Lifeboat Foundation

The development of neuromorphic electronics depends on the effective mimic of neurons. But artificial neurons aren’t capable of operating in biological environments. Organic artificial neurons that work based on conventional circuit oscillators have been created, which require many elements for their implementation. An organic artificial neuron based on a compact nonlinear electrochemical element has been reported. This artificial neuron is sensitive to the concentration of biological species in its surroundings and can also operate in a liquid. The system offers in-situ operation, spiking behavior, and ion specificity in biologically relevant conditions, including normal physiological and pathological concentration ranges. While variations in ionic and biomolecular concentrations regulate the neuronal excitability, small-amplitude oscillations and noise in the electrolytic medium alter the dynamics of the neuron. A biohybrid interface is created in which an artificial neuron functions synergistically with biological membranes and epithelial cells in real-time.

Neurons are the basic units of the nervous system that are used to transmit and process electrochemical signals. They operate in a liquid electrolytic medium and communicate via gaps between the axon of presynaptic neurons and the dendrite of postsynaptic neurons. For effective brain-inspired computing, neuromorphic computing leverages hardware-based solutions that imitate the behavior of synapses and neurons. Neuron like dynamics can be established with conventional microelectronics by using oscillatory circuit topologies to mimic neuronal behaviors. However, these approaches can mimic only specific aspects of neuronal behavior by integrating many transistors and passive electronic components, resulting in a bulky biomemtic circuit unsuitable for direct in situ biointerfacing. Volatile and nonlinear devices based on spin torque oscillators or memristor can increase the integration density and emulate neuronal dynamics.

Combinatorial problems often arise in puzzles, origami, and metamaterial design. Such problems have rare collections of solutions that generate intricate and distinct boundaries in configuration space. Using standard statistical and numerical techniques, capturing these boundaries is often quite challenging. Is it possible to flatten a 3D origami piece without causing damage? This question is one such combinatorial issue. As each fold needs to be consistent with flattening, such results are difficult to predict simply by glancing at the design. To answer such questions, the UvA Institute of Physics and the research center AMOLF have shown that researchers may more effectively and precisely respond to such queries by using machine learning techniques.

Despite employing severely undersampled training sets, Convolutional Neural Networks (CNNs) can learn to distinguish these boundaries for metamaterials in minute detail. This raises the possibility of complex material design by indicating that the network infers the underlying combinatorial rules from the sparse training set. The research team thinks this will facilitate the development of sophisticated, functional metamaterials with artificial intelligence. The team’s recent study examined the accuracy of forecasting the characteristics of these combinatorial mechanical metamaterials using artificial intelligence. Their work has also been published in the Physical Review Letters publication.

The attributes of artificial materials, which are engineered materials, are governed by their geometrical structure rather than their chemical makeup. Origami is one such metamaterial. The capacity of an origami piece to flatten is governed by how it is folded, i.e., its structure, and not by the sort of paper it is made of. More generally, the clever design enables us to accurately regulate a metamaterial’s bending, buckling, or bulging. This can be used for many different things, from satellite solar panels that unfurl to shock absorbers.

The micro-robots consist of a special kind of bacteria.

Scientists have conceived of a new way to deliver cancer-killing compounds, called enterotoxins, to tumors using bionic bacteria that are steered by a magnetic field, according to a report by Inverse.

“Cancer is such a complex disease, it’s hard to combat it with one weapon,” said Simone Schürle-Finke, a micro-roboticist at the Swiss Federal Institute of Technology in Zürich, Switzerland, and one of the authors of the new study.

Continue reading “Scientists use magnets to deliver cancer-killing ‘micro-robots’ into the body” »

For the past two centuries, humans have relied on fossil fuels for concentrated energy; hundreds of millions of years of photosynthesis packed into a convenient, energy-dense substance. But that supply is finite, and fossil fuel consumption has tremendous negative impact on Earth’s climate.

“The biggest challenge many people don’t realize is that even nature has no solution for the amount of energy we use,” said University of Chicago chemist Wenbin Lin. Not even photosynthesis is that good, he said: “We will have to do better than nature, and that’s scary.”

One possible option scientists are exploring is “artificial photosynthesis”—reworking a plant’s system to make our own kinds of fuels. However, the chemical equipment in a single leaf is incredibly complex, and not so easy to turn to our own purposes.

Researchers at UC Riverside are paving the way for diabetes and cancer patients to forget needles and injections, and instead take pills to manage their conditions.

Some drugs for these diseases dissolve in water, so transporting them through the intestines, which receive what we drink and eat, is not feasible. As a result, these drugs cannot be administered by mouth. However, UCR scientists have created a chemical “tag” that can be added to these drugs, allowing them to enter blood circulation via the intestines.

The details of how they found the tag, and demonstrations of its effectiveness, are described in a new Journal of the American Chemical Society paper.

After several years of developing the theoretical ideas, University of Illinois Urbana-Champaign researchers have validated multiple novel predictions about the fundamental mechanism of transport of atoms and molecules (penetrants) in chemically complex molecular and polymer liquid matrices.

The study from Materials Science and Engineering (MatSE) Professor Ken Schweizer and Dr. Baicheng Mei, published recently in Proceedings of the National Academy of Sciences (PNAS), extended the theory and tested it against a large amount of experimental data. MatSE Associate Professor Chris Evans and graduate student Grant Sheridan collaborated on this research by providing additional experimental measurements.

“We developed an advanced, state-of-the art theory to predict how molecules move through complex media, especially in polymer liquids,” Schweizer said. “The theory abstracted what the important features are of the chemically complex molecules and of the polymeric medium that they’re moving through that control their rate of transport.”

In 2021, researchers from Toyota Central R&D Labs developed a large, cost-effective artificial photosynthesis system that produces industrial formate at a solar-to-chemical conversion efficiency (ηSTC) of 10.5%¹. Researchers from the lab say that, to their knowlege, this ηSTC is a first for a one metre squared cell.

Within the next 10 years, the company aims to establish artificial photosynthesis technology for wide-scale production of useful carbon compounds.

Artificial intelligence has long been a hot topic: a computer algorithm “learns” by being taught by examples: What is “right” and what is “wrong.” Unlike a computer algorithm, the human brain works with neurons—cells of the brain. These are trained and pass on signals to other neurons. This complex network of neurons and the connecting pathways, the synapses, controls our thoughts and actions.

Biological signals are much more diverse when compared with those in conventional computers. For instance, neurons in a biological neural network communicate with ions, biomolecules and neurotransmitters. More specifically, neurons communicate either chemically—by emitting the messenger substances such as neurotransmitters—or via electrical impulses, so-called “action potentials” or “spikes”.

Artificial neurons are a current area of research. Here, the efficient communication between the biology and electronics requires the realization of artificial neurons that emulate realistically the function of their biological counterparts. This means artificial neurons capable of processing the diversity of signals that exist in biology. Until now, most artificial neurons only emulate their biological counterparts electrically, without taking into account the wet biological environment that consists of ions, biomolecules and neurotransmitters.

Plastic waste is clogging up our rivers and oceans and causing long-lasting environmental damage that is only just starting to come into focus. But a new approach that combines biological and chemical processes could greatly simplify the process of recycling it.

While much of the plastic we use carries symbols indicating it can be recycled, and authorities around the world make a big show about doing so, the reality is that it’s easier said than done. Most recycling processes only work on a single type of plastic, but our waste streams are made up of a complex mixture that can be difficult and expensive to separate.

On top of that, most current chemical recycling processes produce end products of significantly worse quality that can’t be recycled themselves, which means we’re still a long way from the goal of a circular economy when it comes to plastics.