In this work, three novel re-entrant plate lattice structures (LSs) have been designed by transforming conventional truss-based lattices into hybrid-plate based lattices, namely, flat-plate modified auxetic (FPMA), vintile (FPV), and tesseract (FPT). Additive manufacturing based on stereolithography (SLA) technology was utilized to fabricate the tensile, compressive, and LS specimens with different relative densities (ρ). The base material’s mechanical properties obtained through mechanical testing were used in a finite element-based numerical homogenization analysis to study the elastic anisotropy of the LSs. Both the FPV and FPMA showed anisotropic behavior; however, the FPT showed cubic symmetry. The universal anisotropic index was found highest for FPV and lowest for FPMA, and it followed the power-law dependence of ρ. The quasi-static compressive response of the LSs was investigated. The Gibson–Ashby power law (≈ρn) analysis revealed that the FPMA’s Young’s modulus was the highest with a mixed bending–stretching behavior (≈ρ1.30), the FPV showed a bending-dominated behavior (≈ρ3.59), and the FPT showed a stretching-dominated behavior (≈ρ1.15). Excellent mechanical properties along with superior energy absorption capabilities were observed, with the FPT showing a specific energy absorption of 4.5 J/g, surpassing most reported lattices while having a far lower density.

Satellite and ground-based measurements have demonstrated that Mars' ancient crust was magnetized during its formation and/or cooling and that currently there is no detectable core dynamo (Acuña et al., 1999; Connerney et al., 2004; Johnson et al., 2020). The geographic extent and uniformity of the magnetic field indicates that a steady ambient field persisted for at least a few hundred million years, implying an ancient internally generated magnetic field (Acuña et al., 1999). The Terra Sirenum-Terra Cimmeria (TSTC) region hosts particularly strong remanent magnetization, generating crustal fields predicted to be up to a few thousand nanotesla, more than an order of magnitude stronger than magnetic anomalies observed on other terrestrial planets or the Moon (Figure 1; Langlais et al., 2019). Possible explanations for the strongly magnetized crust in TSTC include one or more of the following: (1) a strong ancient magnetic field; (2) magnetized crust of a large depth/volume (3) magnetic minerals particularly well-suited for recording magnetization, for example, with a strong saturation magnetization; and/ or (4) a magnetized unit with a lithology rich in magnetic minerals (Dunlop & Arkani-Hamed, 2005; Stevenson, 2001).

<p>Auroras are an important probe for characterizing the interaction of solar wind with the induced magnetosphere of Mars and understanding the evolution of Mars’s atmosphere. Since their first discovery in 2005, Mars auroras have been studied extensively, particularly using the observations from NASA’s Mars Atmosphere and Volatile Evolution (MAVEN). Electron auroras with discrete and diffuse morphology are observed on the nightside of Mars whereas proton auroras are observed mainly on the dayside of Mars. Recently the Emirates Mars UV Spectrometer (EMUS) onboard the Emirates Mars Mission (EMM) has discovered new morphologies of sinuous electron auroras and patchy proton auroras on Mars. In this work, we perform comprehensive statistical analyses of aurora observations to understand the processes responsible for the varied auroral activity on Mars. We systematically isolate electron aurora regions from the nightside EMUS observations and characterize their occurrences and emissions with respect to the crustal magnetic fields, IMF, and electron energies measured by MAVEN. We also develop a purely data-driven model of proton auroras on Mars using MAVEN in-situ observations and UV limb scans between 2014-2022 to train an artificial neural network (ANN). We show that the ANN faithfully reconstructs the observed proton aurora limb scans profiles. We use the trained ANN to analyze the influence of Mars’ crustal magnetic field and IMF on the occurrence rates of the proton auroras using gradient-based attribution maps. </p>