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Today's smart devices have short battery lifetimes, high installation and maintenance costs, and rapid obsolescence - all leading to the explosion of electronic waste in the past two decades. These problems will worsen as the number of connected devices grows to one trillion by 2035. Energy harvesting, battery-free devices offer an alternative. Getting rid of the battery reduces e-waste, promises long lifetimes, and enables deployment in new applications and environments. Unfortunately, developing sophisticated inference-capable applications is still challenging. The lack of platform support for advanced (32-bit) microprocessors and specialized accelerators, which can execute dataintensive machine-learning tasks, has held back batteryless devices. 

Battery-free and intermittently powered devices offer long lifetimes and enable deployment in new applications and environments. Unfortunately, developing sophisticated inference-capable applications is still challenging due to the lack of platform support for more advanced (32-bit) microprocessors and specialized accelerators---which can execute data-intensive machine learning tasks, but add complexity across the stack when dealing with intermittent power. We present Protean to bridge the platform gap for inference-capable battery-free sensors. Designed for runtime scalability, meeting the dynamic range of energy harvesters with matching heterogeneous processing elements like neural network accelerators. We develop a modular "plug-and-play" hardware platform, SuperSensor, with a reconfigurable energy storage circuit that powers a 32-bit ARM-based microcontroller with a convolutional neural network accelerator. An adaptive task-based runtime system, Chameleon, provides intermittency-proof execution of machine learning tasks across heterogeneous processing elements. The runtime automatically scales and dispatches these tasks based on incoming energy, current state, and programmer annotations. A code generator, Metamorph, automates conversion of ML models to intermittent safe execution across heterogeneous compute elements. We evaluate Protean with audio and image workloads and demonstrate up to 666x improvement in inference energy efficiency by enabling usage of modern computational elements within intermittent computing. Further, Protean provides up to 166% higher throughput compared to non-adaptive baselines. 

The COVID-19 pandemic has dramatically increased the use of face masks across the world. Aside from physical distancing, they are among the most effective protection for healthcare workers and the general population. Face masks are passive devices, however, and cannot alert the user in case of improper fit or mask degradation. Additionally, face masks are optimally positioned to give unique insight into some personal health metrics. Recognizing this limitation and opportunity, we present FaceBit: an open-source research platform for smart face mask applications. FaceBit's design was informed by needfinding studies with a cohort of health professionals. Small and easily secured into any face mask, FaceBit is accompanied by a mobile application that provides a user interface and facilitates research. It monitors heart rate without skin contact via ballistocardiography, respiration rate via temperature changes, and mask-fit and wear time from pressure signals, all on-device with an energy-efficient runtime system. FaceBit can harvest energy from breathing, motion, or sunlight to supplement its tiny primary cell battery that alone delivers a battery lifetime of 11 days or more. FaceBit empowers the mobile computing community to jumpstart research in smart face mask sensing and inference, and provides a sustainable, convenient form factor for health management, applicable to COVID-19 frontline workers and beyond. 

Structural and ion‐ordering phase transitions limit the viability of sodium‐ion intercalation materials in grid scale battery storage by reducing their lifetime. However, the combination of phenomena in nanoparticulate electrodes creates complex behavior that is difficult to investigate, especially on the single‐nanoparticle scale under operating conditions. In this work, operando single‐particle X‐ray diffraction (oSP‐XRD) is used to observe single‐particle rotation, interlayer spacing, and layer misorientation in a functional sodium‐ion battery. oSP‐XRD is applied to Na_2/3[Ni_1/3Mn_2/3]O₂, an archetypal P2‐type sodium‐ion‐positive electrode material with the notorious P2‐O2 phase transition induced by sodium (de)intercalation. It is found that during sodium extraction, the misorientation of crystalline layers inside individual particles increases before the layers suddenly align just prior to the P2‐O2 transition. The increase in the long‐range order coincides with an additional voltage plateau signifying a phase transition prior to the P2‐O2 transition. To explain the layer alignment, a model for the phase evolution is proposed that includes a transition from localized to correlated Jahn–Teller distortions. The model is anticipated to guide further characterization and engineering of sodium‐ion intercalation materials with P2‐O2 type transitions. oSP‐XRD, therefore, opens a powerful avenue for revealing complex phase behavior in heterogeneous nanoparticulate systems.