Transmembrane Graphene as an Electron Tunnel to Regulate the Intracellular Redox State.

Cellular redox homeostasis is essential for maintaining cellular activities, such as DNA synthesis and gene expression. Inspired by this, new therapeutic interventions have been rapidly developed to modulate the intracellular redox state using artificial transmembrane electron transport. However, current approaches that rely on external electric field polarization can disrupt cellular functions, limiting their in vivo application. Therefore, it is crucial to develop novel electric-field-free modulation methods. In this work, we for the first time found that graphene could spontaneously insert into living cell membranes and serve as an electron tunnel to regulate intracellular reactive oxygen species and NADH based on the spontaneous bipolar electrochemical reaction mechanism. This work provides a wireless and electric-field-free approach to regulating cellular redox states directly and offers possibilities for biological applications such as cell process intervention and treatment for neurodegenerative diseases.

Self-Assembly of Lamellae-in-Lamellae by Double-Tail Cationic Surfactants.

The molecular structures of surfactants play a pivotal role in influencing their self-assembly behaviors. In this work, using simulations and experiments, an unconventional hierarchically layered structure in the didodecyldimethylammonium bromide (DDAB)/water binary system: lamellae-in-lamellae is revealed, a new self-assembly structure in surfactant system. This self-assembly structure refers to a lamellar structure with a shorter periodic length (inner lamellae) embedded in a lamellar phase with a longer periodic length (outer lamellae). The normal vectors of these two lamellar regions orient perpendicularly. In addition, it is observed that this lamellar-in-lamellar phase disappears when the two tails of the cationic surfactants become longer. The formation of the lamellar-in-lamellar architecture arises from multiple interacting factors. The key element is that the short tails of the DDAB surfactants enhance hydrophilicity and rigidity, which facilitates the formation of the inner lamellae. Moreover, the lateral monolayer of the inner lamellae provides shielding from the water and prompts the formation of the outer lamellae. These findings indicate that molecular structures and flexibility can profoundly redirect the hierarchical self-assembly behaviors in amphiphilic systems. More broadly, this work presents a new strategy to deliberately program hierarchical nanomaterials by designing specific surfactant molecules to act as tunable scaffolds, reactors, and carriers.

3D Snapshot: Invertible Embedding of 3D Neural Representations in a Single Image.

3D neural rendering enables photo-realistic reconstruction of a specific scene by encoding discontinuous inputs into a neural representation. Despite the remarkable rendering results, the storage of network parameters is not transmission-friendly and not extendable to metaverse applications. In this paper, we propose an invertible neural rendering approach that enables generating an interactive 3D model from a single image (i.e., 3D Snapshot). Our idea is to distill a pre-trained neural rendering model (e.g., NeRF) into a visualizable image form that can then be easily inverted back to a neural network. To this end, we first present a neural image distillation method to optimize three neural planes for representing the original neural rendering model. However, this representation is noisy and visually meaningless. We thus propose a dynamic invertible neural network to embed this noisy representation into a plausible image representation of the scene. We demonstrate promising reconstruction quality quantitatively and qualitatively, by comparing to the original neural rendering model, as well as video-based invertible methods. On the other hand, our method can store dozens of NeRFs with a compact restoration network (5MB), and embedding each 3D scene takes up only 160KB of storage. More importantly, our approach is the first solution that allows embedding a neural rendering model into image representations, which enables applications like creating an interactive 3D model from a printed image in the metaverse.