Lithium-ion dynamics of garnet-type Li_{6.5}La_{3}Zr_{1.5}Ta_{0.5}O_{12} single crystals were investigated using pulsed field gradient (PFG) NMR and impedance spectroscopy over a wide temperature range up to 700 K. The diffusion coefficient measured by PFG-NMR exhibits non-Arrhenius behavior at elevated temperatures (>400 K). The conductivity diffusion coefficient is consistently higher than the PFG-NMR diffusion coefficient across the entire temperature range, with a Haven ratio of 0.3–0.4, indicating that the motion of mobile Li^{+} ions is correlated. Remarkably, the Haven ratio exhibits little temperature dependence despite the non-Arrhenius behavior, representing the first experimental observation in garnet-type electrolytes and consistent with the molecular dynamics simulations. This indicates that ion-ion correlations remain essentially unchanged over a wide temperature range. Combined with the local Li^{+} ion dynamics obtained from spin-lattice relaxation analysis, these results suggest that long-range diffusion is suppressed at low temperatures due to short-range ordering of Li^{+} ions.
Catalytic decomposition of methane is a more sustainable alternative for hydrogen production compared to established technologies like steam methane reforming and coal gasification. This study reports iron-based catalysts supported on MAl_{2}O_{4} (M=Ca,Mg,Sr,Ba) prepared via a ball-milling or one-pot modified Pechini (sol-gel) method. The synthesized catalysts were evaluated for their CDM performance in the range of 750–800 °C. In general, we show that ball-mill synthesis leads to higher CDM activities than sol-gel synthesized samples, which undergo quick deactivation. Raman spectroscopy and H_{2}-TPR indicate that synthesizing the Fe-MAl_{2}O_{4} catalysts via the sol-gel method results in iron coordination with cations (i.e., M) and with alumina to decrease iron’s reducibility and its overall catalytic activity. For the ball-mill synthesized samples, we find that a 30 wt% Fe-CaAl_{2}O_{4} catalyst tested at optimized reaction conditions of T_{red}=700 °C, T_{rxn}=750 °C, y_{CH4}=0.9, and GHSV=1.25 L·g^{−1}·h^{−1} obtained a competitive carbon yield of 17.86g_{C}/g_{Fe}(5.06g_{C}/g_{Cat}). TEM and SEM of the spent product indicate that the carbon product forms primarily via a tip-growth mechanism, with both filamentous and nano-onion-type carbon being generated. Raman spectroscopy of the spent product determined most of the carbon formed on the ball-milled Fe-MAl_{2}O_{4} catalysts was graphitic in nature with I_{D}/I_{G} ratios ranging between 0.400 and 0.500. Overall, ball-milling appears to be a simple yet effective strategy to prepare dispersed iron catalysts for CDM.
Quantifying the energetic cost of implementing quantum operations is essential for assessing the efficiency and scalability of quantum sensing and information-processing technologies. Here, we introduce a framework for analyzing the interplay between complexity and energy cost of quantum processes. In particular, we apply our framework to a sequential quantum phase estimation protocol, where a phase of physical significance is encoded in a quantum channel. The channel is applied to a probe state repeatedly until the probe is measured and the outcome leads to an estimate on the phase. We establish a trade-off relation between the total energy cost of the protocol and the number of times the channel is applied (complexity), while reaching a desired estimation precision. A sweet spot is located where the two quantities are co-optimized. The principles of our analysis can be adapted to benchmark the energetic requirements in other quantum protocols and devices.
Layered perovskite lithium yttrium titanate (LiYTiO_{4}) has recently emerged as a promising low-potential, ultrahigh-rate anode material for lithium-ion batteries. However, its lattice dynamics and thermal transport behavior remain largely unexplored, limiting a complete assessment of its practical applicability. Here, we combine experimental thermal conductivity measurements with a systematic theoretical study based on a machine-learning neural evolution potential (NEP). Our computational framework integrates the temperature-dependent effective potential method with the Wigner thermal transport (WTT) formalism, explicitly accounting for both diagonal and off-diagonal components of the heat-flux operator. Phonon calculations at 0 K reveal dynamical instabilities arising from rotations of the TiO_{6} octahedra, which are stabilized at finite temperatures through anharmonic phonon renormalization. By incorporating particlelike phonon propagation and wavelike phonon tunneling within the WTT framework, we predict an orientation-averaged room-temperature lattice thermal conductivity (κ_{L}) of 3.6 Wm^{−1} K^{−1}, which is qualitatively consistent with the experimentally measured value of 3.2±0.08 Wm^{−1} K^{−1} for polycrystalline samples. To further elucidate the role of ionic motion at elevated temperatures, we compute κ_{L} using the Green-Kubo approach within equilibrium molecular dynamics simulations based on the trained NEP, yielding improved agreement with experiment relative to the WTT prediction. Our results identify intrinsically low thermal conductivity as a key limitation for the practical application of LiYTiO_{4} and provide broader insight into anharmonic lattice dynamics and thermal transport in complex battery materials.
