Existing vital sign monitoring systems in the neonatal intensive care unit (NICU) require multiple wires connected to rigid sensors with strongly adherent interfaces to the skin. We introduce a pair of ultrathin, soft, skin-like electronic devices whose coordinated, wireless operation reproduces the functionality of these traditional technologies but bypasses their intrinsic limitations. The enabling advances in engineering science include designs that support wireless, battery-free operation; real-time, in-sensor data analytics; time-synchronized, continuous data streaming; soft mechanics and gentle adhesive interfaces to the skin; and compatibility with visual inspection and with medical imaging techniques used in the NICU. Preliminary studies on neonates admitted to operating NICUs demonstrate performance comparable to the most advanced clinical-standard monitoring systems.

Electrochemical reduction of CO2 (CO2RR) provides an attractive pathway to achieve a carbon-neutral energy cycle. Single-atom catalysts (SAC) have shown unique potential in heterogeneous catalysis, but their structural simplicity prevents them from breaking linear scaling relationships. In this study, we develop a feasible strategy to precisely construct a series of electrocatalysts featuring well-defined single-atom and dual-site iron anchored on nitrogen-doped carbon matrix (Fe1–N–C and Fe2–N–C). The Fe2–N–C dual-atom electrocatalyst (DAC) achieves enhanced CO Faradaic efficiency above 80% in wider applied potential ranges along with higher turnover frequency (26,637 h–1) and better durability compared to SAC counterparts. Furthermore, based on in-depth experimental and theoretical analysis, the orbital coupling between the iron dual sites decreases the energy gap between antibonding and bonding states in *CO adsorption. This research presents new insights into the structure–performance relationship on CO2RR electrocatalysts at the atomic scale and extends the application of DACs for heterogeneous electrocatalysis and beyond.

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Sensors that reproduce the complex characteristics of cutaneous receptors in the skin have important potential in the context of artificial systems for controlled interactions with the physical environment. Multimodal responses with high sensitivity and wide dynamic range are essential for many such applications. This report introduces a simple, three-dimensional type of microelectromechanical sensor that incorporates monocrystalline silicon nanomembranes as piezoresistive elements in a configuration that enables separate, simultaneous measurements of multiple mechanical stimuli, such as normal force, shear force, and bending, along with temperature. The technology provides high sensitivity measurements with millisecond response times, as supported by quantitative simulations. The fabrication and assembly processes allow scalable production of interconnected arrays of such devices with capabilities in spatiotemporal mapping. Integration with wireless data recording and transmission electronics allows operation with standard consumer devices.

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.