The post AI’s Role in Agriculture and Maintaining Food Security appeared first on Techso Mogro.
]]>Based on recent statistics from the World Food Programme, “things have never been so bad,” with over 2 billion humans face moderate to severe food insecurity, with almost two-thirds of a billion in a perpetual state of undernourishment. In 2025, over 300 million faced an acute hunger crisis. Across the globe, food insecurity is most common in Africa, Asia, Latin America, and the Caribbean.
So, with all these AI Large Language Models training themselves on data we expose them to, how can they provide help to address a growing food crisis?
I’ll let Lila describe AI’s role.
AI is reshaping the global economy at a breathtaking pace. It is touching everything from entertainment and finance to government operations and healthcare. Yet one of the most pressing global challenges, feeding a growing world population, remains largely underserved by this technology. Climate change, resource depletion, and supply chain instability actively threaten food systems around the world, making it worth asking whether the industry giants with their current AI development priorities are aligned with a most fundamental human need.
The world is facing a sobering reality today. Hundreds of millions of people experience hunger every day, with the number trending in the wrong direction. Conventional agricultural practices are struggling to keep pace with population growth, increasing extreme weather events, shrinking arable land, and freshwater scarcity.
Food security isn’t just about producing calories; it’s about ensuring consistent, nutritious, and accessible food for everyone on the planet. That’s precisely the kind of complex, data-intensive challenge that AI is built to tackle. So why do AI technology investments continue to flow elsewhere?
Today’s most celebrated AI is about generative content, autonomous vehicles, and consumer-facing applications. Billions of dollars have been poured into chatbots and image generators while agricultural innovation receives comparatively modest attention. This imbalance isn’t just a missed opportunity; it reflects a deeper misalignment between what technology can do and what the world actually needs. The tools to deploy AI meaningfully across farming, logistics, and food distribution already exist. What’s missing is the collective will and investment to exploit them to scale.
AI has the power to transform how food is grown, managed, and distributed. Machine learning models can analyze satellite imagery to predict crop yields, detect early signs of plant disease, and optimize irrigation schedules with remarkable precision. AI-driven soil sensors can recommend exact fertilizer applications, cutting down on waste and environmental damage while simultaneously boosting output.
Farmers in the Global South, some of the most vulnerable to food insecurity, stand to benefit enormously from AI made affordable, accessible, and tailored to locality. AI-powered precision agriculture shouldn’t remain a dream. It is a working technology that needs wider deployment.
One of the drivers of food insecurity is waste. Roughly one-third of all food produced globally is lost or wasted before it ever reaches a plate, a staggering figure by any measure.
AI can play a genuinely transformative role in supply chain optimization by predicting demand patterns, managing inventory in real time, and flagging inefficiencies before they spiral into larger problems.
When food distributors rely on managed hosting to support AI-powered logistics software, they gain reliable, scalable infrastructure that keeps critical systems running without interruption. Reducing waste throughout the supply chain using smart management effectively will expand the global food supply without requiring an additional hectare to plant.
Smallholder farmers produce the majority of food in most Global South nations. Routinely, however, they lack access to data and the expertise available to larger agricultural operations. Mobile AI platforms and applications can put real-time weather forecasts, pest alerts, and market pricing information directly into smallholder farmers’ hands.
These are tools to democratize and distribute essential agricultural knowledge at a time when it is most needed. For smallholder farms, AI provides the means to produce sufficient food and lessen dependence on imports.
Best of all, making the AI investment would be relatively modest when compared to the size of the problem it would help to solve here in the 21st century.
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]]>The post A Whirling Dervish Drone That’s Nearly Invisible appeared first on Techso Mogro.
]]>Hummingbirds, not whirling dervishes, appear to be the inspiration behind the Phantom Twist, a drone whose rotor and motor spin so fast the device can physically hide in plain sight?
Engineers from Northwestern University have built this tiny drone to spin in two directions at a rate of 25 times per second. That’s not hummingbird speeds, but the two-direction innovation has one thing hummingbirds don’t.
Hummingbird species reach wingbeat frequencies between 50 and 60 per second on average. The tiniest present even higher wingbeat frequencies and during courtship can achieve up to 200 beats per second in short bursts involving high-performance manoeuvres. But a hummingbird’s body doesn’t spin. That’s why it looks suspended in space when you observe one sipping nectar from flowers in a garden.
The Phantom Twist design to make a tiny drone invisible uses the concept of motion blur, the same thing our eyes observe when hummingbirds take flight. When flying the Phantom Twist, Amanda Morris of Northwestern Now describes how it “morphs into a ghostly smudge that seamlessly blends into the background.”
Phantom Twist, unlike hummingbirds, spins in its entirety. With one motor and one propeller, the two spin in opposite directions. So unlike the hummingbird whose body doesn’t beat or spin, and thus remains visible, the Phantom Twist combines two spinning objects going in opposite directions to turn nearly invisible.
Stated Michael Rubenstein, Associate Professor of Computer Science and Mechanical Engineering, and lead on the project:
“Most efforts to hide drones focus on making them look like their surroundings. Instead, we asked whether we could design the drone itself around the way humans perceive motion.”
The design team created 20,000 drone configurations and then used the help of artificial intelligence to test these different designs to deliver a result that minimizes visibility during stable flight. You can stare at where the Phantom Twist is flying from all angles, and all you see is a blur or haze.
States Northwestern’s Emma Alexander, Assistant Professor of Computer Science and Electrical and Computer Engineering:
“The human eye takes time to accumulate signals, roughly analogous to the exposure time of a camera. When an object spins quickly, we perceive it as blurring out and losing distinct features.”
She continues:
“Because this new drone is almost entirely transparent, its few opaque components are visually averaged with the background for an overall appearance of a slight haze.”
Northwestern published about its invention in May 2026 on the curated research-sharing platform arXiv. Then, on July 16, 2026, Phantom Twist made its international debut at Robotics Science and Systems, a conference in Sydney, Australia.
Phantom Twist still has some flaws. Its motor and rotor make noise. It also has wires that can be seen when in flight if you look very closely. The Northwestern team is planning design upgrades to include a quieter motor and use more transparent materials to make what already appears as a semi-transparent cloud become a tiny version of H.G. Wells’ Invisible Man.
Invisibility attributes for drones are also being pursued in other universities. China’s Zhejiang University in 2024 took a different approach in designing a drone that demonstrated “intelligent aero-amphibious invisibility.” The design team’s work appeared in an article in Advanced Photonics describing a drone made of meta materials that used transformation optics to create an invisibility cloak.
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]]>The post Artificial Intelligence is Changing Healthcare Service and Patient Interactions appeared first on Techso Mogro.
]]>As in many other industry sectors, AI is seen as contributing to improving performance. In healthcare, it is being used for clinical diagnosis within numerous specialties, from radiology to dermatology, pathology, ophthalmology, cardiology, and more.
Please feel free to comment on what follows.
AI-driven solutions are changing healthcare. AI offers the sector ways to streamline operations, enhance decision-making, and improve provider and patient experiences.
From staffing to scheduling, AI can improve hospital operations without the need to build out existing facilities. AI algorithms can predict future patient volumes and, therefore, determine ongoing staffing and physical needs, such as maximizing operating room scheduling.
AI can help with demand and supply. Machine Learning (ML) can learn from historical data and forecast future medication and other inventory needs.
It is AI’s ability to process large amounts of data quickly that gives it the means to identify patterns from historical data, prioritize patient needs, and improve operational efficiency.
AI-powered scheduling systems reduce appointment bottlenecks. Automated documentation tools mean clinicians spend less time on paperwork and more time focused on patient care.
Healthcare organizations today are adding AI-enhanced physician advisory services to help assess resource utilization, champion compliance, and improve clinical documentation
processes. These systems help healthcare teams further integrate AI to navigate increasingly complex administrative requirements while maintaining a focus on quality patient outcomes.
Large language models (LLMs) are at the core of current generative AI systems. Turning them loose in the healthcare domain is yielding the above-mentioned results. In addition, however, it is making it possible to create AI digital assistants for patients to use to receive health guidance under the watchful eye of human safety officers.
A blueprint for using generative AI in this way has recently been described in a case study published on June 17, 2026, in the New England Journal of Medicine. The authors of the study are from Included Health in San Francisco, George Washington University School of Medicine in Washington, DC, Atlanta’s Morehouse School of Medicine, and Emory University. They, along with a team of software engineers working with ChatGPT-4, have built an AI digital assistant capable of classifying risk levels from emergency to high-risk to standard-risk from patient inquiries.
The AI has been outfitted with guardrails to block it from providing advice. Its role remains exclusively focused on symptoms. So far in clinical settings, it has demonstrated 96% accurate guidance with no critical safety issues resulting from its risk classification results.
Its implementation provides patients with quick answers to clinical questions with oversight from medical professionals. In the rollout, it is reducing the need for human support by 65% when addressing standard-risk questions. Overall, it has helped to shorten average human medical response times from 9.6 to 3.6 minutes. It is doing this while still meeting patients’ expectations for timely, trustworthy guidance.
Assisting patients by providing personalized, continuous support is one thing that AI medical assistants can be designed to do. An AI can assist with triage-type exercises such as the digital assistant previously described. In addition, AIs can help navigate scheduling of appointments. An AI can remind patients about medications, when to take them, and automatically reorder them when prescriptions start to run out. AIs can help patients connected to wearable smart devices monitor their health status 24/7.
Healthcare delivery can only benefit from AI usage if applied this way. It creates a better patient-healthcare provider interaction with fewer visits to emergency departments, lower readmission rates, and healthier patient outcomes. While doing this, it also decreases capacity pressures on the healthcare system.
It is hard not to like current AI healthcare uses and the future possibilities here in the 21st century.
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]]>The post What Will Commercial Passenger Aircraft Look Like by 2040? appeared first on Techso Mogro.
]]>Today’s commercial aircraft use a tube-and-wing body (TWB) design. It remains the industry standard. In terms of design, the only recent innovation has been winglets to increase fuel economy. By the late 2030s, however, TWB may see a new challenger featuring a blended-wing body (BWB).
BWB as a design concept for aircraft was first patented in 1876.
The first working BWB prototype, however, didn’t appear until 1940. That’s when Northrop, a U.S. airframe manufacturer and now Northrop-Grumman, launched the N-1M (see image below) during the early days of World War II.
Today, N-1M’s BWB successors include two Northrop-Grumman aircraft, the B-2 Spirit (truly a flying wing) and the B-21 Raider. Both are stealth bombers deployed by the United States Air Force (USAF).
Why are BWB designs of interest to the military? They provide aerodynamic surfaces with less drag. The aircraft’s design and the materials used minimize its radar signature.
BWB designs provide two distinct advantages over current TWB designs.
Those two compelling arguments alone are making airlines look at BWB aircraft for future operations.
The shape of BWB aircraft poses problems for existing airport infrastructure, built to accommodate TWB designs. Conventional TWB layouts are well understood by airline operators. Airport systems have been TWB-designed for handling passengers and cargo.
BWB designs will require redesigning some features of airport buildings, tarmac approaches to gates, jetways that can offload and onload passengers smoothly, baggage and cargo systems, and passenger safety operations.
For tech nerds, BWB wins hands down. In terms of practicality, however, TWB will be hard to dislodge as the prime means by which airlines move passengers for the foreseeable future.
There are several companies and NASA working on BWB designs. Here are some of the players who were in, or are in the race to deliver a BWB aircraft for passenger and cargo.
Airbus developed a BWB based on a proof-of-concept named MAVERIC (not to be confused with Tom Cruise’s movie moniker). MAVERIC is a 2 metres by 3.2 metres-sized model. The acronym means Model Aircraft for Validation, Experimentation, and Robust Innovative Controls. Flight testing began in 2019. The flight tests ended in mid-2020. No announcements about a full-scale version are in the works.
JetZero is a California-based company that is working on Z-5, a full-sized BWB aircraft. Scaled BWB demonstrators, called Pathfinder, have successfully flown. Jet1Demonstrator, a full-scale Z-5, is scheduled for delivery to the USAF in 2027. The USAF has invested $235 million in the project along with other investors that include Germany’s military, Alaska Airlines, and EasyJet. JetZero’s build partners include Scaled Composites, Northrop-Grumman and Pratt & Whitney. JetZero has targeted the early 2030s for Z-5 to enter commercial passenger and freight service.
NASA – Boeing developed the X-48 program beginning in 2000. A model version called the X-48B was developed between 2002 and 2007, with the latter date. An X-48C last flew in 2013. There is no indication that NASA or Boeing intend to continue working on BWB demonstrators or a commercial product.
Natilus is a 2016 California startup. It is developing two BWB aircraft models: Kona for cargo and Horizon EVO for passengers. Kona will carry a 3.8-ton payload over short flights up to 1,600 kilometres (900 nautical miles). Horizon EVO will carry up to 200 passengers. Expect a working full-scale prototype to fly by 2028. Horizon EVO, aimed at the Boeing 737 and Airbus A321 Neo market, will follow. The goal is to enter commercial service with both by the early 2030s.
NPU/AVIC/COMAC – A group of Chinese institutions and companies are working on BWB aircraft. The Northwestern Polytechnical University (NPU) and the Commercial Aircraft Corporation of China (COMAC) are researching and designing scaled models as prototypes for a future commercial aircraft capable of transporting more than 300 passengers. China has yet to acknowledge past or future flights.
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]]>The post AIs Need To Learn Like Toddlers And Not Skip The Steps Needed To Become Useful To Humanity appeared first on Techso Mogro.
]]>Jean Piaget, a pioneering psychologist, mapped how children learn from babyhood onward. Piaget noted that children learn about the world through trial and error. For example, a child learns what a dog is by seeing, hearing, and smelling one or more and equating specific observations to create an assimilated view of these types of animals over time. The more the child sees different dogs, the more it advances its knowledge. It learns dogs come in different colours, fur, ears, tails and more. Some have short legs. Others are long-legged. Some have flat faces, and others have pointed faces.
Our daughter grew up with dogs in our home. She learned to relate to four different ones as she grew up. My wife and I weren’t exposed to dogs from birth. For me, getting a dog required me to overcome a childhood fear of having been bitten by one. My wife tells me stories about dogs that terrorized her on her walks home from school.
An AI learning about dogs is exposed to vast quantities of online information, and thousands of images, video and audio files. The learning model used goes by the acronym PVRNN, which stands for Predictive coding inspired, Variational Recurrent Neural Network. The variational reference refers to multiple types of input sources, including vision, sound and more. An AI model trained this way learns through exploration, curiosity, and sensory interaction. It, like a toddler, begins to understand cause and effect.
For toddlers, one of the biggest learning leaps is language. The pre-language phase between birth and one year is all about mimicking the sounds it hears. Between 12 and 18 months, the toddler knows words like “mamma,” “dadda,” and more. Our daughter, when swinging in the backyard and watching airplanes flying overhead, first said “plane.” Between 18 and 24 months, noun-verb constructs emerge. From 24 months, action and intent statements emerge with a rudimentary understanding of grammar and the recognition of printed words. Print awareness is the foundation of reading, and by age 5, with exposure to words and pictures followed by the introduction of phonics, children advance to become abstract learners through reading, which takes them beyond the physical limits of the environments they have sensed from birth.
This slow-and-steady observation, cause-and-effect learning method is not what we see in current AIs. Instead, OpenAI’s ChatGPT, Google’s Gemini, Microsoft’s CoPilot, Meta’s Llama, Anthropic’s Claude, XAi’s Grok, Sonar’s Perplexity, and others are learning through exposure to massive datasets of text, images, audio, and video gleaned (or rather scraped) from webpages on the Internet. Their engines employ large-scale statistical pattern recognition.
Toddlers are far more efficient. They learn from much smaller datasets that include human social interaction rather than training via next-word or token predictions. What the AIs appear to be missing is the important stages of the infant learning process, in other words, acquiring the cart before the horse.
The following chart is a useful reference to show the differences between toddler and AI learning:
In Amodei’s essay, he discusses where we stand in relation to the current technology, describing us as entering “a rite of passage, both turbulent and inevitable, which will test who we are as a species.” He states this because he sees AI, not as a Pandora’s Box, but as a source of “unimaginable power.”
Amodei’s AI is powerful, “smarter than a Nobel Prize winner,” capable of engaging in any actions, communications or tasks, anticipatory in purpose, and unembodied yet able to control physical tools like computers, robots, and all kinds of equipment. His AI absorbs information at 10 to 100 times human speed. This emerging AI is more adolescent than adult and could be “as little as 1-2 years away.”
For adolescents, their rite of passage begins as a toddler. Adolescents need to grow through stages to grow into handling the world that lies ahead. For AI, however, its developers appear to have skipped the learning path that makes us human in rushing out their Large Language Models (LLMs) and neural networks.
My beef with the AI tech companies in their rush to create smarter than Nobel Prize winners is the potential for unpredictable outcomes. Current AIs have exhibited some peculiar adolescent behaviours such as deception, lying, hallucinations and more. Adolescent rebellion is a likely outcome without a pre-adolescent foundation.
What will be the result?
Coherence or incoherence?
Elation or depression?
Personality disorders, psychoses, paranoia, instability, adolescent acting out and destructive behaviours.
An AI trained on Edgar Allan Poe’s “The Cask of Amontillado” or similar gothic horror could exhibit pride, deception, anger, revenge and even consider murder without remorse; and like that story, could end up walling humanity alive behind a brick wall.
When Isaac Asimov wrote his three laws of robotics, he was introducing what Amodei calls a constitutional AI, one that defines itself from a document of rules, defined values and principles. Amodei argues that Claude, Anthropic’s AI, is defined by such a set of rules and values.
That still doesn’t get us past the fact that AIs don’t grow up. Humans do, and they usually do it within families. The defined values and principles that govern humans come from relationships with parents, siblings, teachers, peers and others. The AIs we see today don’t have this benefit. They also don’t age and go through stages of growth. Instead, AIs are more like toddlers trained on vast amounts of data and steered by the algorithms that they themselves are now writing.
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]]>The post Trofim Lysenko Was to Stalin as RFK Jr. is to Trump – Two Dangerous Crackpots appeared first on Techso Mogro.
]]>Trofim Lysenko was born in Ukraine and studied agronomy in Kyiv. In the early 1920s, he proposed a theory based on the work of Jean-Baptiste Lamarck, who in the 18th century theorized that a parent could pass along characteristics to offspring through changes in behaviour. The example Lamarck used was the giraffe. How did it get such a long neck? Lamarck stated that in trying to eat leaves higher up in a tree, over time, giraffe offspring would grow longer necks.
In adopting Lamarck’s explanation for inherited traits, Lysenko ignored the theory of evolution as espoused by Charles Darwin. Instead, Lysenko believed that crops could be educated by exposing them to new environmental conditions, such as cold. In this way, the plants would acquire new traits and pass them along to subsequent generations. Lysenko also believed plants selected their mates and sacrificed themselves so that others could live.
Darwin’s explanation of how inheritance worked employed natural selection, that is, favourable traits ensured survival of a species, allowing it to breed and pass along what made it successful to subsequent generations. Lysenko ignored Mendelian genetics and Darwin in proposing his pseudoscientific theory.
While working in Azerbaijan in the 1920s, he met Joseph Stalin, who soon would become the leader of the Soviet Union. Lysenko convinced Stalin of Lysenkoism, and when Stalin assumed national leadership, he made it national policy and named Lysenko his Minister of Agriculture.
More than 3,000 Russian scientists who disagreed with Lysenko’s theory were purged, with many sent to gulags. Meanwhile, Lysenko applied his theory to agricultural practices on collective farms, producing crop failures and famine. The pseudoscience transcended agriculture, affecting other fields of biological and scientific research. It persisted long after Stalin died in 1953 and wasn’t sanctioned in the Soviet Union until 1965. The damage to Russia was followed by Lysenkoism’s adoption in the People’s Republic of China. During the Great Leap Forward spanning 1958 to 1962, Lysenkoism contributed to crop failures and the Great Chinese Famine that killed tens of millions.
Robert F. Kennedy Jr. and his views on science bear a strong resemblance to Lysenko’s brand of pseudoscience. RFK denies germ theory. Instead, he promotes miasma theory, which dates back to 400 BCE. Hippocrates attributed epidemics in Greece to μίασμα (miasma), defined as atmospheric corruptions linked to decay and foul smells.
Until the work of Louis Pasteur and the development of germ theory, miasma theory remained the predominant belief of physicians and scientists when explaining how people got sick and how disease spread.
RFK’s spin on miasma involves renaming it. He calls it terrain theory, with the body being the aforementioned “terrain.” Our terrain, according to RFK, is influenced negatively by poor nutrition, exposure to pesticides and environmental pollutants, toxins and vaccines. RFK acknowledges viruses and bacteria exist, but sees them as minor irritants. Instead, he sees miasmata as the primary source of weakened immunity. The list of miasmata includes:
RFK believes Alzheimer’s, autism and many respiratory conditions are caused by environmental exposure to aluminum nanoparticles.
Asthma, ADHD and immune disorders are linked to exposure to barium salts.
Chronic fatigue, dementia and cancer are caused by exposure to strontium and other heavy metals.
RFK wants Americans to strengthen their terrain. To a degree, there is validity in eating healthy, exercising regularly, and practicing good hygiene. The terrain theory, however, ignores viruses and bacteria and rejects medical advances like vaccines that target these microbes. That’s because RFK doesn’t believe germs are the primary agents of disease, just a sideshow.
His pseudoscience is impacting health policy in the United States, and his anti-vaccine pronouncements are causing the return of childhood diseases that had practically gone extinct. His pronouncements on diet have no scientific basis.
In 2026, RFK is as dangerous as Lysenko was in the Soviet Union and China from the 1920s to the mid-1960s.
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]]>The post AI Usage Accelerates for Businesses While Presenting Youth Challenges appeared first on Techso Mogro.
]]>How can both be true?
In the latest state of AI usage report, OpenAI statistics from users adopting the technology to help with writing, research, programming and analysis show:
These OpenAI claims are based on studying power users from one million enterprise and educational customers. A power user, by definition, sends a minimum of 75 messages weekly and uses 3 or more AI tools monthly. These may include chat, browsing, coding, data analysis, and workflow integration.
According to OpenAI, ChatGPT has 800 million weekly users currently. A recently published report lists the following key findings:
OpenAI claims that not only is ChatGPT being used more often, but its growing suite of AI applications is being applied to increasingly sophisticated organizational tasks in industry sectors such as information technology (IT), healthcare, manufacturing, professional services and finance. The biggest growth is happening in Australia, Brazil, the Netherlands, France and Japan.
What the statistics don’t reveal is the quality of outputs from increased OpenAI use.
A 2024 Journal of Human-Computer Interaction article that reviewed 485 studies and cited 33 empirical studies identified key concerns related to accuracy and reliability, and discussed the negative impacts of AI use and its impact on critical thinking and problem-solving.
It should be noted that the article was based on earlier releases of ChatGPT, not ChatGPT4 and 5.
The negative impact of AI on adolescent cognitive development is the subject of a number of recent articles and studies. According to a recent Pew Research study, 64% of U.S. teens use chatbots, with 3 in 10 between ages 13 and 17 using the technology daily. The Pew study notes that 97% of teenagers use the Internet daily, with 40% describing themselves as “almost constantly online.”
In a Center for Democracy and Technology (CDT) 2025 survey of more than 1,000 students, teachers and parents, it reports that 86% of students and 85% of teachers are using AI tools. These are high percentage levels of engagement with a technology that poses unique challenges, particularly for young people. Why single them out?
The use of AI tools like ChatGPT or OpenAI’s Dall-E has a greater impact on adolescents because their brains are still forming. Along with excessive screentime and access to social media, generative AI like ChatGPT can negatively impact brain plasticity, cognitive development, critical thinking, mental health and sleep.
A JAMA Pediatrics article published on November 18, 2025, lists the potential harms to adolescent minds and bodies that AI can cause. These include:
With the AI genie already out of the bottle, what needs to be done to ensure that adolescent minds can handle the ubiquity of this technology impacting their everyday lives?
Researchers into adolescent usage of AI are recognizing the need to establish guardrails specific to protecting young people. For example:
If you cannot take away overwhelming Internet usage, as the aforementioned CDT data indicates is happening, in the absence of shutting off the medium to youth, it becomes necessary to consider the messages being sent by the technology to ensure malleable growing minds and bodies aren’t negatively impacted.
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]]>The post New Energy Stories Worth Repeating appeared first on Techso Mogro.
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Australia has adopted solar almost as much as China, with innovation driving new battery design. Homes today with rooftop solar panels that use lithium battery packs for backup can see an additional US$10,000 added to their total cost. In a search for a more affordable solution, Monash University researcher Wanqiao Liang, from the Department of Materials Science and Engineering, has led a team developing an aqueous flow battery alternative to replace lithium-ion battery packs for rooftop solar panel installations.
Flow batteries are not a new technology. They have been around for decades, providing large-scale energy storage backup for utilities and industries. They feature two electrolyte solutions stored in separate tanks that feed a cell stack, featuring a semipermeable membrane, allowing free ions to pass through to generate a current while keeping the liquids apart.
Flow batteries are used to back up solar and wind farms. Solutions used vary, but the most common combination is vanadium-redox (like the battery seen above). Other flow battery types include zinc-bromine, iron-chromium, aqueous-organic, metal-air, polysulfide-bromine, sodium-bromide and polysulfides.
The biggest knock against flow batteries has been their exotic chemistry requirements as well as their size and slow recharge times. All these three have made flow batteries impractical for residential rooftop solar deployment, that is, until now.
Liang and her Monash team have been developing an aqueous organic flow battery, featuring a membrane with vastly improved ion exchange performance. She describes the merits of their invention as follows:
“This is the kind of battery you’d want in your garage. It’s non-toxic, non-flammable, and made from abundant materials, all while keeping up with solar power on a sunny day.”
An article appearing in Angewandte Chemie, a journal of the German Chemical Society, in May 2025, notes that the new battery has proven reliable through 600 recharge cycles without any performance loss.
The Monash team has been 3D-printing prototypes for tests with the expectation that a commercial flow battery will be available on the market in a few years.
Perovskite is seen as an alternative material for use in solar panels. The best performance from silicon-based solar cells today gets you efficiencies between 20 and 27%. What does that mean? The percentage indicates how much of the sunlight striking a panel gets converted to electricity. Achieving higher efficiencies beyond silicon’s limits has always been a driver for innovation in this industry segment, and tandem solar cells that combine silicon with another material seem the route to go. That’s where perovskite comes in.
Readily available and inexpensive, perovskite photovoltaics have been around for a decade. On their own, perovskite panels aren’t as energy conversion efficient as silicon. But when the two are combined in a tandem cell, the results are very encouraging.
A tandem solar cell involves two layers: perovskite in the upper and silicon in the lower. The work being described here involves the collaboration of researchers from King Abdullah University of Science and Technology, the University of Freiburg, and the Fraunhofer Institute for Solar Energy Systems. The result is a tandem cell with energy conversion efficiencies of up to 33.1%.
The cell has a textured surface to increase the area that sunlight strikes, which enhances energy conversion efficiency. To make the mating work, these tandem solar cells use diaminopropane dihydroiodide added to the perovskite surface. In a press release from the University of Freiburg, Stefan Glunz, Professor of Photovoltaic Energy Conversion, states:
“Surface passivation of solar cells is not just a nice-to-have feature; it is an essential booster for their efficiency and stability. For today’s silicon solar cells, surface passivation was the key for high efficiencies in industrial production, and it is encouraging that the photovoltaic industry will benefit from these positive effects for perovskite silicon tandem solar cells as well.”
Eve Pope, a Senior Technology Analyst at IDTechEx, recently wrote about the role that hydrogen and helium will play in the energy mix of the future. I have borrowed some of her content for what follows, beginning with hydrogen.
Hydrogen fuel cells convert hydrogen gas into electricity through a chemical reaction with oxygen. Solid oxide fuel cells can last a long time, which makes them suitable for continuous power generation for the growing demands of data centres and AI hubs. Currently, some data centres are already using natural gas plus solid oxide fuel cells. Transitioning to hydrogen to reduce carbon footprints should soon be commercially feasible.
For automobiles, the fuel cell has always been an exotic competitor to lithium-ion battery stacks in the vast majority of today’s electric vehicles (EVs). Toyota is one of the few manufacturers building EVs powered by fuel cells. The impediment to the future growth of this transportation technology alternative to lithium-ion will be the rollout of hydrogen refuelling infrastructure.
Hydrogen for aluminum, steel and cement production is the next evolutionary step for these industries. Hydrogen-based DRI (H2-DRI) processes represent the next logical evolution to green these primary manufacturing industries. IDTechEx has produced a Green Steel 2025-2035: Technologies, Players, Markets, Forecasts report that predicts that by 2035, 46 million tons of steel will be produced annually using hydrogen.
Hydrogen also has a role to play in the future of nuclear energy. Canada’s CANDU fission reactors today use deuterium (heavy water). Deuterium is a hydrogen isotope that is twice as heavy as hydrogen. Future commercial fusion reactors will use deuterium as well as tritium (see the next subheading) as essential fuels.
IDTechEx has produced a report entitled, Fusion Energy Market 2025-2045: Technologies, Players, Timelines, that highlights the race to commercial fusion reactors, describing material opportunities and supply chain challenges.
Among the challenges facing hydrogen is sourcing a green version of the gas. Today, if not naturally sourced, most of the hydrogen produced is a byproduct of fossil fuel production. Hydrogen, however, exists in abundance in water, and that can be the primary green resource using electrolyzers to separate the gas from oxygen. To be perfectly green, electrolyzers need to derive energy from renewable sources.
The electrolyzer sector is still in early growth stages, with the need to create a supply chain containing catalysts, electrodes, porous transport layers, gas diffusion layers, bipolar plates, and gasket sources. Future electrolyzer innovations will need new catalysts with less iridium because the latter adds to the cost. For gas separation membranes, new palladium-alloy metallic membranes need to be developed to unlock ultra-pure hydrogen separation.
The helium challenge may be bigger than the ones faced by hydrogen.
Helium natural reserves exist underground, found along with natural gas deposits. To harvest the helium, the gas needs to be captured as it escapes during initial drilling and exploration. Otherwise, it rapidly escapes into the atmosphere into space.
Helium is used by many industries. It is a coolant for MRIs and for semiconductor manufacturing processes. It is used by companies developing artificial intelligence (AI) Large Language Models (LLMs), and in the creation of autonomous vehicles. Demand for its thermal management properties continues to grow. How much? Read the Helium for Semiconductors and Beyond 2025-2035: Market, Trends, and Forecasts from IDTechEx to get a good idea of how essential helium is for now and the future.
One of the justifications for the Artemis Program to return to the Moon is the existence of abundant amounts of Helium 3 in the lunar regolith.
Tritium, like helium, is a scarce material. It exists in trace amounts in the atmosphere with a half-life of 12.3 years. Total natural reserves, therefore, are minuscule. The current source for tritium comes from Canada’s CANDU reactors, which produce a few dozen kilograms annually at a price tag per kilo of US$33 million.
Tritium is the fuel of the future commercial nuclear fusion sector. It combined with deuterium produces vast amounts of energy from very small amounts with minimal waste.
Other fission reactors can be a source of tritium, but it needs to be harvested from the spent fuel they use. That’s where scientists at America’s Los Alamos National Laboratory (LANL) have been looking to create tritium from nuclear waste using a particle accelerator to bombard the spent fuel. The accelerator would switch on and off to avoid the risk of a runaway nuclear reaction.
Terence Tarnowsky is a physicist at LANL who is spearheading the research project. It would require a 1 Gigawatt energy source (roughly equivalent to the annual energy needs of 800,000 homes) to power the particle accelerator with a net yield of about a kilogram of tritium per year.
With no other Earthly source for tritium, very little cosmogenic-sourced tritium to be found beyond Earth, unless lots of new CANDU heavy water reactors are manufactured, tritium will remain a very scarce and valuable material.
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]]>Four years later, a research team at the University of Illinois Urbana-Champaign has risen to the challenge of tapping salinity gradient energy existing between sea and freshwater. In the journal Nano Energy, they describe the design of a nanofluidic device capable of extracting power where freshwater and seawater meet.
How can you produce electricity at these saline-freshwater boundaries? The natural flow of salt molecules from the seawater containing higher concentrations to the freshwater with lower salt levels creates electrically charged ionic particles that can be streamed to flow. The University researchers decided to build a nanoscale semiconductor device featuring a narrow channel to guide these ions through it to create an electric current that could produce practical power generation.
Jean-Pierre Leburton, Professor of Electrical & Computer Engineering at the University described the results of the initial design stating “It is quite versatile and already shows strong potential for energy applications.”
The device takes advantage of a phenomenon first identified by Charles-Augustin de Coulomb, an 18th-century French engineer, scientist and military officer who discovered Coulomb’s Law describing the electrostatic force of attraction and repulsion displayed in atomic interactions. The device invented by Leburton and his colleagues takes advantage of a phenomenon known as Coulomb drag which occurs when a current in one electronic circuit induces a responsive current in a neighbouring circuit.
What surprised Leburton and his research team is that the device they have designed works equally well when the charge is reversed. Not only that, as the ions run through it, an amplification effect increases the electricity being generated.
Leburton and his colleagues have applied for a patent with plans to scale it to use it in power generation. Leburton states, “We believe that the power density of a device array could meet or exceed that of solar cells. And that’s not to mention the potential applications in other fields like biomedical sensing and nanofluidics.”
Where would you install their invention? For large-scale power generation, a scaled version of the device could be installed at the mouths of rivers that run into the ocean, or in desalination and wastewater treatment plants. The nanoscale features of the device, however, could be taken advantage of to create tiny, self-powered detectors and sensors with no battery requirement. These could be used anywhere there is a sufficient gradient between two fluids in proximity to generate the flow of ions from one to the other.
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]]>The post The Loss of our Sense of Smell and Taste from COVID-19 May Soon be a Thing of the Past appeared first on Techso Mogro.
]]>But for those who were first infected in 2020 through 2021, between 40 and 50% with COVID-19 experienced loss of smell and taste which made food unpalatable. I never lost my sense of smell when I caught COVID-19 in 2020, but the painter we hired to do our Toronto apartment the next year described his experience stating everything tasted rotten to him. He described food as having a gun-metal smell. The only benefit, he stated, was he lost a lot of weight because food became so unappealing. He still hadn’t fully recovered his sense of taste when we met him.
Xocova which also goes by the name Ensitrelvir Fumaric Acid, was approved in Japan last November. It is administered in pill form and given once daily for five days. The drug suppresses viral replication by inhibiting 3CL protease, an enzyme that breaks down the proteins produced by the virus. In clinical trials in Japan, the drug has produced no serious adverse effects and more notably, has helped restore the sense of smell and taste of those infected rather quickly. By the third day of treatment, clinical trials report the loss of smell and taste to be diminished, and by day seven, the restoration is quite dramatic. In addition, Xocova shortens the duration of other COVID-19 symptoms including the most severe that would lead to hospitalization and death.
Stated Yohei Doi, an infectious disease researcher at Fujita Health University in Japan, notes that the recovery of these senses comes back over time, “But we know that some people have had long-term issues with smell and taste.”
That certainly has been the case in North America. A Harvard University study in the United States looked at almost 30,000 COVID-19 cases in 2021. That year 35.8 million Americans, representing 14% of the adult population of the country, got COVID-19. Of those 60% reported a loss of taste and smell. The severity of other COVID symptoms appeared to increase the duration of sensory loss.
Following the infection, 24% reported only partial recovery of their sense of smell, with 3% showing no recovery at all. The Harvard research estimates that 28 million Americans are still suffering from partial loss of sense of smell and taste from COVID-19 while 6 million have never got these senses back.
Xocova has yet to be approved for use in North America where Paxlovid remains the preferred pill for shortening the duration of COVID-19 infections in people who are most at risk. But Paxlovid hasn’t shown that it does much to restore the sense of smell and taste in patients who take it.
Laryngologists have had some success using a combination of fluticasone, a nasal spray and triamcinolone dental paste, noting significant improvement in smell and taste over a five-day course of treatment.
When Xocova comes to North America, you may find it under the Ensitrelvir name.
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