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Dynamic 3D Gaussian splatting (3DGS) extends static 3DGS to render dynamic scenes, enabling AR/VR applications with moving objects. However, implementing dynamic 3DGS on edge devices faces challenges: (1) Loading all Gaussian parameters from DRAM for frustum culling incurs high energy costs. (2) Increased parameters for dynamic scenes elevate sorting latency and energy consumption. (3) Limited on-chip buffer capacity with higher parameters reduces buffer reuse, causing frequent DRAM access. (4) Dynamic 3DGS operations are not readily compatible with digital compute-in-memory (DCIM). These challenges hinder real-time performance and power efficiency on edge devices, leading to reduced battery life or requiring bulky batteries. To tackle these challenges, we propose algorithm-hardware co-design techniques. At the algorithmic level, we introduce three optimizations: (1) DRAM-access reduction frustum culling to lower DRAM access overhead, (2) Adaptive tile grouping to enhance on-chip buffer reuse, and (3) Adaptive interval initialization Bucket-Bitonic sort to reduce sorting latency. At the hardware level, we present a DCIM-friendly computation flow that is evaluated using the measured data from a 16 nm DCIM prototype chip. Our experimental results on Large-Scale Real-World Static/Dynamic Datasets demonstrate the ability to achieve high frame rate real-time rendering exceeding 200 frames per second (FPS) with minimal power consumption—merely 0.28 W for static Large-Scale Real-World scenes and 0.63 W for dynamic Large-Scale Real-World scenes. This work successfully addresses the significant challenges of implementing static/dynamic 3DGS technology on resource-constrained edge devices. 

Plastic waste has been a major social and environmental problem. Chemical upcycling of plastic waste into transportation fuels or lubricant oil has emerged as an attractive and promising approach to convert plastic waste into valuable products. However, conventional catalyst systems often require high-pressure hydrogen, prolonged residence time, and the use of noble metal catalysts. Here, we present zero-valent nickel single atoms on Mo-based MXenes (Ni/Mo2TiC2Tx) for effective hydrogenation of polyolefins. In situ spectroscopic and microscopic characterizations demonstrate the formation of Ni–Mo and Ni–C bonds at a low Ni loading, resulting in the formation of dispersed intercalated Ni atoms. The catalysts were then employed to convert polyethylene into fuels and lubricant molecules under atmospheric hydrogen pressure, short vapor residence time (τ < 1 s), and mild reaction temperature (300 °C). We further demonstrated the possibility of tuning the Ni structure by changing the Ni loading, attributed to the strong metal–support interactions (MSIs) with the Mo2TiC2Tx support. Under optimized conditions, 22.5 C% gasoline, 12.0 C% diesel, 19.2 C% jet fuel (JP-8), and 37.4 C% lubricant were produced over 0.5% Ni/Mo2TiC2Tx. This work highlights the potential of utilizing MSIs of Mo2TiC2Tx MXenes to synthesize single-atom catalysts (SACs) for upcycling plastic waste into higher-value products. 

Sustained attention (SA) is essential for maintaining focus over time, with disruptions linked to various neuro- logical and psychiatric disorders. The oscillatory dynamics and functional connectivity in the dorsal fronto- parietal network (dFPN) are crucial in SA. However, the neuronal mechanisms that control the level of SA, especially in response to heightened attentional demands, remain poorly understood. To examine the role of rhythmic synchronization in the dFPN in SA, we recorded local 昀椀eld potential and single unit activity in ferrets that performed the 5-Choice Serial Reaction Time Task (5-CSRTT) under both low and high attentional load. Under high attentional load, dFPN exhibited a pronounced state shift that corresponded with behavioral changes in the animal. Prior to the onset of the target stimulus, animals transitioned from a stationary state, characterized by frontal theta oscillations and dFPN theta connectivity, to an active exploration state associated with sensory processing. This shift was indexed by a suppression of inhibitory alpha oscillations and an increase in excitatory theta and gamma oscillations in parietal cortex. We further show that dFPN theta connectivity predicts per- formance 昀氀uctuations under high attentional load. Together, these results suggest that behavioral strategies for maintaining SA are tightly linked to neuronal state dynamics in the dFPN. Importantly, these 昀椀ndings identify rhythmic synchronization within the FPN as a potential neural target for novel therapeutic strategies for dis- rupted attention. 

Abstract We consider the nuclear absorption of dark matter as an alternative to the typical indirect detection search channels of dark matter decay or annihilation. In this scenario, an atomic nucleus transitions to an excited state by absorbing a pseudoscalar dark matter particle and promptly emits a photon as it transitions back to its ground state. The nuclear excitation of carbon and oxygen in the Galactic Center would produce a discrete photon spectrum in the𝒪(10) MeV range that could be detected by gamma-ray telescopes. Using theBIGSTICKlarge-scale shell-model code, we calculate the excitation energies of carbon and oxygen. We constrain the dark matter-nucleus coupling for current COMPTEL data, and provide projections for future experiments AMEGO-X, e-ASTROGAM, and GRAMS for dark matter masses from ∼ 10 to 30 MeV. We find the excitation process to be very sensitive to the dark matter mass and find that the future experiments considered would improve constraints on the dark matter-nucleus coupling within an order of magnitude. 

Abstract Routine high strain rate impacts from the surrounding environment can cause surface erosion, abrasion, and even catastrophic failure to many structural materials. It is thus highly desirable to develop lightweight, thin, and tough impact resistant coatings. Here, inspired by the structurally robust impact surface of the dactyl club of the peacock mantis shrimp, a silicon carbide and chitosan nanocomposite coating is developed to evaluate its impact resistance as a function of particle loading. High strain rate impact tests demonstrate that coatings with 50% and 60% SiC have optimal performance with the greatest reduction in penetration depth and damage area to the substrate. Post‐impact analysis confirms that these concentrations achieve a balance between stiffness and matrix phase continuity, efficiently dissipating impact energy while maintaining coating integrity. The addition of SiC particles helps dissipate impact energy via interphase effects, particle percolation, and frictional losses due to particle jamming. The formation of these stress paths is also modeled to better understand how the addition of particles improves coating stiffness and the stress distribution as a function of particle loading. These findings highlight the potential of bioinspired materials and their promise to promote innovation and breakthroughs in the development of resilient multifunctional materials. 

De novo design of protein catalysts with high efficiency and stereoselectivity provides an attractive approach toward the design of environmentally benign catalysts. Here, we design proteins that incorporate histidine-ligated synthetic porphyrin and heme ligands. Four of 10 designed proteins catalyzed cyclopropanation with an enantiomeric ratio greater than 99:1. A second class of proteins were designed to catalyze a silicon-hydrogen insertion and were optimized by directed evolution in whole cells. The evolved proteins incorporated features unlikely to be generated by computational design alone, including a proline in an α helix. Molecular dynamics simulations showed that as the proteins evolved toward higher activity, their conformational ensembles narrowed to favor more productive conformations. Our work demonstrates that efficient de novo protein catalysts are designable and should be useful for manifold chemical processes.