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Abstract Confined single metal atoms in graphene‐based materials have proven to be excellent catalysts for several reactions and promising gas sensing systems. However, whether the chemical activity arises from the specific type of metal atom or is a direct consequence of the confinement itself remains unclear.
Iron–nitrogen–carbon (Fe–N–C) catalysts are considered the most active platinum-free alternative for oxygen reduction reaction (ORR), yet the generated reactive oxygen species (ROS) from general mechanistic pathway rapidly impair the ORR activity and stability of Fe–N–C. Herein, we establish and report an ORR pathway-switching strategy to circumvent ROS generation and fundamentally improve the activity and stability of Fe–N–C via DFT guided catalyst design. The constructed Fe–V atomic pair catalyst (Fe 1 V 1 -NC) with N 2 Fe-N 2 -VN 2 configuration enables side-on adsorption of O 2 and subsequent direct-breaking of the O═O bond to form O*, thereby avoiding the formation of ROS radicals. Importantly, there is intersite electron interaction between FeN 4 and VN 4 , which further boosts the ORR activity. Consequently, Fe 1 V 1 -NC exhibits outstanding ORR activity with onset and half-wave (E 1/2 ) potentials at 1.02 and 0.89 V versus RHE, respectively, in 0.1 M KOH. Record-high stability is achieved on Fe 1 V 1 -NC with a minimal decay in E 1/2 by 16 mV over 50000 cycles, surpassing Fe–N–C counterpart and most of the catalysts reported to date. The Fe 1 V 1 -NC-based zinc-air battery reported here demonstrates exceptional durability up to 400 h at 10 mA·cm −2 . This work identifies the intrinsic correlation between ORR pathway, activity, and stability, advancing development of stable catalytic systems.
We demonstrate that atomic-scale surface disorder can control the first-cycle redox sequence of Li-rich layered oxides, eliminating the detrimental process of oxygen release and lattice collapse that degrades performance. In Li1.14Ni0.32Mn0.54O2 (LNMO), a simple chemical treatment introduces oxygen and transition metal (TM) vacancies confined to the particle surface while preserving the bulk layered framework. Multi-modal synchrotron analyses reveal that these vacancies trigger an early oxygen oxidation below 4.4 V, delay nickel oxidation to higher potential, and suppress the formation of covalent Ni4+─O states. This modified pathway prevents irreversible oxygen release, suppresses manganese dissolution, and maintains metal-oxygen coordination at high voltages. Consequently, the treated cathode delivers higher first-cycle Coulombic efficiency (CE), mitigated voltage fade, and superior capacity retention. By directly linking engineered surface disorder to redox reactions and associated structural transformations, this work establishes a general design principle for durable, high-energy-density cathodes.
The quest for efficient solar-driven water splitting, a promising avenue for clean fuel production, faces challenges due to limited solar energy conversion efficiency. Traditional approaches study the overall water splitting as two spatially separate half reactions on two unrelated sites, hindering full utilization of photogenerated charge and water molecules. To overcome these limitations, an integrated cluster heterostructure catalyst on InGaN/GaN semiconductor nanowires is proposed for the effective utilization of photogenerated charge carriers and water molecules on the same redox localization. By establishing the fast charge extraction kinetics based on InGaN/GaN nanowires, the integration of Rh and MoRhOx clusters on the nanowire surface enables simultaneous and fast hydrogen/oxygen evolution reactions at the cluster heterostructure. Furthermore, the integrated strategy can enhance the charge redistribution across the heterostructure between the two clusters, further optimizing adsorption of reaction intermediates on each cluster for boosted photocatalytic water splitting activity. Consequently, the integrated heterostructure triggers a 40-fold increased hydrogen production efficiency in an artificial leaf system. This study provides valuable insights for the rational design of advanced heterostructured photocatalysts for water splitting and beyond.
Many energy absorption applications utilize flexible polymeric foams for their viscoelastic properties. It is desired that the material will perform consistently across repeated compression cycles. This study examines the effect of fatigue at low strain rates on the viscoelasticity of open-cell polyurethane foam. Six polyurethanes of the same base composition with two porosities (70% and 80%) and three chemical indexes (79i, 100i, and 121i) are tested. Large deformation cyclic compression of the foams is conducted on a universal testing system (UTS). These data are then post-processed leveraging dynamic mechanical analysis Fourier transform rheology to characterize changes in the viscoelasticity of the materials over fatigue cycles. Results show that foams can increase or decrease in stiffness up to 10% over 10 4 cycles. Specifically, higher chemical index, higher excitation frequency, and larger excitation amplitude correlate with a more pronounced decrease in stiffness. Damping can also change by 15% and correlates with chemical index and excitation frequency. Consequently, the findings suggest that internal foam structure and bulk material properties as well as applied loading parameters affect the viscoelastic fatigue response of flexible polymeric foams.
By expanding magnetic nanostructures into the third dimension, it is possible to introduce new interactions and realize new forms of magnetic textures and emergent phenomena. Consequently, this unlocks new opportunities for applications in data storage, unconventional computing and sensing by utilizing 3D devices with enhanced functionalities. Connected magnetic nanowires offer a unique platform for applications such as neuromorphic computing due to their tunability and the presence of multiple transport pathways. However to realize this promise, it is necessary to further our understanding of how to locally control the magnetization in 3D, nanowire-based geometries. In this work we show the formation of magnetic domain walls, vortices, anti-vortices, and linked vortex-anti-vortex pairs in interconnected helical nanowire arrays. We show how wire diameter and 3D geometric design can control the states that form and reveal the magnetization reversal mechanism. Hence, we demonstrate this to be a highly tunable system, where the magnetization can be readily reconfigured by an external magnetic field.
The continued scaling of artificial intelligence and telecommunications hardware is increasingly constrained by the power, bandwidth, and area limitations of transistor-based circuits. Neuromorphic processor units, analog oscillators, and active inductors and capacitors rely on complex multi-transistor architectures restricting material choices and incurring energy and footprint overhead. Here, we show that active reactance in electro-thermal memristors provides an intrinsic, material driven route to neuronal oscillator dynamics and signal processing. Using a physics-based compact modeling framework, we bridge negative differential resistance (NDR) and bias-tunable reactance, which underlies spiking dynamics in electro-thermal memristors. Memristors with negative temperature coefficients of resistance (TCR) manifest current-controlled (CC-) NDR and act as active inductors, thus generating spiking above a critical circuit capacitance; whereas memristors with positive TCR manifest voltage-controlled (VC-) NDR and active capacitance, leading to spiking above a critical inductance. By creating a compact model for La 0.7 Ca 0.3 MnO 3 as a representative VC-NDR material and comparing it with LaCoO 3 manifesting CC-NDR, we explain the physical origins of their distinct current-voltage characteristics, reactive phase shifts and consequent spiking behaviors. Finally, we demonstrate tunable filtering enabled by the active reactance of electro-thermal memristors, establishing them as a compact hardware platform for neuronal oscillator functionality and integrated filtering beyond conventional CMOS.
Methane dehydroaromatization (MDA) is one of the most promising technologies for directly transforming methane into aromatics. Unlike the extensively investigated Mo/ZSM-5 catalysts, the structure and, consequently, the catalytic activity of Fe/ZSM-5 are markedly influenced by the method of preparation, as shown here. In this study, we prepared 2 % and 4 % Fe/ZSM-5 catalysts via wet impregnation (WI) and incipient wetness impregnation (IWI). Characterizations (XRD, STEM, UV-Vis, NH 3 -TPD and H 2 -TPR) reveal that 2 %Fe-WI mainly possesses isolated or low-polymerized Fe species within zeolite channels, leading to a rapid activation and a higher benzene yield due to the faster reduction to iron suboxides under MDA conditions. In contrast, 2 %Fe-IWI contains bulk iron oxide aggregates, resulting in a slower activation as these aggregates transform into iron carbide through successive reduction and carbonization. Here, a deactivation kinetic study applied to the 2 % catalysts further demonstrates the quantitative relation between Fe site isolation and catalytic activity. Although both 4 % catalysts inevitably form sizable iron oxide clusters and particles due to the high Fe/Al ratio, similar trends are noted, with the WI catalysts exhibiting a shorter induction/activation period and a higher yield of benzene, paralleling observations made with 2 % catalysts.
The use of γ-Al 2 O 3 -supported Ni catalysts promoted with either Cu or Fe was investigated for the reductive catalytic fractionation (RCF) of hybrid poplar in methanol at 200 and 250 °C. The effectiveness of lignin depolymerization was quantified in terms of the lignin oil production, the quantity and distribution of identifiable monomers present in the lignin oil, and the yield of residual solids. All of the Ni-based catalysts tested provided improved yields of lignin oil and monomers, along with reduced char formation, relative to blank (sans catalyst) runs. The highest monomer yield of 51 % was obtained at 250 °C over a 20 wt.% Ni-5 wt.% Cu/Al 2 O 3 catalyst, the improved performance obtained through Cu promotion being attributed to the ability of Cu to facilitate NiO reduction, resulting in an increased amount of Ni 0 on the catalyst surface and, consequently, improved hydrogenation activity. Finally, the main monomers formed were propanol-, propyl- and propenyl-substituted guaiacol and syringol, the S/G ratio of the products corresponding closely to that in the native lignin.
Abstract Anthropogenic climate change, particularly changes in temperature and precipitation, affects plants in multiple ways. Because plants respond dynamically to stress and acclimate to changes in growing conditions, diagnosing quantitative plant‐environment relationships is a major challenge. One approach to this problem is to quantify leaf responses using spectral reflectance, which provides rapid, inexpensive, and nondestructive measurements that capture a wealth of information about genotype as well as phenotypic responses to the environment. However, it is unclear how warming and drought affect spectra. To address this gap, we used an open‐air field experiment that manipulates temperature and rainfall in 36 plots at two sites in the boreal‐temperate ecotone of northern Minnesota, USA. We collected leaf spectral reflectance (400–2400 nm) at the peak of the growing season for three consecutive years on juveniles (two to six years old) of five tree species planted within the experiment. We hypothesized that these mid‐season measurements of spectral reflectance capture a snapshot of the leaf phenotype encompassing a suite of physiological, structural, and biochemical responses to both long‐ and short‐time scale environmental conditions. We show that the imprint of environmental conditions experienced by plants hours to weeks before spectral measurements is linked to regions in the spectrum associated with stress, namely the water absorption regions of the near‐infrared and short‐wave infrared. In contrast, the environmental conditions plants experience during leaf development leave lasting imprints on the spectral profiles of leaves, attributable to leaf structure and chemistry (e.g., pigment content and associated ratios). Our analyses show that after accounting for baseline species spectral differences, spectral responses to the environment do not differ among the species. This suggests that building a general framework for understanding forest responses to climate change through spectral metrics may be possible, likely having broader implications if the common responses among species detected here represent a widespread phenomenon. Consequently, these results demonstrate that examining the entire spectrum of leaf reflectance for environmental imprints in contrast to single features (e.g., indices and traits) improves inferences about plant‐environment relationships, which is particularly important in times of unprecedented climate change.
An unprecedented precipitation event in the hyper-arid Atacama Desert of Northern Chile occurred in March 2015. Geomorphic alterations to the river channel and the coastal zone, coupled with the exceptional magnitude of the rainfall, caused catastrophic damage and loss of life. On the coast of the El Salado watershed, legacy mine tailings infilled the watershed-ocean connection, while the river channel was altered both by tailings and urbanization. The consequences of this event resulted from the coupling of anthropogenic geomorphic changes with an unusual climate event. Lack of field data, complex geomorphology and sediment loads influenced by human activity make analysing floods in these regions especially challenging. The objective of this work is to improve our understanding of the factors that control flood hazards by using numerical simulations to reconstruct the 2015 flood in El Salado. We carry out unsteady two-dimensional simulations fully coupled with the sediment concentration to identify the influence of tailing deposits, considering high-resolution data of the pre- and post-2015 flood topography. In conclusion, the results highlight the importance of specific event-based studies, using models that can help designing better strategies for climate change adaptation and risk mitigation, while providing information for risk reduction and channel restoration.
ABSTRACT Currently, the breeding programs focus their efforts on identifying and developing tolerant genotypes to adverse conditions, such as drought and high temperatures. In this context, the physiological approach, which involves phenotyping several traits, is useful for breeding programs. Leaf photosynthetic traits have become one of the main objectives to be evaluated for breeders due to their relationship with improving grain yield and biomass production. Gas exchange ( Ge ) and chlorophyll “a” fluorescence ( Chf ) are the main tools to characterize the photosynthetic activity in real time at the leaf level. Consequently, several association studies using proximal and nonproximal sensing (e.g., RGB, thermography) have been developed. However, for the correct application of this breeding approach, it is essential to have a basic knowledge of both the physiological principles involved in the readings and the limitations of phenotyping due to the characteristics of the devices available on the market. This revision also covers other traits, such as the morphological and anatomical characteristics of leaves and roots, and the use of isotopes complementing Ge and Chf measurements.
This study investigates historical and future coastal runoff across the Arctic and sub-Arctic subregions of Alaska, USA, emphasising changes in terrestrial hydrology and their implications for coastal discharge. Using the physically based SnowModel-HydroFlow framework, historical (1991–2020) and future (2071–2100) coastal discharge climatologies were simulated, incorporating meteorological, topographic, and land cover inputs. Results reveal distinct seasonal and spatial patterns in temperature, precipitation, and snow dynamics across the Beaufort, Chukchi, Yukon, and Bristol subregions. Future projections suggest significant warming and shifts in hydrological regimes, such as increased annual precipitation and coastal discharge totals. Following a high emissions, business as usual pathway (SSP5-8.5), coastal discharge hydrographs in the Beaufort and Yukon subregions remain snow dominated with increases in discharge in nearly all months, while Chukchi and Bristol subregions shift from snow dominated to rain dominated. These changes are linked to rising temperatures, decreasing snow precipitation fractions, and altered freezing levels, with implications for water storage, timing, and availability. This research provides insights into the drivers and consequences of hydrological changes in a rapidly warming Arctic, supporting efforts to predict and mitigate the impacts on coastal and marine ecosystems.
ABSTRACT Urban areas experience the impact of natural disasters, such as heatwaves and flash floods, disparately in different neighbourhoods across a city. The demand for precise urban hydrometeorological and hydroclimatological modelling to examine this disparity, and the interacting challenges posed by climate change and urbanisation, has thus surged. The Weather Research and Forecasting (WRF) model has served such operational and research purposes for decades. Recent advancements in WRF, including enhanced numerical schemes and sophisticated urban atmospheric‐hydrological parameterizations, have empowered the simulation of urban geophysical processes at high resolution (~1 km), but even this resolution misses significant urban microclimate variability. This study applies the large‐eddy simulations (LES) mode within WRF, coupled with single‐layer urban canopy models (SLUCM), to enable even finer‐scale modelling (150 m) of the Urban Heat Island (UHI) effect in the Baltimore metropolitan area. We run nine scenarios to evaluate various methods of initializing soil moisture and various spinup lead times, and to assess the impact of WRF's Mosaic approach in depicting subgrid‐scale processes. We evaluate the scenarios by comparing the WRF simulated land surface temperature (LST) against Landsat LST and the WRF simulated hourly 2‐m air temperatures (AT) with observations from eight weather stations across the domain. Results underscore the paramount influence of the lead spinup time on the spatiotemporal distribution of simulated soil moisture, consequently shaping WRF's efficacy in predicting the UHI. Furthermore, interpolating soil moisture‐related parameters from the parent for child domain initialization yields a notable reduction in mean and root‐mean‐squared errors. This improvement was particularly evident in simulations with the longest spinup time, affirming the importance of carefully designing the initialization of soil moisture for improved urban temperature predictions.
ABSTRACT Climate change induces high variability in drought patterns and extreme precipitation indices in rainfed cocoa farming, impacting cocoa production. This study evaluated water stress, meteorological and agricultural drought conditions, and critical extreme precipitation indices in the world's two largest cocoa‐producing nations from 1981 to 2022. The results revealed a significant reduction in total annual precipitation (PRCPTOT), in the last three decades, with the greatest decline in the 1991–2000 and 2011–2022 periods. Ghana experienced the most significant reduction up to 15% (200 mm/year) in the last decade, attributed to a substantial decrease in wet days number (RR1) up to 25 days per year, a reduction in maximum consecutive wet days (CWD) up to 6 days per year, and an increase in maximum consecutive dry days (CDD) up to 15 days per year. Moreover, there was a notable decline in the Simple Daily Intensity Index (SDII), with reductions of up to 4 mm/day in certain areas, contributing to increased drought frequency, severity, and duration. In the most recent decade (2011–2022), particularly during the extremely dry years of 2013 and 2015, cocoa‐growing regions in Ghana (GHA) and eastern Côte d'Ivoire (CIV) experienced prolonged agricultural drought expressed by soil moisture deficit, typically extending from May to September. Additionally, large portions of central and eastern Ghana, as well as northeastern Côte d'Ivoire experienced sustained water stress, with over three consecutive months of total monthly precipitation falling below 100 mm, negatively impacting cocoa productivity. The decrease in the yield in the range of 2.5% to 37% was noted in the dry years and the following years, varying according to the country depending on the severity of the drought. Sensitivity analysis highlights cocoa yield's responsiveness to drought and water stress, particularly in specific years when water stress occurred, such as 1984,1985, 1989, 1995, 1999, 2000, and 2008. Considering the observed trends in precipitation patterns and their impact on cocoa production, it is crucial to acknowledge the inherent uncertainty of future precipitation patterns due to climate change. To address this challenge effectively, our study underscores the importance of identifying and closely monitoring regions currently facing water stress, as determined by precipitation and drought indicators. Over the analysed period (1981–2022), we have noted shifts in the distribution of water‐stressed areas, highlighting the dynamic nature of this issue. Consequently, we advocate for a targeted approach to implement cocoa supplementary irrigation in consistently water‐stressed regions.
Broadband coherent anti-Stokes Raman scattering (BCARS) microscopy is a powerful label-free biological imaging technique, but the raw signal requires careful processing. The vibrationally resonant (Raman) fingerprint signal is usually small compared with instrumental noise sources and the nonresonant background (NRB) inherent in the BCARS signal. Fortunately, the NRB exhibits a systematic phase relationship with the coherent Raman response, acting as a heterodyne amplifier for the weak fingerprint signal. Due to this heterodyne effect, the Raman response can be recovered quantitatively and invariantly across different instruments, provided the NRB shape is known. Even with heterodyne amplification, the amplitudes of fingerprint signal components are often comparable to system noise. Singular value decomposition (SVD), which utilizes spatial information, is often employed for additional noise filtering. Consequently, finding optimal processing parameters to properly distinguish the NRB and Raman responses and suppress noise in the complex BCARS signal requires a reference system that realistically represents the spectral and spatial properties of BCARS signals obtained from biological samples. We present a digital tissue phantom that meets these criteria as a tool for testing candidate signal processing pipelines. The digital phantom is generated with simulated hyperspectral Raman images having system-specific noise and background characteristics. Here, we analyze phantom datasets with differing background and signal-to-noise conditions to evaluate their impact on the performance of multiple signal processing pipelines. Specifically, we investigate the application of a Butterworth filter-based routine to directly estimate the NRB from the BCARS signal. Additionally, we evaluate a Lorentzian wavelet transform as an alternative to the Hilbert transform for extracting the Raman spectrum from the BCARS signal. While we demonstrate this phantom for BCARS, it can be used for any spectroscopic Raman imaging approach.
While wind energy is a key sector of domestic energy production for the United States, operation of wind turbines directly and indirectly adversely affects certain species of birds and bats. The cumulative effect of wind turbine strikes can have both biological and regulatory consequences, and, in some cases, delay permitting and construction or affect ongoing operations. Technology can help quantify and minimize these effects, but the pace of development, acceptance, and adoption of technological solutions is slow. Although adopting cost‐effective technologies may reduce negative effects on wildlife and help achieve both energy production and conservation goals, consensus is lacking among developers, regulators, and the conservation community regarding how to define technology effectiveness and acceptance and how to develop a standardized process for doing so. Removing barriers to technology advancement requires deviating from the status quo. Changes include 1) creating incentives to mitigate impacts, 2) establishing options for research as mitigation, 3) rethinking how research is funded, 4) increasing stakeholder coordination, and 5) increasing the efficiency of research and development. We recommend the creation of a national framework to establish clear criteria and protocols for technology evaluation and adoption.
Resonant field enhancement in optical cavities is provided over only narrow linewidths and for specific spatial modes. Consequently, spectrally restrictive planar Fabry-Pérot cavities have not contributed to date to white-light imaging, which necessitates a highly multimoded broadband field to satisfy the resonance condition. It is shown that introducing judicious angular-dispersion circumvents the fundamental trade-off between cavity linewidth and finesse in a Fabry-Pérot cavity by exciting a 130-nm-bandwidth achromatic resonance across the visible spectrum, which far exceeds the finesse-limited linewidth (0.5 nm), and even exceeds the free spectral range (45 nm). This omni-resonant configuration enables broadband color-imaging over a 100-nm-bandwidth in the visible with minimal spherical and chromatic aberrations. Omni-resonant imaging is demonstrated using coherent and incoherent light, and spatially extended and localized fields comprising stationary and moving objects. This work paves the way to harnessing broadband resonant enhancements for spatially structured fields, as needed for example in solar windows.