Lifeboat Foundation

Nature is a never-ending source of inspiration for scientists, but our artificial devices usually don’t communicate well with the real thing. Now, researchers at Linköping University have created artificial organic neurons and synapses that can integrate with natural biological systems, and demonstrated this by making a Venus flytrap close on demand.

The new artificial neurons build on the team’s earlier versions, which were organic electrochemical circuits printed onto thin plastic film. Since they’re made out of polymers that can conduct either positive or negative ions, these circuits form the basis of transistors. In the new study, the team optimized these transistors and used them to build artificial neurons and synapses, and connect them to biological systems.

When the transistors detect concentrations of ions with certain charges, they switch, producing a signal that can then be picked up by other neurons. Importantly, biological neurons operate on these same ion signals, meaning artificial and natural nerve cells can be connected.

Fully organic rechargeable household batteries are an ideal alternative to traditional metal-based batteries, in particular for reducing pollution to landfill and the environment.

Now researchers at Flinders University, with Australian and Chinese collaborators, are developing an all-organic polymer battery that can deliver a cell voltage of 2.8V—a big leap in improving the energy storage capability of organic batteries.

“While starting with small household batteries, we already know organic redox-active materials are typical electroactive alternatives due to their inherently safe, lightweight and structure-tunable features and, most importantly, their sustainable and environmentally friendly,” says senior lecturer in chemistry Dr. Zhongfan Jia, a research leader at Flinders University’s Institute for Nanoscale Science and Technology.

Imagine making some liquids mix that do not mix, then unmixing them.

In one of the grand challenges of science, a Flinders University device which previously ’unboiled’ egg protein is now unraveling the mystery of incompatible fluids; a development that could enhance many future products, industrial processes and even the food we eat.

Using the highly advanced rapid fluidic flow techniques possible in the Flinders vortex fluidic device (VFD), the Australian research team has capped off 10 years of research to find a way to use clean chemistry to unlock the mystery of ‘mixing immiscibles’.

Tae Seok Moon, associate professor of energy, environmental and chemical engineering at the McKelvey School of Engineering at Washington University in St. Louis, has taken a big step forward in his quest to design a modular, genetically engineered kill switch that integrates into any genetically engineered microbe, causing it to self-destruct under certain defined conditions.

His research was published Feb. 3 in the journal Nature Communications.

Moon’s lab understands microbes in a way that only engineers would, as systems made up of sensors, circuits and actuators. These are the components that allow microbes to sense the world around them, interpret it and then act on the interpretation.

New applications in energy, defense and telecommunications could receive a boost after a team from The University of Texas at Austin created a new type of “nanocrystal gel”—a gel composed of tiny nanocrystals each 10,000 times smaller than the width of a human hair that are linked together into an organized network.

The crux of the team’s discovery is that this new material is easily tunable. That is, it can be switched between two different states by changing the temperature. This means the material can work as an optical filter, absorbing different frequencies of light depending on whether it’s in a gelled state or not. So, it could be used, for example, on the outside of buildings to control heating or cooling dynamically. This type of optical filter also has applications for defense, particularly for thermal camouflage.

The gels can be customized for these wide-ranging applications because both the nanocrystals and the molecular linkers that connect them into networks are designer components. Nanocrystals can be chemically tuned to be useful for routing communications through fiber optic networks or keep the temperature of space craft steady on remote planetary bodies. Linkers can be designed to cause gels to switch based on ambient temperature or detection of environmental toxins.

Cornell chemists have discovered a class of nonprecious metal derivatives that can catalyze fuel cell reactions about as well as platinum, at a fraction of the cost.

This finding brings closer a future where hydrogen fuel cells efficiently power cars, generators and even spacecraft with minimal greenhouse gas emissions.

“These less expensive metals will enable wider deployment of hydrogen fuel cells,” said Héctor D. Abruña, the Émile M. Chamot Professor in the Department of Chemistry and Chemical Biology in the College of Arts and Sciences. “They will push us away from fossil fuels and toward renewable energy sources.”

The basics for tracking include blood biomarkers that have been studied for 50 – 100+ years, depending on the biomarker. Most of these biomarkers are commonly measured at a yearly physical, and are relatively cheap (35 $USD for the standard chemistry panel and complete blood count).

In a paper published by Science, DeepMind demonstrates how neural networks can improve approximation of the Density Functional (a method used to describe electron interactions in chemical systems). This illustrates deep learning’s promise in accurately simulating matter at the quantum mechanical.

In a paper published in the scientific journal Science, DeepMind demonstrates how neural networks can be used to describe electron interactions in chemical systems more accurately than existing methods.

Density Functional Theory, established in the 1960s, describes the mapping between electron density and interaction energy. For more than 50 years, the exact nature of mapping between electron density and interaction energy — the so-called density functional — has remained unknown. In a significant advancement for the field, DeepMind has shown that neural networks can be used to build a more accurate map of the density and interaction between electrons than was previously attainable.

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