Scaling Relationships Research Articles

Amplified temperature sensitivity of extreme precipitation events following heat stress

This study investigates global extreme precipitation events (EPEs) during warm seasons, with a particular focus on EPEs preceded by extreme heat stress (EPE-Hs) and a comparative analysis with those not (EPE-NHs). Using reanalysis product and Earth System Model data, the spatiotemporal characteristics and temperature sensitivities of EPEs are analyzed. Results show that EPE-Hs, while less frequent, have longer duration and greater magnitude compared to EPE-NHs, particularly in high latitude regions. In the future, a significant increase is projected in the characteristics of EPE-Hs, in contrast to the stable duration and magnitude of EPE-NHs. EPE-Hs demonstrate substantially higher temperature sensitivity than EPE-NHs, especially in low latitudes. The precipitation-temperature scaling relationships diverge markedly between EPE-Hs and EPE-NHs, with notable regional variations. These insights are pivotal for crafting region-specific early warning and adaptation strategies to mitigate the risks associated with extreme precipitation under the backdrop of global warming.

Unveiling the mechanism secret of abrasion emissions of particulate matter and microplastics

Recent research highlights that non-exhaust emissions from the abrasion of tires and other organic materials have emerged as a substantial source of airborne particulate matter and marine microplastics. Despite their growing impact, the underlying mechanisms driving these abrasion emissions have remained largely unexplored. In this study, we uncover that abrasion emissions from organic materials are fundamentally governed by a fatigue fracture process, wherein particles are progressively detached from the material surface under cyclic abrasion loads. Our findings demonstrate that these emissions increase significantly only when the applied abrasion loads surpass the material’s toughness threshold. We establish a scaling relationship between the concentration of emitted particulate matter and the measurable crack propagation rate of the organic material, offering a robust quantitative method to estimate abrasion emissions. This work not only introduces a novel mechanistic framework for understanding particulate matter pollution from organic material abrasion but also provides a scientific basis for developing strategies to mitigate emissions of airborne particulates and marine microplastics.

Importing Atomic Rare‐Earth Sites to Activate Lattice Oxygen of Spinel Oxides for Electrocatalytic Oxygen Evolution

Spinel oxides have emerged as highly active catalysts for the oxygen evolution reaction (OER). However, due to covalency competition, the OER process on spinel oxides often follows an arduous adsorbate evolution mechanism (AEM) pathway. Herein, we propose a novel rare‐earth sites substitution strategy to tune the lattice oxygen redox of spinel oxides and bypass the AEM scaling relationship limitation. Taking NiCo2O4 as a model, the incorporation of Ce into the octahedral site induces the formation of Ce‐O‐M (M: Ni, Co) bridge, which triggers charge redistribution in NiCo2O4. The developed Ce‐NiCo2O4 exhibits remarkable OER activity with a low overpotential, satisfactory electrochemical stability, and good practicability in anion‐exchange membrane water electrolyzer. Theoretical analyses reveal that OER on Ce‐NiCo2O4 surface follows a more favorable lattice oxygen mechanism (LOM) pathway and non‐concerted proton‐electron transfers compared to pure NiCo2O4, as further verified by pH‐dependent behavior and in situ Raman analysis. 18O‐labeled electrochemical mass spectrometry directly demonstrates that oxygen originates from the lattice oxygen of Ce‐NiCo2O4 during OER. It is discovered that electron delocalization of Ce 4f states triggers charge redistribution in NiCo2O4 through the Ce‐O‐M bridge, favoring antibonding state occupation of Ni‐O bonding in [Ce‐O‐Ni] site, thereby activating lattice oxygen redox of NiCo2O4 in OER.

Importing Atomic Rare-Earth Sites to Activate Lattice Oxygen of Spinel Oxides for Electrocatalytic Oxygen Evolution.

Spinel oxides have emerged as highly active catalysts for the oxygen evolution reaction (OER). However, due to covalency competition, the OER process on spinel oxides often follows an arduous adsorbate evolution mechanism (AEM) pathway. Herein, we propose a novel rare-earth sites substitution strategy to tune the lattice oxygen redox of spinel oxides and bypass the AEM scaling relationship limitation. Taking NiCo2O4 as a model, the incorporation of Ce into the octahedral site induces the formation of Ce-O-M (M: Ni, Co) bridge, which triggers charge redistribution in NiCo2O4. The developed Ce-NiCo2O4 exhibits remarkable OER activity with a low overpotential, satisfactory electrochemical stability, and good practicability in anion-exchange membrane water electrolyzer. Theoretical analyses reveal that OER on Ce-NiCo2O4 surface follows a more favorable lattice oxygen mechanism (LOM) pathway and non-concerted proton-electron transfers compared to pure NiCo2O4, as further verified by pH-dependent behavior and in situ Raman analysis.18O-labeled electrochemical mass spectrometry directly demonstrates that oxygen originates from the lattice oxygen of Ce-NiCo2O4 during OER. It is discovered that electron delocalization of Ce 4f states triggers charge redistribution in NiCo2O4 through the Ce-O-M bridge, favoring antibonding state occupation of Ni-O bonding in [Ce-O-Ni] site, thereby activating lattice oxygen redox of NiCo2O4 in OER.

P-Block Aluminum Single-Atom Catalyst for Electrocatalytic CO2 Reduction with High Intrinsic Activity.

Atomically dispersed transition metal sites on nitrogen-doped carbon catalysts hold great potential for the electrochemical CO2 reduction reaction (CO2RR) to CO due to their encouraging selectivity. However, their intrinsic activity is restricted by the hurdle of the high energy barrier of either *COOH formation or *CO desorption due to the scaling relationship. Herein, we discover a p-block aluminum single-atom catalyst (Al-NC) featuring an Al-N4 site that enables disentangling this hurdle, which endows a moderate reaction kinetic barrier for *COOH formation and *CO desorption, as validated by in situ attenuated total reflection infrared spectroscopy and theoretical simulations. As a result, the developed Al-NC shows a CO Faradaic efficiency (FECO) of up to 98.76% at -0.65 V vs RHE and an intrinsic catalytic turnover frequency of 3.60 s-1 at -0.99 V vs RHE, exceeding those of the state-of-the-art Ni-NC and Fe-NC counterparts. Moreover, it also delivers a partial CO current of 309 mA·cm-2 at 93.65% FECO and 605 mA at >85% FECO in a flow cell and membrane electrode assembly (MEA), respectively. Strikingly, when using low-concentration CO2 (30%) as the feedstock, this catalyst can still deliver a partial CO current of 240 mA at >80% FECO in the MEA. Considering the earth-abundant character of the Al element and the high intrinsic activity of the Al-NC catalyst, it is a promising alternative to today's transition metal-based single-atom catalysts.

The Performance of Ethylene Partial Oxidation on Group IB Metal Stepped Surfaces

Group IB metal catalysts, particularly Ag, Au, and Cu, exhibit particular selectivity for ethylene oxide (EO) formation, while Ag demonstrating the highest performance so far. Previous studies have explored the EO formation mechanism on (100) and (111) surfaces of group IB metals, but the reaction mechanism on the (211) facet (analogous to the edge sites of a catalyst particle) remains poorly understood. Herein, we fill in the knowledge gap by analyzing ethylene partial oxidation to EO on the (211) surfaces of Ag, Au, and Cu through density functional theory (DFT) calculations, scaling relationship analysis, and microkinetic modeling. Our study demonstrates that the (211) surface decreases the energy barrier for the dissociation of oxygen molecules into oxygen atoms, while unfavorable for the production of EO. Therefore, we should preserve an appropriate concentration of (211) surface on the nanoparticles when designing catalysts for the ethylene epoxidation reaction.

Regulating Local Coordination Sphere of Ir Single Atoms at the Atomic Interface for Efficient Oxygen Evolution Reaction.

Single-atom catalysts dispersed on an oxide support are essential for overcoming the sluggishness of the oxygen evolution reaction (OER). However, the durability of most metal single-atoms is compromised under harsh OER conditions due to their low coordination (weak metal-support interactions) and excessive disruption of metal-Olattice bonds to enable lattice oxygen participation, leading to metal dissolution and hindering their practical applicability. Herein, we systematically regulate the local coordination of Irsingle-atoms at the atomic level to enhance the performance of the OER by precisely modulating their steric localization on the NiO surface. Compared to conventional Irsingle-atoms adsorbed on NiO surface, the atomic Ir atoms partially embedded within the NiO surface (Iremb-NiO) exhibit a 2-fold increase in Ir-Ni second-shell interaction revealed by X-ray absorption spectroscopy (XAS), suggesting stronger metal-support interactions. Remarkably, Iremb-NiO with tailored coordination sphere exhibits excellent alkaline OER mass activity and long-term durability (degradation rate: ∼1 mV/h), outperforming commercial IrO2 (∼26 mV/h) and conventional Irsingle-atoms on NiO (∼7 mV/h). Comprehensive operando X-ray absorption and Raman spectroscopies, along with pH-dependence activity tests, identified high-valence atomic Ir sites embedded on the NiOOH surface during the OER followed the lattice oxygen mechanism, thereby circumventing the traditional linear scaling relationships. Moreover, the enhanced Ir-Ni second-shell interaction in Iremb-NiO plays a crucial role in imparting structural rigidity to Ir single-atoms, thereby mitigating Ir-dissolution and ensuring superior OER kinetics alongside sustained durability.

Substrate topography regulating membrane adhesion mediated by receptor–ligand bonds

Cell adhesion can be significantly influenced by the topography of the substrate surface. However, how the adhesion molecules essentially respond to this topographical stimulus is not fully understood yet. Here, we employ an effective Monte Carlo simulation to systematically investigate a fluctuating membrane interacting with a curved substrate via adhesive proteins. Interestingly, results show that, compared with the flat substrate, curved substrates regulate the membrane adhesion in a bond length dependent manner. The effects of the substrate surface amplitude and wavelength on the number of molecular bonds and adhesion pattern are also extracted from the scaling relationship between the characteristic lateral length of the membrane and the local substrate curvature radius. Furthermore, the local substrate curvature is found to select the bond distribution in terms of the bond length and stiffness. The results suggest that the bond stiffness enhances the clustering of molecular bonds, mainly due to synergistic interactions among these molecular bonds.

Integrating Active Learning and DFT for Fast-Tracking Single-Atom Alloy Catalysts in CO2-to-Fuel Conversion.

Electrocatalytic carbon dioxide reduction (CO2RR) technology enables the conversion of excessive CO2 into high-value fuels and chemicals, thereby mitigating atmospheric CO2 concentrations and addressing energy scarcity. Single-atom alloys (SAAs) possess the potential to enhance the CO2RR performance by full utilization of atoms and breaking linear scaling relationships. However, quickly screening high-performance metal portfolios of SAAs remains a formidable challenge. In this study, we proposed an active learning (AL) framework to screen high-performance catalysts for CO2RR to yield fuels such as CH4 and CH3OH. After four rounds of AL iterations, the ML model attained optimal prediction performance with the test set R2 of approximately 0.94 and successful prediction was achieved for the binding free energy of *CHO, *COH, *CO, and *H on 380 catalyst surfaces with an accuracy within 0.20 eV. Subsequent analysis of the SAA catalysts' activity, selectivity, and stability led to the discovery of eight previously unexplored SAA catalysts for CO2RR. Notably, the surface activity of Ti@Cu(100), Au@Pt(100), and Ag@Pt(100) shines prominently. Utilizing DFT calculations, we elucidated the complete reaction pathway of the CO2RR on the surfaces of these catalysts, confirming their high catalytic activity with limiting potentials of -0.11, -0.34, and -0.46 eV, respectively, which are significantly lower than those of pure Cu catalysts. The results showcase the exceptional predictive prowess of AL, providing a valuable reference for the design of CO2RR catalysts.

A scaling relationship between thermodynamic and hydrodynamic interactions in protein solutions

Weak protein interactions are associated with a broad array of biological functions and are often implicated in molecular dysfunction accompanying human disease. In addition, these interactions are a critical determinant in the effective manufacturing, stability, and administration of biotherapeutic proteins. Despite their prominence, much remains unknown about how molecular attributes influence the hydrodynamic and thermodynamic contributions to the overall interaction mechanism. To systematically probe these contributions, we have evaluated self-interaction in a diverse set of proteins that demonstrate a broad range of behaviors from attractive to repulsive. Analysis of the composite trending in the data provides a convenient interconversion among interaction parameters measured from the concentration dependence of the molecular weight, diffusion coefficient, and sedimentation coefficient, as well as insight into the relationship between thermodynamic and hydrodynamic interactions. We find relatively good agreement between our data and a model for interacting hard spheres in the range of weak self-association. In addition, we propose an empirically derived, general scaling relationship applicable across a broad range of self-association and repulsive behaviors.

Modelling global mesozooplankton biomass using machine learning

Mesozooplankton are a crucial link between primary producers and higher trophic levels and play a vital role in marine food webs, biological carbon pumps, and sustaining fishery resources. However, the global distribution of mesozooplankton biomass and the relevant controlling mechanisms remain elusive. We compared four machine learning algorithms (Boosted Regression Trees, Random Forest, Artificial Neural Network, and Support Vector Machine) to model the spatiotemporal distributions of global mesozooplankton biomass. These algorithms were trained on a compiled dataset of published mesozooplankton biomass observations with corresponding environmental predictors from contemporaneous satellite observations (temperature, chlorophyll, salinity, and mixed layer depth). We found that Random Forest achieved the best predictive accuracy with R2 and RMSE (Root Mean Standard Error) of 0.57 and 0.39, respectively. Also, the global distribution of mesozooplankton biomass predicted by the Random Forest model was more consistent with the observational data than other models. We used the Random Forest model to create a global map of mesozooplankton biomass which serves as a reference for validating process-based ecosystem models. The model outputs confirm that environmental factors, especially surface Chl a, a proxy for prey availability, significantly correlate with the spatiotemporal distribution of mesozooplankton biomass. The scaling relationship between the mesozooplankton biomass and Chl a can be used as an emergent constraint for model validation and development. Moreover, our model predicts that the global mesozooplankton biomass will decrease by 3% by the end of this century under the “business-as-usual” scenarios, potentially reducing fishery production and carbon sequestration. Our study contributes to predicting global mesozooplankton biomass and provides deep insights into the underlying environmental impacts on the distribution of mesozooplankton biomass.

Allometric Growth and Scaling of Body Form of the Spadenose Shark (Scoliodon laticaudus).

The versatility of the shark body form is suggested to be one of the key factors underlying their evolutionary success and persistence. Nevertheless, sharks exhibit a huge diversity of body forms and morphological adaptations. More subtly, it is increasingly evident that in many species, morphology varies through ontogeny. Multiple competing hypotheses exist explaining both the function of specific morphological structures and the interspecific distribution of these ontogenetic morphological shifts. However, existing studies are restricted to a small number of mostly large-bodied species. In this study, we report allometric scaling relationships from functionally important morphological structures in the spadenose shark (Scoliodon laticaudus). We find that a mosaic of isometric and allometric growth underlies the scaling trends in this species and that cases of allometry are consistent with an ontogenetic shift in diet. Moreover, our results refute suggestions that small-bodied sharks grow isometrically. Given the small number of existing studies of ontogenetic morphometry in sharks and the life-history/ecological characteristics of S. laticaudus, this study is a valuable contribution to our understanding of the adaptive value of ontogenetic morphological shifts in elasmobranchs.

Scaling Relationships between Local Magnitude, Seismic Moment, and Radiated Seismic Energy for the 18 April 2019 Xiulin, Taiwan, Earthquake Sequence: Implications for Source Non-Self-Similarity

Abstract Using local seismograms, we obtain the source parameters of the 2019 ML 6.32 Xiulin, Taiwan, earthquake sequence to establish scaling relationships among the local magnitude (ML), seismic moment (M0), moment magnitude (Mw), and radiated seismic energy (ES) for ML<4.0 events. The key findings are as follows: (1) logES∝2.0ML, which is consistent with previous studies; (2) logM0∝1.0ML, which leads to Mw∝0.67ML; and (3) the scaled energy (ES/M0) is proportional to M0. These relationships imply that the source duration (T) should be independent of M0. Further analysis via forward waveform modeling reveals a very weak correlation between T and M0 (i.e., logT∝0.03logM0), suggesting that T should be nearly constant. These findings indicate that the source properties of the 2019 Xiulin earthquake sequence were not self-similar. In addition, the two relationships, logES∝2.0ML and logM0∝1.0ML, suggest that ES/M0 should increase with M0 rather than ES/M0 tending to be a constant.

Patterns of early embryogenesis and growth in the olfactory system of chick (Gallus gallus domesticus) based on iodine-enhanced micro-computed tomography.

The vertebrate olfactory system entails a complex set of neural/support structures that bridge morphogenetic regions. The developmental mechanisms coordinating this bridge remain unclear, even for model organisms such as chick, Gallus gallus. Here, we combine previous growth data on the chick olfactory apparatus with new samples targeting its early embryogenesis. The purpose is to illuminate how early developmental dynamics integrate with scaling relationships to produce adult form and, potentially, evolutionary patterns. Olfactory structures, including epithelium, turbinate, nerve, and olfactory bulb, are considered in the context of neighboring nasal and brain structures. Axonal outgrowth from the olfactory epithelium, which eventually connects receptor neurons with the brain, begins earlier than previously established. This dynamic marks the beginning of a complex pattern of early differential growth wherein the olfactory bulbs scale with positive allometry relative to both brain volume and turbinate area, which in turn scale isometrically with one another. The mechanisms driving observed patterns of organogenesis and growth remain unclear awaiting experimental evidence. We discuss competing hypotheses, including the possibility that broad-based isometry of olfactory components reflects constraints imposed by high levels of functional/structural integration. Such integration would include the frontonasal prominence having a strong influence on telencephalic patterning.

Independent Control Over the H/OH Adsorption: Breaking the Trade-Off Between H/OH-Adsorption and H2O-Dissociation of Platinum-Group Metal Electrocatalyst for Hydrogen Evolution Reaction.

Platinum-group metals catalysts (such as Rh, Pd, Ir, Pt) have been the most efficient hydrogen evolution reaction (HER) electrocatalysts due to their moderate H adsorption strength, while the high H2O-dissociation barrier in alkaline media restrains the catalytic performance of PGM catalysts. However, the optimization of the H2O-dissociation barrier and *H/*OH binding energy toward their individual optima is limited due to the constraints of their scaling relationship on a single active site. Here, a coordinatively unsaturated "M─Ox─W" (M = Rh, Pd, Ir, Pt) active area is constructed, where H and OH species are anchored on Pt-group metal sites and inactive W sites for individual regulation. By combining experiments and density functional theory calculations, the introduction of extra OH-adsorption sites (coordinatively unsaturated WO3-x) avoids the competitive adsorption of H and OH on the single site, while the enhanced OH-adsorption capacity on the coordinatively unsaturated WO3-x effectively facilitates the adsorption/dissociation of interfacial H2O. As a result, the representative Rh-WO3-x catalyst exhibits outstanding catalytic activity and durability for HER. The findings of this work not only provide valuable insights for the design of efficient PGM catalysts for HER but also shed light on the development of electrocatalysts for other catalytic reactions.

Correlation-Based Predictions of Gas Solute Diffusivity in Ionic Liquid Solvents Based on Solvent-Accessible Surface Area.

In our prior study [Olowookere, F. V.; Turner, C. H. J. Phys. Chem. B 2023, 127(42), 9144-9154], we introduced a new scaling relationship to predict gas solute diffusion at challenging conditions, focusing on CO2 and SO2 diffusion in multivalent ionic liquid (IL) solvents. This work extends our initial exploratory study into a much broader array of systems, encompassing additional solutes (N2, CH4, C2H6, C3H8, C3H8O, and H2O) and a variety of different ionic liquid species ([Bzmim3]3+, [Bzmim4]4+, [BMIM]+, [EMIM]+, [HMIM]+, [NapO2]2-, [BzO3]3-, [BF4]-, [Tf2N]-, and [PF6]-). Our study demonstrates a remarkably robust logarithmic correlation between solute diffusion and solvent accessible surface area (SA) across 20 different additional systems. We perform comprehensive analyses of the underlying molecular phenomena responsible for this correlation, including solute lifetime distributions, void space dynamics, and Voronoi tessellation, in order to elucidate a stronger mechanistic understanding of this behavior. Our findings highlight a direct link between the solvent accessible SA and the size of the void domains. Overall, our scaling approach provides an efficient and reliable approach for predicting diffusion from analyses of short simulations at higher temperatures.

Better than linear strength scaling of multifunctional ceramic truss lattice materials

Driven by the goal of creating exceptionally strong and lightweight thermal insulators to enable the operation of a vacuum airship on Venus, conditions where ceramic truss lattice materials provide better than linear scaling of strength with respect to variations in relative density have been found. This enhanced scaling relationship is a consequence of the pressure sensitive shear strength of ceramic materials. A new Gibson-Ashby type scaling relationship is developed between strength and relative density. Elementary analysis is used to formulate theoretical limits for the compressive strength, minimum density, and minimum thermal conductivity for truss lattice materials subjected to hydrostatic pressure loads. Shape optimization using the covariance matrix adopted evolutionary strategy (CMA-ES) and highly resolved finite element models is conducted on silicon carbide Kelvin cells with variable cross-section axisymmetric struts considering three failure modes: buckling, tensile rupture, and shear failure. The optimized designs closely adhere to and validate the newly developed analytical scaling relationship with better than linear strength scaling. These optimized designs are found to withstand the extreme loading conditions on Venus while providing up to 43 kgm3 of buoyancy. The thermal conductivity of the optimized designs are computed and found to be less than 0.5 WmK, with one design outperforming silica aerogels at elevated temperature.

Transport of a comb-like polymer across a nanochannel subject to a pulling force.

We investigate the dynamics of comb-like polymer translocation through a nanochannel using three-dimensional Langevin dynamics simulations based on a coarse-grained chain model. A comprehensive set of simulations are performed to examine the effects of system parameters such as the grafting densityρof the side chains, the polymer chain length, the nanochannel dimensions, and the magnitude of the pulling force on the translocation dynamics. For a given polymer chain length, keeping the backbone length is constant while varyingρ, we have found that the dependence of the mean translocation time⟨τ⟩onρis non-monotonic, with a maximum translocation time for a specificρat which the translocation is the slowest. The simulation results also show that⟨τ⟩is not significantly affected by the channel width above a certain radius, while the comb-like polymer translocation is hindered by a narrower channel due to increased interactions between the chain monomers and the channel. In addition,⟨τ⟩increases linearly with the nanochannel length. A linear scaling relationship between the mean translocation time⟨τ⟩and the chain lengthNof polymer is obtained,⟨τ⟩∼N. Similarly, the dependence of⟨τ⟩on the backbone chain sizeNbbhas a quasi-linear dependence,⟨τ⟩∼Nbb. On the other hand, the translocation velocityvfollows a power-law relationship with the polymer chain lengthNasv∼N-1. The mean translocation time also shows an inverse linear relationship with the magnitude of the pulling forceF,⟨τ⟩∼F-1. The power-law relationships discovered in this study contribute to the fundamental understanding of the comb polymer translocation dynamics and to establishing a framework for further investigations in this field.

Inferring the metabolic rate of bone

The bone organ is poorly represented in comparative research on mammalian mass-specific metabolic rates. As a first order attempt to remedy this, from the literature we collected mass-specific metabolic rates for all major organs except for the bone organ, and by subtraction infer the rate for the bone organ. The scaling relationships are given of each whole-organ mass-specific metabolic rate and of the relationship between whole-organ metabolic rate and body mass. Scaling of the lung, adipose depot and bone organ with body mass is higher than would be expected by ¾ power scaling. We interpret the similar scalings of bone and the adipose depot in light of their evolved regulation of whole-body metabolism. We also briefly examine the supra-¾ power scaling of the lung as well as the independence of the mass-specific metabolic rate of the heart from body mass. The bone organ exhibits relatively high energy expenditure with increasing body size. The bone marrow and its medullary adipocyte store may be responsible for engendering the greater share of the bone organ's energetic cost.

Tandem Electroreduction of Nitrate to Ammonia Using a Cobalt–Copper Mixed Single‐Atom/Cluster Catalyst with Synergistic Effects

AbstractElectrochemical conversion of waste nitrate (NO3−) to ammonia (NH3) for environmental applications, such as carbon‐neutral energy sources and hydrogen carriers, is a promising alternative to the energy‐intensive Haber–Bosch process. However, increasing the energy efficiency is limited by the high overpotential and selectivity. Herein, a Co─Cu mixed single‐atom/cluster catalyst (Co─Cu SCC) is demonstrated—with well‐dispersed Co and Cu active sites anchored on a carbon support—that delivers high NH3 Faradaic efficiency of 91.2% at low potential (–0.3 V vs. RHE) due to synergism between the heterogenous active sites. Electrochemical analyses reveal that Cu in Co─Cu SCC preferentially catalyzes the NO3−‐to‐NO2− pathway, whereupon Co catalyzes the NO2−‐to‐NH3 pathway. This tandem electroreduction bypasses the rate‐determining steps (RDSs) for Co and Cu to lower the reaction energy barrier and surpass scaling relationship limitations. The electrocatalytic performance is amplified by the subnanoscale catalyst to increase the partial current density of NH3 by 2.3 and 5.4 times compared to those of individual Co, Cu single‐atom/cluster catalysts (Co SCC, Cu SCC), respectively. This Co─Cu SCC is operated stably for 32 h in a long‐term bipolar membrane (BPM)‐based membrane electrode assembly (MEA) system for high‐concentration NH3 synthesis to produce over 1 m NH3 for conversion into high‐purity NH4Cl at 2.1 g day−1.