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Nanotexturing as a method to reduce dust accumulation on solar panels

Choi et al. | Jan 30, 2025

Nanotexturing as a method to reduce dust accumulation on solar panels

Dust accumulation on solar panels can reduce electricity output by 20–50%, posing a major challenge for solar energy collection. Instead of altering panel design, we explored a simpler approach by modifying surface energy through nanotexturing, predicting that hydrophobic surfaces would repel both water and dust. This study found that treating glass and silicone surfaces with potassium hydroxide (KOH) for 13 and 10 minutes, respectively, created optimal nanotextures (445 nm for glass, 205 nm for silicone), significantly reducing dirt accumulation and improving solar energy capture.

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The impact of attending a more selective college on future income

Ho et al. | Oct 16, 2024

The impact of attending a more selective college on future income

Debates around legacy preferences, recruited athletes, and affirmative action in U.S. college admissions often focus on the belief that graduating from a more selective institution leads to higher future earnings. The study hypothesized a positive correlation between college selectivity and future income due to enhanced resources and opportunities.

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Jet optimization using a hybrid multivariate regression model and statistical methods in dimuon collisions

Chunduri et al. | Jun 09, 2024

Jet optimization using a hybrid multivariate regression model and statistical methods in dimuon collisions
Image credit: Chunduri, Srinivas and McMahan, 2024.

Collisions of heavy ions, such as muons result in jets and noise. In high-energy particle physics, researchers use jets as crucial event-shaped observable objects to determine the properties of a collision. However, many ionic collisions result in large amounts of energy lost as noise, thus reducing the efficiency of collisions with heavy ions. The purpose of our study is to analyze the relationships between properties of muons in a dimuon collision to optimize conditions of dimuon collisions and minimize the noise lost. We used principles of Newtonian mechanics at the particle level, allowing us to further analyze different models. We used simple Python algorithms as well as linear regression models with tools such as sci-kit Learn, NumPy, and Pandas to help analyze our results. We hypothesized that since the invariant mass, the energy, and the resultant momentum vector are correlated with noise, if we constrain these inputs optimally, there will be scenarios in which the noise of the heavy-ion collision is minimized.

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