Lifeboat Foundation

Researchers at Japan’s National Institute for Materials Science (NIMS) have developed a device capable of taking hundreds of times more electrochemical measurements than conventional devices. By analyzing the device’s large amounts of data, the team has identified molecular mechanisms that enable electrogenic bacteria to efficiently generate electricity even when subjected to a wide range of electrode potentials.

This technique can also be used to analyze materials interacting with microorganisms (e.g., biodegradable plastics), potentially facilitating efforts to discover innovative microbial degradable materials.

The work was published in the journal Patterns in October, 2022.

Covering interstellar distances in a human lifetime is far from easy. Going at 1 percent of the speed of light, it would take over 400 years to reach the closest star, and we have not been able to propel any spacecraft even close to that speed. But a new method aims to get to those speeds and maybe more – and it takes inspiration from the mighty albatross.

Chemical propulsion can be very useful in achieving high speeds pretty quickly, but there’s the drawback in that you need to carry the fuel with you, which means you need to be able to generate more thrust to shift the extra fuel and so on. It’s a huge issue when it comes to rocket science. A realistic alternative is ion propulsion, used to slowly and successfully maneuver the Dawn spacecraft, but it would take an equally long time to reach enough speed with such a steady but small acceleration.

Solar sails hold a more intriguing possible approach. Proposals such as the Breakthrough Starshot see lasers used to massively accelerate a spacecraft the size of a credit card to one-fifth the speed of light. But, you need to build a very powerful laser. A similar method using sunlight might also work, although not up to such a high speed.

Amino acids have been found in meteorites, and now an experiment shows how they might have been created by chemical reactions in these space rocks.

CRISPR, the Nobel Prize-winning gene editing technology, is poised to have a profound impact on the fields of microbiology and medicine yet again.

A team led by CRISPR pioneer Jennifer Doudna and her longtime collaborator Jill Banfield has developed a clever tool to edit the genomes of bacteria-infecting viruses called bacteriophages using a rare form of CRISPR. The ability to easily engineer custom-designed phages —which has long eluded the research community —could help researchers control microbiomes without antibiotics or harsh chemicals, and treat dangerous drug-resistant infections. A paper describing the work was recently published in Nature Microbiology.

“Bacteriophages are some of the most abundant and diverse biological entities on Earth. Unlike prior approaches, this editing strategy works against the tremendous genetic diversity of bacteriophages,” said first author Benjamin Adler, a postdoctoral fellow in Doudna’s lab. “There are so many exciting directions here—discovery is literally at our fingertips.”

Researchers at Tohoku University, the University of Messina, and the University of California, Santa Barbara (UCSB) have developed a scaled-up version of a probabilistic computer (p-computer) with stochastic spintronic devices that is suitable for hard computational problems like combinatorial optimization and machine learning.

Moore’s law predicts that computers get faster every two years because of the evolution of semiconductor chips. While this is what has historically happened, the continued evolution is starting to lag. The revolutions in machine learning and artificial intelligence means much higher computational ability is required. Quantum computing is one way of meeting these challenges, but significant hurdles to the practical realization of scalable quantum computers remain.

A p-computer harnesses naturally stochastic building blocks called probabilistic bits (p-bits). Unlike bits in traditional computers, p-bits oscillate between states. A p-computer can operate at room-temperature and acts as a domain-specific computer for a wide variety of applications in machine learning and artificial intelligence. Just like quantum computers try to solve inherently quantum problems in quantum chemistry, p-computers attempt to tackle probabilistic algorithms, widely used for complicated computational problems in combinatorial optimization and sampling.

How far away could an artificial brain be? Perhaps a very long way off still, but a working analogue to the essential element of the brain’s networks, the synapse, appears closer at hand now.

That’s because a device that draws inspiration from batteries now appears surprisingly well suited to run artificial neural networks. Called electrochemical RAM (ECRAM), it is giving traditional transistor-based AI an unexpected run for its money—and is quickly moving toward the head of the pack in the race to develop the perfect artificial synapse. Researchers recently reported a string of advances at this week’s IEEE International Electron Device Meeting (IEDM 2022) and elsewhere, including ECRAM devices that use less energy, hold memory longer, and take up less space.

The artificial neural networks that power today’s machine-learning algorithms are software that models a large collection of electronics-based “neurons,” along with their many connections, or synapses. Instead of representing neural networks in software, researchers think that faster, more energy-efficient AI would result from representing the components, especially the synapses, with real devices. This concept, called analog AI, requires a memory cell that combines a whole slew of difficult-to-obtain properties: it needs to hold a large enough range of analog values, switch between different values reliably and quickly, hold its value for a long time, and be amenable to manufacturing at scale.

Government support is needed, however, to help consumers overcome heat pumps’ higher upfront costs relative to alternatives. The costs of purchasing and installing a heat pump can be up to four times as much as those for a gas boiler. Financial incentives for heat pumps are now available in 30 countries.

In the IEA’s most optimistic scenario – in which all governments achieve their energy and climate pledges in full – heat pumps become the main way of decarbonising space and water heating worldwide. The agency estimates that heat pumps have the potential to reduce global carbon dioxide (CO₂) emissions by at least 500 million tonnes in 2030 – equal to the annual CO₂ emissions of all cars in Europe today. Leading manufacturers report promising signs of momentum and policy support and have announced plans to invest more than US$4 billion in expanding heat pump production and related efforts, mostly in Europe.

Opportunities also exist for heat pumps to provide low-temperature heat in industrial sectors, especially in the paper, food, and chemicals industries. In Europe alone, 15 gigawatts of heat pumps could be installed across 3,000 facilities in these three sectors, which have been hit hard by recent rises in natural gas prices.

In the philosophy of mind, the multiple realizability thesis contends that a single mental kind (property, state, event) can be realized by many distinct physical kinds. A common example is pain. Many philosophers have asserted that a wide variety of physical properties, states, or events, sharing no features in common at that level of description, can all realize the same pain. This thesis served as a premise in the most influential argument against early theories that identified mental states with brain states (psychoneural, or mind-brain identity theories). It also served in early arguments for functionalism. Nonreductive physicalists later adopted this premise and these arguments (usually without alteration) to challenge all varieties of psychophysical reductionism. The argument was even used to challenge the functionalism it initially was offered to support. Reductionists (and other critics) quickly offered a number of responses, initially attacking either the anti-reductionist or anti-identity conclusion from the multiple realizability premise, or advocating accounts of the reduction relation that accommodated multiple realizability. More recently it has become fashionable to attack the multiple realizability premise itself. Most recently the first book-length treatment of multiple realizability and its philosophical import has appeared.

This entry proceeds mostly chronologically, to indicate the historical development of the topic. Its principle focus is on philosophy of mind and cognitive science, but it also indicates the more recent shift in emphasis to concerns in the metaphysics of science more generally. It is worth mentioning at the outset that multiple realizability has been claimed in physics (e.g., Batterman 2000), biochemistry (Tahko forthcoming) and synthetic biology (Koskinen 2019a, b). After more than fifty years of detailed philosophical discussion there still seems to be no end in sight for novel ideas about this persistent concern.

Two categories of nanofabrication technologies are known as top-down and bottom-up approaches [5]. For the former, nanosized materials are prepared through the rupture of bulk materials to fine particles, and such a process is usually conducted by diverse physical and mechanical techniques like lithography, laser ablation, sputtering, ball milling and arc-discharging [6, 7]. These techniques themselves are simple, and nanosized materials can be produced quickly after relatively short technological process, but expensive specialized equipment and high energy consumption are usually inevitable. Meanwhile, a variety of efficient chemical bottom-up methods, where atoms assemble into nuclei and then form nanoparticles, have been intensively studied to synthesize and modulate nanomaterials with specific shape and size [8].

Indeed, chemical methodologies, including but not limited to, aqueous reaction using chemical reducing agents (e.g. hydrazine hydrate and sodium borohydride), electrochemical deposition, hydrothermal/solvothermal synthesis, sol–gel processing, chemical liquid/vapor deposition, have been developed up to now [5, 6]. These approaches can not only produce diverse nanomaterials with fairly high yields, but also endow fine controllability in tailoring nanostructures and properties of the products. Nevertheless, they have been encountering some serious challenges of harsh reaction conditions (e.g. pH and temperature), potential risks in human health and environment, and low cost-effectiveness. Moreover, there are biosafety concerns on products synthesized chemically using hazardous reagents, which restricts their applications in many areas, particularly in medicines and pharmaceuticals [9].

Impressively, biological methodology is becoming a favourite in nanomaterial synthesis nowadays to address challenges in chemical synthesis. Compared to chemical routes, biosynthesis using natural and biological materials as reducing, stabilizing and capping agents are simple, energy-and cost-effective, mild and environment-friendly, which is termed as “Green Chemistry” [2, 6]. More significantly, the biologically synthesized nanomaterials have much better competitiveness in biocompatibility, compared to those chemically derived counterparts. On the one hand, the biogenic nanomaterials are free from toxic contamination of by-products that are usually involved in chemical synthesis process; on the other hand, the biosynthesis do not need additional stabilizing agents because either the used organisms themselves or their constituents can act as capping and stabilizing agents and the attached biological components in turn form biocompatible envelopes on the resultant nanomaterials, leading to actively interact with biological systems [2]. As one of the most abundant biological resources, some microorganisms have adapted to habitat contaminated with toxic metals, and thus evolved powerful tactics for remediating polluted environment while recycling metal resources [7, 10], and some review articles on the biosynthesis of MNPs using diverse microorganisms including bacteria, yeast, fungi, alga, etc. and their applications have been published in recent years [1, 2, 6, 7, 10].