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Implantable electronics are of great interest owing to their capability for real-time and continuous recording of cellular−electrical activity. Nevertheless, as such systems involve direct interfaces with surrounding biofluidic environments, maintaining their long-term sustainable operation, without leakage currents or corrosion, is a daunting challenge. Herein, we present a thin, flexible semiconducting material system that offers attractive attributes in this context. The material consists of crystalline cubic silicon carbide nanomembranes grown on silicon wafers, released and then physically transferred to a final device substrate (e.g., polyimide). The experimental results demonstrate that SiC nanomembranes with thicknesses of 230 nm do not experience the hydrolysis process (i.e., the etching rate is 0 nm/day at 96 °C in phosphate-buffered saline (PBS)). There is no observable water permeability for at least 60 days in PBS at 96 °C and non-Na + ion diffusion detected at a thickness of 50 nm after being soaked in 1× PBS for 12 days. These properties enable Faradaic interfaces between active electronics and biological tissues, as well as multimodal sensing of temperature, strain, and other properties without the need for additional encapsulating layers. These findings create important opportunities for use of flexible, wide band gap materials as essential components of long-lived neurological and cardiac electrophysiological device interfaces.

Three-dimensional (3D), submillimeter-scale constructs of neural cells, known as cortical spheroids, are of rapidly growing importance in biological research because these systems reproduce complex features of the brain in vitro. Despite their great potential for studies of neurodevelopment and neurological disease modeling, 3D living objects cannot be studied easily using conventional approaches to neuromodulation, sensing, and manipulation. Here, we introduce classes of microfabricated 3D frameworks as compliant, multifunctional neural interfaces to spheroids and to assembloids. Electrical, optical, chemical, and thermal interfaces to cortical spheroids demonstrate some of the capabilities. Complex architectures and high-resolution features highlight the design versatility. Detailed studies of the spreading of coordinated bursting events across the surface of an isolated cortical spheroid and of the cascade of processes associated with formation and regrowth of bridging tissues across a pair of such spheroids represent two of the many opportunities in basic neuroscience research enabled by these platforms.

Advances in Large Language Models (LLMs) have inspired a surge of research exploring their expansion into the visual domain. While recent models exhibit promise in generating abstract captions for images and conducting natural conversations, their performance on textrich images leaves room for improvement. In this paper, we propose the Contrastive Reading Model (Cream), a novel neural architecture designed to enhance the language-image understanding capability of LLMs by capturing intricate details typically overlooked by existing methods. Cream integrates vision and auxiliary encoders, complemented by a contrastive feature alignment technique, resulting in a more effective understanding of textual information within document images. Our approach, thus, seeks to bridge the gap between vision and language understanding, paving the way for more sophisticated Document Intelligence Assistants. Rigorous evaluations across diverse tasks, such as visual question answering on document images, demonstrate the efficacy of Cream as a state-of-the-art model in the field of visual document understanding. We provide our codebase and newly-generated datasets at https://github.com/naver-ai/cream.

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