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The earth abundant and environmentally friendly element iron (Fe) forms various functional materials of metallic iron, iron oxides, iron carbides, natural iron ore, and iron-based metallic-organic frameworks. The Fe-based materials have been intensively studied as oxygen carriers, catalysts, adsorbents, and additives in bioenergy production. This review was to provide a fundamental understanding of the syntheses and characteristics of various Fe-based materials for further enhancing their functionalities and facilitating their applications in various bioenergy conversion processes. The syntheses, characteristics, and applications of various iron-based materials for bioenergy conversion published in peer-reviewed articles were first reviewed. The challenges and perspectives of the wide applications of those functional materials in bioenergy conversion were then discussed. The functionalities, stability, and reactivity of Fe-based materials depend on their structures and redox phases. Furthermore, the phase and composition of iron compounds change in a process. More research is needed to analyze the complex phase and composition changes during their applications, and study the type of iron precursors, synthesizing conditions, and the use of promoters and supports to improve their performance in bioenergy conversion. More studies are also needed to develop multifunctional Fe-based materials to be used for multi-duties in a biorefinery and develop green processes to biologically, economically, and sustainably produce those functional materials at a large scale.

 

Fast kinetics of solid‐state batteries at the device level is not adequately explored to achieve fast charging and discharging. In this work, a leap forward is achieved for fast kinetics in full cells with high cathode loading and areal capacity. This kinetic improvement is achieved by designing a hierarchical structure of electrode composites. In the cathode, the authors’ design enables high areal capacities above 3 mAh cm⁻²to be stably cycled at high current densities of ≈13–40 mA cm⁻², yielding a C‐rate from 5 to 10 C. In the anode, the authors’ design breaks the common rule of the negative correlation between critical C‐rate and the discharge voltage that is observed in most other anodes. The overall design enables the fast cycling of such batteries for over 4000 cycles at room temperature and 5 C charge‐rate. The design principles unveiled by this work help to understand critical kinetic processes in battery devices that limit the fast cycling at high cathode loading and speed up the design of high‐performance solid‐state batteries.

 

Processing social information from faces is difficult for individuals with autism spectrum disorder (ASD). However, it remains unclear whether individuals with ASD make high-level social trait judgments from faces in the same way as neurotypical individuals. Here, we comprehensively addressed this question using naturalistic face images and representatively sampled traits. Despite similar underlying dimensional structures across traits, online adult participants with self-reported ASD showed different judgments and reduced specificity within each trait compared with neurotypical individuals. Deep neural networks revealed that these group differences were driven by specific types of faces and differential utilization of features within a face. Our results were replicated in well-characterized in-lab participants and partially generalized to more controlled face images (a preregistered study). By investigating social trait judgments in a broader population, including individuals with neurodevelopmental variations, we found important theoretical implications for the fundamental dimensions, variations, and potential behavioral consequences of social cognition.

 

We employ a sample of 135 873 RR Lyrae stars (RRLs) with precise photometric-metallicity and distance estimates from the newly calibrated P–ϕ31–R21–[Fe/H] and Gaia G band P–R21–[Fe/H] absolute magnitude–metallicity relations of Li et al., combined with available proper motions from Gaia EDR3, and 6955 systemic radial velocities from Gaia DR3 and other sources, in order to explore the chemistry and kinematics of the halo of the Milky Way (MW). This sample is ideally suited for characterization of the inner- and outer-halo populations of the stellar halo, free from the bias associated with spectroscopically selected probes, and for estimation of their relative contributions as a function of Galactocentric distance. The results of a Gaussian mixture model analysis of these contributions are broadly consistent with other observational studies of the halo, and with expectations from recent MW simulation studies. We apply the hdbscan clustering method to the specific energies and cylindrical actions (E, Jr, Jϕ, Jz), identifying 97 dynamically tagged groups (DTGs) of RRLs, and explore their associations with recognized substructures of the MW. The precise photometric-distance determinations (relative distance errors on the order of 5 per cent or better), and the resulting high-quality determination of dynamical parameters, yield highly statistically significant (low) dispersions of [Fe/H] for the stellar members of the DTGs compared to random draws from the full sample, indicating that they share common star-formation and chemical histories, influenced by their birth environments.

 

It has been hypothesized that the ventral stream processing for object recognition is based on a mechanism called cortically local subspace untangling. A mathematical abstraction of object recognition by the visual cortex is how to untangle the manifolds associated with different object categories. Such a manifold untangling problem is closely related to the celebrated kernel trick in metric space. In this paper, we conjecture that there is a more general solution to manifold untangling in the topological space without artificially defining any distance metric. Geometrically, we can either embed a manifold in a higher-dimensional space to promote selectivity or flatten a manifold to promote tolerance. General strategies of both global manifold embedding and local manifold flattening are presented and connected with existing work on the untangling of image, audio, and language data. We also discuss the implications of untangling the manifold into motor control and internal representations.