Outer planet frontier of geoscience
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Generating multi-scale Li-ion battery cathode particles with radial grain architectures using stereological generative adversarial networks
Understanding structure-property relationships of Li-ion battery cathodes is crucial for optimizing rate-performance and cycle-life resilience. However, correlating the morphology of cathode particles, such as in LiNi0.8Mn0.1Co0.1O2 (NMC811), and their inner grain architecture with electrode performance is challenging, particularly, due to the significant length-scale difference between grain and particle sizes. Experimentally, it is not feasible to image such a high number of particles with full granular detail. A second challenge is that sufficiently high-resolution 3D imaging techniques remain expensive and are sparsely available at research institutions. Here, we present a stereological generative adversarial network-based model fitting approach to tackle this, that generates representative 3D information from 2D data, enabling characterization of materials in 3D using cost-effective 2D data. Once calibrated, this multi-scale model can rapidly generate virtual cathode particles that are statistically similar to experimental data, and thus is suitable for virtual characterization and materials testing through numerical simulations. A large dataset of simulated particles with inner grain architecture has been made publicly available.
Sustaining the planet by sustaining ourselves
The author transitions his career in oncology to one in planetary health. The career pivot begins after he recognizes similarities between the pandemic and the climate crisis. The author determines that stepping away from his role as chair of radiology for a one-year sabbatical is the most efficient way to learn about sustainability. The author explains the process of his sabbatical and offers guidance for those in oncology who are also considering sabbaticals. He concludes by listing five lessons about sustainability and describing his future plans.
MADS31 supports female germline development by repressing the post-fertilization programme in cereal ovules
The female germline of flowering plants develops within a niche of sporophytic (somatic) ovule cells, also referred to as the nucellus. How niche cells maintain their own somatic developmental programme, yet support the development of adjoining germline cells, remains largely unknown. Here we report that MADS31, a conserved MADS-box transcription factor from the B-sister subclass, is a potent regulator of niche cell identity. In barley, MADS31 is preferentially expressed in nucellar cells directly adjoining the germline, and loss-of-function mads31 mutants exhibit deformed and disorganized nucellar cells, leading to impaired germline development and partial female sterility. Remarkably similar phenotypes are observed in mads31 mutants in wheat, suggesting functional conservation within the Triticeae tribe. Molecular assays indicate that MADS31 encodes a potent transcriptional repressor, targeting genes in the ovule that are normally active in the seed. One prominent target of MADS31 is NRPD4b, a seed-expressed component of RNA polymerase IV/V that is involved in epigenetic regulation. NRPD4b is directly repressed by MADS31 in vivo and is derepressed in mads31 ovules, while overexpression of NRPD4b recapitulates the mads31 ovule phenotype. Thus, repression of NRPD4b by MADS31 is required to maintain ovule niche functionality. Our findings reveal a new mechanism by which somatic ovule tissues maintain their identity and support germline development before transitioning to the post-fertilization programme.
AI can outperform humans in predicting correlations between personality items
We assess the abilities of both specialized deep neural networks, such as PersonalityMap, and general LLMs, including GPT-4o and Claude 3 Opus, in understanding human personality by predicting correlations between personality questionnaire items. All AI models outperform the vast majority of laypeople and academic experts. However, we can improve the accuracy of individual correlation predictions by taking the median prediction per group to produce a “wisdom of the crowds” estimate. Thus, we also compare the median predictions from laypeople, academic experts, GPT-4o/Claude 3 Opus, and PersonalityMap. Based on medians, PersonalityMap and academic experts surpass both LLMs and laypeople on most measures. These results suggest that while advanced LLMs make superior predictions compared to most individual humans, specialized models like PersonalityMap can match even expert group-level performance in domain-specific tasks. This underscores the capabilities of large language models while emphasizing the continued relevance of specialized systems as well as human experts for personality research.
Progressively smaller glacier lake outburst floods despite worldwide growth in lake area
Glacier lake outburst floods (GLOFs) may originate from larger lakes as these continue to grow with ongoing glacier retreat; however, this perception remains poorly supported in available GLOF databases. Here we mapped the areas of 1,686 glacier lakes, just before they drained, across 13 glaciated mountain regions outside polar regions and examined the trends in pre-GLOF lake areas between 1990 and 2023. We found that pre-GLOF lake areas barely changed, or even decreased, regionally through time, even as the total lake area, and thus hazard potential, grew overall. This counterintuitive finding reflects limits to growing GLOF magnitudes, such as the decoupling of lakes and parent glaciers, the development of wide, low-gradient outlets or human management. Across all regions, pre-GLOF lake areas depend on a few ice-dammed lakes, which have produced ten times more reported outbursts and ten times larger outbursts than moraine- and bedrock-dammed lakes. The latter two dam types will continue to impound growing amounts of meltwater, thereby accounting for most of the overall GLOF hazard potential, while ice-dammed lakes will shrink with deglaciation. As these lake types will evolve differently in the twenty-first century, we call for customized simulations of GLOF outflows and impacts, given the growing exposure of critical infrastructure.
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