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Schematic overview of an electrochemical sensor for ROS detection, showing different potential sensing materials, transducer, and signal processing. 

Managing stress is essential for mental and physical health, yet current methods rely on subjective self-assessments or indirect physiological measurements, often lacking accuracy. Existing wearable sensors primarily target a single stress hormone, cortisol, using single-point measurements that fail to capture real-time changes and distinguish between acute and chronic stress. To address this, we present Stressomic, a wearable multiplexed microfluidic biosensor for noninvasive monitoring of cortisol, epinephrine, and norepinephrine in sweat. Stressomic integrates iontophoresis-driven sweat extraction with bursting valve-regulated microfluidic channels for continuous sampling and analysis. Gold nanodendrite–decorated laser-engraved graphene electrodes achieve picomolar-level sensitivity, enabling simultaneous detection of multiple stress hormones. Electrochemical assays and human studies demonstrate that Stressomic reliably tracks hormone fluctuations in response to physical, psychological, and pharmacological stressors. Distinct temporal patterns reveal the dynamic interplay between the hypothalamic-pituitary-adrenal axis and the sympathetic nervous system. This platform enables continuous, multiplexed stress profiling, offering opportunities for early detection of maladaptive responses, personalized stress management, and deeper insights into stress biology. 

Small electrodes capable of detecting Mn dissolution and oxygen evolution are placed near operating Mn-based lithium-ion battery cathodes to track their degradation, informing on mechanism and revealing how additives might help decrease degradation. 

To manage water resources and forecast river flows, hydrologists seek to understand how water moves from precipitation, through watersheds, into river channels. However, we lack fundamental information on the spatial distribution and physical controls on global hydrologic processes. This information is needed to provide theoretical support for large-domain model simulations. Here, to address this issue, we present a global, searchable database of 400 research watersheds with published descriptions of dominant hydrologic flow pathways. This knowledge synthesis approach leverages decades of grant funding, fieldwork effort and local expertise. We use the database to test longstanding hypotheses about the roles of climate, biomes and landforms in controlling hydrologic processes. We show that aridity predicts the depth of water flow pathways and that terrain and biomes predict the prevalence of lateral flow pathways. These new data and search capabilities support efficient hypothesis testing to investigate emergent patterns that relate landscape organization to hydrologic function. 

Abstract Recent advancements in wearable sensor technologies have enabled real-time monitoring of physiological and biochemical signals, opening new opportunities for personalized healthcare applications. However, conventional wearable devices often depend on rigid electronics components for signal transduction, processing, and wireless communications, leading to compromised signal quality due to the mechanical mismatches with the soft, flexible nature of human skin. Additionally, current computing technologies face substantial challenges in efficiently processing these vast datasets, with limitations in scalability, high power consumption, and a heavy reliance on external internet resources, which also poses security risks. To address these challenges, we have developed a miniaturized, standalone, chip-less wearable neuromorphic system capable of simultaneously monitoring, processing, and analyzing multimodal physicochemical biomarker data (i.e., metabolites, cardiac activities, and core body temperature). By leveraging scalable printing technology, we fabricated artificial synapses that function as both sensors and analog processing units, integrating them alongside printed synaptic nodes into a compact wearable system embedded with a medical diagnostic algorithm for multimodal data processing and decision making. The feasibility of this flexible wearable neuromorphic system was demonstrated in sepsis diagnosis and patient data classification, highlighting the potential of this wearable technology for real-time medical diagnostics. 

Semiconductor moiré superlattices, characterized by their periodic spatial light emission, unveil a new paradigm of engineered photonic materials. Here, we show that ferroelectric moiré domains formed in a twisted hexagonal boron nitride (t-hBN) substrate can modulate light emission from an adjacent semiconductor MoSe₂monolayer. The electrostatic potential at the surface of the t-hBN substrate provides a simple way to confine excitons in the MoSe₂monolayer. The excitons confined within the domains and at the domain walls are spectrally separated because of a pronounced Stark shift. Moreover, the patterned light emission can be dynamically controlled by electrically gating the ferroelectric domains, introducing a functionality beyond other semiconductor moiré superlattices. Our findings chart an exciting pathway for integrating nanometer-scale moiré ferroelectric domains with various optically active functional layers, paving the way for advanced nanophotonics and metasurfaces.