia production. Converting nitrate ions electrochemically, which are commonly found in industrial wastewater and polluted groundwater, into ammonia offers a viable approach for both wastewater treatment and ammonia production yet limited by low producibility and scalability. Here we report a versatile and scalable solution-phase synthesis of high-entropy single-atom nanocages (HESA NCs) in which Fe and other five metals-Co, Cu, Zn, Cd, and In-are isolated via cyano-bridges and coordinated with C and N, respectively. Incorporating and isolating the five metals into the matrix of Fe resulted in Fe-C5 active sites with a minimized symmetry of lattice as well as facilitated water dissociation and thus hydrogenation process. As a result, the Fe-HESA NCs exhibited a high selectivity toward NH3 from the electrocatalytic reduction of nitrate with a Faradaic efficiency of 93.4% while maintaining a high yield rate of 81.4 m h -1 mg -1 .
A general flame aerosol route to kinetically stabilized metal-organic frameworks
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PRISMS-PF: A general framework for phase-field modeling with a matrix-free finite element method
Abstract A new phase-field modeling framework with an emphasis on performance, flexibility, and ease of use is presented. Foremost among the strategies employed to fulfill these objectives are the use of a matrix-free finite element method and a modular, application-centric code structure. This approach is implemented in the new open-source PRISMS-PF framework. Its performance is enabled by the combination of a matrix-free variant of the finite element method with adaptive mesh refinement, explicit time integration, and multilevel parallelism. Benchmark testing with a particle growth problem shows PRISMS-PF with adaptive mesh refinement and higher-order elements to be up to 12 times faster than a finite difference code employing a second-order-accurate spatial discretization and first-order-accurate explicit time integration. Furthermore, for a two-dimensional solidification benchmark problem, the performance of PRISMS-PF meets or exceeds that of phase-field frameworks that focus on implicit/semi-implicit time stepping, even though the benchmark problem’s small computational size reduces the scalability advantage of explicit time-integration schemes. PRISMS-PF supports an arbitrary number of coupled governing equations. The code structure simplifies the modification of these governing equations by separating their definition from the implementation of the numerical methods used to solve them. As part of its modular design, the framework includes functionality for nucleation and polycrystalline systems available in any application to further broaden the phenomena that can be used to study. The versatility of this approach is demonstrated with examples from several common types of phase-field simulations, including coarsening subsequent to spinodal decomposition, solidification, precipitation, grain growth, and corrosion.