With the advent of Big Data, current computational architectures are proving to be insufficient. Difficulties in decreasing transistors’ size, large power consumption and limited operating speeds .
MIT PhD student Nina Andrejevic is training her machine-learning models to hunt for new and useful traits in materials. With her twin sister Jovana, she has developed a method for testing material samples to predict the presence of topological characteristics that is faster and more versatile than other methods.
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Single-atom electrocatalysts (SACs) toward hydrogen evolution reaction (HER) have been extensively studied owing to their high mass activity and atom utilization. Although platinum (Pt) based SACs have been reported frequently, optimizing the metal–support interaction to achieve low valence state Pt species is still a challenge. Here, the carbon supported α-MoC1−x nanoparticles are used to anchor zero-valent Pt single atoms (PtSA/α-MoC1−x@C) as electrocatalyst for pH-universal HER. The PtSA/α-MoC1−x@C with optimized Pt loading of 0.75 wt% shows a low overpotential (21, 12, and 36 mV at 10 mA cm–2) and high turnover frequencies (27.00, 31.98, and 21.39 H2 s–1 at 100 mV) for HER under alkaline, acidic, and neutral electrolyte conditions. Experimental evidence combing density functional theory calculations confirm that the charge polarization leads to a zero-valence state of Pt single atom and further optimized the adsorption/desorption energy of intermediates, further acce