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Urea is a common waste in agriculture runoff and has also been proposed as a promising oxidizable molecule in urea electrolysis for hydrogen production from wastewater. However, the overpotential of the electrochemical urea oxidation reaction (UOR) is high due to the complicated six-electron transfer process on most metal catalysts. The competition with oxygen evolution reaction (OER) further limits catalyst options for UOR. The most promising and studied catalysts for UOR are Ni-based catalysts. Here we study the reactivity of the basal β-NiOOH(001) surface for UOR and study the effects of metal doping (Mn, Fe, Co, and Cu) on the phase transformation from β-Ni(OH)2 to β-NiOOH, UOR, and OER pathways using density functional theory (DFT) calculations. The introduction of Mn and Fe dopants facilitates the formation of catalytically active β-NiOOH phase, and also favors the adsorption of urea compared to the undoped β-NiOOH surface, thereby significantly benefitting the overall UOR. Moreover, comparison of the effect of dopants on UOR and OER provides fundamental understanding of the competition between UOR and OER and how the dopants influence the reaction selectivity and competition. This work sheds light on the structure-property relationship of Ni-catalysts in urea oxidation and provides design principles for functional Ni-based materials, which will help accelerate the development of efficient UOR catalysts. 

We study how open source communities describe participation and control through version controlled governance documents. Using a corpus of 710 projects with paired snapshots, we parse text into actors, rules, actions, and objects, then group them and measure change with entropy for evenness, richness for diversity, and Jensen Shannon divergence for drift. Projects define more roles and more actions over time, and these are distributed more evenly, while the composition of rules remains stable. These findings indicate that governance grows by expanding and balancing categories of participation without major shifts in prescriptive force. The analysis provides a reproducible baseline for evaluating whether future AI mediated workflows concentrate or redistribute authority. 

Creating engaging cybersecurity education materials typically requires months of development time and specialized expertise. This paper describes how we used generative AI to address this challenge. We utilized Claude AI to generate a complete interactive platform that teaches students basic microelectronics through IoT hacking. Through iterative prompting, we generated more than 15,000 lines of functional code, including interactive visualizations, Python security tools, and gamified quizzes with real-time leaderboards. The curriculum guides students through the evolution of computing—from vacuum tubes to modern IoT devices—then helps them apply this foundation to discover real vulnerabilities. We implemented this platform at a GenCyber summer camp with 40 participants, where students identified actual security issues in AmpliPi audio systems—opensource network audio devices designed for multi-room audio distribution—including password weaknesses and denial of service flaws. The entire development process took only three weeks instead of the typical several months. The AI produced quality educational content, although we reviewed everything for technical accuracy and ethical considerations. During the camp, students remained engaged through competitive elements and hands-on labs, learning both theoretical concepts and practical skills. The students used AI-generated tools, including working implementations of SlowLoris and dictionary attacks, to test real systems. Our experience demonstrates that generative AI can efficiently create effective cybersecurity education materials that remain technically current. All materials are publicly available on GitHub for educational use. This approach could help educators stay on track with the rapidly evolving technology despite traditional curriculum development constraints. 

Abstract Secreted proteins are crucial for the structure and functions of the human epidermis, but the full repertoire of the keratinocyte secretome has not been experimentally defined. In this study, we performed mass spectrometry on conditioned media from primary human keratinocytes, identifying 406 proteins with diverse roles in adhesion, migration, proliferation, proteolysis, signal transduction, and innate immunity. To leverage this new dataset, we developed a novel colony formation assay-based CRISPR screen to investigate the functions of uncharacterized secreted proteins on epidermal stem cells. The screen identified six candidate proteins that promoted proliferation of epidermal progenitors and two proteins that inhibited it. Secreted frizzled-related protein-1 (SFRP1) was the most potent inhibitor. We discovered that SFRP1 restrained clonogenic keratinocyte proliferation by inhibiting Wnt signaling as well as blocking ectopic expression of leukemia inhibitory factor (LIF). Collectively, our study expands our knowledge of the keratinocyte secretome, establishes a novel CRISPR screen to assess the function of non-cell autonomous factors, and highlights SFRP1’s role in regulating epidermal balance.