Enhancing the Initial Coulombic Efficiency of Sodium-Ion Batteries via Highly Active Na2 S as Presodiation Additive.

Recently, sodium-ion batteries (SIBs) have received considerable attention for large-scale energy storage applications. However, the low initial Coulombic efficiency of traditional SIBs severely impedes their further development. Here, a highly active Na2 S-based composite is employed as a self-sacrificial additive for sodium compensation in SIBs. The in situ synthesized Na2 S is wrapped in a carbon matrix with nanoscale particle size and good electrical conductivity, which helps it to achieve a significantly enhanced electrochemical activity as compare to commercial Na2 S. As a highly efficient presodiation additive, the proposed Na2 S/C composite can reach an initial charge capacity of 407 mAh g-1 . When 10 wt.% Na2 S/C additive is dispersed in the Na3 V2 (PO4 )3 cathode, and combined with a hard carbon anode, the full cell achieves 24.3% higher first discharge capacity, which corresponds to a 18.3% increase in the energy density from 117.2 to 138.6 Wh kg-1 . Meanwhile, it is found that the Na2 S additive does not generate additional gas during the initial charging process, and under an appropriate content, its reaction product has no adverse impact on the cycling stability and rate performance of SIBs. Overall, this work establishes Na2 S as a highly effective additive for the construction of advanced high-energy-density SIBs.

Can human-machine feedback in a smart learning environment enhance learners' learning performance? A meta-analysis.

Objective: The human-machine feedback in a smart learning environment can influences learners' learning styles, ability enhancement, and affective interactions. However, whether it has stability in improving learning performance and learning processes, the findings of many empirical studies are controversial. This study aimed to analyze the effect of human-machine feedback on learning performance and the potential boundary conditions that produce the effect in a smart learning environment. Methods: Web of Science, EBSCO, PsycINFO, and Science Direct were searched for publications from 2010 to 2022. We included randomized controlled trials with learning performance as outcome. The random effects model was used in the meta-analysis. The main effect tests and the heterogeneity tests were used to evaluate the effect of human-machine feedback mechanism on learning performance, and the boundary conditions of the effect were tested by moderating effects. Moreover, the validity of the meta-analysis was proved by publication bias test. Results: Out of 35 articles identified, 2,222 participants were included in this study. Human-machine interaction feedback had significant effects on learners' learning process (d = 0.594, k = 26) and learning outcomes (d = 0.407, k = 42). Also, the positive effects of human-machine interaction feedback were regulated by the direction of feedback, the form of feedback, and the type of feedback technique. Conclusion: To enhance learning performance through human-machine interactive feedback, we should focus on using two-way and multi-subject feedback. The technology that can provide emotional feedback and feedback loops should be used as a priority. Also, pay attention to the feedback process and mechanism, avoid increasing students' dependence on machines, and strengthen learners' subjectivity from feedback mechanism.

Early Onset of Impairments of Interpersonal Motor Synchrony in Preschool-Aged Children with Autism Spectrum Disorder.

Characteristics of interpersonal motor synchrony in individuals with autism spectrum disorder (ASD) have been investigated only in older children and adolescents, which calls for investigations in younger samples. The interpersonal motor synchrony was compared between preschool-aged children with (n = 23) and without ASD (n = 24) during free plays with familiar teachers. Children with ASD exhibited reduced synchrony of the upper body and trunk compared with typically developing (TD) children. Moreover, the degree of synchrony in ASD group was not above than chance. For autistic children, interpersonal motor synchrony was negatively correlated with aspects of autistic traits. The results suggest that the impairment of interpersonal motor synchrony has an onset earlier than school age and is a potential pathway for understanding autistic traits.

Interface Engineering via Regulating Electrolyte for High-Voltage Layered Oxide Cathodes-Based Li-Ion Batteries.

Li-rich and Ni-rich layered oxides as next-generation high-energy cathodes for lithium-ion batteries (LIBs) possess the catalytic surface, which leads to intensive interfacial reactions, transition metal ion dissolution, gas generation, and ultimately hinders their applications at 4.7 V. Here, robust inorganic/organic/inorganic-rich architecture cathode-electrolyte interphase (CEI) and inorganic/organic-rich architecture anode-electrolyte interphase (AEI) with F-, B-, and P-rich inorganic components through modulating the frontier molecular orbital energy levels of lithium salts are constructed. A ternary fluorinated lithium salts electrolyte (TLE) is formulated by mixing 0.5 m lithium difluoro(oxalato)borate, 0.2 m lithium difluorophosphate with 0.3 m lithium hexafluorophosphate. The obtained robust interphase effectively suppresses the adverse electrolyte oxidation and transition metal dissolution, significantly reduces the chemical attacks to AEI. Li-rich Li1.2 Mn0.58 Ni0.08 Co0.14 O2 and Ni-rich LiNi0.8 Co0.1 Mn0.1 O2 in TLE exhibit high-capacity retention of 83.3% after 200 cycles and 83.3% after 1000 cycles under 4.7 V, respectively. Moreover, TLE also shows excellent performances at 45 °C, demonstrating this inorganic rich interface successfully inhibits the more aggressive interface chemistry at high voltage and high temperature. This work suggests that the composition and structure of the electrode interface can be regulated by modulating the frontier molecular orbital energy levels of electrolyte components, so as to ensure the required performance of LIBs.

Application of Machine Learning Techniques to Detect the Children with Autism Spectrum Disorder.

Early detection of autism spectrum disorder (ASD) is highly beneficial to the health sustainability of children. Existing detection methods depend on the assessment of experts, which are subjective and costly. In this study, we proposed a machine learning approach that fuses physiological data (electroencephalography, EEG) and behavioral data (eye fixation and facial expression) to detect children with ASD. Its implementation can improve detection efficiency and reduce costs. First, we used an innovative approach to extract features of eye fixation, facial expression, and EEG data. Then, a hybrid fusion approach based on a weighted naive Bayes algorithm was presented for multimodal data fusion with a classification accuracy of 87.50%. Results suggest that the machine learning classification approach in this study is effective for the early detection of ASD. Confusion matrices and graphs demonstrate that eye fixation, facial expression, and EEG have different discriminative powers for the detection of ASD and typically developing children, and EEG may be the most discriminative information. The physiological and behavioral data have important complementary characteristics. Thus, the machine learning approach proposed in this study, which combines the complementary information, can significantly improve classification accuracy.