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Electrocatalytic nanocarbon (EN) is a class of material receiving intense interest as a potential replacement for expensive, metal-based electrocatalysts for energy conversion and chemical production applications. The further development of EN will require an intricate knowledge of its catalytic behaviors, however, the true nature of their electrocatalytic activity remains elusive. This review highlights work that contributed valuable knowledge in the elucidation of EN catalytic mechanisms. Experimental evidence from spectroscopic studies and well-defined molecular models, along with the survey of computational studies, is summarized to document our current mechanistic understanding of EN-catalyzed oxygen, carbon dioxide and nitrogen electrochemistry. We hope this review will inspire future development of synthetic methods and in situ spectroscopic tools to make and study well-defined EN structures.

 

High-performance piezoelectrics benefit transducers and sensors in a variety of electromechanical applications.The materials with the highest piezoelectric chargecoefficients (d33) are relaxor-PbTiO3 crystals, which were discovered two decades ago. We successfully grew Sm-doped Pb(Mg1/3Nb2/3)O3-PbTiO3 (Sm-PMN-PT) single crystals with even higher d33 values ranging from 3400 to 4100 picocoulombs per newton, with variation below 20%over the as-grown crystal boule, exhibiting good property uniformity. We characterized the Sm-PMN-PTon the atomic scale with scanning transmission electron microscopy and made first-principles calculations to determine that the giant piezoelectric properties arise fromthe enhanced local structural heterogeneity introduced by Sm3+ dopants. Rare-earth doping is thus identified as a general strategy for introducing local structural heterogeneity in order to enhance the piezoelectricity of relaxor ferroelectric crystals. 

Using medical images to evaluate disease severity and change over time is a routine and important task in clinical decision making. Grading systems are often used, but are unreliable as domain experts disagree on disease severity category thresholds. These discrete categories also do not reflect the underlying continuous spectrum of disease severity. To address these issues, we developed a convolutional Siamese neural network approach to evaluate disease severity at single time points and change between longitudinal patient visits on a continuous spectrum. We demonstrate this in two medical imaging domains: retinopathy of prematurity (ROP) in retinal photographs and osteoarthritis in knee radiographs. Our patient cohorts consist of 4861 images from 870 patients in the Imaging and Informatics in Retinopathy of Prematurity (i-ROP) cohort study and 10,012 images from 3021 patients in the Multicenter Osteoarthritis Study (MOST), both of which feature longitudinal imaging data. Multiple expert clinician raters ranked 100 retinal images and 100 knee radiographs from excluded test sets for severity of ROP and osteoarthritis, respectively. The Siamese neural network output for each image in comparison to a pool of normal reference images correlates with disease severity rank (ρ = 0.87 for ROP andρ = 0.89 for osteoarthritis), both within and between the clinical grading categories. Thus, this output can represent the continuous spectrum of disease severity at any single time point. The difference in these outputs can be used to show change over time. Alternatively, paired images from the same patient at two time points can be directly compared using the Siamese neural network, resulting in an additional continuous measure of change between images. Importantly, our approach does not require manual localization of the pathology of interest and requires only a binary label for training (same versus different). The location of disease and site of change detected by the algorithm can be visualized using an occlusion sensitivity map-based approach. For a longitudinal binary change detection task, our Siamese neural networks achieve test set receiving operator characteristic area under the curves (AUCs) of up to 0.90 in evaluating ROP or knee osteoarthritis change, depending on the change detection strategy. The overall performance on this binary task is similar compared to a conventional convolutional deep-neural network trained for multi-class classification. Our results demonstrate that convolutional Siamese neural networks can be a powerful tool for evaluating the continuous spectrum of disease severity and change in medical imaging.

 

Undiagnosed dehydration compromises health outcomes across many populations. Existing dehydration diagnostics require invasive bodily fluid sampling or are easily confounded by fluid and electrolyte intake, environment, and physical activity limiting widespread adoption. We present a portable MR sensor designed to measure intramuscular fluid shifts to identify volume depletion.

Fluid loss is induced via a mouse model of thermal dehydration (37°C; 15‐20% relative humidity). We demonstrate quantification of fluid loss induced by hyperosmotic dehydration with multicomponent T2 relaxometry using both a benchtop NMR system and MRI localized to skeletal muscle tissue. We then describe a miniaturized (~1000 cm³) portable (~4 kg) MR sensor (0.28 T) designed to identify dehydration‐induced fluid loss. T2 relaxometry measurements were performed using a Carr‐Purcell‐Meiboom‐Gill pulse sequence in ~4 min.

T2 values from the portable MR sensor exhibited strong (R²= 0.996) agreement with benchtop NMR spectrometer. Thermal dehydration induced weight loss of 4 to 11% over 5 to 10 h. Fluid loss induced by thermal dehydration was accurately identified via whole‐animal NMR and skeletal muscle. The portable MR sensor accurately identified dehydration via multicomponent T2 relaxometry.

Performing multicomponent T2 relaxometry localized to the skeletal muscle with a miniaturized MR sensor provides a noninvasive, physiologically relevant measure of dehydration induced fluid loss in a mouse model. This approach offers sensor portability, reduced system complexity, fully automated operation, and low cost compared with MRI. This approach may serve as a versatile and portable point of care technique for dehydration monitoring.