Energy Cycle Research Articles

Atmospheric Moisture Decreases Midlatitude Eddy Kinetic Energy

Abstract There is compelling evidence that atmospheric moisture may either increase or decrease midlatitude eddy kinetic energy (EKE). We reconcile these findings by using a hierarchy of idealized atmospheric models to demonstrate that moisture energizes individual eddies given fixed large-scale background winds and temperatures but makes those background conditions less favorable for eddy growth. For climates similar to the present day, the latter effect wins out, and moisture weakens midlatitude eddy activity. The model hierarchy includes a moist two-layer quasigeostrophic (QG) model and an idealized moist general circulation model (GCM). In the QG model, EKE increases when moisture is added to simulations with fixed baroclinicity, closely following a previously derived scaling. But in both models, moisture decreases EKE when environmental conditions are allowed to vary. We explain these results by examining the models’ mean available potential energy (MAPE) and by calculating terms in the models’ Lorenz energy cycles. In the QG model, the EKE decreases because precipitation preferentially forms on the poleward side of the jet, releasing latent heat where the model is relatively cold and decreasing the MAPE, hence the EKE. In the moist GCM, the MAPE primarily decreases because the midlatitude stability increases as the model is moistened, with reduced meridional temperature gradients playing a secondary role. Together, these results clarify moisture’s role in driving the midlatitude circulation and also highlight several drawbacks of QG models for studying moist processes in midlatitudes. Significance Statement Dry models of the atmosphere have played a central role in the study of large-scale atmospheric dynamics. But we know that moisture adds much complexity, associated with phase changes, its effect on atmospheric stability, and the release of latent heat during condensation. Here, we take an important step toward incorporating moisture into our understanding of midlatitude dynamics by reconciling two diverging lines of literature, which suggest that atmospheric moisture can either increase or decrease midlatitude eddy kinetic energy. We explain these opposing results by showing that moisture not only makes individual eddies more energetic but also makes the environment in which eddies form less favorable for eddy growth. For climates similar to the present day, the latter effect wins out such that moisture decreases atmospheric eddy kinetic energy. We demonstrate this point using several different idealized atmospheric models, which allow us to gradually add complexity and to smoothly vary between moist and dry climates. These results add fundamental understanding to how moisture affects midlatitude climates, including how its effects change in warmer and moisture climates, while also highlighting some drawbacks of the idealized atmospheric models.

Revolutionizing Battery Longevity by Optimising Magnesium Alloy Anodes Performance

This research explores the enhancement of electrochemical performance in magnesium batteries by optimising magnesium alloy anodes, explicitly focusing on Mg-Al and Mg-Ag alloys. The study’s objective was to determine the impact of alloy composition on anode voltage stability and overall battery efficiency, particularly under extended cycling conditions. The research assessed the anodes’ voltage behaviour and internal resistance across magnesium bis(trifluoromethanesulfonyl)imide (Mg(TFSI)2) electrolyte formulations using a systematic setup involving cyclic voltammetry on the anode and electrochemical impedance spectroscopy. The Mg-Al alloy demonstrated superior performance, with minimal voltage drop and lower resistance increase than the Mg-Ag alloy. The results showed that the Mg-Al alloy maintained over 85% energy efficiency after 100 cycles, significantly outperforming the Mg-Ag alloy, which exhibited increased degradation and efficiency reduction to approximately 80%. These findings confirm that incorporating aluminium into magnesium anodes stabilises the anode voltage and enhances the overall battery efficiency by mitigating degradation mechanisms. Consequently, the Mg-Al alloy is identified as an up-and-coming candidate for use in advanced battery technologies, offering energy density and cycle life improvements. This study lays the groundwork for future research to refine magnesium alloy compositions further to boost battery performance.

Satellite remote sensing reveals the footprint of biodiversity on multiple ecosystem functions across the NEON eddy covariance network

Abstract Biodiversity relates to ecosystem functioning by modulating biogeochemical cycles of carbon, water, energy, and nutrients within and between multiple biotic and abiotic components of the ecosystems. However, large-scale, systematic measurements of plant biodiversity are still lacking, and the effects of biodiversity on measured biogeochemical processes are understudied. Here, we combined alpha (α) and beta (β) taxonomic measurements, spectral diversity from satellite observations, structural properties of the vegetation, and climatic drivers to assess the effect of biodiversity on ecosystem functional properties. Ecosystem functional properties were computed from eddy-covariance fluxes at 44 sites of the National Ecological Observatory Network. Based on the spectral variation hypothesis, we used the near-infrared reflectance of vegetation (NIRv) derived from Sentinel-2 satellite imagery to compute Rao’s quadratic entropy (Rao Q), a distance metric related to spatial heterogeneity. Using an automatic model averaging technique, we found that biodiversity proxies hold substantial explanatory power when predicting several ecosystem functions related to carbon and water exchange. In particular, NIRv-based Rao Q (RaoQNIRv) reflected positive biodiversity effects on productivity, as expected from the literature. In contrast, traditional taxonomic α-diversity indices were generally not selected as relevant predictors of the ecosystem functional properties. Yet, β-diversity strongly contributed to the prediction of carbon use efficiency, surface conductance, and water use efficiency. We also found that the RaoQNIRv is less affected by issues of saturation and bare soil contribution compared to RaoQNDVI. We show that spectral heterogeneity based on remotely sensed NIRv holds the potential for globally characterizing the biodiversity-ecosystem functioning relationship (BEF). While systematic measurements of taxonomic diversity co-located at biogeochemical measurement stations could reduce the uncertainty surrounding the BEF relationship at whole-ecosystem scale, remotely- sensed metrics characterizing important functional and structural diversity aspects of the landscape will be crucial for continuous spatiotemporal monitoring of biodiversity with relevant implications for ecosystem services to humankind.

Intensification of submesoscale frontogenesis and forward energy cascade driven by upper-ocean convergent flows

Upper-ocean fronts are an important component of the global climate system, regulating both the oceanic energy cycle and material transports. In the common paradigm, upper-ocean fronts are generated by frontogenesis at the mesoscale (20–300 km), driven predominantly by confluent horizontal flows initiated by a background straining field. However, the mechanisms by which this frontogenesis extends down to and influences the submesoscale (0.2–20 km), which dominates vertical transports in the ocean, are still understudied. Here, we provide direct observational evidence that submesoscale frontogenesis, defined as the rate at which submesoscale buoyancy gradients intensify, is closely linked to convergent flows. Analysis of year-long measurements by a mooring array in the North Atlantic indicates that both the upper-ocean frontogenetic rate and the horizontal convergence exhibit strong seasonality and scale dependence, with larger magnitudes in winter and at smaller horizontal scales (down to at least 2 km). The frontogenetic rate is found to correlate more strongly with horizontal convergence as the scale decreases, suggesting that convergent flows are the main driver of submesoscale frontogenesis. Crucially, a rapid forward cascade of kinetic energy and enhanced vertical velocities preferentially occur during periods of submesoscale frontogenesis. Our findings highlight a mechanism underpinning the key role of submesoscale fronts in the oceanic kinetic energy cascade and as a focus of vertical transports, and call for a parameterization of such effects in climate-scale ocean models.

Optimized Soil Moisture Mapping Strategies on the Tibetan Plateau Using Downscaled and Interpolated Maps as Mutual Covariates

Accurate high-resolution soil moisture maps are crucial for a better understanding of hydrological processes and energy cycles. Mapping strategies such as downscaling and interpolation have been developed to obtain high-resolution soil moisture maps from multi-source inputs. However, research on the optimization performance of integrating downscaling and interpolation, especially through the use of mutual covariates, remains unclear. In this study, we compared four methods—two standalone methods based on downscaling and interpolation strategies and two combined methods that utilize soil moisture maps as mutual covariates within each strategy—in a case study of daily soil moisture mapping at a 1 km resolution in the Tibetan Plateau. We assessed mapping performance in terms of prediction accuracy and differences in spatial coverage. The results indicated that introducing interpolated soil moisture maps into the downscaling strategy significantly improved prediction accuracy (RMSE: −5.94%, correlation coefficient: +14.02%) but was limited to localized spatial coverage (6.9% of grid cells) near in situ sites. Conversely, integrating downscaled soil moisture maps into the interpolation strategy resulted in only modest gains in prediction accuracy (RMSE: −1.07%, correlation coefficient: +1.04%), yet facilitated broader spatial coverage (40.4% of grid cells). This study highlights the critical differences between downscaling and interpolation strategies in terms of accuracy improvement and spatial coverage, providing a reference for optimizing soil moisture mapping over large areas.

The Global Atmospheric Energy Cycle in TaiESM1: Present and Future

AbstractThe Lorenz Energy Cycle (LEC) in the Taiwan Earth System Model Version 1 (TaiESM1) historical simulation is calculated and compared with ERA5 to evaluate the model performance from the thermodynamic aspect. The future change of LEC is accessed by comparing the SSP5‐8.5 and historical simulations in TaiESM1. TaiESM1 reasonably simulates the global mean, seasonal cycle, and spatial patterns of the energy reservoirs with larger values in the mean energy components and smaller in the eddy energy components. The energy cycle in TaiESM1 is about 35%–45% stronger than ERA5, except from December to February. The impact of global warming on the LEC is different at the vertical levels. The influence of meridional temperature gradient change is the dominant factor in the intensity of the energy cycle, and the change in static stability only contributes to the lower troposphere. Lifting the tropopause in the tropics increases the meridional temperature gradient and produces more zonal mean potential energy (PM) in the upper troposphere. PM is the primary driver of the LEC and leads to a more active energy cycle in the upper troposphere. As the tropical tropospheric depth increases and the mid‐latitude eddy activities become more active, more (less) energy is stored in the upper (lower) troposphere, and the energy conversion processes tend to become stronger (weaker) in the upper (lower) troposphere.

Exploring Topography Downscaling Methods for Hyper‐Resolution Land Surface Modeling

AbstractHyper‐resolution land surface modeling provides an unprecedented opportunity to simulate locally relevant water and energy cycles. However, the available meteorological forcing data is often insufficient to fulfill the requirements of hyper‐resolution modeling. Here, we developed a comprehensive downscaling framework based on topography‐adjusted methods and automated machine learning (AutoML). With this framework, a 90 m and hourly atmospheric forcing data set was developed from ERA5 data at a 0.25° resolution, and the Common Land Model (CoLM) was then forced with the developed forcing data over two complex terrain regions (the Heihe River Basin and Upper Colorado River Basin). We systematically evaluated the downscaled forcing and the CoLM outputs against both in situ observations and gridded data. The ground‐based validation results suggested consistent improvements for all downscaled forcing variables with mean RMSE improved by 6.362%–95.86%. The downscaled forcings, which incorporated detailed topographic features, offered improved magnitude estimates, achieving a comparable level of performance to that of regional reanalysis forcing data. The downscaled forcing driving the CoLM model showed comparable or better skills in simulating water and energy fluxes, as verified by in situ validations. The hyper‐resolution simulations provided a detailed and more reasonable description of land surface processes and attained similar spatial patterns and magnitudes with high‐resolution land surface data, especially over highly elevated areas. Additionally, this study highlighted the benefits of using mountain radiation theory‐based shortwave radiation downscaling models and AutoML‐assisted precipitation downscaling models. These findings emphasized the significance of integrating topography‐based downscaling methods for hillslope‐scale simulations.

Lignin-Based N-Carbon Dots Anchoring NiCo2S4/Graphene Hydrogel Exhibits Excellent Performance as Anodes for Hybrid Supercapacitor

A NiCo2S4/N-CDs/RGO ternary composite hydrogel was prepared via a one-step hydrothermal method, utilizing lignin-based nitrogen-doped carbon dots as a bridge connecting NiCo2S4 and graphene. The specific capacitance of NiCo2S4/N-CDs/RGO significantly outperforms that of the GH and NiCo2S4/RGO electrodes, achieving 1050 F g−1. The 3D mesh porous hydrogel structure mitigates NiCo2S4 nanoparticle aggregation, providing a larger specific surface area for enhanced charge storage. The abundant functional groups of N-CDs interact with Ni (II) and Co (III) cations, favoring NiCo2S4 particle synthesis. Additionally, an assembled solid-state asymmetric supercapacitor employing NiCo2S4/N-CDs/RGO as the positive electrode exhibited excellent energy density (68.4 Wh kg−1) and cycle stability (82% capacitance retention after 10,000 constant current charge–discharge cycles).

Unleashing the synergistic potential of 2D-2D nanosheet based S-scheme heterojunction: Cooperative boosting of CO2 reduction to solar fuel and biomass valorization.

Harnessing inexhaustible solar energy for CO2 valorization is substantial step toward achieving carbon-neutral energy cycle. However, CO2 conversion often exhibits slow kinetics, necessitating the utilization of sacrificial agents making the process economically unfeasible. In the ongoing quest for sustainable and economically feasible CO2 valorization, herein the photoreduction of CO2 to CO coupled with biomass-based alcohol oxidation to fine chemicals is reported via Bi2WO6/g-C3N4 (BWO/g-CN) 2D-2D nanosheet based S-scheme heterojunction. Importantly, BWO/g-CN-60 exhibits highest photocatalytic activity with CO production rate of 6.87 mmol g-1 h-1, accompanied by >98% selectivity and selective oxidation of veratryl alcohol to veratraldehyde, with notable yield of 42% in 6 h under simulated solar light. The apparent quantum yield (AQY) of 14.3% is achieved for CO production at the wavelength of 420 nm. Additionally, the formed heterostructure results in enhanced charge separation and accelerated charge transfer kinetics as validated by PL, EIS, and photocurrent studies. EPR, 13CO2 labeling, DFT studies, and various controlled experiments provided deeper insight into the mechanism of underlying photo-redox process. Thus, the current study presents a sustainable paradigm for CO2 mitigation by converting it into solar fuel, while synergistically producing the fine chemicals through effectively harnessing the full potential of charge carriers.

Turbulent Energy and Carbon Fluxes in an Andean Montane Forest—Energy Balance and Heat Storage

High mountain rainforests are vital in the global energy and carbon cycle. Understanding the exchange of energy and carbon plays an important role in reflecting responses to climate change. In this study, an eddy covariance (EC) measurement system installed in the high Andean Mountains of southern Ecuador was used. As EC measurements are affected by heterogeneous topography and the vegetation height, the main objective was to estimate the effect of the sloped terrain and the forest on the turbulent energy and carbon fluxes considering the energy balance closure (EBC) and the heat storage. The results showed that the performance of the EBC was generally good and estimated it to be 79.5%. This could be improved when the heat storage effect was considered. Based on the variability of the residuals in the diel, modifications in the imbalances were highlighted. Particularly, during daytime, the residuals were largest (56.9 W/m2 on average), with a clear overestimation. At nighttime, mean imbalances were rather weak (6.5 W/m2) and mostly positive while strongest underestimations developed in the transition period to morning hours (down to −100 W/m2). With respect to the Monin–Obukhov stability parameter ((z − d)/L) and the friction velocity (u*), it was revealed that the largest overestimations evolved in weak unstable and very stable conditions associated with large u* values. In contrast, underestimation was related to very unstable conditions. The estimated carbon fluxes were independently modelled with a non-linear regression using a light-response relationship and reached a good performance value (R2 = 0.51). All fluxes were additionally examined in the annual course to estimate whether both the energy and carbon fluxes resembled the microclimatological conditions of the study site. This unique study demonstrated that EC measurements provide valuable insights into land-surface–atmosphere interactions and contribute to our understanding of energy and carbon exchanges. Moreover, the flux data provide an important basis to validate coupled atmosphere ecosystem models.

CoFe2O4 with the In-Situ Formed Oxygen Vacancies and Co Particles as an Efficient Bifunctional Catalyst for Rechargeable Zinc-Air Batteries.

Rechargeable zinc-air batteries (RZABs) are considered as one of the most promising clean energy device due to their abundant resources, low cost and environmental friendliness. However, their energy efficiency and cycle life are far from satisfactory due to the poor activity and stability of bi-functional electrocatalyst in air cathode. In this work, an efficient bi-functional catalyst (rGO-CoFe2O4/Co) was derived from its precursor (rGO-CoFe2O4) through a simple annealing process. Electrochemical measurements prove that rGO-CoFe2O4/Co with the in-situ formed Co nano particles and rich oxygen vacancies appears excellent oxygen reduction reaction and oxygen evolution reaction catalytic activity compared to its counterpart. Its half-wave potential is 0.81 V (vs RHE) and the OER overpotential is only 310 mV (vs RHE). In addition, rechargeable zinc-air batteries assembled with rGO-CoFe2O4/Co show the highest peak power density (128.9 mW cm-2) and cycling stability compared to rGO-CoFe2O4 and commercial Pt/C-RuO2 catalysts. This work provides a simple strategy for the design of advanced bifunctional catalysts.

The Impact of Transport Costs on Business Decision-Making in Rural Areas

Mobility challenges in rural areas can have a negative impact on economic and demographic development. People in these areas often have to move to urban areas to find quality jobs or services, which can lead to a decline in population, and economic activity. In this paper we analyze the factors influencing business decisions in rural areas. Public transport infrastructure based on short energy cycles using solar energy represents a solution to boost business decision management in rural areas.

A bottom-up framework to investigate environmental and techno-economic aspects of lithium-ion batteries: A case study of conventional vs. pre-lithiated lithium-ion battery cells

As per-lithiation emerges as a promising technology for the next generation of lithium-ion battery cells, aimed at enhancing energy density and cycle life, it is crucial to evaluate its technological, production cost, and environmental impacts on an industrial scale. In this study, we developed a cradle-to-gate process-based model framework using LiNi0.8Mn0.1Co0.1O2 (NMC811)/Graphite as a reference cell to propose industrial-scale roll-to-roll direct contact pre-lithiation method for NMC811/Prelithiated-Graphite (prGr) and NMC811/Prelithiated-Graphite-Silicon (prGrSi) cells. Our findings indicate that pre-lithiated cells exhibit a gravimetric energy density increase of approximately 11%–27% compared to the baseline of 288 Wh.kg−1. This improvement could offset or even reduce the environmental impacts associated with the additional lithium foil component inserted into the cell during production. Economically, our analysis underscores the significant influence of lithium foil unit price, which is a critical factor in the overall cost of this technology, and a stable material market is crucial for its success. Overall, the investigation into environmental and economic aspects suggests that the prospects for industrialization and adoption are more promising than challenging in the near term.

Comparative analysis of advanced deep learning models for predicting evapotranspiration based on meteorological data in bangladesh.

Evapotranspiration is one of the crucial elements in water balance equations and plays a pivotal role in the water and energy cycle of an area. An accurate and precise estimation and prediction of reference evapotranspiration (ETo) is necessary for regional management of water resources and irrigation scheduling. The challenge of predicting daily evapotranspiration with limited meteorological data in Bangladesh. This study aims to predict daily evapotranspiration using limited meteorological data of Bangladesh by three deep learning (CNN, GRU, LSTM) and one hybrid (CNN-GRU) model. The novel method of hybrid CNN-GRU models, which have not been commonly used for this purpose. The performance of models was evaluated by five accuracy matrices R2, RMSE, MAE, MAPE, and CE and comparison is visualized by radar graphs. The study's novelty lies in the use of hybrid CNN-GRU models to estimate reference evapotranspiration, as this algorithm has not been commonly used for this purpose. In the case of the Rangpur station, the hybrid CNN-GRU algorithm outperformed other models, achieving the best values across various statistical metrics during both the training and testing phases. The highest correlation coefficient values of approximately 0.994 and 0.995. Moreover, during training and testing stages, the hybrid model had the lowest MAE (0.076, 0.068) and RMSE (0.138, 0.106) at the Rangpur station. Additionally, in the Sreemangal station, it can be notable that the statistical parameter RSME found superior results in the hybrid model around 0.225 and 0.174, respectively. In addition, the highest R2 and CE values were noted as 0.986, 0.987 and 0.985, 0.986 during the training and testing phases, respectively. The comparison suggests that the hybrid model will be best suited for prediction with the limited meteorological data. The outcome of the present research signifies the ability of deep learning methods in the prediction of evapotranspiration and the dominant variables affecting the changes the in context of Bangladesh.

The Egyptian 24-h movement guidelines for children and adolescents: an integration of physical activity, sedentary behaviour, sleep and eating habits—by the Egyptian Academy of Bone and Muscle health

BackgroundThe objectives of this work were as follows: (1) to develop holistic recommendations for physical activity, sleep, sedentary behaviours and eating habits adapted to children and adolescent life stage (5–17 years) and targeting optimal metabolic cycle and achieving energy balance; (2) to provide evidence-based information that can be used by the government, policy makers, healthcare professionals, community, schools as well as families to endorse active, healthy, living in apparently healthy children and youth aged 5–17 years, and as a base for monitoring the activities, particularly the physical ones, on the people level. The Egyptian Academy of Bone and Muscle Health followed an established guideline development process to create the Egyptian 24-h movement clinical guideline for children and adolescents. Online databases (PubMed, Embase and Cochrane Library) were searched for relevant peer-reviewed studies that met the a priori inclusion criteria.ResultsA total of 41 studies met the inclusion criteria. Leveraging evidence from the review of the literature led to the development of 11 key questions covering 8 domains. Fifteen statements focus on the overall identified targets through integration of the movement activities and eating behaviour. Results revealed a major change in the previous basic understandings as it shifts away from segregated into integrated movement behaviour and dietary habit paradigm. Based on this, the final guideline was developed providing evidence-based recommendations for a “Healthy 24-h day”, for Egyptian children and adolescents.ConclusionThe 24-h composition of movement behaviours has principal implications for health at all ages. The developed guideline provides an up-to-date evidence-based recommendation towards a holistic approach for favourable daily life activities and adopts a feasible perspective by outlining these activities within a 24-h period. In addition, eating and dietary elements have been included to complete the energy cycle. Children and adolescents who meet the 24-h movement guidelines generally report more favourable health indicators than those who do not.

Evaluating Land Surface Temperature Variation and Its Responses to Climate Change and Human Activities on the Southeastern Tibetan Plateau

ABSTRACTThe Yarlung Zangbo River Basin (YZRB), situated within the Qinghai‐Tibetan Plateau, has experienced significant alterations due to global warming and vegetation greening. This region serves as a critical indicator of the interplay between vegetation growth and climatic fluctuations, as evidenced by substantial changes in spatiotemporal land surface temperature (LST) over recent decades. In this research, we assessed the components of the water and energy cycles from 1980 to 2015 utilising the variable infiltration capacity (VIC) model to generate a continuous daily LST data over a 35‐year period. Subsequently, we analysed the variations in LST and identified the influence of environmental factors on temperature changes. Notably, while greening was observed, LST exhibited an upward trend. By differentiating the effects of climatic and anthropogenic factors on LST, we found that climate was the predominant influence, accounting for a contribution rate of 70.36% from 1980 to 1995. In contrast, human activities became the primary driver of LST changes, contributing 55% after 1995. Grasslands with moderate coverage demonstrated potential cooling effects. Among the various environmental factors examined, albedo exhibited a negative and delayed impact on LST, while temperature, precipitation and evapotranspiration were positively correlated with LST, displaying relatively synchronous variations. Additionally, soil moisture and the normalised difference vegetation index (NDVI) were identified as leading contributors to positive changes in LST. This study enhances the understanding of the mechanisms influencing LST and provides essential insights for socio‐economic development in areas with sensitive ecosystem.

Application of indoor thermal energy cycle and visual communication simulation scene based on Machine vision and optical image enhancement

Traditional thermal analysis methods have limitations in complex environments, and innovative technologies are urgently needed to improve the accuracy of indoor thermal monitoring and optimization. In this paper, machine vision and optical image enhancement technology are combined to simulate the indoor heat energy cycle process to achieve more efficient heat management and visual communication, so as to improve the energy utilization efficiency and residential comfort of buildings. In this paper, a high-resolution camera is used to capture real-time images of indoor environment, image quality is enhanced by image processing algorithm, and heat distribution characteristics are extracted. The machine learning model is used to analyze the dynamic changes of the thermal energy cycle, and the visual model of the indoor thermal energy cycle is constructed by combining the fluid dynamics theory. The experimental results show that the proposed method can effectively identify and display the key areas of indoor heat circulation and their changing trends. The simulation results show that the optical enhanced images can show different temperature regions more clearly, directly reflect the heat flow situation, and have a high agreement with the actual measurement data. Therefore, the indoor thermal energy cycle simulation method based on machine vision and optical image enhancement not only improves the accuracy and real-time performance of thermal energy analysis, but also provides visual support for indoor environmental management.

Dual-functions of the carbon-confined oxygen on the capacitance and cycle stability enhancements of Zn-ion capacitors

Zinc-ion capacitors (ZICs) are promising energy storage devices due to their balance between the energy and power densities inherited from Zn-ion batteries and supercapacitors, respectively. However, the low specific capacitance of carbon cathode materials and the dendrite growth on Zn anode have set fatal drawbacks to their energy density and cycle stability. Herein, we demonstrate that, in 1 M Zn(CF3SO3)2/DMF (N, N-dimethylformamide) electrolyte, confining oxygen in carbon cathode materials via high-energy ball milling can synergistically introduce additional pseudocapacitance on the cathode side while suppressing the dendrite growth on Zn anode side, which jointly lead to high energy density (94 Wh kg−1 at 448 W kg−1) and long cycle stability of ZICs. The hydroxyl group in carbon cathode can be transformed to C—O—Zn together with the release of protons during the initial discharge, which in turn stimulates the defluorination of CF3SO3− anions and formation of ZnF2 on both cathode and anode. The ZnF2 formed on the surface of the Zn anode suppresses the dendrite growth by regulating the Zn2+ deposition/stripping in a reticular structure, resulting in the excellent cycle stability. This work provides a facile strategy to rationally design and construct high energy and stable ZICs through engineering the oxygen-bearing functional groups in carbon cathode materials.

Copper-Based Para-Phthalic Acid Metal-Organic Framework: An Efficient Anodic material for Super-capattery Hybrid Systems

The battery-supercapacitor hybrid, an asymmetric energy storage system, has garnered considerable attention due to its outstanding energy storage capabilities. Anodic materials have become a central focus, driven by the quest for enhanced energy density, power density, cycle life, and overall performance. In this study, we have synthesized copper-based para-phthalic acid (PTA) metal-organic frameworks (MOFs) via a facile hydrothermal method to be employed as the positive electrode material in a battery-supercapacitor hybrid matrix. The initial testing indicates the battery-type nature of the prepared electrode along with maximum specific capacity of 260.5 C g-1 at 0.7 A g-1. Subjection of Cu-PTA MOF electrode to real device configuration (Super-capattery device) results in considerable specific capacity of 152.2 C g-1 along with specific energy and power of 35.9 Wh kg-1 and 5950 W kg-1, respectively at 0.3 A g-1. The device also demonstrated a stability potential of 92.15% when tested for 5000 continuous charge-discharge cycles. The present study presents Cu-PTA MOF an exciting addition for advancing current state of research in the field of energy storage technology.

Coordination polymer derived transition metal phosphide/carbon composites for bifunctional oxygen electrocatalyst

Developing nonprecious electrocatalysts with bifunctional performances for oxygen reduction (ORR) and evolution reactions (OER) remains a crucial challenge in rechargeable Zn-air batteries (RZABs). In this study, we report the synthesis of a three-dimensional (3D) porous N, P-doped carbon-wrapped cobalt phosphide composite (Co2P@3DNPC) via direct calcination of a novel organic/inorganic porous coordination polymer by an in-situ phosphating strategy. DFT calculations demonstrate the intricate interactions occurring during the PEI-directed grinding self-assembly process among Co2+, phytic acid (PA), and polyethylenimine (PEI). Specifically, Co2+ ions initially adsorb onto PEI molecules before integrating with PA to form a 3D coordination polymer matrix. As-fabricated Co2P@3DNPC composite exhibits impressive ORR/OER bifunctional performances, with a half-wave potential of 0.78 V and an overpotential of 1.71 V, respectively. Its bifunctional activities enable a power density of 148.5 mW cm–2 in rechargeable ZABs, with remarkable stability (> 480 h) during a discharge-charge cycle. The interconnected porous structure and embedded Co2P nanoparticles optimize the electrode-electrolyte interfacial contact, boosting energy density and cycle life of as-assembled ZABs. This innovative approach paves the way for efficient, cost-effective production of bifunctional electrocatalysts for RZABs.