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Derivation of human toxicokinetic parameters and internal threshold of toxicological concern for tenuazonic acid through a human intervention trial and hierarchical Bayesian population modeling

Tenuazonic acid (TeA), a mycotoxin produced by Alternaria alternata, contaminates various food commodities and is known to cause acute and chronic health effects. However, the lack of human toxicokinetic (TK) data and the reliance on external exposure estimates have stalled a comprehensive risk assessment for TeA.

Feasibility of meeting future battery demand via domestic cell production in Europe

Batteries are critical to mitigate global warming, with battery electric vehicles as the backbone of low-carbon transport and the main driver of advances and demand for battery technology. However, the future demand and production of batteries remain uncertain, while the ambition to strengthen national capabilities and self-sufficiency is gaining momentum. In this study, leveraging probabilistic modelling, we assessed Europe’s capability to meet its future demand for high-energy batteries via domestic cell production. We found that demand in Europe is likely to exceed 1.0 TWh yr−1 by 2030 and thereby outpace domestic production, with production required to grow at highly ambitious growth rates of 31–68% yr−1. European production is very likely to cover at least 50–60% of the domestic demand by 2030, while 90% self-sufficiency seems feasible but far from certain. Thus, domestic production shortfalls are more likely than not. To support Europe’s battery prospects, stakeholders must accelerate the materialization of production capacities and reckon with demand growth post-2030, with reliable industrial policies supporting Europe’s competitiveness.

Spotiphy enables single-cell spatial whole transcriptomics across an entire section

Spatial transcriptomics (ST) has advanced our understanding of tissue regionalization by enabling the visualization of gene expression within whole-tissue sections, but current approaches remain plagued by the challenge of achieving single-cell resolution without sacrificing whole-genome coverage. Here we present Spotiphy (spot imager with pseudo-single-cell-resolution histology), a computational toolkit that transforms sequencing-based ST data into single-cell-resolved whole-transcriptome images. Spotiphy delivers the most precise cellular proportions in extensive benchmarking evaluations. Spotiphy-derived inferred single-cell profiles reveal astrocyte and disease-associated microglia regional specifications in Alzheimer’s disease and healthy mouse brains. Spotiphy identifies multiple spatial domains and alterations in tumor–tumor microenvironment interactions in human breast ST data. Spotiphy bridges the information gap and enables visualization of cell localization and transcriptomic profiles throughout entire sections, offering highly informative outputs and an innovative spatial analysis pipeline for exploring complex biological systems.

First-principles and machine-learning approaches for interpreting and predicting the properties of MXenes

MXenes are a versatile family of 2D inorganic materials with applications in energy storage, shielding, sensing, and catalysis. This review highlights computational studies using density functional theory and machine-learning approaches to explore their structure (stacking, functionalization, doping), properties (electronic, mechanical, magnetic), and application potential. Key advances and challenges are critically examined, offering insights into applying computational research to transition these materials from the lab to practical use.

Surface-hydroxylated single-atom catalyst with an isolated Co-O-Zn configuration achieves high selectivity in regulating active species

Single-atom catalysts (SACs) are emerging as potent tools for the selective regulation of active species, offering substantial promise for green and sustainable Fenton catalysis. However, current SACs face limitations due to the specificity of their supports, which only allow selective regulation within certain oxidant systems. This constraint makes targeted regulation across different systems challenging. In response, this study designs a SAC, termed CoSAs-ZnO, featuring surface hydroxylation and an isolated asymmetric Co-O-Zn configuration. This SAC can realize a nearly 100% selective generation of sulfate radicals (SO4•−) and singlet oxygen (1O2) in peroxymonosulfate (PMS) and peracetic acid (PAA) systems, respectively. Moreover, the PMS-activated system can efficiently treat electron-deficient-dominated and refractory benzoic acid wastewater, achieving 100.0% removal in multiple consecutive pilot-scale experiments. The PAA-activated system facilitates the rapid conversion of benzyl alcohol to benzaldehyde, with a high selectivity of 89.0%. Detailed DFT calculations reveal that the surface hydroxyl groups on ZnO play a critical role in modulating the adsorption configurations of the oxidants, thus enabling the selective generation of specific active species in each system. This study provides insights into the design of SACs for multifunctional applications and paves the way for their deployment in wastewater treatment and high-value chemical conversion.

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