Unraveling the Cationic and Anionic Redox Reactions in a Conventional Layered Oxide Cathode

Increasing interest in high-energy lithium-ion batteries has triggered the demand to clarify the reaction mechanism in battery cathodes during high-potential operation. However, the reaction mechanism often involves both transition-metal and oxygen activities that remain elusive. Here we report a comprehensive study of both cationic and anionic redox mechanisms of LiNiO2 nearly full delithiation. Selection of pure LiNiO2 removes the complication of multiple transition metals. Using combined X-ray absorption spectroscopy, resonant inelastic X-ray scattering, and operando differential electrochemical mass spectrometry, we are able to clarify the redox reactions of transition metals in the bulk and at the surface, reversible lattice oxygen redox, and irreversible oxygen release associated with surface reactions. Many findings presented here bring attention to different types of oxygen activities and metal-oxygen interactions in layered oxides, which are of crucial importance to the advancement of a Ni-rich layered oxide cathode for high capacity and long cycling performance

Atrial fibrillation

This issue of eMedRef provides information to clinicians on the pathophysiology, diagnosis, and therapeutics of atrial fibrillation

A Rule-Based Method for Determining the Degree of Student Satisfaction of a Web-Based Learning Environment

Student essays representing their individual reflections on a collaborative web-based course in International Business are computationally analyzed according to a classification scheme based on a set of a priori fuzzy categories. This classification method enables the identification of themes and trends in the student responses that can be used to illustrate an overall evaluation of the personal learning experiences for this course. By processing the classification results using a computational neural network, we can depict the clustering intensity of thematic elements and illustrate the strength of dependencies between classification attribute values topologically using a self-organizing map (SOM), which provides a pattern recognition visualization. The resulting SOM can then be used to compare successive depictions for future iterations of new thematic data from student self-evaluations

Photoemission evidence for crossover from Peierls-like to Mott-like transition in highly strained VO2_2

We present a spectroscopic study that reveals that the metal-insulator transition of strained VO$_2$ thin films may be driven towards a purely electronic transition, which does not rely on the Peierls dimerization, by the application of mechanical strain. Comparison with a moderately strained system, which does involve the lattice, demonstrates the crossover from Peierls- to Mott-like transitions

Automated Ecological Assessment of Physical Activity: Advancing Direct Observation.

Technological advances provide opportunities for automating direct observations of physical activity, which allow for continuous monitoring and feedback. This pilot study evaluated the initial validity of computer vision algorithms for ecological assessment of physical activity. The sample comprised 6630 seconds per camera (three cameras in total) of video capturing up to nine participants engaged in sitting, standing, walking, and jogging in an open outdoor space while wearing accelerometers. Computer vision algorithms were developed to assess the number and proportion of people in sedentary, light, moderate, and vigorous activity, and group-based metabolic equivalents of tasks (MET)-minutes. Means and standard deviations (SD) of bias/difference values, and intraclass correlation coefficients (ICC) assessed the criterion validity compared to accelerometry separately for each camera. The number and proportion of participants sedentary and in moderate-to-vigorous physical activity (MVPA) had small biases (within 20% of the criterion mean) and the ICCs were excellent (0.82-0.98). Total MET-minutes were slightly underestimated by 9.3-17.1% and the ICCs were good (0.68-0.79). The standard deviations of the bias estimates were moderate-to-large relative to the means. The computer vision algorithms appeared to have acceptable sample-level validity (i.e., across a sample of time intervals) and are promising for automated ecological assessment of activity in open outdoor settings, but further development and testing is needed before such tools can be used in a diverse range of settings

Exploring neighborhood environments and active communting in Chennai, India

Few studies assess built environment correlates of active commuting in low-and-middle-income countries (LMICs), but the different context could yield distinct findings. Policies and investments to promote active commuting remain under-developed in LMICs like India, which grapples with traffic congestion, lack of activity-supportive infrastructure, poor enforcement of traffic rules and regulations, air pollution, and overcrowding. This cross-sectional study investigated associations between home neighborhood environment characteristics and active commuting in Chennai, India. Adults (N = 370, 47.2% female, mean age = 37.9 years) were recruited from 155 wards in the metropolitan area of Chennai in southern India between January and June 2015. Participants self-reported their usual mode of commute to work, with responses recoded into three categories: (1) multi-modal or active commuting (walking and bicycling; n = 56); (2) public transit (n = 52); and (3) private transport (n = 111). Environmental attributes around participants’ homes were assessed using the Neighborhood Environment Walkability Scale for India (NEWS-India). Associations between environmental characteristics and likelihood of active commuting and public transit use were modeled using logistic regression with private transport (driving alone or carpool) as the reference category, adjusting for age, gender, and household car ownership. Consistent with other international studies, participants living in neighborhoods with a mix of land uses and a transit stop within a 10-minute walk from home were more likely to use active commuting (both p < 0.01). Land-use mix was significantly associated with the use of public transit compared to private transport (adjusted odds ratio (aOR) = 5.2, p = 0.002). Contrary to findings in high-income countries, the odds of active commuting were reduced with improved safety from crime (aOR = 0.2, p = 0.003), aesthetics (aOR = 0.2, p = 0.05), and street connectivity (aOR = 0.2, p = 0.003). Different environmental attributes were associated with active commuting, suggesting that these relationships are complex and may distinctly differ from those in high-income countries. Unexpected inverse associations of perceived safety from crime and aesthetics with active commuting emphasize the need for high-quality epidemiologic studies with greater context specificity in the study of physical activity in LMICs. Findings have public health implications for India and suggest that caution should be taken when translating evidence across countries

Protecting against researcher bias in secondary data analysis: challenges and potential solutions

Analysis of secondary data sources (such as cohort studies, survey data, and administrative records) has the potential to provide answers to science and society’s most pressing questions. However, researcher biases can lead to questionable research practices in secondary data analysis, which can distort the evidence base. While pre-registration can help to protect against researcher biases, it presents challenges for secondary data analysis. In this article, we describe these challenges and propose novel solutions and alternative approaches. Proposed solutions include approaches to (1) address bias linked to prior knowledge of the data, (2) enable pre-registration of non-hypothesis-driven research, (3) help ensure that pre-registered analyses will be appropriate for the data, and (4) address difficulties arising from reduced analytic flexibility in pre-registration. For each solution, we provide guidance on implementation for researchers and data guardians. The adoption of these practices can help to protect against researcher bias in secondary data analysis, to improve the robustness of research based on existing data

Visible light-driven H2 production over highly dispersed Ruthenia on Rutile TiO2 nanorods

The immobilization of miniscule quantities of RuO2 (~0.1%) onto one-dimensional (1D) TiO2 nanorods (NRs) allows H2 evolution from water under visible light irradiation. Rod-like rutile TiO2 structures, exposing preferentially (110) surfaces, are shown to be critical for the deposition of RuO2 to enable photocatalytic activity in the visible region. The superior performance is rationalized on the basis of fundamental experimental studies and theoretical calculations, demonstrating that RuO2(110) grown as 1D nanowires on rutile TiO2(110), which occurs only at extremely low loads of RuO2, leads to the formation of a heterointerface that efficiently adsorbs visible light. The surface defects, band gap narrowing, visible photoresponse, and favorable upward band bending at the heterointerface drastically facilitate the transfer and separation of photogenerated charge carriers.Peer ReviewedPostprint (published version