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1. Similarity in spatial structure constrains ecosystem relationships: Building a macroscale understanding of lakes

   https://doi.org/10.1111/geb.12781  

   Lapierre, Jean‐Francois ; Collins, Sarah_M ; Seekell, David_A ; Spence_Cheruvelil, Kendra ; Tan, Pang‐Ning ; Skaff, Nicholas_K ; Taranu, Zofia_E ; Fergus, C_Emi ; Soranno, Patricia_A ( September 2018 , Global Ecology and Biogeography)

   We aimed to measure the dominant spatial patterns in ecosystem properties (such as nutrients and measures of primary production) and the multi‐scaled geographical driver variables of these properties and to quantify how the spatial structure of pattern in all of these variables influences the strength of relationships among them.

   We studied > 8,500 lakes in a 1.8 million km²area of Northeast U.S.A. Data comprised 10‐year medians (2002–2011) for measured ecosystem properties, long‐term climate averages and recent land use/land cover variables.

   We focused on ecosystem properties at the base of aquatic food webs, including concentrations of nutrients and algal pigments that are proxies of primary productivity.

   We quantified spatial structure in ecosystem properties and their geographical driver variables using distance‐based Moran eigenvector maps (dbMEMs). We then compared the similarity in spatial structure for all pairs of variables with the correlation between variables to illustrate how spatial structure constrains relationships among ecosystem properties.

   The strength of spatial structure decreased in order for climate, land cover/use, lake ecosystem properties and lake and landscape morphometry. Having a comparable spatial structure is a necessary condition to observe a strong relationship between a pair of variables, but not a sufficient one; variables with very different spatial structure are never strongly correlated. Lake ecosystem properties tended to have an intermediary spatial structure compared with that of their main drivers, probably because climate and landscape variables with known ecological links induce spatial patterns.

   Our empirical results describe inherent spatial constraints that dictate the expected relationships between ecosystem properties and their geographical drivers at macroscales. Our results also suggest that understanding the spatial scales at which ecological processes operate is necessary to predict the effects of multi‐scaled environmental changes on ecosystem properties.

    

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2. Dinuclear nitrido-bridged osmium complexes inhibit the mitochondrial calcium uniporter and protect cortical neurons against lethal oxygen–glucose deprivation

   https://doi.org/10.1039/d2cb00189f  

   Woods, Joshua J. ; Novorolsky, Robyn J. ; Bigham, Nicholas P. ; Robertson, George S. ; Wilson, Justin J. ( January 2023 , RSC Chemical Biology)

   We have investigated the biological properties of the osmium analogue of a potent ruthenium-based mitochondrial calcium uniporter inhibitor and have found it to possess distinct properties.

    

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3. Spectroscopic techniques to probe magnetic anisotropy and spin–phonon coupling in metal complexes

   https://doi.org/10.1039/D3DT03609J  

   Hand, Adam T. ; Watson-Sanders, Brandon D. ; Xue, Zi-Ling ( March 2024 , Dalton Transactions)

   Magnetic anisotropy and spin–phonon coupling are key properties of single-molecule magnets. The use of far-IR and Raman magneto-spectroscopies (FIRMS and RaMS), and inelastic neutron scattering (INS) to determine the magnetic properties is highlighted.

    

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4. Learning from models: high-dimensional analyses on the performance of machine learning interatomic potentials

   https://doi.org/10.1038/s41524-024-01333-3  

   Liu, Yunsheng ; Mo, Yifei ( July 2024 , npj Computational Materials)

   Machine learning interatomic potential (MLIP) has been widely adopted for atomistic simulations. While errors and discrepancies for MLIPs have been reported, a comprehensive examination of the MLIPs’ performance over a broad spectrum of material properties has been lacking. This study introduces an analysis process comprising model sampling, benchmarking, error evaluations, and multi-dimensional statistical analyses on an ensemble of MLIPs for prediction errors over a diverse range of properties. By carrying out this analysis on 2300 MLIP models based on six different MLIP types, several properties that pose challenges for the MLIPs to achieve small errors are identified. The Pareto front analyses on two or more properties reveal the trade-offs in different properties of MLIPs, underscoring the difficulties of achieving low errors for a large number of properties simultaneously. Furthermore, we propose correlation graph analyses to characterize the error performances of MLIPs and to select the representative properties for predicting other property errors. This analysis process on a large dataset of MLIP models sheds light on the underlying complexities of MLIP performance, offering crucial guidance for the future development of MLIPs with improved predictive accuracy across an array of material properties.

    

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5. Tuning the Intrinsic Properties of Carbon Nitride for High Quantum Yield Photocatalytic Hydrogen Production

   https://doi.org/10.1002/advs.201800820  

   Rahman, Mohammad Z. ; Davey, Kenneth ; Mullins, C. Buddie ( August 2018 , Advanced Science)

   The low quantum yield of photocatalytic hydrogen production in carbon nitride (CN) has been improved upon via the modulation of both the extrinsic and intrinsic properties of the material. Although the modification of extrinsic properties has been widely investigated in the past, recently there has been growing interest in the alteration of intrinsic properties. Refining the intrinsic properties of CN provides flexibility in controlling the charge transport and selectivity in photoredox reactions, and therefore makes available a pathway toward superior photocatalytic performance. An analysis of recent progress in tuning the intrinsic photophysical properties of CN facilitates an assessment of the goals, achievements, and gaps. This article is intended to serve this purpose. Therefore, selected techniques and mechanisms of the tuning of intrinsic properties of CN are critically discussed here. This article concludes with a recommendation of the issues that need to be considered for the further enhancement in the quantum efficiency of CN photocatalysts.

    

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