Existing liquid sensing solutions typically fail to achieve real-world scalability. Current channel-based methods (e.g., RF, acoustics) approach this problem through a consumer-centric classification paradigm. However, their measurements become tightly coupled to non-repeatable channel responses and heterogeneous hardware. As a result, systems require massive, ad-hoc data collection to cover these variations, yet still fail to generalize across the practically infinite diversity of products. In this paper, we propose a paradigm shift to targeted verification and repurpose the identity tag as a sensing interface. We present AquaCode, the first liquid quality assessment system seamlessly integrated into retail checkout platforms. Instead of inferring liquid types via unstable channels, AquaCode harnesses Liquid-Solid Interface (LSI), a novel sensing modality that measures charge transfer dynamics directly at the outer container wall, making it immune to external multipath interference. Crucially, AquaCode leverages the checkout platform to provide standardized mechanical excitation, converting the erratic motion artifacts of handheld sensing into a deterministic signal source. This enables a robust verification model: the system compares the runtime LSI signal against a manufacturer-defined golden standard encoded in a low-cost passive tag. We design a specialized analog frontend to extract these faint, high-impedance signals and a lightweight algorithm for 1-to-1 verification. We achieve a recall of 0.905 for identifying low-quality drinks. We believe AquaCode offers a scalable path to ubiquitous liquid safety.

The integration of Large Language Models (LLMs) with mobile devices is set to transform mental health care accessibility and quality. This paper introduces MindGuard, an autonomous LLM agent that utilizes mobile sensor data and engages in proactive, personalized conversations while ensuring user privacy through local processing. Unlike traditional mental health AI tools, MindGuard enables real-time, context-aware interventions by dynamically adapting to users' emotional and physiological states. The real-world implementation demonstrates its effectiveness with the ultimate goal of creating an accessible, scalable, and personalized mental healthcare ecosystem for anyone with smart mobile devices.

This paper reviews current progress on flexible and stretchable transistors and sensors for the next-generation multiplex electronics (commonly referred to as “electronics skin” or “e-skin”) that is capable of simultaneous detection of multiple information from a variety of surfaces including human skin. Flexible chemical sensors for sweat analysis as well as physical sensors for detecting tactile force, bending, and temperature will be discussed, with emphasis on materials, detection mechanisms, and device demonstration to realize multiplex human-interactive devices. Next, system integration enabling the real-time monitoring of health conditions is also demonstrated as a proof of concept. Finally, perspectives on e-skin for moving toward realizing practical wearable electronics in the market are discussed. This paper targets the translation of the nano- and flexible technologies from academic innovations to industrial practical applications with high-impact breakthroughs.