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1. Geometrical Relations between Slab Dip and the Location of Volcanic Arcs and Back-arc Spreading Centers

   https://doi.org/10.5281/zenodo.7636704  

   Ha, Goeun; Montési, Laurent G.; Zhu, Wenlu (January 2023, Zenodo)

   The dataset includes the measurements of individual subduction zones defined in the convergence-parallel, trench-perpendicular, and spreading-parallel direction. </p>  </p> Table S3. Location of each trench, arc, and back-arc defined in a direction parallel to the convergence, and the corresponding distance from the trench to the arc (D_TA), subarc slab depth (H), and from the trench to the back-arc spreading center (D_TB). The slab dip is measured at 50km (Dip50), 100km (Dip100), and 200km (Dip200) and averaged from 0 to 50 km (Dip050), 0 to 100km (Dip0100), 0 to 200km (Dip0200), and 50 to 200km (Dip50200). </p> Table S4. Location of each trench, arc, and back-arc defined in a direction perpendicular to the trench, and the corresponding distance from the trench to the arc (D_TA), subarc slab depth (H), and from the trench to the back-arc spreading center (D_TB). The slab dip is measured at 50km (Dip50), 100km (Dip100), and 200km (Dip200) and averaged from 0 to 50 km (Dip050), 0 to 100km (Dip0100), 0 to 200km (Dip0200), and 50 to 200km (Dip50200). </p> Table S5. Location of each trench, arc, and back-arc defined in a direction parallel to the spreading direction, and the corresponding distance from the trench to the arc (D_TA), subarc slab depth (H), and from the trench to the back-arc spreading center (D_TB). The slab dip is measured at 50km (Dip50), 100km (Dip100), and 200km (Dip200) and averaged from 0 to 50 km (Dip050), 0 to 100km (Dip0100), 0 to 200km (Dip0200), and 50 to 200km (Dip50200). </p>  </p> 

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2. Clustering with Neighborhoods

   https://doi.org/10.4230/LIPIcs.ISAAC.2021.6  

   Huang, Hongyao; Klimenko, Georgiy; Raichel, Benjamin (November 2021, International Symposium on Algorithms and Computation (ISAAC))

   Ahn, Hee-Kap; Sadakane, Kunihiko (Ed.)

   In the standard planar k-center clustering problem, one is given a set P of n points in the plane, and the goal is to select k center points, so as to minimize the maximum distance over points in P to their nearest center. Here we initiate the systematic study of the clustering with neighborhoods problem, which generalizes the k-center problem to allow the covered objects to be a set of general disjoint convex objects C rather than just a point set P. For this problem we first show that there is a PTAS for approximating the number of centers. Specifically, if r_opt is the optimal radius for k centers, then in n^O(1/ε²) time we can produce a set of (1+ε)k centers with radius ≤ r_opt. If instead one considers the standard goal of approximating the optimal clustering radius, while keeping k as a hard constraint, we show that the radius cannot be approximated within any factor in polynomial time unless P = NP, even when C is a set of line segments. When C is a set of unit disks we show the problem is hard to approximate within a factor of (√{13}-√3)(2-√3) ≈ 6.99. This hardness result complements our main result, where we show that when the objects are disks, of possibly differing radii, there is a (5+2√3)≈ 8.46 approximation algorithm. Additionally, for unit disks we give an O(n log k)+(k/ε)^O(k) time (1+ε)-approximation to the optimal radius, that is, an FPTAS for constant k whose running time depends only linearly on n. Finally, we show that the one dimensional version of the problem, even when intersections are allowed, can be solved exactly in O(n log n) time. 

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3. Characterization of N ⁺ Abundances in the Terrestrial Polar Wind Using the Multiscale Atmosphere‐Geospace Environment

   https://doi.org/10.1029/2023JA032311  

   Albarran, R M; Varney, R H; Pham, K; Lin, D (May 2024, Journal of Geophysical Research: Space Physics)

   Abstract The High‐latitude Ionosphere Dynamics for Research Applications (HIDRA) model is part of the Multiscale Atmosphere‐Geospace Environment model under development by the Center for Geospace Storms NASA DRIVE Science Center. This study employs HIDRA to simulate upflows of H⁺, He⁺, O⁺, and N⁺ions, with a particular focus on the relative N⁺concentrations, production and loss mechanisms, and thermal upflow drivers as functions of season, solar activity, and magnetospheric convection. The simulation results demonstrate that N⁺densities typically exceed He⁺densities, N⁺densities are typically ∼10% O⁺densities, and N⁺concentrations at quiet‐time are approximately 50%–100% of N⁺concentrations during storm‐time. Furthermore, the N⁺and O⁺upflow fluxes show similar trends with variations in magnetospheric driving. The inclusion of ion‐neutral chemical reactions involving metastable atoms is shown to have significant effects on N⁺production rates. With this metastable chemistry included, the simulated ion density profiles compare favorably with satellite measurements from Atmosphere Explorer C and Orbiting Geophysical Observatory 6. 

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4. Catalytic Ammonia Oxidation to Dinitrogen by a Nickel Complex

   https://doi.org/10.1002/anie.202213462  

   Stephens, David N.; Szilagyi, Robert K.; Roehling, Paige N.; Arulsamy, Navamoney; Mock, Michael T. (November 2022, Angewandte Chemie International Edition)

   Abstract We report a nickel complex for catalytic oxidation of ammonia to dinitrogen under ambient conditions. Using the aryloxyl radical 2,4,6‐tri‐tert‐butylphenoxyl (^tBu₃ArO⋅) as a H atom acceptor to cleave the N−H bond of a coordinated NH₃ligand up to 56 equiv of N₂per Ni center can be generated. Employing theN‐oxyl radical 2,2,6,6‐(tetramethylpiperidin‐1‐yl)oxyl (TEMPO⋅) as the H‐atom acceptor, up to 15 equiv of N₂per Ni center are formed. A bridging Ni‐hydrazine product identified by isotopic nitrogen (¹⁵N) studies and supported by computational models indicates the N−N bond forming step occurs by bimetallic homocoupling of two paramagnetic [Ni]−NH₂fragments. Ni‐mediated hydrazine disproportionation to N₂and NH₃completes the catalytic cycle. 

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5. Effect of Phosphorus Modulation in Iron Single‐Atom Catalysts for Peroxidase Mimicking

   https://doi.org/10.1002/adma.202209633  

   Ding, Shichao; Barr, Jordan Alysia; Lyu, Zhaoyuan; Zhang, Fangyu; Wang, Maoyu; Tieu, Peter; Li, Xin; Engelhard, Mark H.; Feng, Zhenxing; Beckman, Scott P.; et al (March 2023, Advanced Materials)

   Abstract Fe–N–C single‐atom catalysts (SACs) exhibit excellent peroxidase (POD)‐like catalytic activity, owing to their well‐defined isolated iron active sites on the carbon substrate, which effectively mimic the structure of natural peroxidase's active center. To further meet the requirements of diverse biosensing applications, SAC POD‐like activity still needs to be continuously enhanced. Herein, a phosphorus (P) heteroatom is introduced to boost the POD‐like activity of Fe–N–C SACs. A 1D carbon nanowire (FeNCP/NW) catalyst with enriched Fe–N₄active sites is designed and synthesized, and P atoms are doped in the carbon matrix to affect the Fe center through long‐range interaction. The experimental results show that the P‐doping process can boost the POD‐like activity more than the non‐P‐doped one, with excellent selectivity and stability. The mechanism analysis results show that the introduction of P into SAC can greatly enhance POD‐like activity initially, but its effect becomes insignificant with increasing amount of P. As a proof of concept, FeNCP/NW is employed in an enzyme cascade platform for highly sensitive colorimetric detection of the neurotransmitter acetylcholine. 

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