Search for: All records

The selection and design of charge integration methods remain an outstanding challenge in materials chemistry. In complex materials like electrides, this challenge is amplified by the small charge and complex shape of electride wave functions. For these reasons, popular integration methods, such as the Bader method, usually fail to assign any charge to the bare electrons in an electride. To address this challenge, we developed an algorithm that instead partitions the charge based on the electron localization function (ELF), a popular scheme for visualizing chemically important features in molecules and solids. The algorithm uses Bader segmentation of the ELF to find the electride electrons and Voronoi segmentation of the ELF to identify atoms. We apply this method, “BadELF”, to the quantification of atomic radii and oxidation states in both ionic compounds and electrides. For ionic compounds, we find that the BadELF method yields radii that agree closely with Shannon crystal radii, while the oxidation states agree closely with the Bader method. When they are applied to electrides, however, only the BadELF algorithm yields chemically meaningful charges. We argue that the BadELF method provides a useful strategy to identify electrides and obtain new insight into their most essential property: the quantity of electrons within them. 

Marsh plant species shoot height, weight and diameters for Rowley River tidal creeks associated with long term fertilization experiments, Rowley and Ipswich, MA. The TIDE project aims to simulate eutrophication on a large scale by the addition of NO3- aiming to reach 70μM concentrations from May to September every year during the growing season. This fertilization of the marsh has been going on at Sweeney Creek since the 2004 growing season through 2012 and at Clubhead Creek in 2005 and from 2009 till 2019. 

Abstract Fluoride–ion batteries are a promising alternative to lithium–ion batteries with higher theoretical capacities and working voltages, but they have experienced limited success due to the poor ionic conductivities of known electrolytes and electrodes. Here, we report a high-throughput computational screening of 9747 fluoride-containing materials in search of fluoride-ion conductors. Via a combination of empirical, lightweight DFT, and nudged elastic band (NEB) calculations, we identified >10 crystal systems with high fluoride mobility. We applied a search strategy where calculations are performed in any order (decoupled), computational resources are reassigned based on need (dynamic), and predictive models are repeatedly updated (iterative). Unlike hierarchical searches, our decoupled, dynamic, and iterative framework (DDI) began by calculating high-quality barrier heights for fluoride-ion mobility in a large and diverse group of materials. This high-quality dataset provided a benchmark against which a rapid calculation method could be refined. This accurate method was then used to measure the barrier heights for 6797 fluoride–ion pathways. The final dataset has allowed us to discover many fascinating, high-performance conductors and to derive the design rules that govern their performance. These materials will accelerate experimental research into fluoride–ion batteries, while the design rules will provide an improved foundation for understanding ionic conduction. 

Background Multiple strategies can be used when self-monitoring diet, physical activity, and perceived stress, but no gold standards are available. Although self-monitoring is a core element of self-management and behavior change, the success of mHealth behavioral tools depends on their validity and reliability, which lack evidence. African American and Latina mothers in the United States are high-priority populations for apps that can be used for self-monitoring of diet, physical activity, and stress because the body mass index (BMI) of mothers typically increases for several years after childbirth and the risks of obesity and its’ sequelae diseases are elevated among minority populations. Objective To examine the intermethod reliability and concurrent validity of smartphone-based self-monitoring via ecological momentary assessments (EMAs) and use of daily diaries for diet, stress, and physical activity compared with brief recall measures, anthropometric biomeasures, and bloodspot biomarkers. Methods A purposive sample (n=42) of primarily African American (16/42, 39%) and Latina (18/42, 44%) mothers was assigned Android smartphones for using Ohmage apps to self-monitor diet, perceived stress, and physical activity over 6 months. Participants were assessed at 3- and 6-month follow-ups. Recall measures included brief food frequency screeners, physical activity assessments adapted from the National Health and Nutrition Examination Survey, and the nine-item psychological stress measure. Anthropometric biomeasures included BMI, body fat, waist circumference, and blood pressure. Bloodspot assays for Epstein–Barr virus and C-reactive protein were used as systemic load and stress biomarkers. EMAs and daily diary questions assessed perceived quality and quantity of meals, perceived stress levels, and moderate, vigorous, and light physical activity. Units of analysis were follow-up assessments (n=29 to n=45 depending on the domain) of the participants (n=29 with sufficient data for analyses). Correlations, R2 statistics, and multivariate linear regressions were used to assess the strength of associations between variables. Results Almost all participants (39/42, 93%) completed the study. Intermethod reliability between smartphone-based EMAs and diary reports and their corresponding recall reports was highest for stress and diet; correlations ranged from .27 to .52 (P<.05). However, it was unexpectedly low for physical activity; no significant associations were observed. Concurrent validity was demonstrated for diet EMAs and diary reports on systolic blood pressure (r=−.32), C-reactive protein level (r=−.34), and moderate and vigorous physical activity recalls (r=.35 to.48), suggesting a covariation between healthy diet and physical activity behaviors. EMAs and diary reports on stress were not associated with Epstein–Barr virus and C-reactive protein level. Diary reports on moderate and vigorous physical activity were negatively associated with BMI and body fat (r=−.35 to −.44, P<.05). Conclusions Brief smartphone-based EMA use may be valid and reliable for long-term self-monitoring of diet, stress, and physical activity. Lack of intermethod reliability for physical activity measures is consistent with prior research, warranting more research on the efficacy of smartphone-based self-monitoring of self-management and behavior change support. 

High-magnitude storm events such as Hurricane Sandy are powerful agents of geomorphic change in coastal marshes, potentially altering their surface elevation trajectories. But how do a storm’s impacts vary across a large region spanning a variety of wetland settings and storm exposures and intensities.We determined the short-term impacts of Hurricane Sandy at 223 surface elevation table–marker horizon stations in estuarine marshes located across the northeast region of the United States by comparing poststorm surface elevation change with pre-storm elevation trends.We hypothesized that the storm’s effect on marsh elevation trends would be influenced by position relative to landfall (right or left) and distance from landfall. The structural equation model presented predicts that marshes located to the left of landfall were more likely to experience an elevation gain greater than expected, and this positive deviation from pre-storm elevation trends tended to have a greater magnitude than those experiencing negative deviations (elevation loss), potentially due to greater sediment deposition. The magnitude of negative deviations from elevation change in marshes to the right of landfall was greater than for positive deviations, with a greater effect in marshes within 200 km of landfall, potentially from the extent and magnitude of storm surge. Overall, results provide an integrated picture of how storm characteristics combined with the local wetland setting are important to a storm’s impact on surface elevation, and that the surface elevation response can vary widely among sites across a region impacted by the same storm. 

Brain age (BA), distinct from chronological age (CA), can be estimated from MRIs to evaluate neuroanatomic aging in cognitively normal (CN) individuals. BA, however, is a cross-sectional measure that summarizes cumulative neuroanatomic aging since birth. Thus, it conveys poorly recent or contemporaneous aging trends, which can be better quantified by the (temporal) pace P of brain aging. Many approaches to map P, however, rely on quantifying DNA methylation in whole-blood cells, which the blood–brain barrier separates from neural brain cells. We introduce a three-dimensional convolutional neural network (3D-CNN) to estimate P noninvasively from longitudinal MRI. Our longitudinal model (LM) is trained on MRIs from 2,055 CN adults, validated in 1,304 CN adults, and further applied to an independent cohort of 104 CN adults and 140 patients with Alzheimer’s disease (AD). In its test set, the LM computes P with a mean absolute error (MAE) of 0.16 y (7% mean error). This significantly outperforms the most accurate cross-sectional model, whose MAE of 1.85 y has 83% error. By synergizing the LM with an interpretable CNN saliency approach, we map anatomic variations in regional brain aging rates that differ according to sex, decade of life, and neurocognitive status. LM estimates of P are significantly associated with changes in cognitive functioning across domains. This underscores the LM’s ability to estimate P in a way that captures the relationship between neuroanatomic and neurocognitive aging. This research complements existing strategies for AD risk assessment that estimate individuals’ rates of adverse cognitive change with age.