A recently flagged and yet-to-be peer-reviewed paper highlights that the planet Uranus is in a stable orbital resonance with a minor planet, a Centaur named 2015 OU₁₉₄. They have a 3:4 orbital resonance, for every 4 orbits that Uranus completes around the Sun, 2015 OU₁₉₄ completes 3. This locks the two in a coordinated, celestial orbital dance, keeping them from drifting apart or colliding. Scientists suggest that it has lasted for 100o years or even a million years and is likely to continue for another half a million years.
In the vast space between Uranus and Neptune, 2015 OU₁₉₄ follows a nearly circular orbit. Centaurs are rocky and icy objects that are found between Jupiter and Neptune. They usually exhibit chaotic, shifting orbits. A stable, long-term dance like this is extraordinary. This also hints at hidden gravitational relationships between the bodies in the outer solar system.
Researchers led by Daniel Bamberger from the Northolt Branch Observatories in Germany scanned through the archival data of 2017 and 2018 from the observation of 2015 OU₁₉₄. and extended the data points from 1 to 3.5 years, this longer observation provided them true nature of the orbital resonance. They published a research paper titled ‘A minor planet in an outer resonance with Uranus’, uncovering the extraordinary findings.
Moreover, other centaurs like 2013 RG₉₈ have also been revealed, which had been engaged in an orbital dance of 3:4 resonance with Uranus. Other candidates like 2014 NX₆₅ also show a strong gravitational influence from Neptune.
These also imply that a similar relationship may also be valid for more objects in the outer Solar System, and these partnerships might be fundamental to the understanding of that region.
By the year 2100, nearly a billion people could be living with drastically reduced access to river water. The finding comes from a new study that used some of the most advanced Earth systems models available.
The research was led by scientists at Northeastern University, specifically from the Institute for Experiential AI and the Department of Civil and Environmental Engineering.
The researchers reassessed climate model performance and found that improved simulations shift our expectations about future water availability.
Earth systems models simulate the planet’s processes – from the atmosphere and oceans to land surfaces and human activities. They’re critical tools for understanding future climate impacts. But not all models are created equal.
The researchers compared two generations of climate models: CMIP5 and the newer CMIP6.
The CMIP6 system had a clear edge. It used finer spatial resolution – every 100 kilometers instead of 500 – and more advanced physics, including better handling of land, ocean, and ice systems.
“The CMIP6 also did a better job of incorporating comprehensive physics, such as physics of land, ocean and ice into climate model equations,” noted Puja Das, a post-doctoral research fellow at Northeastern University.
Some climate models handle cloud formation and atmospheric convection through a process known as parameterization.
“There are some critical parameterizations that need to be correct. We saw that the models that use those parameterizations are performing well,” said Das.
Understanding future water availability means looking at where the stakes are highest. That’s why the researchers zeroed in on the world’s largest river basins – systems that support vast populations and economies.
“We chose the 30 biggest river basins around the world, including the Amazon, Congo, Ganges, Brahmaputra and Nile rivers,” said Das. “We were trying to see how the runoff in those river basins, or water availability in those river basins, are presented in climate models.”
Runoff – the water that flows from land into rivers – is a key part of that equation. It feeds into drinking water systems, sustains agriculture, and powers hydroelectric plants. Even small drops in runoff can cause major disruptions.
“Population estimates are important because they give policymakers an idea of what to expect in terms of the availability of food, water and energy,” explained Das.
The results were sobering. The five most accurate climate models predict that 40% of these rivers will experience reduced runoff by the end of the century. That would affect roughly 850 million people – more than triple the number that was previously estimated.
Better resolution and physics help, but that’s not enough. The researchers wanted to be sure the improved models actually matched historical data and worked well with each other. That’s where skill and consensus come in.
“She’s saying that higher resolution, better parameterization and more physics components do add value. We try to look at all these kinds of metrics based on skills and consensus and that’s what Das has done,” said study co-author Auroop Ganguly, a professor at Northeastern.
However, even better models can come with trade-offs. As precision improves, the range of possible outcomes – known as uncertainty bounds – can actually grow wider.
“Higher resolutions, more intricate parameterizations and comprehensive physics are hypothesized to improve model projections, but this is not guaranteed until the models are thoroughly evaluated against observations (skills) and against each other (consensus),” said Das.
The research didn’t stop with one future scenario. The team also ran the models through five different emissions pathways. As expected, a lower-emissions world had better results.
“We saw that if there is a greener world, the water availability will be higher and fewer people will be impacted because of the decrease in water availability,” said Das.
Even in the best-case scenario, drying rivers will still threaten hundreds of millions of people with water shortages.
“We found that 500 million people (would be affected) instead of 900 million people, but water availability will still decrease in certain parts of the world.”
The goal of the research is to inform two key groups: policymakers and water resource managers who use these models for future planning, and scientists and modelers working to improve their accuracy.
By identifying the most reliable models and highlighting their projections, the study equips both groups to make more informed decisions in a rapidly changing world.
The full study was published in the journal npj Climate and Atmospheric Science.
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Scientists invented a pocket-sized model of the most common form of amyotrophic lateral sclerosis (ALS). The “disease-on-a-chip,” made using stem cells, could pave the way for new treatments for the progressive condition, the researchers say.
In ALS, the brain and spinal-cord cells that control voluntary muscle movements — known as motor neurons — break down and die. As a result, the brain can no longer send signals to the muscles, leading to symptoms of muscle weakness and paralysis, as well as trouble speaking, swallowing and breathing.
In a study published July 3 in the journal Cell Stem Cell, scientists unveiled a new model of sporadic ALS, which accounts for up to 95% of ALS cases and occurs spontaneously without a clear genetic cause or known family history. The platform mimics the early stages of the disease and does so more accurately than previous lab models could.
To build the model, researchers collected blood cells from young-onset ALS patients, all under age 45, and healthy male donors, whose cells were used to build a “healthy” chip, for comparison. The blood cells were reprogrammed into induced pluripotent stem cells (iPSCs), which can be turned into any type of cell in the body. The stem cells were then turned into spinal motor neurons, which normally enable movement and degenerate in ALS.
A second set of iPSCs was turned into cells similar to the blood-brain barrier (BBB), which helps prevent harmful germs and toxins from entering the brain. The spinal neurons were seeded into one channel within the chip, while the BBB cells were placed in another channel.
Separated by a porous membrane, the two chambers were then perfused with nutrient-rich fluid to mimic continuous blood flow. The resulting “spinal-cord chip” maintained both sets of cells for up to about a month and helped the neurons mature beyond what models without flowing fluids allowed.
Related: Scientists invent 1st ‘vagina-on-a-chip’
The basic chip was developed by the biotech company Emulate and then customized for use in the ALS model by researchers at Cedars-Sinai in Los Angeles, California.
Earlier models of ALS also used iPSC-derived neurons and structures mimicking those found in the brain, but they lacked dynamic flow, making it hard to capture specific aspects of the disease.
“Our previous models were static, like a dish of cells sitting still, and couldn’t differentiate between ALS and healthy cells,” said study co-author Clive Svendsen, executive director of the Board of Governors Regenerative Medicine Institute at Cedars-Sinai. “We recreated an in vitro [lab dish] environment that breathes and flows like human tissue, which allowed us to detect early differences in ALS neurons.”
Other experts agree. “Unlike most lab models that lack vascular features and dynamic flow, this chip improves neuron health and maturation,” said Dr. Kimberly Idoko, a board-certified neurologist and medical director at Everwell Neuro, who was not involved in the study. “It captures early disease signals in ALS that are often hard to detect,” Idoko told Live Science in an email.
With their ALS and healthy chips in hand, the researchers analyzed the activity of more than 10,000 genes across all the cells. One of the most striking findings was abnormal glutamate signaling in the neurons within the ALS chip.
Glutamate is a major excitatory chemical messenger, meaning it makes neurons more likely to fire and send on a message to additional neurons; its counterpart, GABA, is inhibitory. The team saw increased activity in glutamate receptor genes and decreased activity in GABA receptor genes in the motor neurons, compared to the healthy chip.
“We were intrigued to find this increase in glutamate activity,” Svendsen said. “Although there was no visible neuron death, we hypothesize this hyperexcitability could trigger degeneration at later stages.”
This finding aligns with long-standing theories about ALS, which suggest that boosted glutamate signalling contributes to nerve damage. It also corresponds with the mechanism of the ALS drug riluzole, which blocks glutamate. The new chip adds to the evidence for this mechanism and could help reveal how it manifests in the earliest stages, before symptoms would be evident in a patient, Svendsen suggested.
While Idoko praised the model, she noted it lacks glial cells — additional nervous-system cells involved in ALS — and doesn’t capture the late-stage degeneration seen in ALS. “However, a model like this could conceivably be useful for early drug screening, to study how a drug might cross a barrier similar to the blood-brain barrier, in preparation for animal or human studies,” she said.
The team is now working toward maintaining the cells in the model for up to 100 days. They also would like to incorporate other cell types, like muscle cells, to fully mimic ALS progression. As motor neurons die off in the disease, muscle cells also waste away.
“Our goal is now to build models where more neurons die, so we can better map disease pathways and test treatments in a human-like setting,” Svendsen said. For now, the chip offers a window into ALS’s earliest molecular changes and a tool to figure out how to detect and slow the disease before irreversible damage occurs.
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Motor neurons, shown with their axons (nerve fibers) labeled in green, are seen growing on a spinal cord organ-chip developed by Cedars-Sinai. | Credit: The Svendsen Lab at Cedars-Sinai
Scientists invented a pocket-sized model of the most common form of amyotrophic lateral sclerosis (ALS). The “disease-on-a-chip,” made using stem cells, could pave the way for new treatments for the progressive condition, the researchers say.
In ALS, the brain and spinal-cord cells that control voluntary muscle movements — known as motor neurons — break down and die. As a result, the brain can no longer send signals to the muscles, leading to symptoms of muscle weakness and paralysis, as well as trouble speaking, swallowing and breathing.
In a study published July 3 in the journal Cell Stem Cell, scientists unveiled a new model of sporadic ALS, which accounts for up to 95% of ALS cases and occurs spontaneously without a clear genetic cause or known family history. The platform mimics the early stages of the disease and does so more accurately than previous lab models could.
To build the model, researchers collected blood cells from young-onset ALS patients, all under age 45, and healthy male donors, whose cells were used to build a “healthy” chip, for comparison. The blood cells were reprogrammed into induced pluripotent stem cells (iPSCs), which can be turned into any type of cell in the body. The stem cells were then turned into spinal motor neurons, which normally enable movement and degenerate in ALS.
A second set of iPSCs was turned into cells similar to the blood-brain barrier (BBB), which helps prevent harmful germs and toxins from entering the brain. The spinal neurons were seeded into one channel within the chip, while the BBB cells were placed in another channel.
Separated by a porous membrane, the two chambers were then perfused with nutrient-rich fluid to mimic continuous blood flow. The resulting “spinal-cord chip” maintained both sets of cells for up to about a month and helped the neurons mature beyond what models without flowing fluids allowed.
Related: Scientists invent 1st ‘vagina-on-a-chip’
The basic chip was developed by the biotech company Emulate and then customized for use in the ALS model by researchers at Cedars-Sinai in Los Angeles, California.
Earlier models of ALS also used iPSC-derived neurons and structures mimicking those found in the brain, but they lacked dynamic flow, making it hard to capture specific aspects of the disease.
“Our previous models were static, like a dish of cells sitting still, and couldn’t differentiate between ALS and healthy cells,” said study co-author Clive Svendsen, executive director of the Board of Governors Regenerative Medicine Institute at Cedars-Sinai. “We recreated an in vitro [lab dish] environment that breathes and flows like human tissue, which allowed us to detect early differences in ALS neurons.”
Other experts agree. “Unlike most lab models that lack vascular features and dynamic flow, this chip improves neuron health and maturation,” said Dr. Kimberly Idoko, a board-certified neurologist and medical director at Everwell Neuro, who was not involved in the study. “It captures early disease signals in ALS that are often hard to detect,” Idoko told Live Science in an email.
With their ALS and healthy chips in hand, the researchers analyzed the activity of more than 10,000 genes across all the cells. One of the most striking findings was abnormal glutamate signaling in the neurons within the ALS chip.
Glutamate is a major excitatory chemical messenger, meaning it makes neurons more likely to fire and send on a message to additional neurons; its counterpart, GABA, is inhibitory. The team saw increased activity in glutamate receptor genes and decreased activity in GABA receptor genes in the motor neurons, compared to the healthy chip.
“We were intrigued to find this increase in glutamate activity,” Svendsen said. “Although there was no visible neuron death, we hypothesize this hyperexcitability could trigger degeneration at later stages.”
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This finding aligns with long-standing theories about ALS, which suggest that boosted glutamate signalling contributes to nerve damage. It also corresponds with the mechanism of the ALS drug riluzole, which blocks glutamate. The new chip adds to the evidence for this mechanism and could help reveal how it manifests in the earliest stages, before symptoms would be evident in a patient, Svendsen suggested.
While Idoko praised the model, she noted it lacks glial cells — additional nervous-system cells involved in ALS — and doesn’t capture the late-stage degeneration seen in ALS. “However, a model like this could conceivably be useful for early drug screening, to study how a drug might cross a barrier similar to the blood-brain barrier, in preparation for animal or human studies,” she said.
The team is now working toward maintaining the cells in the model for up to 100 days. They also would like to incorporate other cell types, like muscle cells, to fully mimic ALS progression. As motor neurons die off in the disease, muscle cells also waste away.
“Our goal is now to build models where more neurons die, so we can better map disease pathways and test treatments in a human-like setting,” Svendsen said. For now, the chip offers a window into ALS’s earliest molecular changes and a tool to figure out how to detect and slow the disease before irreversible damage occurs.
Columbia University Engineers, funded by the Defense Advanced Research Projects Agency (DARPA), have developed a “robot metabolism” process that gives robots the unusual ability to consume other robots and use their components to heal, grow, and improve themselves.
In a statement, the researchers behind the ominous-sounding work say that the ability of robots to maintain and potentially improve their physical components and capabilities, while artificial intelligence systems are already expanding their own autonomous capabilities, is a necessary step toward “self-sustaining robot ecologies.”
“True autonomy means robots must not only think for themselves but also physically sustain themselves,” explained Philippe Martin Wyder, a researcher at Columbia Engineering and the University of Washington and the lead author of a study detailing the Columbia team’s research. “Just as biological life absorbs and integrates resources, these robots grow, adapt, and repair using materials from their environment or from other robots.”
Unlike biological organisms, robots lack the ability to change, repair, or improve their physical state without outside assistance. The Columbia researchers say this lack of autonomy has left modern-day robots “stuck,” as their physical forms are typically closed systems, “that can neither grow nor self-repair, nor adapt to their environment.” The problem is further exacerbated by the rapid increase in robot intelligence, leaving their physical capabilities increasingly far behind.
“Robot minds have moved forward by leaps and bounds in the past decade through machine learning, but robot bodies are still monolithic, unadaptive, and unrecyclable,” explained Hod Lipson, a Professor of Innovation and chair of the Department of Mechanical Engineering at Columbia University, and co-author of the study. “Biological bodies, in contrast, are all about adaptation – lifeforms can grow, heal, and adapt.”
Lipson said this biological adaptability stems from the fact that “modular” biological organisms can “use and reuse” components from other biological systems, such as amino acids. Ideally, the researcher says we will ultimately have to give robots the same capabilities, which he considers a nascent form of ‘machine metabolism.’
Thanks to funding from DARPA and the National Science Foundation (NSF), Wyder and colleagues have found a way to change that situation, starting with a robotic magnet stick called a “Truss Link.” Inspired by the Geomag toy, the researchers describe a Truss Link as a “simple, bar-shaped module equipped with free-form magnetic connectors that can expand, contract, and connect with other modules at various angles.” This versatility and set of capabilities allow the Truss Link to form increasingly complex physical structures.
In a series of laboratory experiments, the team demonstrated how these links could self-assemble into two-dimensional shapes. The team also showed how these two-dimensional shapes could “morph” into three-dimensional, functioning robots.
In the next phase, demonstrating robot metabolism, the team’s robots further improved their abilities and functionality by integrating new components into their design. In one example of these robots “growing” into more capable machines without outside interference, one of the team’s three-dimensional, tetrahedron-shaped robots “integrated” an additional link it used as a walking stick. According to the researchers, this simple self-improvement increased the robot’s downhill speed by more than 66.5%.
Lipson admits that the thought of robots able to adapt, improve, grow, and even potentially self-reproduce “conjures some bad sci-fi scenarios.” However, the director of the Creative Machines Lab, where the work was conducted, stated that keeping robots reliant on humans, even for simple maintenance, will become increasingly impractical as the number of robotic systems we encounter and interact with in our daily lives is expected to grow.
“The reality is that as we hand off more and more of our lives to robots – from driverless cars to automated manufacturing, and even defense and space exploration, who is going to take care of these robots? We can’t rely on humans to maintain these machines. Robots must ultimately learn to take care of themselves.”
In the study’s conclusion, the team said more research is needed to expand the potential applications of robot metabolism. However, they also envision a future where robotic systems, ranging from autonomous cars to autonomous spacecraft, will live in robot ecologies where machines can independently maintain themselves. Based on their research, this could include the ability to grow and adapt to unforeseen tasks and environments like biological organisms already do.
“By imitating nature’s approach—building complex structures from simple building blocks—robot metabolism paves the way for autonomous robots capable of physical development and long-term resilience,” they explained.
In his final thoughts, Wyder said robot metabolism will bridge the gap between AI and the physical world, thereby allowing these systems, which can already advance cognitively, to match that expansion with the ability to adapt and grow physically, “creating an entirely new dimension of autonomy.” This self-reliance could impact several industries and potentially make robot metabolism as commonplace as the intelligent AI people use daily.
“Initially, systems capable of Robot Metabolism will be used in specialized applications such as disaster recovery or space exploration,” the researcher concluded. “Ultimately, it opens up the potential for a world where AI can build physical structures or robots just as it does today, writes or rearranges the words in your email.”
Funded by DARPA and the NSF AI Institute in Dynamic Systems, “Robot Metabolism: Towards machines that can grow by consuming other machines” was published in Science Advances.
Christopher Plain is a Science Fiction and Fantasy novelist and Head Science Writer at The Debrief. Follow and connect with him on X, learn about his books at plainfiction.com, or email him directly at christopher@thedebrief.org.
Microscopes have long been scientists’ eyes into the unseen, revealing everything from bustling cells to viruses and nanoscale structures.
However, even the most powerful optical microscopes have been limited by a fundamental physical rule known as the diffraction limit, which prevents them from clearly seeing anything smaller than about 200 nanometers—far too large to capture single atoms.
This limitation has stood in the way of observing how light interacts with individual atoms or molecules, a critical step for advancing materials science, electronics, and quantum research.
Now, a team of international researchers has overcome this challenge. They’ve developed a novel imaging technique called ULA-SNOM (ultralow tip oscillation amplitude scattering-type scanning near-field optical microscopy), which can optically resolve features just one nanometer in size—small enough to see individual atoms with light.
In short, the scientists have developed an optical microscope that lets us watch light behave at the level of single atoms—a feat once thought possible only with electron-based microscopy tools.
The breakthrough could revolutionize how we study matter at its fundamental level, and reshape everything from how solar cells are built to how we understand chemical reactions and quantum systems.
To break past the resolution limits of traditional optics, the team built on a technique called scattering-type scanning near-field optical microscopy (s-SNOM). In s-SNOM, a sharp metal tip is illuminated with a laser and scanned across the surface of a material.
The light scatters off the surface in patterns that reveal nanoscale details. However, typical s-SNOM setups only reach resolutions of about 10 to 100 nanometers. Which is too big for atomic-scale imaging.
Using their novel approach, ULA-SNOM, the researchers managed to reduce the movement of the scanning tip to an incredibly tiny level. In this method, the tip oscillates with an amplitude of only 0.5 to 1 nanometer, which is about the width of three atoms.
This precise motion was found to be large enough to pick up optical signals, but small enough to detect the finest structural details. A larger amplitude would degrade the optical resolution, and any smaller would overwhelm the signal with noise.
The tip itself was made of polished silver, carefully shaped using a focused ion beam to ensure a smooth and stable surface. A visible red laser with a wavelength of 633 nanometers and six milliwatts of power was directed at the tip, producing a phenomenon called a plasmonic cavity, a tiny, confined pocket of light formed between the tip and the sample surface.
This cavity was squeezed into a volume just one cubic nanometer in size, which allowed it to interact with the material at the scale of single atoms. To keep this delicate setup stable, the entire experiment was carried out in ultrahigh vacuum and at an ultra-cold temperature of eight Kelvin (−265°C).
These cryogenic conditions eliminated vibrations and contamination, helping the tip stay precisely positioned just a nanometer above the surface. Then, to filter out background light and enhance the real signal, the team used a specialized method called self-homodyne detection, which made the optical data clearer and more reliable. At this point, the ULA-SNOM microscope setup was ready for testing.
The team used their ULA-SNOM setup to image single-atom-thick silicon islands placed on a silver surface. Despite the fact that these silicon layers were just one atom tall, the microscope was able to clearly show where the silicon ended and the silver began, not just in terms of shape, but in how each material responded to light.
This result confirmed that the system could capture true optical contrast at atomic resolution. The microscope also offered something unique. It could gather different kinds of information at the same time.
Along with optical signals, the setup also measured electrical conductivity and mechanical forces using built-in scanning tunneling microscopy (STM) and atomic force microscopy capabilities.
Moreover, by analyzing how the tip responded at different vibration frequencies (harmonics), the team was able to separate signals from different sources. The fourth harmonic, in particular, revealed the clearest differences in optical behavior between materials.
When the scientists compared the spatial resolution with that of a traditional STM—a powerful instrument used to image surfaces at the atomic scale—they found the optical images from ULA-SNOM matched its detail, about one nanometer, nearly identical to the 0.9-nanometer resolution of the STM.
For the first time, researchers could see clearly how a single atom or defect affects the optical behavior of a material. The development can potentially lead to precise design of nanostructures in electronics, the discovery of new photonic materials, or even better solar cells that absorb light more efficiently.
Furthermore, scientists could use this technique to study quantum dots, single-molecule sensors, or biological structures with a level of detail that was previously impossible.
However, ULA-SNOM requires cryogenic cooling, ultrahigh vacuum, carefully shaped metal tips, and stable laser systems, tools available only in specialized labs. Hopefully, future studies will focus on making the approach more practical, accessible, and scalable.
The study is published in the journal Science Advances.
While many science books would have you believe the Earth’s lower mantle—the layer deep below the crust—is smooth, it’s actually made up of a mountainous-like topography that moves and changes just like the crust above it. Further, research shows that this lower mantle contains two continent-sized structures, which researchers have dubbed big lower-mantle basal structures, or BLOBS.
We don’t know exactly what these BLOBS consist of, but scientists suspect they could be made up of the same materials surrounding them. In fact, new research published in the journal Communications Earth & Environment suggests that the planet’s volcanic activity may be driven by volcanic plumes that move with their origins.
The origins in question, researchers believe, could be the BLOBS found deep within the Earth. These mysterious structures appear to be the driving force behind the Earth’s volcanic history, and while there are scientists hard to work trying to prove that, looking at past simulations has painted a pretty clear picture to work with.
To start with, the researchers used computer models to simulate the movements of the BLOBS over 1 billion years ago. These models showed that the BLOBS produced mantle plumes that were sometimes tilted or even rose up higher. This suggests that the eruptions seen over the past billion years likely took place above the BLOBS, or at least very close to them.
The researchers believe that this data shows that the Earth’s volcanic activity could somehow be linked to the BLOBS, despite how deep they are in the Earth. The findings are “encouraging,” the researchers note in a post on The Conversation, as it suggests that future simulations may be able to predict where mantle plumes will strike next. This could help us create a general volcano warning system.
Despite being destructive—the Hunga volcano eruption of 2022 continues to set records years later—large volcanic eruptions also have the ability to create new islands and landmasses. Knowing where they occur—or where they occurred in the past—could help us save lives and better understand how our planet formed at different points in history. Of course, we still have a lot to learn about the mysterious BLOBS found deep in the Earth. But this research is a smoking gun that could open the door for tons of new discoveries and revelations.
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On Episode 169 of This Week In Space, Rod Pyle and Tariq Malik are joined by Rob Manning, JPL’s Chief Engineer Emeritus, to look back at the Mariner 4 Mars mission 60 years later.
Six decades ago this week, the Mariner 4 probe sped past Mars, the first to succeed in this then-brash undertaking. The technology was unbelievably primitive, yet effective, sending back 22 low-resolution video frames of the Red Planet. On that day, the wee hours of July 15 at JPL in Pasadena, the Mars of the romantics died. What had long been viewed as a slightly colder, somewhat drier, near-twin of Earth ended up having just a trace of an atmosphere and looked more like the moon–bone dry and pummelled by craters.
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Finally, did you know you can launch your own SpaceX rocket? Model rocket maker Estes’ stunning scale model of a Falcon 9 rocket that you can pick up now. The launchable model is a detailed recreation of the Falcon 9 and retails for $149.99. You can save 10% by using the code IN-COLLECTSPACE at checkout, courtesy of our partners collectSPACE.com.
This Week in Space covers the new space age. Every Friday we take a deep dive into a fascinating topic. What’s happening with the new race to the moon and other planets? When will SpaceX really send people to Mars?
Join Rod Pyle and Tariq Malik from Space.com as they tackle those questions and more each week on Friday afternoons. You can subscribe today on your favorite podcatcher.
Rod Pyle is an author, journalist, television producer and Editor-in-Chief of Ad Astra magazine. He has written 18 books on space history, exploration, and development, including Space 2.0, Innovation the NASA Way, Interplanetary Robots, Blueprint for a Battlestar, Amazing Stories of the Space Age, First On the Moon, and Destination Mars
In a previous life, Rod produced numerous documentaries and short films for The History Channel, Discovery Communications, and Disney. He also worked in visual effects on Star Trek: Deep Space Nine and the Battlestar Galactica reboot, as well as various sci-fi TV pilots. His most recent TV credit was with the NatGeo documentary on Tom Wolfe’s iconic book The Right Stuff.
Responsible for Space.com’s editorial vision, Tariq Malik has been the Editor-in-Chief of Space.com since 2019 and has covered space news and science for 18 years. He joined the Space.com team in 2001, first as an intern and soon after as a full-time spaceflight reporter covering human spaceflight, exploration, astronomy and the night sky. He became Space.com’s managing editor in 2009. As on-air talent has presented space stories on CNN, Fox News, NPR and others.
Tariq is an Eagle Scout (yes, he earned the Space Exploration merit badge), a Space Camp veteran (4 times as a kid, once as an adult), and has taken the ultimate “vomit comet” ride while reporting on zero-gravity fires. Before joining Space.com, he served as a staff reporter for The Los Angeles Times covering city and education beats. He has journalism degrees from the University of Southern California and New York University.