In academic and industrial chemistry societies, green chemistry and sustainability are the focus of emergent efforts to address grand global challenges; these priorities are now reflected in the guidelines for chemistry education as the "Normal Expectations" for ACS-approved Bachelor's degree programs. Furthermore, the phenomena posed by the United Nation's Sustainable Development Goals (SDGs) and sustainability issues have enormous potential to inspire undergraduate science students. However, in the case of the undergraduate chemistry curriculum, integrating sustainability challenges-often the result of complex chemistry interacting at the boundaries of social or environmental systems-presents design challenges for curriculum developers working with learners' nascent knowledge base. To meet this crucial need for sustainability education, successful integration of green and sustainable chemistry into the curriculum requires a deep knowledge of how people learn and an understanding of how to use this knowledge to design curricula and curricular materials to support learning. In this article, we offer a case study designed using our framework to incorporate green chemistry and sustainability; the purpose of this case study is to engage students in constructing an explanation of the underlying chemistry as well as defining a sustainability problem with respect to stakeholders' needs. Our design efforts are demonstrated through examples of students' engagement with and responses to the scaffolded case study design.
Shared accountability records are often used by parties who may never agree about causation, responsibility, or normative interpretation. For such records, neutrality cannot be achieved by omitting contested information, because accountability requires preserving the claims parties made, with their sources and provenance. Nor can neutrality be achieved by asserting one contested interpretation as the shared base. This paper defines a neutral substrate as a shared representational layer that provides stable reference while making no object-level substrate-layer commitments to causal or normative propositions. The central design constraint is that, when causal and normative propositions are contestable across admissible frameworks and the substrate's referential commitments are common ground, the substrate's neutrality is guaranteed at design time if and only if its foundational layer is restricted to those referential commitments and attribution propositions whose attributional basis is fixed by them. Causal and normative content may still be represented, but not as object-level foundational-layer commitments: it may appear there only as the content of attributed assertions with provenance, made by some identified framework, source, agent, institution, record, or document. The representational machinery used here is standard: reification, attribution, and provenance. The contribution is the constraint: a checkable condition on the foundational layer of a shared record, stated together with the assumptions it depends on and the boundary condition under which the constraint does not apply. A neutral substrate says enough to preserve accountability, but it does not turn one party's interpretation into an object-level substrate-layer commitment. The constraint does not apply at that layer when the referential regime or attributional basis is contested among the frameworks in play.
The Fraunhofer diffraction of quantum particles from materials with sharp electron-density edges or symmetric bond structures is ubiquitous. In contrast, diffraction from atoms with characteristic asymptotically-diffused electron distribution is far less intuitive, although known for many years. The current study unravels an unusual diffraction mechanism of elastic electrons from diffused atomic diffractors. Consequently, the fringe pattern converted to the Fourier reciprocal space maps out the effective scattering potential, which is not accessible in direct measurements. This may benefit benchmarking theory models, advances in atom-holography, plasma and astrophysical diagnostics, and accessing time-resolved potential landscapes. The study employs relativistic partial wave analysis with atoms modeled in the Dirac-Fock formalism and performs e-Cd measurements in absolute scale. Analysis for Mg, Ba, and Ra targets demonstrates the universality of the mechanism.
Implementation of hybrid model architecture in Retrieval-Augmented Generation (RAG) systems has increased in recent years where it adopts knowledge graph to retrieve entities, relationships and path alongside vector retrieval which increase the quality of retrieval compared to traditional RAG. The knowledge graphs used in these systems are constructed automatically from data after the appearance of Large Language Models (LLMs). However, in many of these automatically constructed knowledge graphs lack explicit conceptual structure. Most nodes are labelled with generic, surface level names, especially the ones constructed on unstructured texts. This limits the type-aware retrieval of nodes and narrows the potential of the query system. Simultaneously, recent works has shown that ontologies can be generated from unstructured text using Large Language Models and prompt engineering. Yet, their integration with GraphRAG pipelines remained unexplored. This paper proposes a unified framework in which a knowledge graph and an ontology are automatically generated from the same source text and then aligned so that the ontology is used to relabel and enrich the knowledge graph. The enriched graph supports type-aware retrieval, enabling GraphRAG to answer queries even when surface terms do not explicitly appear in the text. We have proposed the method and discussed with a case demonstration how ontology-guided relabeling increases semantic coverage, improves retrieval generality, and strengthens the interpretability of graph-based results.
The integration of sixth generation (6G) wireless communications, quantum information technologies, and autonomous artificial intelligence (AI) agents is emerging as an important and challenging research area in telecommunications. Although 6G promises extremely high data rates, ultra low latency, and ubiquitous connectivity, it also introduces significant security concerns. In particular, quantum computing threatens traditional cryptographic techniques used in current communication systems. This challenge requires new approaches for designing secure 6G infrastructures. In this context, this paper explores the integration of autonomous AI agents, which are reasoning enabled systems, with quantum communication technologies such as Quantum Key Distribution (QKD) and Post Quantum Cryptography (PQC). A literature review is conducted to examine current research on 6G security, quantum communications, and agentic AI, followed by the identification of research gaps and future directions. The paper also formulates and investigates key research questions related to quantum safe 6G security. In addition, applications across transformative 6G domains are discussed with a focus on quantum safe security concerns in five critical areas.
