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In Situ Synthesis of PbS Shell on CsPbBr3 QD for Enhanced Stability and Photoluminescence
Full text linkHalide perovskite quantum dots (HPQDs) have attracted considerable attention for optoelectronic applications due to their outstanding optical properties, including high photoluminescent quantum yield (PLQY), tunable band gap, high absorption coefficients, etc. However, their practical implementation is limited by poor stability under ambient conditions such as moisture, heat, and light. To overcome these challenges, surface passivation and shelling engineering have emerged as effective strategies to enhance HPQD stability by encapsulating their surface with a protective material. Conventional shelling techniques, however, often face limitations related to lattice mismatch and interfacial incompatibility. In this study, we report the successful synthesis of PbS@CsPbBr3 (shell@core) QDs via an in situ shelling process employing an activated sulfur precursor. This approach enables low-temperature shell growth which mitigates the thermal degradation of the CsPbBr3 QDs core. The resulting PbS@CsPbBr3 QDs exhibit enhanced PLQY and stability, while retaining their intrinsic spectral properties. Additionally, we systematically examined the influence of shelling stoichiometry and temperature on the structural and optical properties of the PbS@CsPbBr3 QDs. This work not only presents a robust strategy for stabilizing HPQDs but also offers valuable insights into the rational design of surface passivation methods
Sustainability Hub Newsletter - May 2025
Full text linkHappy May Bearcats! Read all about exciting recent events on campus, as well as advice for what to grow or read this summer! Congratulations to our graduating readers, and best of luck to you all
Enhancing Online Education Through Sentiment Analysis and Complex Systems Modelling
Full text linkThis research explores the application of sentiment analysis through the lens of complex systems modelling to enhance the quality of online certification courses, with a particular focus on global platforms such as Coursera. The COVID-19 pandemic catalyzed significant growth in online learning, creating an urgent need for adaptive and student-centric approaches to ensure relevance and effectiveness. Leveraging unstructured textual data from student reviews of courses, this study integrates methodologies from systems science, computer science, and education to address real-world challenges in online education. By employing both lexicon-based (SentiWordNet and VADER) and supervised machine learning techniques (Multinomial Naive Bayes, Support Vector Machine, and Stochastic Gradient Descent), the research conducts a detailed sentiment analysis to identify patterns, emergent behaviours, and feedback loops inherent in course design and delivery. Findings reveal that Support Vector Machine achieves the highest accuracy at 97.3%, offering insights that guide iterative improvements in course content and pedagogical strategies. The study demonstrates how interdisciplinary approaches to sentiment analysis can inform responsive education environments, aligning with broader societal goals of accessibility, inclusivity, and quality in online learning ecosystems
Gravity of Ideas: Mapping Science at BU
Full text linkWe plan to showcase a hyperbolic map that visualizes 1.1 million papers by 14,490 BU researchers, tracking topic shifts over time. A working visualization can be found at https://github.com/skojaku/bu-art-202
3D Printed CNT/TPU Triboelectric Nanogenerator for Load Monitoring of Total Knee Replacement
Full text linkThis study presents the development and characterization of a novel triboelectric nanogenerator (TENG) designed as a self-powered sensor for load monitoring in total knee replacement (TKR) implants. The triboelectric layers comprise a 3D-printed thermoplastic polyurethane (TPU) matrix with carbon nanotube (CNT) nanoparticles and kapton tape, sandwiched between two copper electrodes. To optimize sensor performance, the proposed CNT/TPU TENG sensor is fabricated with varying CNT concentrations and thicknesses, enabling a comprehensive analysis of how material composition and structural parameters influence energy harvesting efficiency. The 1% CNT/TPU composite demonstrates the highest power output among the tested samples. The solid CNT/TPU-based TENG generated the apparent output power of 4.1 μW under a cyclic compressive load of 2100 N, measured across a 1.6 GΩ load resistance and over a nominal contact area of 15.9 cm², while the foam CNT/TPU film achieved a higher apparent output power of 6.9 μW measured across a 0.9 GΩ load resistance with the same nominal area. The generated power is sufficient to operate a power management and ADC circuit based on our earlier work. The sensors exhibit a stable open-circuit voltage of 320 V for the foam layer and 275 V for the solid one. Sensitivities are 80.50 mV/N (≤ 1600 N) and 24.60 mV/N (\u3e 1600 N) for foam CNT/TPU film, demonstrating the integrated sensor capability f or wide-range force sensing on TKR implants. The foam CNT/TPU-based TENG maintained stable performance over 16,000 load cycles, confirming its potential f or long-term use inside the TKR. Additionally, the dielectric constant of the CNT/TPU composite was found to increase with increasing CNT concentration. The proposed CNT/TPU TENG sensor offers a broad working range and robust energy-harvesting efficiency, making it appropriate for self-powered load sensing in biomedical applications