Nonstructural carbohydrates (NSC) buffer plants against carbon imbalances, yet their partitioning between storage and soluble pools remains elusive at global scales. We compiled a dataset of starch to soluble sugar ratio (St : Su) for 308 woody species across 220 sites world-wide and introduce a dimensionless index that integrates storage and demand while minimizing methodological artifacts. St : Su was strongly associated with growth, identifying it as a key axis of carbon allocation. Foliage consistently exhibited lower St : Su than lignified organs, reflecting a division between transient and conservative pools. Conifers accumulated more starch in foliage but less in stems relative to angiosperms, while leaf habits and mycorrhizal associations further modulated organ-specific strategies. Contrary to expectation, foliar and root St : Su varied little among biomes, but stems exhibited higher ratios in tropical rainforests than in boreal or arid regions, reflecting differences in species composition and adaptive storage under disturbance. Phylogeny constrained stem storage, whereas climatic variability, rather than mean conditions, dominated allocation in leaves and roots. These findings establish St : Su as a robust functional trait linking allocation strategies, growth, and resilience, which can be used to improve vegetation model prediction of forest productivity and mortality under climate variability.

Black phosphorus (bP), a two-dimensional van der Waals material with a phosphine-like basal plane, offers a promising but underexplored platform for surface organometallic chemistry. Here we demonstrate the first dual organometallic functionalization of bP using two chemically orthogonal protocols applied sequentially: (i) direct coordination of Re(CO)3Cl to the bP surface and (ii) tethering Re or Ru complexes via ortho-quinone anchors. Together, these strategies establish bP as a versatile platform for programmable, molecularly precise, multimetal architectures.

This study presents a cradle-to-grave lifecycle analysis of energy use and greenhouse gas (GHG) emissions for U.S. medium- and heavy-duty vehicles across current (2021) and future (2035) technologies using the Greenhouse gas, Regulated Emissions, and Energy use in Technologies (GREET) model with industry-vetted assumptions. Results vary across vehicle classes but point to common trends: today, battery electric vehicles (BEVs) offer significant (10-60%) GHG emissions reduction compared to diesel internal combustion engine vehicles and are the lowest emissions option per ton-mile of cargo movement, followed by hydrogen fuel cell electric vehicles (FCEVs) (5-50% emissions reduction). Emissions savings depend largely on the duty cycle and fuel economy of the vehicle type. Future vehicle technology advancements result in comparable emission reductions associated with BEVs and hydrogen FCEVs. Weight-limited BEV trucks see less per-ton-mile emissions reduction due to the impact of battery weight on increased vehicle weight and reduced payload capacity. By 2035, improvements in vehicle efficiency can reduce emissions across all powertrains. However, very low levels of emissions require switching vehicles' use-phase fuel/energy to low-carbon fuels and electricity. Renewable diesel, e-fuels, hydrogen produced from natural gas with carbon capture and storage or renewables, and use of low-carbon electricity can all achieve over 70% reduction in GHG emissions from the current day diesel-based internal combustion engine vehicle.

We investigated microhydrated transition metal-ethylenediaminetetraacetic acid (EDTA) dianion complexes [EDTA · M(II)]2- · nH2O (M = Ni, Cu, and Zn; n = 1, 2) using cryogenic photoelectron spectroscopy in conjunction with theoretical calculations. The measured spectra of [EDTA · Ni/Zn(II)]2- · nH2O closely resemble those of their corresponding bare dianions, with a systematic shift toward the high electron binding energy side upon stepwise hydration and featuring a hexadentate metal-EDTA binding motif. In contrast, the spectra of hydrated [EDTA · Cu(II)]2- exhibit broadened first detachment bands, implying the coexistence of multiple metal-ligand binding forms, as a consequence of the Jahn-Teller effect associated with its d9 electron arrangement. These findings showcase molecular-level insights into the structural adaptability of EDTA-based supramolecular assemblies comprising transition metal ions, water molecules, and multidentate ligand environments.

Abstract Understanding how the short‐term evolution of synoptic weather patterns influence Mesoscale Convective Systems (MCSs) is essential, as these systems are responsible for over half of central U.S. flash floods, leading to substantial socioeconomic and water resource management impacts. This study analyzes long‐term MCS data, flash flood reports, and atmospheric reanalyses from 2007 to 2017 using a machine learning clustering algorithm to examine how the synoptic weather patterns evolve prior to MCS initiation. While the clusters reflect seasonal and regional differences in MCS occurrence, they do not consistently distinguish between MCSs that do and do not produce flash floods. Systems in the southern Great Plains are more flood‐prone when a synoptic‐scale forcing, located near the system, drives strong water vapor transport from the nearby moisture source. More generally under different synoptic weather patterns, a broader precipitating area is the most dominant factor governing MCS flash flood potential.

Non-targeted liquid chromatography tandem high-resolution mass spectrometry (LC-MS/MS) is increasingly applied for the structure-resolved chemical analysis of dissolved organic matter (DOM). With new developments in MS instrumentation and analysis software, the approach has gained substantial momentum over the past decade. However, achieving high-quality analytical data that is reproducible and comparable across laboratories can be a bottleneck in non-targeted metabolomics and organic matter chemical analysis, especially for data reuse in repository-scale analyses. Understanding the capabilities as well as challenges of comparing LC-MS/MS data from different laboratories is necessary for inferring global trends from public data sets. To illuminate instrumentation factors that drive differences and variability, we used a standardized data analysis pipeline, including classical (CMN) and feature-based molecular networking (FBMN), to analyze data from a ring trial by 24 laboratories on identical sample sets of algal and DOM extracts that were mixed in predefined concentrations and spiked with standards. Our results showed that data sets from similar mass spectrometer types with unified instrument parameters were qualitatively comparable, resolving the same general trends and shared mass spectral features. Interlaboratory comparability was best for high-intensity features, while low-intensity features showed greater detection variability. Our analysis also highlights challenges when comparing data from instruments with different acquisition rates or operating with less standardized methods. Lastly, we provide recommendations for data integration, public data sharing, standardization, and best practices for standardized LC-MS/MS data acquisition, which will be critical for long-term time series and intercomparability of DOM chemical analyses.

Evolutionary conservation has been considered a hallmark of essential basic functions in cells. Therefore, the study of evolutionarily conserved post-translational modifications (PTMs) can provide insight into their role in protein function. In this context, mass spectrometry can identify and quantify thousands of PTM sites. However, a major bottleneck lies in analyzing the large amounts of data collected by the mass spectrometer. Here we address the need for a protein sequence alignment tool for multiple PTMs across several species. We developed a tool named PTMOverlay that takes peptide identification output files and overlays PTM sites onto multiple protein sequence alignments. Examining 31 bacteria isolates, we combined their protein sequences with select PTM types, including acetylation, phosphorylation, monomethylation, dimethylation, and trimethylation. The tool revealed a variety of conserved modification sites on the bacterial central carbon metabolism. Further structural analysis revealed possible interactions between methylated arginine and lysine residues with phosphothreonine/serine sites on the homodimer interface of enolase. Overall, this tool can parse large amounts of mass spectrometry data and allows for more informed and efficient selection of sites for future studies of protein function.

Abstract Historical datasets of tropical cyclone–driven storm surge and waves at moderate coastal resolution are scarce, limiting coastal hazard analysis and AI/ML-based surrogate model development where field observations remain sparse. We present a publicly available hindcast database of surge and wave conditions for 232 U.S. landfalling and impactful storms (1981–2021). We applied the coupled ADCIRC+SWAN system across the entire U.S. North Atlantic and Gulf coastline on a coastal-refined unstructured mesh achieving practical nearshore resolution ( ~100-500 m) to have a computational feasibility. We forced simulations with parametric wind fields from the Generalized Asymmetric Holland Model fitted to NOAA best-track data. For each event, we provide hourly NetCDF files containing water surface elevation, significant wave height, and peak wave period. Users can apply these fields as boundary conditions for higher-resolution local models, train ML model predictors, and conduct coastwide extreme-value analyses. We validated simulations against numerous NOAA tide gauges and NDBC buoys, demonstrating robust water level skill with documented wave biases. This comprehensive basin-scale database enables coastal flood hazard assessment across multiple decades of historical storm surge events.