Technology Engineering Synthetic skin reveals hidden'Mona Lisa' when exposed to heat The octopus-inspired material could lead to better camouflage technology for the military and beyond. Breakthroughs, discoveries, and DIY tips sent six days a week. Octopuses and their cephalopod cousins have long fascinated biologists with their seemingly supernatural shapeshifting. The cephalopods rapidly change color and texture, blending into their surroundings and evading predators. This natural camouflage is a remarkable bit of biology that engineers have tried to replicate, albeit with limited success.

Our muscles are nature's actuators. The sinewy tissue is what generates the forces that make our bodies move. In recent years, engineers have used real muscle tissue to actuate "biohybrid robots" made from both living tissue and synthetic parts. By pairing lab-grown muscles with synthetic skeletons, researchers are engineering a menagerie of muscle-powered crawlers, walkers, swimmers, and grippers. But for the most part, these designs are limited in the amount of motion and power they can produce.

A rubber duck that was stuck to a seaside rock for more than a year has proved the strength of a new sticky material. The adhesive could be used in deep-sea robots and repair work, or as surgical glue for medical procedures. "We developed a super-adhesive hydrogel that works extremely well even underwater – something very few materials can achieve," says Hailong Fan at Shenzhen University in China. Hydrogels are stretchy and soft materials. Fan, then at Hokkaido University in Japan, and his colleagues analysed 24,000 sticky protein sequences from many different organisms to identify the stickiest combinations of amino acids, the building blocks of proteins.

Breakthroughs, discoveries, and DIY tips sent every weekday. One of the major barriers they haven't overcome is the ability to "feel" sensations like a human. Although researchers have tried various sensors to give robots a rudimentary sense of touch, these systems are often costly, inaccurate, and limited to detecting only one type of sensation at a time. But that may be about to change. Researchers from the University of Cambridge and University College London have developed a new type of responsive "synthetic skin."

Flexible electronic skins that simultaneously sense touch and bend are desired in several application areas, such as to cover articulated robot structures. This paper introduces a flexible tactile sensor based on Electrical Impedance Tomography (EIT), capable of simultaneously detecting and measuring contact forces and flexion of the sensor. The sensor integrates a magnetic hydrogel composite and utilizes EIT to reconstruct internal conductivity distributions. Real-time estimation is achieved through the one-step Gauss-Newton method, which dynamically updates reference voltages to accommodate sensor deformation. A convolutional neural network is employed to classify interactions, distinguishing between touch, bending, and idle states using pre-reconstructed images. Experimental results demonstrate an average touch localization error of 5.4 mm (SD 2.2 mm) and average bending angle estimation errors of 1.9$^\circ$ (SD 1.6$^\circ$). The proposed adaptive reference method effectively distinguishes between single- and multi-touch scenarios while compensating for deformation effects. This makes the sensor a promising solution for multimodal sensing in robotics and human-robot collaboration.

Peptides are ubiquitous and important biologically derived molecules, that have been found to self-assemble to form a wide array of structures. Extensive research has explored the impacts of both internal chemical composition and external environmental stimuli on the self-assembly behaviour of these systems. However, there is yet to be a systematic study that gathers this rich literature data and collectively examines these experimental factors to provide a global picture of the fundamental rules that govern protein self-assembly behavior. In this work, we curate a peptide assembly database through a combination of manual processing by human experts and literature mining facilitated by a large language model. As a result, we collect more than 1,000 experimental data entries with information about peptide sequence, experimental conditions and corresponding self-assembly phases. Utilizing the collected data, ML models are trained and evaluated, demonstrating excellent accuracy (>80\%) and efficiency in peptide assembly phase classification. Moreover, we fine-tune our GPT model for peptide literature mining with the developed dataset, which exhibits markedly superior performance in extracting information from academic publications relative to the pre-trained model. We find that this workflow can substantially improve efficiency when exploring potential self-assembling peptide candidates, through guiding experimental work, while also deepening our understanding of the mechanisms governing peptide self-assembly. In doing so, novel structures can be accessed for a range of applications including sensing, catalysis and biomaterials.

Inspired by recent advancements in brain organoid systems, researchers have designed a simple hydrogel-electrode array that not only can "play" Pong, but improve its gameplay over time. Debuted by Atari in 1972, Pong is one of the most rudimentary but influential video games of all time. Although it just features two player paddles and a pixelated "ball" ricocheting between them, it still serves as a helpful benchmark for training not just artificial intelligence and neural networks, but also organoid intelligence, or OI. Grown from stem cells into rudimentary "brains," these OI systems may one day provide promising alternatives to more traditional hardware. But both AI and OI are extremely complex, costly industries--what if much simpler arrays could achieve similar results?

In the 1997 Robin Williams flick Flubber, an absent-minded professor creates a sentient ball of goo with incredible capabilities. Now, more than 25 years later, scientists have made a surprising discovery that could bring Flubber into the real world. Researchers from the University of Reading have created a non-living'hydrogel brain' which is capable of playing the video game Pong. Using a plate of electrodes hooked up to the classic game, the water-based jelly even managed to get 10 per cent better as it practised. While it might not be quite as bouncy as Robin Williams' invention, the researchers believe this breakthrough could change the future of artificial intelligence.

An inanimate glob of ion-laced jelly can play the computer game Pong and even improve over time. Researchers plan further experiments to explore whether it can handle more complex computations and hope it could eventually be used to control robots. Inspired by previous research that used brain cells in a dish to play Pong, Vincent Strong and his colleagues at the University of Reading, UK, decided to try playing the tennis-like game with an even simpler material. They took a polymer material containing water and laced it with ions to make it responsive to electrical stimuli. When electricity is passed through the material, those ions move to the source of the current, dragging water with them and causing the gel to swell.

Researchers have found a soft and squidgy water-rich gel is not only able to play the video game Pong, but gets better at it over time. The findings come almost two years after brain cells in a dish were taught how to play the 1970s classic, a result the researchers involved said showed "something that resembles intelligence". The team behind the latest study said that while they were inspired by that work, they were not claiming their hydrogel was sentient. "We are claiming that it has memory, and through that memory it can improve in performance by gaining experience," said Dr Vincent Strong, the first author of the research, from the University of Reading. Strong said the work could offer a simpler way to develop algorithms for neural networks – models that underpin AI systems including Chat GPT – noting that at present they are based on how biological structures work.