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Ravinder Dahiya on Muck Rack

Ravinder Dahiya

As seen in: Nature, The Independent (UK), Wiley Online Library, IEEE Spectrum, The Conversation, The Daily Beast, The Next Web, The Scotsman, IFL Science, Inverse and
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Articles

Deterministic roll-contact printing of ultradense aligned nanowire arrays for high-performance flexible electronics

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By Adamos Christou, Abhishek Singh Dahiya, Ravinder Dahiya, Ayoub Zumeit
| Nature Verified
Abstract Semiconducting nanowires (NWs) are versatile building blocks for mechanically compliant electronics, with applications spanning sensing, displays, and neuromorphic computing. However, translating their exceptional one-dimensional transport properties into large-area device arrays requires scalable assembly methods capable of simultaneously controlling NW density, alignment, and material purity - a combination that has remained elusive.
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Building on strength: a decade of progress in flexible electronics - npj Flexible Electronics

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By Ravinder Dahiya
| Nature Verified
As npj Flexible Electronics enters its tenth anniversary year, the journal does so from a position of strength. Over the past decade, flexible electronics has evolved from an emerging research direction into a mature and influential field, and the journal has grown alongside it1. This anniversary offers an opportunity not only to reflect on progress, but also to recognise how a strong foundation enables new ambition, deeper impact, and a clearer sense of direction for the years ahead.
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Ultrafast visual perception beyond human capabilities enabled by motion analysis using synaptic transistors - Nature Communications

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By Shengbo Wang, Jingwen Zhao, Liangbing Zhao, Xiaoyu Guo, Yue Cheng, Cong Li, Weihao Ma, Chenyu Tang, Zhenyu Xu, Ningli Wang, Luigi G. Occhipinti, Arokia Nathan, Ravinder Dahiya, Huaqiang Wu, Li Tao, Shuo Gao, Tongming Pu
| Nature Verified
Abstract Optical flow, inspired by biological visual systems, calculates spatial motion vectors aiming to enable robotics to excel in dynamic environments. However, current algorithms, despite human-competitive task performance on benchmark datasets, suffer from significant time delays, limiting practical deployment.
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