Microalgae can convert light energy into measurable electrical current, making them promising living biomaterials for bioelectronic applications such as sustainable power generation, environmental sensing, wearable devices, and biosensors. However, different applications require different electrical outputs, environmental tolerances, and growth conditions, while relevant information is currently scattered across individual studies.

This project aims to make microalgae easier to identify and evaluate for specific bioelectronic applications. It combines an experimentally characterized library of freshwater microalgae collected at DKU with a literature-based database containing information on taxonomy, habitat, growth conditions, photosynthetic performance, environmental tolerance, and electrical output.
The collected data will support a machine-learning-assisted platform that allows researchers to specify an intended application, desired electrical-output range, and operating conditions and receive ranked candidate microalgae. Predictions will be experimentally validated using DKU strains.

By integrating biological characterization, electrical measurements, machine learning, and access to living microalgal strains, this project seeks to bridge the gap between microalgal research and practical bioelectronic device development.

ChatDKU is an institutionally supported, open-source large language model platform already deployed at Duke Kunshan University. This project will use ChatDKU as a real-world research testbed for an undergraduate team to build and evaluate a new LLM agent addressing one clearly defined campus need, such as academic support, career development, research discovery, or student services.

The central research question is whether an agentic system combining retrieval, tool use, planning, source verification, and calibrated abstention can complete complex campus tasks more accurately and reliably than a conventional retrieval-augmented generation (RAG) chatbot while satisfying privacy, security, and access-control requirements.

The student team will lead the literature review, problem formulation, system design, implementation, experimentation, and dissemination. Students will first identify a focused campus use case and formulate a testable research hypothesis. They will then build a conventional RAG baseline, implement the proposed agent on the ChatDKU framework, and develop a reproducible bilingual evaluation benchmark. The system will be evaluated in terms of factual accuracy, citation correctness, information freshness, task completion, robustness, latency, usability, and privacy and security risks.

Depending on the selected use case, the project may investigate multilingual interaction, automatic answer verification, prompt-injection defenses, multi-agent collaboration, or privacy-aware agent design. The project gives students an opportunity to contribute to a deployed open-source AI platform and complete an end-to-end research cycle from problem formulation and system development to rigorous evaluation and academic dissemination. Strong contributors may also have opportunities to continue collaborating with the ChatDKU research group on follow-on research projects.

Any campus pilot will proceed only after the relevant technical, privacy, data-governance, security, stakeholder, and human-subject approvals.

This project addresses the systemic neglect of janitorial staff, where frontline workers-mostly migrant employees in their 50s and 60s-face inadequate rest spaces, wage suppression, health hazards, and cultural invisibility. Despite the institution’s global reputation, janitorial staff are relegated to poor working conditions. Based on preliminary interviews and observations, this staff is cramped in utility closets for meals and breaks, prohibited from sitting or resting in public areas under threat of being labeled “slackers.”

The proposed initiative, “Restoring Dignity,” adopts a two-stage approach. First, it maps existing rest stations and advocates converting underused storage rooms into proper break areas with seating, water, and climate control. Funds will be used for a preliminary resting area, and perceptions and effects will be evaluated before and after.

Second, it launches a campus awareness campaign-including photography exhibitions, oral history interviews, and student pledges-to render this labor visible and cultivate respect, while integrating the issue into service-learning curricula.

Implementation proceeds through research, pilot interventions, and scaling, with the goal of transforming both physical conditions and institutional culture. Grounded in the principle that basic labor rights are not charity, the project aligns with broader policy trends while challenging the structural and cultural forces that render essential workers invisible. By simultaneously addressing spatial injustice and social recognition, it aims to ensure that those who maintain the campus can do so with dignity, rest, and respect.

Spectroscopy is a powerful technique for studying and identifying materials, but commercial spectrometers are often expensive and complex. This project aims to design and build a simple, low-cost, and compact spectrometer using accessible optical and electronic components. Students will explore the basic principles of spectroscopy and develop a working prototype through hands-on design, fabrication, assembly, calibration, and testing. The project will involve optical alignment, detector integration, basic electronics, data collection, and spectral analysis. The prototype will be tested using several well-defined reference samples, such as polystyrene, silicon, and selected simple organic materials, to evaluate its ability to detect and distinguish characteristic spectral features. The project has both academic and practical value. Students will apply fundamental concepts in optics and spectroscopy to a real scientific instrument while gaining experience in experimental design, fabrication, testing, and data analysis. The project will also investigate how a low-cost optical system can achieve useful spectral measurements compared with established laboratory instruments. The project is intentionally limited in scope and designed to be feasible within one year. The main stages will be system design, prototype fabrication, calibration, measurement, and performance evaluation. The expected outcome is a functional proof-of-concept spectrometer and a documented evaluation of its capabilities and limitations.

As embodied AI moves from digital models into robots, factories, logistics systems, healthcare, and public services, its environmental responsibility becomes distributed across global networks of critical materials, semiconductors, computing infrastructure, manufacturing, transportation, operation, maintenance, and end-of-life recovery. Existing assessments often estimate a total environmental footprint but do not clearly explain who is responsible for which impact, what evidence supports that judgment, or how different rules may change organizational behavior.

This project asks:

How can environmental responsibility in embodied-AI global supply chains be recorded, verified, and shared in ways that improve both accountability and incentives?

Students will select one clearly bounded embodied-AI system, map its principal actors, resources, decisions, and environmental effects, and build a provenance-aware dataset from credible public evidence. They will compare at least three responsibility-allocation mechanisms-for example, direct operational control, lifecycle contribution, economic value, or capacity to prevent harm-and examine how these rules may affect disclosure, procurement, production, mitigation, and responsibility shifting.

The central Minimum Viable Product will be a functional Environmental-Responsibility Ledger and Mechanism Lab. Its interactive interface will make supply-chain relationships, evidence sources, assumptions, uncertainty, missing data, and alternative responsibility allocations visible and open to comparison.

The project will incubate students as scientists, entrepreneurs, and philanthropists: conducting interdisciplinary research for international dissemination; translating the findings into a responsible startup demonstration; and returning knowledge to society through an open website, stakeholder workshop, tutorial, and multimedia educational resources.

The project will make substantive contributions to:

• SDG 9 – Industry, Innovation and Infrastructure, by advancing responsible and sustainable embodied-AI infrastructure;
• SDG 12 – Responsible Consumption and Production, by improving lifecycle transparency, responsible procurement, and circular supply-chain thinking;
• SDG 13 – Climate Action, by connecting verifiable environmental evidence with incentives for mitigation;
• SDG 17 – Partnerships for the Goals, by bringing together

students, researchers, industry, policymakers, sustainability experts, and international-standards contributors.