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Lithium cobalt oxide (LiCoO2) and graphite-based Li-ion batteries have been widely applied for consumer electronics because of the long cycle life and easy preparation. However, the limited capacity for traditional materials hampers the practical application for high energy-density battery. Conventional electrolyte system could not satisfy the need for high-capacity materials. Here, methylene methanedisulfonate (MMDS) was chosen as electrolyte additive for enhancing the available capacity for LiCoO2 and graphite-based battery. The effect of MMDS on the LiCoO2 cathode and graphite anode was investigated via multi electrochemical methods. It was found that the capacity for cells with MMDS electrolyte additive increases (from 142.6 mAh g−1 for pristine to 193.4 mAh g−1 on LiCoO2/Li battery, from 275.5 mAh g−1 for pristine to 407.0 mAh g−1 on graphite/Li battery). The experimental results indicate that improved capacity by MMDS electrolyte additive can be attributable to the stabilized interface on both cathode and anode sides, leading to superior interfacial Li+ kinetics and mitigated bulk structural degradation, which was further confirmed by the ex-situ electrochemical and structural characterization. [ABSTRACT FROM AUTHOR]

Nickel-rich LiNixCoyMn1−x−yO2 cathode materials have been studied to increase the energy density of lithium-ion batteries due to their high capacity and low cost. However, the complex side reaction in the electrode/electrolyte interphase and cracks inside the secondary particles would decrease the stability of crystalline structure and interface, which further affects the cycling life. Here, a thermodynamically stabilized interface was constructed on the surface of single crystalline nickel-rich cathode material via an acid solution treatment. Structural characterization results confirm the enhanced structural reversibility during electrochemical cycling. Electrochemical data suggests accelerated kinetics for lithium ion diffusion process after treatment. Consequently, modified nickel-rich material delivers significantly enhanced capacity from 131.0 mAh/g for pristine material to 266.2 mAh/g at 0.1 C rate and improved cycling stability. These results suggest it is a moderate method for optimizing the available capacity of high-Ni cathode materials. [ABSTRACT FROM AUTHOR]

In the pursuit of high-energy density lithium-ion battery, Li-rich Mn-based oxide cathode has gained great attention with unexpectedly high capacity, low cost and excellent thermal stability. However, the cause for the sluggish kinetics remains a mystery, hindering the application of Li-rich cathode material. Here, we reveal the interfacial instability is the driving force for the sluggish kinetics, which severely blocks the interfacial Li-ion transport and triggers fast battery failure. Through rice-husk carbon (RHC) and spinel phase modification on Li-rich Mn-based oxide Li1.2Ni0.16Mn0.56Co0.08O2 (LNMC), the poor lithium ion diffusion and interfacial degradation can be effectively prevented, delivering a high specific capacity of around 300 mAh/g and excellent rate performance. These findings provide a universal method to prepare high electrochemical performance Li-rich oxide materials. Through synergetic modification, the Li-rich Mn-based cathode delivers a high available capacity of 300 mAh/g via accelerated Li+ intercalation kinetics [ABSTRACT FROM AUTHOR]

Background: The lack of racial diversity in science, technology, engineering, and mathematics (STEM) disciplines is perhaps one of the most challenging issues in the United States higher education system. The issue is not only concerning diverse students, but also diverse faculty members. One important contributing factor is the faculty hiring process. To make progress toward equity in hiring decisions, it is necessary to better understand how applicants are considered and evaluated. In this paper, we describe and present our study based on a survey of current STEM faculty members and administrators who examined applicant qualifications and characteristics in STEM faculty hiring decisions. Results: There are three key findings of the present research. First, we found that faculty members placed different levels of importance on characteristics and qualifications for tenure track hiring and non-tenure track hiring. For example, items related to research were more important when evaluating tenure track applicants, whereas items related to teaching and diversity were more important when evaluating non-tenure track applicants. Second, faculty members' institutional classification, position, and personal identities (e.g., gender, race/ethnicity) had an impact on their evaluation criteria. For instance, we found men considered some diversity-related items more important than women. Third, faculty members rated the importance of qualifications with diversity, equity, and inclusion (DEI)-related constructs significantly lower than qualifications that did not specify DEI-related constructs, and this trend held for both tenure track and non-tenure track faculty hiring. Conclusions: This study was an attempt to address the issue of diversity in STEM faculty hiring at institutions of higher education by examining how applicant characteristics are considered and evaluated in faculty hiring practices. Emphasizing research reputation and postdoctoral reputation while neglecting institutional diversity and equitable and inclusive teaching, research, and service stunt progress toward racial diversity because biases—both implicit and explicit, both positive and negative—still exist. Our results were consistent with research on bias in recruitment, revealing that affinity bias, confirmation bias, and halo bias exist in the faculty hiring process. These biases contribute to inequities in hiring, and need to be addressed before we can reach, sustain, and grow desired levels of diversity. [ABSTRACT FROM AUTHOR]

Low mental health literacy is a public health concern in China and contributes to the low underutilization of mental health resources. We extended the current conceptualization of mental health literacy and developed a comprehensive self-report measure of mental health literacy in the Chinese context, the Mental Health Literacy Questionnaire (MHLQ). We generated items based on existing literature, extensive qualitative interviews with mental health professionals and community members. We revised these items based on expert review to examine content validity, after which we conducted a pilot test with a representative sample of 734 urban and rural community members to illicit feedback on wording clarity and select items within appropriate difficulty levels (i.e., correct rate). We then examined dimensionality and item performance using multidimensional item response theory analyses in a combined sample of mental health professionals (n = 122), community members (n = 121), and college students (n = 144). We concluded a six-factor structure for the MHLQ encompassing mental health knowledge, mental illness knowledge, attitudes towards mental health, attitudes towards mental illness, behaviors regarding mental health promotion, and behaviors regarding mental illness coping. The scale yielded adequate internal reliability estimates and test-retest agreement, and convergent evidence and known-group comparisons results in support of construct validity. The six-factor MHLQ largely supported our extended theoretical conceptualization of the mental health literacy in the Chinese context. Future studies are needed to further validate the factor structure and examine measurement invariance. [ABSTRACT FROM AUTHOR]

Involving students in the co-design of educational curricula and practices can benefit both students and teachers. Students who participate in co-design may show better learning or increased agency or engagement. In the present study, we investigated what kind of science knowledge or practices can be learned by student co-designers while engaging in co-design practices and how that learning happens with six high school students. We created a model to guide the analysis of students' learning with technology in co-designing processes. The results revealed that students learned engineering design process even if no explicit instruction on engineering learning was given. Also, our analysis suggested that co-designing with technology enabled learning of the engineering design process and potentially furthered learning of science because it promoted knowledge integration. The results have implications for understanding and enhancing engineering design and science learning through co-designing with technology. [ABSTRACT FROM AUTHOR]

It is greatly significant to combine data with diverse structures and supply a unified view of these data for the user, especially for sharing data residing in heterogeneous data sources via the internet. This paper introduces a fast and novel method to data integration among different systems, which is based on ontology similarity in a language agnostic way. The fundamental ontological entities are extracted from multiple data sources according to the same mapping rules. By means of the improved edit distance algorithm, similarity measurement consists of determining the levels of similarity among the ontological entities for aiding in the construction of data integration platform. A web-service based architecture is presented along with the set of layers designed to achieve rapid data integration from different aspects such as data center, ontology extraction and similarity measurement, which aims to make this architecture more flexible. The prototype implemented by the proposed approach shows satisfying results against other techniques. [ABSTRACT FROM AUTHOR]

We introduce Atropos, an ITK-based multivariate n-class open source segmentation algorithm distributed with ANTs (). The Bayesian formulation of the segmentation problem is solved using the Expectation Maximization (EM) algorithm with the modeling of the class intensities based on either parametric or non-parametric finite mixtures. Atropos is capable of incorporating spatial prior probability maps (sparse), prior label maps and/or Markov Random Field (MRF) modeling. Atropos has also been efficiently implemented to handle large quantities of possible labelings (in the experimental section, we use up to 69 classes) with a minimal memory footprint. This work describes the technical and implementation aspects of Atropos and evaluates its performance on two different ground-truth datasets. First, we use the BrainWeb dataset from Montreal Neurological Institute to evaluate three-tissue segmentation performance via (1) K-means segmentation without use of template data; (2) MRF segmentation with initialization by prior probability maps derived from a group template; (3) Prior-based segmentation with use of spatial prior probability maps derived from a group template. We also evaluate Atropos performance by using spatial priors to drive a 69-class EM segmentation problem derived from the Hammers atlas from University College London. These evaluation studies, combined with illustrative examples that exercise Atropos options, demonstrate both performance and wide applicability of this new platform-independent open source segmentation tool. [ABSTRACT FROM AUTHOR]