The magnetic and structural properties of the recently discovered pnictogen/chalcogen-free superconductor LaFeSiH have been investigated by 57Fe synchrotron M & ouml;ssbauer source spectroscopy, X-ray and neutron powder diffraction, and 29Si nuclear magnetic resonance spectroscopy. In contrast with earlier work suggesting the presence of an orthorhombic and magnetic ground state as in underdoped Fe -based pnictides, our results unambiguously establish that LaFeSiH is in fact similar to strongly overdoped Fe -based pnictides: there is no magnetic order (including under hydrostatic pressure up to 18.8 GPa), nor even fluctuating local moments and the system remains tetragonal down to 2 K. This raises the prospect of enhancing the Tc of LaFeSiH by reducing its carrier concentration through appropriate chemical substitutions.

The lattice dynamics of the superconducting materials LaFeSiH and LaFeSiO 1 - delta as well as their intermetallic precursor LaFeSi are investigated by polarized Raman spectroscopy and first-principles calculations, together with X-ray and advanced electron diffraction techniques for their structural analysis. We find that the Fe-dominated Raman-active modes reflect the chemical peculiarities of these silicides compared to their pnictide counterparts, with enhanced structural couplings between the FeSi layer and the spacer that can be related to the ionic vs . covalent character of the latter. In addition, we find signatures of enhanced electron-phonon coupling for some of the Raman-active modes. Beyond that, our study reveals intriguing Fe-based Raman features as well as structural subtleties in LaFeSiH suggesting that this superconductor may formally be non-centrosymmetric.

The emergence of alternative stable states in forest systems has significant implications for the functioning and structure of the terrestrial biosphere, yet empirical evidence remains scarce. Here, we combine global forest biodiversity observations and simulations to test for alternative stable states in the presence of evergreen and deciduous forest types. We reveal a bimodal distribution of forest leaf types across temperate regions of the Northern Hemisphere that cannot be explained by the environment alone, suggesting signatures of alternative forest states. Moreover, we empirically demonstrate the existence of positive feedbacks in tree growth, recruitment and mortality, with trees having 4-43% higher growth rates, 14-17% higher survival rates and 4-7 times higher recruitment rates when they are surrounded by trees of their own leaf type. Simulations show that the observed positive feedbacks are necessary and sufficient to generate alternative forest states, which also lead to dependency on history (hysteresis) during ecosystem transition from evergreen to deciduous forests and vice versa. We identify hotspots of bistable forest types in evergreen-deciduous ecotones, which are likely driven by soil-related positive feedbacks. These findings are integral to predicting the distribution of forest biomes, and aid to our understanding of biodiversity, carbon turnover, and terrestrial climate feedbacks.

Reservoirs exert a profound influence on the cycling of dissolved organic matter (DOM) in inland waters by altering flow regimes. Biological incubations can help to disentangle the role that microbial processing plays in the DOM cycling within reservoirs. However, the complex DOM composition poses a great challenge to the analysis of such data. Here we tested if the interpretable machine learning (ML) methodologies can contribute to capturing the relationships between molecular reactivity and composition. We developed time-specific ML models based on 7-day and 30-day incubations to simulate the biogeochemical processes in the Three Gorges Reservoir over shorter and longer water retention periods, respectively. Results showed that the extended water retention time likely allows the successive microbial degradation of molecules, with stochasticity exerting a non-negligible effect on the molecular composition at the initial stage of the incubation. This study highlights the potential of ML in enhancing our interpretation of DOM dynamics over time.