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On-surface synthesis made the fabrication of extended, atomically precise π-conjugated nanostructures on solid supports possible, with graphene nanoribbons (GNRs) and porphyrin-derived oligomers standing out. To date, examples combining these two prominent material classes are scarce, even though the chemically versatile porphyrins and the atomistic details of the nanographene spacers promise an easy tunability of structural and functional properties of the resulting hybrid structures. Here, we report the on-surface synthesis of extended benzenoid-and nonbenzenoid-coupled porphyrin–graphene nanoribbon hybrids by sequential Ullmann-type and cyclodehydrogenation reactions of a tailored Zn(II) 5,15-bis(5-bromo-1-naphthyl)porphyrin (Por(BrNaph)2) precursor on Au(111) and Ag(111).

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While our attention is mostly directed towards ever smaller-integrated silicon circuits providing faster and faster computing, there’s another area of integrated electronics that operates at a much lower speed which we should be following. Thin-film flexible circuitry will provide novel ways to place electronics where a bulky or expensive circuit board with traditional components might be too expensive or inappropriate, and Wikichip is here to remind us of a Leuven university team who’ve created what is claimed to be the fastest thin-film flexible microprocessor yet. Some of you might find it familiar, it’s our old friend the 6502.

The choice of an archaic 8-bit processor might seem a strange one, but we can see the publicity advantage — after all, you’re reading about it here because of it being a 6502. Plus there’s the advantage of it being a relatively simple and well-understood architecture. It’s no match for the MHz clock speeds of the original with an upper limit of 71.4 kHz, but performance is not the most significant feature of flexible electronics. The production technology isn’t quite ready for the mainstream so we’re unlikely to be featuring flexible Commodore 64s any time soon, but the achievement is the impressive feat of a working thin-film flexible microprocessor.

Meanwhile, if you’re curious about the 6,502, we took a look at the life of its designer, [Chuck Peddle].

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We have all had the experience of one of our electronic devices overheating. Needless, to say that when that happens, it becomes dangerous both for the device and its surroundings. But considering the speed at which devices work, is overheating avoidable?

A 740 percent increase in power per unit.

Researchers at the University of Illinois at Urbana-Champaign (UIUC) and the University of California, Berkeley (UC Berkeley) have recently devised an invention that could cool down electronics more efficiently than other alternative solutions and enable a 740 percent increase in power per unit, according to a press release by the institutions published Thursday.

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A hand-held laser pointer produces no noticeable recoil forces when it is “fired” — even though it emits a directed stream of light particles. The reason for this is simply because of its relatively enormous mass compared to the very tiny recoil impulses that the light particles cause when they leave the laser pointer.

However, it has long been clear that optical recoil forces can indeed have a significant effect on correspondingly small particles. For example, the tails of comets point away from the Sun partly due to light pressure. The propulsion of light spacecraft via light sails has also been discussed repeatedly, most recently in connection with the “starshot” project, in which a fleet of miniature spacecraft is to be sent to Alpha Centauri.

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Does the breakthrough to general AI need more data and computing power above all else? Yann LeCun, Chief AI Scientist at Metaon the recent debate about scaling sparked by Deepmind’s Gato.

The recent successes of large AI models such as OpenAI’s DALL-E 2, Google’s PaLM and Deepmind’s Flamingo have sparked a debate about their significance for progress towards general AI. Deepmind’s Gato has recently given a particular boost to the debate, which has been conducted publicly, especially on Twitter.

Gato is a Transformer model trained with numerous data modalities, including images, text, proprioception or joint moments. All training data is processed by Gato in a token sequence similar to those of large language models. Thanks to the versatile training, Gato can text, describe images, play video games or control robotic arms. Deepmind tested the AI model with over 600 benchmarks.

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