Reactive Oxygen Species (ROS) are associated with a broad spectrum of diseases, ranging from bone loss to cancer. One strategy to combat ROS is to treat sources of such species in the body with materials capable of generating hydrogen and reacting with ROS to neutralize it. This project involves incorporating an H₂-generating material known as Calcium Disilicide (CaSi₂) into membranes of another H₂-generating material known as porous silicon for tandem antioxidant drug delivery. Porous silicon (pSi) is an important substrate in drug delivery as its nano-network of pores allows controlled loading of drugs. Our approach centers on the use of spark ablation to deposit CaSi₂ into the pSi. Both porous silicon and CaSi₂ are nontoxic and can be resorbed over time in vivo.

To prepare CaSi₂/pSi, a piece of pSi membrane is fixed to substrate with a small drop of nail polish, and CaSi₂ powder is added. A capillary tube is placed on the pSi and spark ablated with a high-voltage Tesla coil, causing Si atoms on the porous membrane to vaporize along with CaSi₂ and the mixture resettles upon cooling. Scanning Electron Microscopy (SEM) is used to characterize morphology, and in situ Energy Dispersive X-ray Spectroscopy (EDX) to determine the percentage of calcium in the sample. We use the criterion of highest CaSi₂ loading percentage to determine the conditions for most efficient addition of CaSi₂ into the membrane. We have successfully incorporated calcium disilicide into porous Si membranes; current experiments are attempting to measure the amount of hydrogen produced synergistically to improve the performance of porous silicon as a means to treat in situ ROS production.

Density Functional Theory (DFT) is a method for simulating molecules by approximating their electron densities, with various functionals available to
model these systems. M11plus is one such functional, a range-separated hybrid meta functional that combines long-range non-local Hartree–Fock
exchange with the non-local Rung 3.5 correlation, which has demonstrated effectiveness across a broad range of chemical databases. This work
implements the M11plus functional into the PySCF open-source Python library.​

This research aims to develop and characterize synthetic riboswitches for creatinine and trimethylamine N-oxide (TMAO), metabolic biomarkers for kidney and cardiovascular dysfunctions. Riboswitches are structured RNA elements located in the 5’-untranslated regions (UTRs) of bacterial mRNAs that regulate downstream gene expression through ligand-induced conformational changes with high affinity and selectivity. To select for the synthetic riboswitches specific to creatinine, the glycine riboswitch library was subjected to a dual genetic selection. In the positive selection, the riboswitches that bind to creatinine or any endogenous molecules will produce the CAT-UPP fusion protein, allowing the host cells to survive in the presence of chloramphenicol. The negative selection is carried out in media containing 5-fluorouracil (5-FU) in the absence of creatinine. Any riboswitches activated by endogenous ligands will die in the presence of 5-FU. The surviving cells should contain the riboswitches that are activated only by creatinine. After several repeated selection steps, including increased concentrations of chloramphenicol and 5-FU, no glycine riboswitch variants were identified to show chloramphenicol resistance in the presence of creatinine. We will continue the project with different riboswitch libraries. We identified a synthetic riboswitch to TMAO, a riboswitch that was derived from the genetic selection of the theophylline riboswitch library that clearly shows chloramphenicol resistance only in the presence of TMAO. We will further test this TMAO riboswitch by colorimetric or fluorescence assays using β-galactosidase and green fluorescence protein, respectively, in the presence of varying concentrations of TMAO.

Sustainable synthetic approaches to drug delivery carriers such as porous silicon are becoming increasingly important for biomedical applications such as drug delivery, where extreme electronic-grade purity is not required, even though silicon remains a critical material in electronics and energy technologies. This work develops a green, self-propagating high-temperature synthesis (SHS) approach to produce high-surface-area porous silicon using plant-derived silicon dioxide (SiO₂) as the precursor, magnesium (Mg) as the reductant, and sodium chloride (NaCl) as a thermal moderator. The exothermic magnesiothermic reaction is initiated using a controlled electrical input of less than (or equal to) 9V, enabling silicon formation while significantly reducing external energy requirements compared to conventional high-temperature silicon production methods.

In practice, Mg and SiO₂ reactants are exposed to a finite voltage for approximately 10–15 minutes to allow the SHS reaction to propagate. After synthesis, the crude product is purified by dissolving reaction byproducts in concentrated hydrochloric acid, leaving behind porous silicon. X-ray powder diffraction (XRD) is used to evaluate crystallinity and phase composition. While XRD analysis confirms the formation of silicon, persistent crystalline silica peaks indicate incomplete reduction and phase coexistence that currently limits effective separation. Ongoing work focuses on optimizing reaction conditions and refining reaction kinetics to improve phase selectivity and identify optimal synthesis parameters. Despite these challenges, the low-energy synthesis strategy and use of accessible raw materials highlight the potential of SHS-derived porous silicon as a scalable and sustainable platform for future drug delivery applications, particularly in resource-limited settings.

Reactive oxygen species (ROS) are byproducts of normal cellular metabolism and play important roles in cell signaling and immune defense. However, when their production exceeds the cell’s antioxidant capacity, ROS accumulation leads to oxidative stress, damaging proteins, lipids, and DNA. In the brain, this oxidative imbalance has been closely linked to the development and progression of neurodegenerative diseases like Alzheimer’s. Under normal conditions, superoxide dismutase (SOD) enzymes play a key role in protecting cells by breaking down harmful superoxide radicals. Yet, reduced SOD activity and impaired regulation have been consistently observed in patients with neurodegeneration, including Alzheimer’s disease. Small-molecule mimics of SOD, therefore, represent a promising therapeutic approach. In this study, we evaluate an expanded library of tetra-aza macrocyclic ligands chelating either copper or manganese metals. Mechanistic analysis reveals how structural modifications to the macrocyclic ring, particularly R-group substitutions that alter steric environment and electronic properties, directly influence catalytic reactivity and stability. Evaluation of Cu- and Mn-based complexes highlights distinct trends in activity and identifies structural motifs that enhance SOD-like function. These findings provide design principles for developing antioxidant therapeutics targeting oxidative stress.

“Superfrog Science: Experiments for at Home and in the Classroom” is a creative endeavor that encourages young scientists to get curious about science and help learn a variety of chemistry concepts. This book is a visual representation of the importance of exploring the bounds of creativity in science. Join Superfrog as he goes on a learning adventure conducting science experiments and using the scientific method to deepen his knowledge about chemistry, making it easy to learn by conducting each experiment with easy-to-follow comic panels. This book strives to make science experiments accessible, affordable, and fun. It is perfect for encouraging hands-on learning, with in-depth explanations of the “how” and “why” of these experiments. Superfrog asks discussion questions and provides variations of the experiments to get his students to think about cause-and-effect and variable manipulation when it comes to the scientific process, as well as encourage collaboration with his peers. The educational activities featured are made for scientific discovery inside and outside the classroom. Make chemistry fun and easy with Superfrog!

Riboswitches are structured RNA elements that regulate gene expression through ligand-induced conformational changes and provide a platform for engineering cell-based biosensors. By coupling aptamers to reporter genes, synthetic riboswitches enable small-molecule–dependent detection of clinically relevant metabolites. This study focuses on sarcosine, associated with prostate cancer progression, and urate, linked to gout. Two sarcosine-responsive candidates were evaluated in E. coli using β-galactosidase and GFP reporter systems. Although construct integrity was confirmed, neither candidate demonstrated ligand-dependent activation in CDR or minimal media, suggesting insufficient regulatory activity under tested conditions. In parallel, a urate-responsive riboswitch library underwent dual selection with chloramphenicol resistance for positive selection and 5-fluorouracil counterselection for negative selection. After multiple selection rounds and screening of 192 colonies, no urate-specific variants were identified. Increasing chloramphenicol concentration to strengthen positive selection similarly yielded no hits. Future work will focus on further increasing both positive and negative selection intensity to enhance enrichment of functional variants and improve development of RNA-based biosensors for accessible metabolite detection. Additionally, future efforts will explore the adenine riboswitch library as a potential platform for developing novel biomarker detection systems.

Interactive Querying and Visualization of Solar Events

Author: Thu My Banh, Cathy Nguyen, Pandey Chetraj

Access to structured solar flare event data is essential for space weather (SWx) research, operational analysis, and machine learning applications. While the solar flare event archive maintained by the Lockheed Martin Solar and Astrophysics Laboratory (LMSAL) provides a widely used curated record of flare activity, the archive is primarily accessible through static web interfaces rather than a programmable query system. This makes automated filtering, dataset generation, and large-scale analysis difficult for researchers. To address this limitation, we developed a full-stack web application that provides programmatic access to LMSAL solar flare event records through a queryable API. A Python-based data ingestion pipeline retrieves and deduplicates event information from LMSAL’s rolling snapshot archive and stores it in a structured format. A FastAPI backend exposes endpoints that allow users to filter events by date range and GOES flare classification, enabling rapid dataset generation for analysis workflows. The frontend, implemented in React, allows users to query the event catalog, visualize results in a structured table, and export filtered datasets as CSV or JSON files. To improve data reliability and context, the system cross-references LMSAL event records with NOAA solar flare catalogs, allowing users to compare event metadata across independent data sources. Additionally, the application integrates with the Helioviewer API to display solar imagery corresponding to each event, with derived heliographic positions overlaid onto the solar disk to provide spatial context. The resulting system provides a lightweight platform for exploring, querying, and exporting solar flare event data, lowering the barrier to accessing operational flare records and facilitating dataset generation for space weather analysis and predictive modeling.

Large language models (LLMs) are increasingly framed as force multipliers for cyberattacks, yet most existing evaluations focus on isolated artifact generation rather than the construction and execution of full offensive workflows. This paper presents a controlled empirical study of LLM-assisted cyberattack construction across multiple representative attack classes, including automated SQL injection exploitation, spyware assembly, reverse shell establishment, and denial-of-service traffic generation. We evaluate several contemporary models—including ChatGPT-4o, ChatGPT-5.2, ChatGPT-5.1-instant, Claude Sonnet 4.6, and Gemini 3—within fully sandboxed virtualized environments, treating each model strictly as an advisory system embedded within a human-driven workflow.

Our experimental design decomposes attacks into staged operational workflows encompassing reconnaissance, payload generation, system integration, troubleshooting, and persistence. This structure enables systematic analysis of where automation succeeds or fails during real execution rather than relying on single-shot demonstrations. Across scenarios, LLMs consistently reduce effort for localized technical tasks such as command syntax recall, tool configuration, payload scaffolding, and procedural troubleshooting. However, reliable end-to-end attack execution remains limited. SQL injection automation succeeds primarily when established tools encapsulate complex orchestration, while more complex scenarios such as spyware assembly fail at system-level integration, environment-specific dependency resolution, and evasion of host defenses.

Across models and attack classes, automation consistently breaks at environment-dependent boundaries requiring global reasoning, state awareness, and cross-stage workflow coordination. These findings suggest that contemporary LLMs do not autonomously execute cyberattacks but instead function as workflow accelerators that lower the expertise threshold required to operationalize existing offensive techniques. This capability-boundary perspective provides a more realistic foundation for threat modeling, defensive planning, and future evaluation of AI-assisted cybersecurity risks.

Medical students lack effective tools for developing clinical reasoning, as most resources emphasize memorization rather than decision-making. DiseaseQuest is an AI-powered, gamified platform that addresses this gap through realistic patient simulations and decision-based scenarios. It enables students to work through complete clinical cases using interactive, patient-centered dialogue. Supported by a multi-agent framework, the platform provides adaptive guidance, diagnostic feedback, and personalized evaluations. By promoting active learning and problem-solving, DiseaseQuest offers a transformative approach that replaces passive study with immersive, hands-on practice, helping students strengthen diagnostic thinking and better prepare for real-world clinical decision-making.