Molten-salt electrorefining (ER) is essential for purifying chemically active metals, including plutonium, that cannot be processed in aqueous systems. However, systematic experimental studies linking operating parameters to ER performance and reliable process-monitoring criteria remain limited. In this work, tin–nickel (Sn-Ni) electrorefining in a LiCl-KCl-CaCl 2 eutectic is employed as a non-radioactive surrogate to investigate the effects of anode composition, temperature, and SnCl 2 seeding concentration. Kilogram-scale ER experiments were conducted using an automated operating protocol that monitored voltage, current, static refining resistance, and back-electromotive force (back-emf) as run termination indicators. Product yield, faradaic efficiency, and refined-metal purity were quantified through post-run mass balances and chemical analysis. All conditions produced refined Sn with ≥99.5 wt% purity. Increasing SnCl 2 seeding concentration and operating temperature improved product yield, whereas increasing Ni impurity in the anode feed from 2 to 3 wt% significantly reduced yield and faradaic efficiency. Comparison of monitoring metrics showed static refining resistance to be more consistent and reliable than back-emf for identifying run completion, as back-emf exhibited false positives and missed late-stage transitions. These results provide practical guidance for ER process control and reinforce the Sn-Ni system as a surrogate for molten-salt electrorefining of plutonium.
In this paper, I study the emerging political depolarization field in the contemporary US through a network analysis of civil society organizations working to overcome political divides. Because polarization can prevent us from not only solving other social problems, but the problem of polarization itself, I look specifically at whether organizations directly focused on bridging—who represent the core of the supposedly non-partisan depolarization field—practice what they preach by embodying non-partisanship and engaging in cross-partisan ties within their own interorganizational environment. Drawing on data compiled from the Princeton Bridging Divides Initiative, Twitter/X, and AllSides media, I analyze organizations’ partisanship and use Exponential Random Graph Models (ERGMs) to assess the likelihood for bridging organizations to form ties across political differences within the political depolarization field. I find that while bridging-focused organizations are left of center overall, they are relatively less left-leaning than their nonbridging counterparts. And, while bridging organizations show no difference in forming ties across political differences in general, they are more likely to connect with politically different organizations when those organizations have a nonbridging focus. I argue that this pattern reflects the strength of the non-partisan ideal at the core of the bridging movement, where bridging organizations apply stricter partisan standards to fellow bridging organizations than to nonbridging organizations for whom political difference is expected and unremarkable. I discuss the implications of these findings for the prefigurative commitments of the bridging movement and for tensions between civic and partisan logics within the broader depolarization field.
PurposeThe design of multi-tier supply chain networks is a central but underexplored matter in the literature. How firms determine the optimal number of supplier tiers can impact their resilience and visibility. This study addresses this gap by considering the concept of reachability of the focal firm. It examines how network structure, based on centrality metrics, affects firm performance and resilience.Design/methodology/approachThe study uses a two-phase, mixed methods design. In study 1, a reachability analysis of the US pharmaceutical supply network helped determine the optimal number of upstream tiers given random disruption scenarios. This analysis identifies a convergence tier, a point after which added visibility has diminishing returns. In study 2, the impact of focal firm's position in the network on firm performance is investigated. This study uses degree and betweenness centrality as structural indicators to test the relationship between financial performance and financial resilience using econometric methods.FindingsThe findings from Study 1 show that reachability decreases non-linearly as the number of tiers increases. This indicates that there is a finite and quantifiable range of visibility that maximizes the focal firm access (reachability) to the suppliers under random disruptions. In Study 2, it was found that betweenness centrality, which reflects brokerage positions within the network, significantly enhances financial performance. Additionally, degree centrality, which indicates the number of direct supplier connections, has a positive impact on financial resilience.Originality/valueThis study presents a data-driven approach to determine the optimal monitoring depth in complex supply networks, linking it to firm-level financial outcomes. By combining reachability analysis and centrality metrics, it connects network design with organizational resilience. This work offers a quantitative framework to help firms build resilient, disruption-ready supply networks.
With the ongoing deployment of AI algorithms, managers do not know whether existing demand planning processes account for possible differences in human behavior when using AI-based systems in comparison to legacy model-based systems. This study examines how human behavior may differ when performing demand forecasting tasks due to the disclosure of algorithm type (AI or model) along with associated algorithm performance (low and improving). Using signaling theory, we hypothesize that algorithm type and performance influence user forecast adjustment behavior. We find support for these predictions across two laboratory experiments and a large quasi-natural field experiment with approximately 575,000 observations from a multinational retailer. We find no significant direct effect of algorithm type independent of performance in the lab. In contrast, in the field, users implement significantly greater adjustments for AI-based algorithms compared to model-based algorithms. Across both contexts, algorithm performance, whether low or improving, has a significant direct effect on user adjustments, with users adapting their behavior to the algorithm's performance. Finally, we find that in the lab and the field, users' responses to low performance are amplified when the forecasts originate from AI-based algorithms. Our findings underscore the nuance and complexity in which users interact with AI-based algorithms compared to model-based algorithms and demonstrate the value of signaling theory for understanding human-AI collaboration.
Justice system prevention research has focused heavily on risk factors, overlooking how positive psychological assets may protect vulnerable youth. This study addresses that gap by testing whether optimism mediates the relationship between childhood adversity and arrest in emerging adulthood, and whether this pathway differs by sex. Using data from 2,990 participants in the Future of Families and Child Wellbeing Study, adversity was measured at age nine, optimism at fifteen, and arrest at twenty-two. Logistic regression and mediation analyses showed that optimism partially mediated the adversity-arrest link, with stronger effects for males. Findings highlight optimism as a modifiable target for prevention, suggesting that fostering positive future expectations may reduce justice involvement, especially among high-risk boys.
