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In this study, the authors address the current climate concern of high CO₂ levels by testing solid forms of hydroxide for CO₂ reduction and designing a drone to fly it in ambient air!

Microplastics can have detrimental effects on various wildlife, as well as pollute aquatic and atmospheric environments. This study focused on air samples collected from five locations to investigate microplastic concentrations in atmospheric fallout from indoor and outdoor settings, through a process utilizing a hand-held vacuum pump and a rotameter. The authors found that the difference between the average number of microplastic fragments and fibers collected from all locations was not large enough to be statistically significant. The results collected in this study will contribute to knowledge of the prevalence of airborne microplastics.

Zeolithic imidazolate framework-8 (ZIF-8) is a specific metal-organic framework that has favorable qualities for use in an air filter and is known to be capable of adsorbing particulate matter. Therefore, the objective of this experiment was to determine the effectiveness of ZIF-8 in adsorbing polar, gaseous air pollutants, specifically nitrogen dioxide and hydrogen sulfide. In order to determine effectiveness, the percent change in concentration for various gases after the application of ZIF-8 crystals was measured via Fourier-transform infrared spectroscopy (FTIR). The work highlights crystals as a potentially promising alternative or addition to current filter materials to reduce atmospheric pollution.

Here the authors investigated whether frequent use of generative AI tools triggers upward social comparison and negatively impacts users' performance self-esteem. Based on a survey of 121 adults, they found no significant relationship between AI usage and self-esteem, concluding that current chatbot interactions do not pose the same psychological threat as peer-based comparisons.

Large language models are increasingly used to solve math problems, but their ability to handle multi-step reasoning remains uncertain. In this study, students tested whether different prompting styles could improve LLM accuracy on challenging AIME math questions and found that detailed step-by-step solutions did not significantly outperform simpler prompts. These results suggest that improving LLM mathematical reasoning may require deeper model-level advances rather than changes in prompting style alone.

This study investigated perceptions of the emerging workforce toward the use of artificial intelligence in hiring, specifically for assessing subjective "culture fit." Through a mixed-methods survey of 150 high school and early-college students in Nepal, we found a significant disconnect between organizational adoption of AI and the profound skepticism of young job candidates, who express deep concerns about fairness, transparency, and the potential for AI to perpetuate systemic discrimination.

Advertising platforms like Google Ads use AI to drive the algorithms used to maximize advertisers benefits. This study shows that AI does not adjust it strategy based on auction type and highlights the limitations of AI running without explicit guidance.

This study explored how green spaces, climate, traffic, and air quality (GCTA) collectively influence asthma-related emergency department visits in the U.S using machine learning models and explainable AI.

The authors studied the chemoreception of moon jellyfish in response to food, and developed an AI tool to identify track and quantify the pulsation of swimming jellyfish.

In this study, three models are used to test the hypothesis that data-centric artificial intelligence (AI) will improve the performance of machine learning.