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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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PID and fuzzy logic optimization of the pitch control of wind turbines

Zhou et al. | Jan 28, 2025

PID and fuzzy logic optimization of the pitch control of wind turbines
Image credit: The authors

Wind turbines are a valuable source of renewable energy, but face challenges related to unpredictable wind speed. The turbine must be able to control its angle to catch enough wind to generate electricity, while avoiding excess wind that may damage the turbine. Zhou and Wang explore different types of smart turbine controllers to see which appears optimal for electricity generation.

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Machine learning predictions of additively manufactured alloy crack susceptibilities

Gowda et al. | Nov 12, 2024

Machine learning predictions of additively manufactured alloy crack susceptibilities

Additive manufacturing (AM) is transforming the production of complex metal parts, but challenges like internal cracking can arise, particularly in critical sectors such as aerospace and automotive. Traditional methods to assess cracking susceptibility are costly and time-consuming, prompting the use of machine learning (ML) for more efficient predictions. This study developed a multi-model ML pipeline that predicts solidification cracking susceptibility (SCS) more accurately by considering secondary alloy properties alongside composition, with Random Forest models showing the best performance, highlighting a promising direction for future research into SCS quantification.

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The use of computer vision to differentiate valley fever from lung cancer via CT scans of nodules

El Kereamy et al. | Nov 12, 2024

The use of computer vision to differentiate valley fever from lung cancer via CT scans of nodules

Pulmonary diseases like lung cancer and valley fever pose serious health challenges, making accurate and rapid diagnostics essential. This study developed a MATLAB-based software tool that uses computer vision techniques to differentiate between these diseases by analyzing features of lung nodules in CT scans, achieving higher precision than traditional methods.

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