NH4 + Pre-Intercalation and Mo Doping VS2 to Regulate Nanostructure and Electronic Properties for High Efficiency Sodium Storage.

Sodium-ion hybrid capacitors (SIHCs) have attracted much attention due to integrating the high energy density of battery and high out power of supercapacitors. However, rapid Na+ diffusion kinetics in cathode is counterbalanced with sluggish anode, hindering the further advancement and commercialization of SIHCs. Here, aiming at conversion-type metal sulfide anode, taking typical VS2 as an example, a comprehensive regulation of nanostructure and electronic properties through NH4 + pre-intercalation and Mo-doping VS2 (Mo-NVS2) is reported. It is demonstrated that NH4 + pre-intercalation can enlarge the interplanar spacing and Mo-doping can induce interlayer defects and sulfur vacancies that are favorable to construct new ion transport channels, thus resulting in significantly enhanced Na+ diffusion kinetics and pseudocapacitance. Density functional theory calculations further reveal that the introduction of NH4 + and Mo-doping enhances the electronic conductivity, lowers the diffusion energy barrier of Na+, and produces stronger d-p hybridization to promote conversion kinetics of Na+ intercalation intermediates. Consequently, Mo-NVS2 delivers a record-high reversible capacity of 453 mAh g-1 at 3 A g-1 and an ultra-stable cycle life of over 20 000 cycles. The assembled SIHCs achieve impressive energy density/power density of 98 Wh kg-1/11.84 kW kg-1, ultralong cycling life of over 15000 cycles, and very low self-discharge rate (0.84 mV h-1).

Severe psychological distress among patients with epilepsy during the COVID-19 outbreak in southwest China.

OBJECTIVE: To compare the severity of psychological distress between patients with epilepsy and healthy controls during the COVID-19 outbreak in southwest China, as well as identify potential risk factors of severe psychological distress among patients with epilepsy. METHODS: This cross-sectional case-control study examined a consecutive sample of patients older than 15 years treated at the epilepsy center of West China Hospital between February 1 and February 29, 2020. As controls, sex- and age-matched healthy visitors of inpatients (unrelated to the patients) were also enrolled during the same period. Data on demographics and attention paid to COVID-19 were collected by online questionnaire, data on epilepsy features were collected from electronic medical records, and psychological distress was evaluated using the 6-item Kessler Psychological Distress Scale (K-6). Potential risk factors of severe psychological distress were identified using multivariate logistic regression. RESULTS: The 252 patients and 252 controls in this study were similar along all demographic variables except family income. Patients with epilepsy showed significantly higher K-6 scores than healthy controls and spent significantly more time following the COVID-19 outbreak (both P < .001). Univariate analyses associated both diagnosis of drug-resistant epilepsy and time spent paying attention to COVID-19 with severe psychological distress (defined as K-6 score >12; both P ≤ .001). Multivariate logistic regression identified two independent predictors of severe psychological distress: time spent paying attention to COVID-19 (odds ratio [OR] = 1.172, 95% confidence interval [CI] = 1.073-1.280) and diagnosis of drug-resistant epilepsy (OR = 0.283, 95% CI = 0.128-0.623). SIGNIFICANCE: During public health outbreaks, clinicians and caregivers should focus not only on seizure control but also on mental health of patients with epilepsy, especially those with drug-resistant epilepsy. K-6 scores > 12 indicate severe psychological distress. This may mean, for example, encouraging patients to engage in other activities instead of excessively following media coverage of the outbreak.

Frustrated random walks: A faster algorithm to evaluate node distances on connected and undirected graphs.

Researchers have designed many algorithms to measure the distances between graph nodes, such as average hitting times of random walks, cosine distances from DeepWalk, personalized PageRank, etc. Successful though these algorithms are, still they are either underperforming or too time consuming to be applicable to huge graphs that we encounter daily in this big data era. To address these issues, here we propose a faster algorithm based on an improved version of random walks that can beat DeepWalk results with more than 10 times acceleration. The reason for this significant acceleration is that we can derive an analytical formula to calculate the expected hitting times of this random walk quickly. There is only one parameter (the power expansion order) in our algorithm, and the results are robust with respect to its changes. Therefore, we can directly find the optimal solution without fine tuning of model parameters. Our method can be widely used for fraud detection, targeted ads, recommendation systems, topic-sensitive search, etc.