Machine learning-augmented materials design is an emerging method for rapidly developing new materials. It is especially useful for designing new nanoarchitectured materials, whose design parameter space is often large and complex. Metal-agent dealloying, a materials design method for fabricating nanoporous or nanocomposite from a wide range of elements, has attracted significant interest. Here, a machine learning approach is introduced to explore metal-agent dealloying, leading to the prediction of 132 plausible ternary dealloying systems. A machine learning-augmented framework is tested, including predicting dealloying systems and characterizing combinatorial thin films via automated and autonomous machine learning-driven synchrotron techniques. This work demonstrates the potential to utilize machine learning-augmented methods for creating nanoarchitectured thin films.

Background Retaining highly qualified science, technology, engineering, and mathematics (STEM) teachers is imperative for meeting demands of the twenty-first century STEM workforce. While multiple studies have revealed several factors that influence teacher retention, little work has examined how these factors interact with one another. This study explored the relationship between two critical factors that relate to teachers’ decisions to remain in the profession: teacher identity and communities of practice (CoP) networks. Results Drawing upon scholarship on science teacher identity, CoP, and social network theory, we demonstrate a quantitative relationship between perceived network bridging roles and career commitment, which is mediated through teacher identity. Conclusions The findings from this study have both implications for scholarship in teacher retention and science teacher identity development. Potential solutions for improving novice teachers’ self-image through providing opportunities to grow their professional networks both locally and regionally/nationally are suggested.

As a sexual attitude in this paper, Sexual Double Standard (SDS) refers to gender differences in permissible sexual behavior and is used to refer specifically to stricter standards for women. Specifically, it can be divided into different degrees of permission for certain sexual behaviors between men and women, and differences in evaluation between men and women for the same sexual behaviors. Based on the literature review, this study proposes a hypothesis about gender orientation's effect on implicit SDS and carries out the IAT test (Implicit Association Test) of Implicit SDS. The result show that straight people do have implicit SDS for homosexual people, and gay people have more negative SDS than lesbian people. The significance of this work is to carry out research on minority groups under the SDS, because previous research rarely investigates homosexual groups. We expected that the existing research results can be used to solve the problem of gender inequality in society and eliminate outdated gender discrimination and discrimination against the LGBT community.