Anthrobots: These remarkable spheroid-shaped multicellular biological robots, or biobots, are not the products of advanced robotics laboratories but are instead born from the inherent potential of adult human somatic progenitor seed cells.
Advanced Science is a high-impact, interdisciplinary science journal covering materials science, physics, chemistry, medical and life sciences, and engineering.
Researchers may have identified the missing component in the chemistry of the Venusian clouds that would explain their color and splotchiness in the UV range, solving a long-standing mystery.
What are the clouds of Venus made of? Scientists know it’s mainly made of sulfuric acid droplets, with some water, chlorine, and iron. Their concentrations vary with height in the thick and hostile Venusian atmosphere. But until now they have been unable to identify the missing component that would explain the clouds’ patches and streaks, only visible in the UV range.
In a new study published in Science Advances, researchers from the University of Cambridge synthesised iron-bearing sulfate minerals that are stable under the harsh chemical conditions in the Venusian clouds.
Current technologies of bioinspired and neuromorphic electronics still lack a universal framework for integration into everyday life. This Perspective highlights how bioinspired electronics with soft electrochemical matter based on organic mixed conductors can potentially enable the integration of diverse forms of intelligence everywhere.
In the vast realm of scientific discovery and technological advancement, there exists a hidden frontier that holds the key to unlocking the mysteries of the universe. This frontier is Pico Technology, a domain of measurement and manipulation at the atomic and subatomic levels. The rise of Pico Technology represents a seismic shift in our understanding of precision measurement and its applications across diverse fields, from biology to quantum computing. Pico Technology, at the intersection of precision measurement and quantum effects, represents the forefront of scientific and technological progress, unveiling the remarkable capabilities of working at the picoscale, offering unprecedented precision and reactivity that are reshaping fields ranging from medicine to green energy.
Unlocking the Picoscale World
At the heart of Pico Technology lies the ability to work at the picoscale, a dimension where a picometer, often represented as 1 × 10^−12 meters, reigns supreme. The term ‘pico’ itself is derived from the Greek word ‘pikos’, meaning ‘very small’. What sets Pico Technology apart is not just its capacity to delve deeper into smaller scales, but its unique ability to harness the inherent physical, chemical, mechanical, and optical properties of materials that naturally manifest at the picoscale.
The new method from ETH Zurich departs from traditional carbon capture, relying on temperature or pressure, minimizing energy consumption.
The details of the study, led by Maria Lukatskaya, Professor of Electrochemical Energy Systems at ETH Zurich, were published in the journal ACS.
Acid switch
The team at ETH Zurich utilized the principle that CO2 exists in its gaseous form in acidic aqueous solutions. In contrast, in alkaline aqueous solutions, it undergoes a reaction to produce carbonates, referred to as salts of carbonic acid. This chemical transformation is reversible, and the acidity level of a liquid decides whether it contains CO2 or carbonates.
An organic artificial neuron that is based on a compact nonlinear electrochemical element can operate in a liquid and responds to the concentration of biological species in its surroundings, allowing its behaviour to be modulated, for example, by interfacing with the membranes of living cells.
When young, these neurons signal fatty tissues to release energy fueling the brain. With age, the line breaks down. Fat cells can no longer orchestrate their many roles, and neurons struggle to pass information along their networks.
Using genetic and chemical methods, the team found a marker for these neurons—a protein called Ppp1r17 (catchy, I know). Changing the protein’s behavior in aged mice with genetic engineering extended their life span by roughly seven percent. For an average 76-year life span in humans, the increase translates to over five years.
The treatment also altered the mice’s health. Mice love to run, but their vigor plummets with age. Reactivating the neurons in elderly mice revived their motivation, transforming them from couch potatoes into impressive joggers.
Bipolar membranes (BPMs) are a class of ion-exchange membranes typically comprising a cation-and an anion-exchange layer. While these membranes have recently been integrated in various electrochemical devices for a wide range of application, the processes underlying their operation are not yet fully understood.
Researchers at the Massachusetts Institute of Technology (MIT) recently developed a new mechanistic model that explains the forward bias polarization mechanisms of BPMs in mixed electrolytes with varying acidities and basicities. Their model, introduced in Nature Energy, could guide the development of strategies to overcome the issue of ionic blockades, which can adversely impact the performance of forward bias BPM devices.
“We were initially trying to design an electrolyzer that converts carbon dioxide CO2 into useful feedstocks or fuels using bipolar membranes (BPMs),” Yogesh Surendranath, co-author of the paper, told Tech Xplore. “To provide a little context, CO2 electrolyzers are most efficient when operating with alkaline electrolyte solutions such as potassium hydroxide, but because CO2 is an acid gas, it reacts with alkaline solutions to produce carbonate solutions over time.”
Dubbed the chemical’ Reactome,’ the system is claimed to be trained using a dataset that includes 39,000 pharmaceutically relevant reactions.
The innovative system merges automated experiments with AI, offering accelerated insights into chemical interactions for a quicker drug design process.
Using #AI to define the chemical “reactome”—the important functional sites in small molecules.
High-throughput experimentation (HTE) has great utility for chemical synthesis. However, robust interpretation of high-throughput data remains a challenge. Now, a flexible analyser has been developed on the basis of a machine learning-statistical analysis framework, which can reveal hidden chemical insights from historical HTE data of varying scopes, sizes and biases.