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Abstract Plasmon-driven photocatalysis offers a unique means of leveraging nanoscale light–matter interactions to convert photon energy into chemical energy in a chemoselective and regioselective manner under mild reaction conditions. Plasmon-driven bond cleavage in molecular adsorbates represents a critical step in virtually all plasmon-mediated photocatalytic reactions and has been identified as the rate-determining step in many cases. This review article summarizes critical insights concerning plasmon-triggered bond-cleaving mechanisms gained through combined experimental and computational efforts over the past decade or so, elaborating on how the plasmon-derived physiochemical effects, metal–adsorbate interactions, and local chemical environments profoundly influence chemoselective bond-cleaving processes in a diverse set of molecular adsorbates ranging from small diatomic molecules to aliphatic and aromatic organic compounds. As demonstrated by several noteworthy examples, insights gained from fundamental mechanistic studies lay a critical knowledge foundation guiding rational design of nanoparticle–adsorbate systems with desired plasmonic molecule-scissoring functions for targeted applications, such as controlled release of molecular cargos, surface coating of solid-state materials, and selective bond activation for polymerization reactions. 

Abstract The scarcity of measured data for defect identification often challenges the development and certification of additive manufacturing processes. Knowledge transfer and sharing have become emerging solutions to small-data challenges in quality control to improve machine learning with limited data, but this strategy raises concerns regarding privacy protection. Existing zero-shot learning and federated learning methods are insufficient to represent, select, and mask data to share and control privacy loss quantification. This study integrates differential privacy in cybersecurity with federated learning to investigate sharing strategies of manufacturing defect ontology. The method first proposes using multilevel attributes masked by noise in defect ontology as the sharing data structure to characterize manufacturing defects. Information leaks due to the sharing of ontology branches and data are estimated by epsilon differential privacy (DP). Under federated learning, the proposed method optimizes sharing defect ontology and image data strategies to improve zero-shot defect classification given privacy budget limits. The proposed framework includes (1) developing a sharing strategy based on multilevel attributes in defect ontology with controllable privacy leaks, (2) optimizing joint decisions in differential privacy, zero-shot defect classification, and federated learning, and (3) developing a two-stage algorithm to solve the joint optimization, combining stochastic gradient descent search for classification models and an evolutionary algorithm for exploring data-sharing strategies. A case study on zero-shot learning of additive manufacturing defects demonstrated the effectiveness of the proposed method in data-sharing strategies, such as ontology sharing, defect classification, and cloud information use. 

Abstract The intricate interdependence of food, energy, and water (FEW) systems necessitates effective and coordinated educational efforts across various contexts to equip students with the skills to tackle FEW challenges. As an emerging interdisciplinary field, understanding educators’ and education researchers’ views on the FEW-Nexus perspective, self-efficacy, needs, and approaches to promoting community engagement are vital to facilitating the growth of this field. The National Collaborative for Research on Food, Energy, and Water Education (NC-FEW) is an NSF-funded, emergent, interdisciplinary community of educators and discipline-based education researchers engaged in sustained network and capacity building around FEW-Nexus. We present initial survey findings from 166 NC-FEW members, predominantly postsecondary faculty from varied disciplines. Our goal was to understand their views of FEW-Nexus perspective, self-efficacy in FEW-Nexus-specific teaching and education research, instructional design, and community engagement. The findings suggest that FEW-Nexus educators in the NC-FEW community view the Nexus as a blend of diverse concepts and themes, emphasizing the necessity of establishing a concrete definition of the nexus perspective. Their self-efficacy levels were higher in general STEM teaching (mean = 4.03) and STEM education research (mean = 3.61) compared to FEW-Nexus-specific teaching (mean = 3.43) and education research (mean = 3.18). Respondents reported feeling moderately connected to the FEW-Nexus educator community (mean = 2.21). They also outlined anticipated community benefits and contributions to promoting teaching and learning in the FEW-Nexus. These findings highlight the significance of boosting FEW-Nexus educators’ self-efficacy and building a stronger sense of community, having important implications for professional development in emerging fields and broader educational reform endeavors.