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The First2 Network is a coalition of individuals from multiple universities, K-12 schools, industry, and government organizations from a rural eastern U.S. state who collaborate to ensure that rural, first-generation undergraduate students are prepared and motivated to persist in their science, technology, engineering, and mathematics (STEM) major. Since its inception in 2018, this National Science Foundation-funded project has utilized student summer immersive experiences for incoming freshmen and Networked Improvement Communities to produce replicable best practices, campus student clubs, student ambassador programs, institutional teams, statewide conferences, and many other methods, all for the purpose of promoting student STEM persistence across the state. This study employs social network analysis to explore the structure, growth, and impact of the connections across this Network over the five years of its existence. Social network analysis metrics indicate that the Network grew both in size and connectivity until 2022 when policy changes led to more institutional localization for the purpose of sustainability. Students have formed robust connections with other Network members throughout the course of the project, leading to a higher STEM persistence rate among students in the Network than average at their university. Faculty from different universities across the state have made connections, which has increased productivity as a result of network membership. The available data suggests that the Network has had a positive impact on both student retention and faculty collaboration, which should be sustained and have a positive impact on STEM persistence throughout the state in years to come. 

The First2 Network is a coalition of individuals from multiple universities, K-12 schools, industry, and government organizations from a rural eastern U.S. state who collaborate to ensure that rural, first-generation undergraduate students are prepared and motivated to persist in their science, technology, engineering, and mathematics (STEM) major. Since its inception in 2018, this National Science Foundation-funded project has utilized student summer immersive experiences for incoming freshmen and Networked Improvement Communities to produce replicable best practices, campus student clubs, student ambassador programs, institutional teams, statewide conferences, and many other methods, all for the purpose of promoting student STEM persistence across the state. This study employs social network analysis to explore the structure, growth, and impact of the connections across this Network over the five years of its existence. Social network analysis metrics indicate that the Network grew both in size and connectivity until 2022 when policy changes led to more institutional localization for the purpose of sustainability. Students have formed robust connections with other Network members throughout the course of the project, leading to a higher STEM persistence rate among students in the Network than average at their university. Faculty from different universities across the state have made connections, which has increased productivity as a result of network membership. The available data suggests that the Network has had a positive impact on both student retention and faculty collaboration, which should be sustained and have a positive impact on STEM persistence throughout the state in years to come. 

This research-to-practice paper describes an experiment designed to understand educational opportunities valued by students. Engineering education has, since the advent of ABET's EC-2000, operated using an outcomes-based paradigm predominantly focused on preparing engineers for the workforce. Engineering departments create curricula based on this paradigm that are more rigid than most other disciplines, thereby limiting the opportunities students have to explore beyond established curricular boundaries. The outcome-based paradigm limits students' agency in engineering education to pursue growth in unique, individual ways. Recognizing these challenges, the Electrical and Computer Engineering Department at Bucknell University is adapting Amartya Sen's Capability Approach, which emphasizes student agency. In contrast to top-down approaches to curriculum design that focus narrowly on students' mastery of defined content areas, we focus on enabling students to develop the abilities needed to live a life aligned with their values. Rather than ensuring students achieve mandated outcomes, the focus is on providing opportunities, which students actively choose to transform into achievements. This study sought to better understand the opportunities that students value. The department first created a capabilities list that classified several opportunities that are of potential importance in engineering education. To gather feedback from students in the department, we offered two focus groups to discuss our capabilities list and a follow-up survey to formally elicit student valuation of capabilities. In addition, we offered an experimental course that promoted an opportunity-based engineering education model that nurtures both academic and personal growth. Student reflections from this class were analyzed using inductive coding with multiple coders, categorizing portions of students' reflections that align with our capabilities list. This study reveals the opportunities students highly regard to be better equipped to live a life they value. 

This work in progress (WIP) research paper describes student use of representations in engineering design. While iterative design is not unique to engineering, it is one of the most common methods that engineers use to address socio-technical problems. The use of representations is common across design methodologies. Representations are used in design to serve as external manifestations of internal thought processes that make abstract concepts tangible, enhance communication by providing a common language, enable iteration by serving as a low-effort way to explore ideas, encourage more empathetic design by capturing users' perspectives, visualize the problem space, and promote divergent thinking by providing different ways to visualize ideas. While representations are a key aspect of design, the effective use of representations is a learned process which is affected by other factors in students' education. This study sought to understand how students' perceptions of the role of representations in design changed over the course of a one-semester design course. Small student teams created representations in a three-stage process-problem exploration, convergence to possible solutions, and prototype generation-that captured their evolving understanding of a socio-technical issue and response to it. The authors hypothesize that using effective representations can help develop skills in convergence in undergraduate students; one of engineering's contributions to convergent problem solving is design. More specifically, this research looked at students' use of design representations to develop convergent understanding of ill-defined socio-technical problems. The research questions focus on how students use representations to structure sociotechnical design problems and how argumentation of their chosen solution path changed over time. To answer these questions this study analyzed student artifacts in a third-year design course supported by insights on the process of representation formation obtained from student journals on the design process and a self-reflective electronic portfolio of student work. Based on their prior experiences in engineering science classes, students initially viewed design representations as time-bound (e.g. homework) problems rather than as persistent tools used to build understanding. Over time their use of representations shifted to better capture and share understanding of the larger context in which projects were embedded. The representations themselves became valued reflections on their own level of understanding of complex problems, serving as a self-reflective surface for the status of the larger design problem. 

This research-to-practice full paper presents and approach to bringing convergence to the undergraduate engineering context. Convergence is the process of integrating a variety of ideas, skills, and methods to create new ideas, skills, and methods in order to address complex, socially relevant challenges like the UN Sustainable Development Goals [1] and the National Academy of Engineering's (NAE) Grand Challenges [2]. In the US, the National Science Foundation (NSF) has been a major driver of convergence related research and has focused on work primarily at the graduate level and beyond. To explore how convergence concepts translate to an undergraduate engineering context this research to practice paper describes a taxonomy that translates convergent knowledge, skills, and mindsets into the domain of undergraduate engineering education. While we do not believe it is reasonable to expect undergraduates to engage with convergence in the same way as graduate students or postdoctoral scholars, we believe that they can develop in areas that will allow them to engage in convergent work later in their careers. This paper first defines convergence and then examines the challenges and opportunities related to developing a student's ability to do convergent work in an undergraduate context. The developed taxonomy outlines the knowledge, skills, mindsets, and structures that support convergent work from the larger research literature, and adapts these to an undergraduate context. The taxonomy is then used to conduct a gap analysis of an undergraduate electrical and computer engineering degree program. This analysis is based on the syllabi. This work was conducted in the context of an electrical and computer engineering department situated in a medium-sized primarily undergraduate liberal arts institution in the mid-Atlantic region. As the challenges and opportunities are similar to but also unique to this institution this work forms a rich case study that can inform similar efforts in other institutions and contexts where a similar gap analysis may be beneficial. The goal of this work is to enable others to analyze an their existing student experience to see what aspects of convergence are currently included. 

The material family halide perovskites has been critical in recent room-temperature radiation detection semiconductor research. Cesium lead bromide (CsPbBr3) is a halide perovskite that exhibits characteristics of a semiconductor that would be suitable for applications in various fields. In this paper, we report on the correlations between material purification and crystal material properties. Crystal boules of CsPbX3 (where X = Cl, Br, I, or mixed) were grown with the Bridgman growth method. We describe in great detail the fabrication techniques used to prepare sample surfaces for contact deposition and sample testing. Current–voltage measurements, UV–Vis and photocurrent spectroscopy, as well as photoluminescence measurements, were carried out for material characterization. Bulk resistivity values of up to 3.0 × 109 Ω∙cm and surface resistivity values of 1.3 × 1011 Ω/□ indicate that the material can be used for low-noise semiconductor detector applications. Preliminary radiation detectors were fabricated, and using photocurrent measurements we have estimated a value of the mobility–lifetime product for holes (μτ)h of 2.8 × 10−5 cm2/V. The results from the sample testing can shed light on ways to improve the crystal properties for future work, not only for CsPbX3 but also other halide perovskites. 

Materials composed of spin-1 antiferromagnetic (AFM) chains are known to adopt complex ground states that are sensitive to the single-ion-anisotropy (SIA) energy ( $D$), and intrachain ( $J_0$) and interchain ( $J_{1,2}^{\prime }$) exchange energy scales. While theoretical and experimental studies have extended this model to include various other energy scales, the effect of the lack of a common SIA axis is not well explored. Here we investigate the magnetic properties of $\mathrm{Ni}\left(\mathrm{pyrimidine}\right){\left({\mathrm{H}}_2\mathrm{O}\right)}_2{\left({\mathrm{NO}}_3\right)}_2$, a chain compound where the tilting of Ni octahedra leads to a twofold alternation of the easy-axis directions along the chain. Muon-spin relaxation measurements indicate a transition to long-range order at $T_{\text{N}}=2.3\mathrm{K}$and the magnetic structure is initially determined to be antiferromagnetic and collinear using elastic neutron diffraction experiments. Inelastic neutron scattering measurements were used to find $J_0=5.107\left(7\right)\mathrm{K}, D=2.79\left(1\right)\mathrm{K},J_1^{\prime }=0.00\left(5\right)\mathrm{K}, J_2^{\prime }=0.18\left(3\right)\mathrm{K}$, and a rhombic anisotropy energy $E=0.19\left(9\right)\mathrm{K}$. Mean-field modeling reveals that the ground state structure hosts spin canting of $\varphi \approx 6.5^{\circ }$, which is not detectable above the noise floor of the elastic neutron diffraction data. Monte Carlo simulation of the powder-averaged magnetization, $M\left(H\right)$, is then used to confirm these Hamiltonian parameters, while single-crystal $M\left(H\right)$simulations provide insight into features observed in the data. Published by the American Physical Society2025