Until recently, Mars was studied only through telescopes and some data that was recorded from early spacecraft. It was thought that Mars’ polar regions were the only significant reservoirs of ice, mainly in the form of frozen carbon dioxide (dry ice). The rest of Mars seemed too dry for frozen water to form, and there were no means to detect what was under the surface of the planet. The glaciers on Mars were thought to be composed mainly of rock and dust, with a thin layer of ice covering them.
This view started to change with planetary science missions like the Mars Odyssey and the Phoenix Lander. Having a closer look at the red planet unveiled the existence of hydrogen in the soil and water ice just a few inches below the surface. Water was found on Mars, and it wasn’t as rare as previously thought. However, with the new study published in the journal Icarus, scientists Yuval Steinberg, Oded Aharonson, and Isaac Smith describe Martian glaciers as consisting of 80% ice. This new, radar-based study opens myriad new possibilities for planetary science and even future human-led missions to Mars.
Read more: What’s Happening To Earth Right Now Can’t Be Explained By Climate Models
What’s Special About These Glaciers?
Frozen water puddle – Just_super/Getty Images
Martian glaciers are of special interest to scientists because they help us understand the planet’s climate history. Understanding Mars’ past may help us better plan for the future of our own planet. This was the main sphere of interest for Weizmann Institute of Science graduate, Steinberg, who conducted this study alongside mentors and colleagues from the Planetary Science Institute in Tucson and York University.
They began their research by combing through old studies, just to realize many of those had been performed on different sites, with different methods. That means the results from different glaciers across the planet were not comparable. There was no discernible pattern, so the team decided to test five separate glacier sites and use the same techniques on all of them. That would allow them to process all the data in the same way, compare them, and draw a single conclusion.
They used the Shallow Radar instrument mounted on the Mars Reconnaissance Orbiter (MRO), a NASA spacecraft designed to study climate and water presence on the red planet. The Shallow Radar allowed Steinberg and the team to gather crucial data from five different spots on different glaciers. Once the analysis of the data was performed, it was clear that each glacier had the same electrical signature. In other words, all of the analyzed glaciers had the same composition. It was determined that by volume, Mars glaciers are four-fifths ice, and the rest is rocks and various debris that encase this ice. This uniformity of results means that either there was one glaciation event on Mars, or multiple ice periods that occurred under very similar circumstances.
What Does This Discovery Mean To Humankind?
Two astronauts walking on Martian surface – Frame Stock Footage/Shutterstock
Mars, as we know it today, is a barren wasteland. However, the new study suggests it wasn’t always like this. The presence of high-purity ice means Mars once had an abundance of snowfall and frost that, over time, led to the formation of these large glaciers at different latitudes over the planet’s surface. Knowing that the glaciers consist of 80% ice, scientists can now model how much water once circulated in the planet’s atmosphere. This also enables us to understand how the climate on Mars has changed with the planet’s tilt. Following these changes on the nearby planet might let us understand how to combat climate change on Earth.
That said, the presence and purity of ice also mean possibilities for future missions on Mars, especially if we plan to send people to the surface of the red planet. If we develop ways of using the water already on the planet, it would mean more efficient crewed missions. The pure ice can be mined for drinking water or even fuel, which means Mars could sustain human life on its own. There would be no need to haul all the necessary water from Earth.
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Exoplanets used to be fringe objects in astronomy. Now, they are popular subjects for testing ideas about the composition of the universe.
A new study proposes that some gas giants might steadily collect dark matter in their cores until the buildup tips into a collapse that forms a tiny black hole.
Mehrdad Phoroutan-Mehr, a graduate researcher at the University of California, Riverside (UCR), led the work with postdoctoral researcher Tara Fetherolf.
Planets help detect dark matter
Astronomers now have thousands of worlds to work with across many ages, sizes, and orbits. NASA’s exoplanet archive lists nearly 6,000 confirmed planets as of mid 2025.
Gas giants are especially handy for this kind of test. They have huge volumes and cold interiors compared with stars, which makes any extra energy source easier to notice.
The team modeled how heavy, non-annihilating dark matter would move through a Jupiter-like world. The particles would gradually lose speed, settle into the center, and pile up.
Once that central clump passes a critical mass, gravity wins. The clump collapses into a black hole that either begins to feed on the surrounding gas or evaporates if it is small enough.
How planets may form black holes
The researchers focused on the superheavy, non-annihilating case scenario because captured particles never remove themselves by destroying each other. That makes the buildup relentless.
Under those conditions, the core can collapse on observable timescales in some planets. The possibility extends to multiple collapses throughout a single planet’s lifetime if capture continues.
In plain language, if the parameters line up, a Jupiter-mass world could quietly become a Jupiter-mass black hole. The object would keep the planet’s mass but lose the planet’s bulk.
“As the central concentration of dark matter grows in gaseous planets, a black hole may form and then accrete the surrounding material. Discovering a black hole with the mass of a planet would be a major breakthrough,” noted Phoroutan-Mehr.
Signs of planet black holes
A planet that turns into a black hole would still tug its star in the same way, so radial-velocity and astrometric signals would persist. Transit dips, however, could vanish because the body shrinks far below a star-crossing silhouette.
That mismatch creates a check. If we see stellar wobbles that imply a giant planet but repeated photometry no longer shows transits, the system deserves a closer look.
Microlensing adds a different angle. A compact lens and a puffier planet of the same mass bend light nearly the same way in simple events, so single light curves will not easily separate them.
Population statistics can help. If many high-mass “planets” near the galactic center lack transits yet show dynamical signals, that pattern would be hard to blame on chance alone.
How telescopes can check
The Nancy Grace Roman Space Telescope’s Galactic Bulge Time-Domain survey will capture tens of thousands of micro-lensing events in fields rich in dark matter. That is exactly where this model expects stronger capture and faster collapses.
Roman’s microlensing plus precise astrometry can flag planetary-mass compact objects and map their distribution. If planet-mass black holes cluster where the dark matter density is higher, that would lift this idea above speculation.
There is also a thermal route. Independent work showed that dark matter annihilation could heat exoplanets, raising their infrared glow beyond what cooling alone predicts.
Small black holes could also evaporate by Hawking radiation, dumping high-energy particles into the planet or, if the particles escape, into space. Either outcome offers a potential signal, though today’s instruments lack the sensitivity to chase the faintest cases.
Limits of the idea
This is not a blanket prediction that all gas giants are doomed. Many parameters must align, from the particle mass to the scattering cross section and the local dark matter density.
The absence of planet-mass black holes would be informative as well. If Jupiter-like worlds remain intact in regions where the model expects collapses, that would carve away at the allowed properties of dark matter.
The researchers also noted that multiple observational methods must be combined. No single transit, lens, or velocity signal will settle the question in isolation.
There are limitations in the physics too. Capture and drift times depend on interior temperature and density profiles, which are still debated for many planets, and even modest changes can shift the thresholds.
Why this matters for dark matter
The research could turn an ordinary planetary survey into a particle-physics experiment. We already collect the data while hunting for new worlds.
A confirmed planet-mass black hole would point straight at a non-annihilating, very heavy particle candidate. That would push the conversation about dark matter beyond the ranges probed by common terrestrial detectors.
Even a null result tightens the net. If transit catalogs remain full and consistent with radial-velocity and astrometric counts, those absences pin down cross sections that would have produced collapses.
The framework invites new cross-checks as the exoplanet census grows. Roman’s catalogs, plus follow-up from ground observatories and future direct-imaging missions, set the stage for a decisive test.
The study is published in the journal Physical Review D.
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NASA will announce its new astronaut class next month, and you’ll be able to watch the unveiling live.
The 2025 class of NASA astronaut candidates will be announced Sept. 22 during a livestreamed event at NASA’s Johnson Space Center in Houston that begins at 12:30 p.m. EDT (1630 GMT; 11:30 a.m. local time in Houston).
You can catch coverage of the event on NASA+, Netflix, Amazon Prime, NASA’s YouTube channel, and the agency’s X account. Space.com will also simulcast the event, if NASA makes its stream available.
The selectees were narrowed down from a field of 8,000 applicants. The new astronaut candidates will next undergo roughly two years of training at NASA before being certified as full astronauts, eligible for future missions.
NASA also plans to host several media briefings on Sept. 23 and Sept. 24 to discuss the Artemis 2 mission that will send four astronauts around the moon no earlier than April 2026. These events will stream live on NASA’s YouTube channel and X account (as well as at Space.com, if possible).
On board Artemis 2 will be NASA astronauts Reid Wiseman (commander), Victor Glover (pilot) and Christina Koch (mission specialist), and Canadian Space Agency astronaut and mission specialist Jeremy Hansen. Glover will be the first Black astronaut ever to fly a moon mission, while Koch will be the first woman and Hansen the first non-American.
Here are the scheduled briefings for Artemis 2:
Breaking space news, the latest updates on rocket launches, skywatching events and more!
Tuesday, Sept. 23:
11 a.m. EDT (10 a.m. local time, 1500 GMT): Mission overview briefing with the following participants
Lakiesha Hawkins, acting deputy associate administrator, Exploration Systems Development Mission Directorate, NASA Headquarters
Charlie Blackwell-Thompson, Artemis launch director, NASA’s Kennedy Space Center in Florida
Judd Frieling, lead Artemis 2 ascent flight director, NASA Johnson
Jeff Radigan, lead Artemis 2 flight director, NASA Johnson
Rick Henfling, lead Artemis 2 entry flight director, NASA Johnson
Daniel Florez, test director, Exploration Ground Systems, NASA Kennedy
1 p.m. EDT (12 p.m. local time, 1700 GMT): Artemis 2 science and technology briefing with the following participants:
Matt Ramsey, Artemis 2 mission manager, NASA Headquarters
Debbie Korth, deputy Orion Program manager, NASA Johnson
Jacob Bleacher, manager, Science, Technology Utilization, and Integration, Exploration Systems Development Mission Directorate, NASA Headquarters
Mark Clampin, acting deputy associate administrator, Science Mission Directorate, NASA Headquarters
Wednesday, Sept. 24
10 a.m. EDT (12 p.m. local time, 1400 GMT): Artemis 2 crew news conference featuring:
Mehta S. Age-related macular degeneration. Prim Care. 2015;42:377–91.
PubMed
Google Scholar
Keenan TDL, Clemons TE, Domalpally A, Elman MJ, Havilio M, Agrón E, et al. Retinal specialist versus artificial intelligence detection of retinal fluid from oct: age-related eye disease study 2: 10-year follow-on study. Ophthalmology. 2021;128:100–9.
PubMed
Google Scholar
Okada M, Mitchell P, Finger RP, Eldem B, Talks SJ, Hirst C, et al. Nonadherence or nonpersistence to intravitreal injection therapy for neovascular age-related macular degeneration: a mixed-methods systematic review. Ophthalmol. 2021;128:234–47.
Google Scholar
Kang C, Lo JE, Zhang H, Ng SM, Lin JC, Scott IU, et al. Artificial intelligence for diagnosing exudative age-related macular degeneration. Cochrane Database Syst Rev. 2024;10:CD015522. https://doi.org/10.1002/14651858.CD015522.pub2.
Article
PubMed
Google Scholar
Pepe MS. The statistical evaluation of medical tests for classification and prediction. Oxford University Press; 2003.
Lijmer JG, Mol BW, Heisterkamp S, Bonsel GJ, Prins MH, van der Meulen JH, et al. Empirical evidence of design-related bias in studies of diagnostic tests. JAMA. 1999;282:1061–6.
PubMed
Google Scholar
Dong L, Yang Q, Zhang RH, Wei WB. Artificial intelligence for the detection of age-related macular degeneration in color fundus photographs: A systematic review and meta-analysis. EClinicalMedicine. 2021;35:100875. https://doi.org/10.1016/j.eclinm.2021.100875.
Article
PubMed
PubMed Central
Google Scholar
Wynants L, Van Calster B, Collins GS, Riley RD, Heinze G, Schuit E, et al. Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal. BMJ. 2020;369:m1328. https://doi.org/10.1136/bmj.m1328. Update in: BMJ. 2021 Feb 3;372:n236. https://doi.org/10.1136/bmj.n236. Erratum in: BMJ. 2020 Jun 3;369:m2204. https://doi.org/10.1136/bmj.m2204.
Wan B, Caffo B, Vedula SS. A unified framework on generalizability of clinical prediction models. Front Artif Intell. 2022;5:872720. https://doi.org/10.3389/frai.2022.872720.
Article
PubMed
PubMed Central
Google Scholar
Efthimiou O, Seo M, Chalkou K, Debray T, Egger M, Salanti G. Developing clinical prediction models: a step-by-step guide. BMJ. 2024;386:e078276. https://doi.org/10.1136/bmj-2023-078276.
Article
PubMed
PubMed Central
Google Scholar
Collins GS, Dhiman P, Ma J, Schlussel MM, Archer L, Van Calster B, et al. Evaluation of clinical prediction models (part 1): from development to external validation. BMJ. 2024;384:e074819. https://doi.org/10.1136/bmj-2023074819.
Article
PubMed
PubMed Central
Google Scholar
Riley RD, Archer L, Snell KIE, Ensor J, Dhiman P, Martin GP, et al. Evaluation of clinical prediction models (part 2): how to undertake an external validation study. BMJ. 2024;384:e074820. https://doi.org/10.1136/bmj-2023-074820.
Article
PubMed
PubMed Central
Google Scholar
Riley RD, Snell KIE, Archer L, Ensor J, Debray TPA, van Calster B, et al. Evaluation of clinical prediction models (part 3): calculating the sample size required for an external validation study. BMJ. 2024;384:e074821. https://doi.org/10.1136/bmj-2023-074821.
Article
PubMed
PubMed Central
Google Scholar
Wolff RF, Moons KGM, Riley RD, Whiting PF, Westwood M, Collins GS, et al. PROBAST Group†. PROBAST: A Tool to Assess the Risk of Bias and Applicability of Prediction Model Studies. Ann Intern Med. 2019;170:51–8. https://doi.org/10.7326/M18-1376.
Article
PubMed
Google Scholar
Collins GS, Moons KGM, Dhiman P, Riley RD, Beam AL, Van Calster B, et al. TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods. BMJ. 2024;385:e078378. https://doi.org/10.1136/bmj-2023-078378. Erratum in: BMJ. 2024 Apr 18;385:q902. https://doi.org/10.1136/bmj.q902.
To elucidate the role of LSWI in altering interfacial properties, this study focuses on the behavior of the EDL at the surface of composite carbonate reservoir rocks composed of both calcite and quartz. Using MD simulations, we systematically investigated how key brine ions (Na⁺, Cl⁻, Mg²⁺, and Ca²⁺) interact with the heterogeneous surface under varying salinity conditions. The mineralogical contrast between calcite and quartz provides a unique framework to assess ion-specific preferences and localized EDL formation. Our results highlight that salinity not only affects the overall EDL thickness but also modulates the spatial distribution of ions at the distinct calcite–quartz interfaces. These observations reveal critical insights into how composite rock–brine interactions contribute to the effectiveness of the LSWI mechanism, as detailed in the following section.
Table 3 Energy interactions between rock surface and various components (H-water, O-water, oil, Na+, Cl–) in three different systems, all interaction energies are reported in (kcal/mol).
Table 3 presents the energy interactions between components (H-water, O-water, oil, Na+, Cl−) in three different systems (DW, SW, and FW) for two rock composite tissues, CaCO3 and SiO2. The negative values indicate attractive forces, while positive values indicate repulsive forces. The values highlighted in blue in Table 3 are obtained by summing the values of the two rows directly above them.
These values reflect the cumulative interaction energy across all pairs within the specified groups. The unusually high values arise due to the large number of particle pairs contributing to the total energy. The reported values represent the average of the total cumulative energy across all such pairs during production simulation time. This approach accounts for temporal fluctuations and provides a representative value for the interaction energy.
Interaction between Na+ and the rock surface
Table 3 details notable observations regarding the negative interaction between Na + and CaCO3 (values of – 185 kcal/mol for DW, – 789 kcal/mol for SW, and − 1539 kcal/mol for FW). As salinity increases, so does the adsorption capacity of CaCO3 rock; this suggests that CaCO3 rock has an increased capacity for Na+ adsorption50, which indicates a favorable interaction of Na + ions with CaCO3 tissue.
Na + exhibits an initial negative interaction (value of – 80 kcal/mol), expressed by attraction to the SiO2 surface. However, as salinity increases, this effect becomes positive (values of 374 kcal/mol for SW and 208 kcal/mol for FW) due to the occupation of the oxygen atoms of the SiO2 rock surface with Na+ ions in the SW system. indicating that Na+ repulsively affects the SiO2 surface and causes it to retreat from the surface. This is an unfavorable interaction for the EOR mechanism for two reasons: first, it increases the thickness of the EDL; second, it destroys the integrity of the stern layer that previously appeared on the surface of pure calcite, which was representative of the reservoir rock9,15,50,51. In turn, the instability of the stern layer leads to a decrease in EOR.
Nevertheless, when considering the overall interaction of Na⁺ with the entire composite rock surface, the net interaction becomes increasingly negative with salinity. This suggests that the overall force exerted by Na+ is attractive to the entire surface of the reservoir rock, indicating a favorable interaction for the EOR mechanism.
Interaction between Cl– and the rock surface
Another significant observation in Table 3 is the interaction between Cl⁻ ions and CaCO₃ surfaces. Cl⁻ exhibits a repulsive interaction with calcite that becomes increasingly pronounced with rising salinity, as reflected by interaction energies of + 658 kcal/mol, + 1479 kcal/mol, and + 2627 kcal/mol for the IW, SW, and FW systems, respectively. These increasingly positive values indicate that Cl⁻ ions are positioned farther away from the calcite surface at higher salinities. This behavior is further supported by the ion number density profiles shown in Fig. 3, which demonstrate that Cl⁻ distributions become more spatially localized under high-salinity conditions. The greater separation between Cl⁻ and the calcite surface reduces the hydrophilicity of the CaCO₃ phase, thereby negatively affecting key mechanisms related to EOR. This interpretation is consistent with the findings of Dastjerdi et al.51. In contrast, Cl− and SiO2 show a negative interaction at the beginning of the DW system, wherein Cl− is adsorbed to the SiO2 rock. The interaction energy increases to − 667 kcal/mol in the SW system as salinity rises, whereas it decreases to − 476 kcal/mol in FW. This suggests that Cl⁻ ions cease to interact significantly with the SiO₂ surface beyond a certain concentration. In the FW system, this behavior is attributed to the saturation of available surface sites, where Cl⁻ ions occupy all accessible Si sites on the SiO₂ surface. Briefly, it is significant to note that for all three systems under study, the total interaction between Cl− and the reservoir rock surface is positive (repulsion from the rock surface). On the other hand, Cl− prefers to adsorb to the SiO2 part of the reservoir rock surface. However, the increase in the Cl− is one of the main factors in creating the difference in electrical charge and, as a result, reduces the hydrophilicity of the rock surface, which leads to a decrease in EOR, Which is also consistent with Ding et al.52 observations.
We analyzed the electrostatic interactions of Na⁺ and Cl⁻ ions with the rock surface to quantify the charge-dependent interaction disparity. A stronger attractive force was observed between Na⁺ ions and the negatively charged rock surface, leading to a net positive interaction energy difference favoring Na⁺ adsorption. Meanwhile, the rock surface with Cl− ions exhibits a negative electrostatic force. The difference in electrostatic force values determines the interaction strength of the entire reservoir rock. The discrepancy in this electric charge provides a force-type value on the entire reservoir rock surface. In the ninth row (∑Na++Cl−) of Table 3, the interaction rates between Na+ and Cl− are listed in Table 3. The interaction energy value ranges from 209 kcal/mol in low-salinity to 764 kcal/mol. The FW system has recorded the most significant increase, leading to the highest reduction in the thickness of the EDL layer. At high salinities, the increasing electric charge difference force between brine ions is the primary factor influencing the reduction in EDL layer thickness. To validate this assertion, the findings derived from molecular dynamics were utilized. The charge density, density profile, and radial distribution function (RDF) plots are presented.
(1) The charge density and density profile:
Figure 2 displays the density diagrams for DW, SW, and FW systems on the right-hand side, while their charge diagram is on the left. The figure displays the salinity increase from top to bottom. purple and yellow lines indicate an increase in both value and area under the curve, which represents the increase in the electric force difference between Na+-Cl−. This information is also presented in Table 3. It is quantified by numbers. However, the density graphs on the right-hand side of Fig. 2 illustrate the increase in density of Na+ and Cl− ions (represented by the blue and green lines) as salinity increases. This change in density is believed to be the primary factor influencing the alteration of surface wettability, which is in agreement with the study24. Based on the findings, it can be inferred that the development of the EDL on a charged surface is influenced by the salinity and ionic compositions. Injecting low-ionic strength LSW decreases the repulsive electrostatic forces between the interfaces of oil-brine and brine-rock. This leads to the expansion of the EDL, resulting in the thickening of a thin water layer that separates oil and rock. As a consequence, the wettability of the system shifts towards a water-wet state, facilitating the separation of oil and increasing the oil’s relative permeability.
Fig. 2
The charge density is on the left side, and the density profile is on the right for DW, SW, and FW systems from top to bottom.
The expansion of EDL in the rock surface
Rock surfaces that come into contact with water attract positively charged cations, which are typically hydrated with water molecules, called the Stern layer, and it is devoid of anions. The positive charge of this layer can be caused by two possible causes. First, while the calcite plane is a neutral surface, the outermost atoms of the surface are sets of oxygen atoms of carbonate groups, resulting in relative polarity of the surface with a partial negative charge. Second, polar water molecules create a monolayer on the surface, increasing its polarity. The slab’s improved polarity with partial negative charge due to oxygen atoms makes it a favorable substrate for Na+ ions adsorption. Beyond the positive Stern layer, adjacent to the Stern layer is the Diffuse layer, which comprises liberated ions and has a greater concentration of counter ions. The term “EDL” denotes these two layers, and its thickness has been believed to be a significant factor in the mechanism by which wettability changes53.
Ion number-density profiles as a function of distance from a cleaved calcite–quartz composite for (A) SW and (B) FW. Gray shading denotes the atomic planes of the rock surfaces; the green region marks the Stern layer (SL), where counterions are tightly adsorbed, and the pink region indicates the diffuse layer (DL), where ion densities decay into the bulk.
In SW (Fig. 3A), the SL thickness is approximately 1.5 Å, with Na⁺ and Cl⁻ peaks that are relatively broad and a modest contribution of Ca²⁺/Mg²⁺ reflecting lower ionic strength (interaction energy ≈ 437 kcal/mol). In FW (Fig. 3B), the SL compresses to ~ 1.0 Å, dominated by sharply defined Na⁺ and Cl⁻ peaks (interaction energy ≈ 765 kcal/mol), in agreement with observations by Bourg and Sposito54. while divalent cations (Ca²⁺/Mg²⁺) remain less abundant near the surface due to competition and their stronger hydration, specifically for Mg2+ ions which exhibit a greater separation from the quartz surface compared to the calcite surface, indicating weaker direct surface interactions with quartz. This behavior is discussed in detail in Fig. 4. Beyond the SL, the first Cl⁻ peak shifts from ~ 10 Å in SW to ~ 12 Å in FW, and both Na⁺ and Cl⁻ distributions become more localized under high salinity. These results confirm that elevating brine salinity compacts the Stern layer and intensifies interfacial electrostatic interactions, thereby promoting ion bridging that enhances oil adhesion and drives the system toward oil-wet conditions, This finding aligns with the results reported by Gu et al.15 and Tian et al.33.
Fig. 3
Ion number density profiles for Na⁺, Cl⁻, Ca²⁺, and Mg²⁺, illustrating the formation of EDL on a composite calcite–quartz surface for (a) SW and (b) FW systems.
Figure 4a illustrates the RDF of Na+ ions interacting with oxygen on the surface of the CaCO3 reservoir rock. The RDF is plotted using a cut-off radius of 12 Å. The RDF is calculated separately for DW, SW, and FW. This allows for analyzing and studying the probable phenomena occurring near the rock within the specified distance. The initial peak is observed at a radius of 2.125 Å, indicating the beginning of Na+ ions pairing. The corresponding values are (23, 7, 6). This phrase describes the initial and immediate interaction between Na+ ions and the surface of CaCO3. The rise in salinity has resulted in a decrease in this correlation, primarily caused by the heightened electric charge difference force between Na+ and Cl−. This, in turn, leads to a reduction in the thickness of the EDL layer and subsequently decreases the quantity of Na+ molecules in the stern layer. Another contributing cause to the peak reduction in this picture is the development of ion bridges due to increased salinity. This phenomenon has been demonstrated in previous study55.
Fig. 4
Radial distribution function for CaCO3–Na+ interaction (a) and CaCO3–Cl− interaction (b).
In contrast, Fig. 4b illustrates the correlation between Cl− atoms and the CaCO3 surface, with DW, SW, and FW. Notably, the first peak appears at a radius of 3.875 Å, indicating that the pairing with calcite commences at a distance of 1.75 Å from the Na+ ions (Stern layer). Due to the reasons stated previously, an increase in salinity has an inverse relationship with the number of Cl− molecules that tend to pair with calcite.
Figure 4 presents the RDFs of CaCO₃–Na+, CaCO₃–Cl− for different brine systems. This figure provides insights into the affinity of Na⁺, Cl⁻ions for interacting with the calcite surface in the simulated reservoir environment. As expected, Na⁺ ions, which are typically hydrated by water molecules, initially associate with oxygen atoms on the CaCO₃ surface. With increasing salinity, the number of Na⁺ ion pairs decreases, leading to a higher electrostatic force disparity between Na⁺ and Cl⁻ ions. Consequently, this results in a decrease in the thickness of the electrical double layer (EDL), as demonstrated in Fig. 2a–c by the rising density profiles and surface charge densities of Na⁺ lines and Cl⁻ lines across the three systems: DW (A), SW (B), and FW (C).
The presence of Na+ and Cl− ions arranged in a layered structure can be noticed on the surface of calcite in brines, as shown in Figs. 4 and 6. The Na+ RDF peak adjacent to calcite, within the first compact hydration layer indicates a solid-like form. This shows that the cation is strongly bound directly to the substrate without any water molecules in between. It is important to note that Na+ ions are more likely to be adsorbed onto the outermost oxygen of the calcite tissue. At the same time, water molecules are more likely to be adsorbed above the calcium of calcite rock. The AFM experiment conducted by Ricci et al.56 provides evidence for the preferential localization of Na+ cations on a calcite surface when in contact with an electrolyte solution. The presence of dangling oxygen of calcite atoms, specifically located above the calcium of calcite atoms in the calcite tissue, is responsible for the gap observed between the first peak of water molecules and the peak corresponding to Na+ ions near the calcite tissue as it is shown in Fig. 4a.
Figure 5 depicts the presence of Na+ and Cl− ions on the quartz surface. Figure 5a depicts the RDF of Na+ ions interacting with the quartz surface of the reservoir rock under different salinity conditions: DW, SW, and FW salinity. In the DW system, the first peak measurement at a radius of 3.875 Å yields a count of 8, suggesting a significant occurrence of Na-quartz rock pairings. As salinity increases, the affinity of Na+ to bond with quartz diminishes. However, Fig. 5b illustrates the bonding between Cl− ions and the quartz surface. This bonding is shown by the first peak at a radius of 3.375 Å, which corresponds to a value of 1.2. Conversely, the Na+ of Cl− ions exhibits no inclination to establish a connection with quartz rock at lower radii, but its concentration rises at larger radii. It is important to observe that as salinity increases, the propensity of Cl− to interact with SiO2 rock diminishes.
Fig. 5
The radial distribution function for SiO2-Na+ interaction in above and SiO2-Cl− interaction at the down.
Figure 6a displays the RDF CaCO3–Owater for all three states of the system. It indicates that as salinity increases, there is a higher likelihood of water oxygen linking with calcite rock (shown by the first peak at (:approx:) 2.37 Å in Fig. 6a), with a recorded value of (:approx:) 6.25. The direction of water molecules towards calcite rock is mostly influenced by the oxygen atoms in the water. As salinity increases, the quantity of ions in the porous medium also increases. This causes the EDL to become thinner, resulting in a greater amount of water oxygen molecules that are inclined to bond with calcite. in agreement with recent experimental and simulation results55.
Fig. 6
The radial distribution function for CaCO3–O water interaction in above and CaCO3–H water interaction at the down.
The RDF CaCO3–O–H of water in Fig. 6b show that the calcite surface is hydrated by a well-organized layer of water molecules. The density peaks at distances of (:approx:) 2.3 and 3.1 Å from the surface, which are marked by two horizontal black lines along Fig. 6. These consist of a highly condensed, solid-like layer of water that directly covers the calcite tissue, followed by a less dense and slightly less organized layer of hydration in all levels of salinity. In all salinities, the densities and thicknesses of these hydration layers are nearly identical. The monolayer of water directly above the calcite is almost solid, supporting the mineral’s continued wetness57. The X-ray reflectivity results published by Fenter et al.55 are consistent with the formation of two water mono-layers on a calcite surface. Furthermore, it was recently proven that calcite’s highly ordered crystal structure initiates the creation of these well-structured hydration layers58.
Figure 6b depicts the pairing between Hwater atoms and Cacalcite rock. The initial peak indicates a 50% decrease ((:text{g}left(text{r}right)approx:) 3.12) in the Hwater atoms pairing with Cacalcite rock compared to Owater atoms ((:text{g}left(text{r}right)approx:) 6.25) in Fig. 6a. Furthermore, it signifies that the peak of water Hwater atoms is present at a greater distance, i.e. 3.2 Å, which supports the findings of Ghatee and Koleini 2017 study59 and indicates that water is adsorbed to the outermost calcium atoms of calcite through the oxygen atoms.
Figure 7a represents the RDF of SiO2-Owater for three systems. The first peak, recorded at g(r) ≈ 1.5, is situated at a distance of ≈ 3.1 Å. The increase in salinity encourages atom pairing with quartz rock. However, Fig. 7b displays the RDF SiO2-H water, which produces the first peak at a greater distance, i.e. 3.3, suggesting the connection of quartz rock and water from the oxygen edge. Previous investigation36 support these findings.
Fig. 7
The radial distribution function for SiO2_Owater interaction in above (a) and SiO2_Hwater interaction at the down (b).
A distinct variation has been seen when comparing the interaction between water’s hydrogen and oxygen atoms in quartz and calcite. Water molecules (oxygen and hydrogen) exhibit a comparable process of pairing with quartz rock. Regarding the orientation of water molecules (oxygen and hydrogen), the orientation with quartz rock seems nearly identical. However, in contrast to calcite, quartz does not have a clearly defined peak at the beginning of the graph like calcite. This is a significant difference between the two rock composite tissues, which proves that there is no indication or propensity for a hydrated layer to form on the quartz surface. Conversely, Fig. 6 displays the calcite surface, which indicates the propensity to form hydration layers in all salinities.
In Fig. 8A, the RDF of CaCo3–Mg2+ in SW exhibits a strong inner-sphere peak at ≈ 2,8 Å (g(r) ≈ 4.8), followed by well‐defined second and third hydration‐shell peaks at ≈ 4 Å and ≈ 5 Å. This first‐shell distance and magnitude agree closely with MD reports of direct Mg²⁺ coordination to CaCo3 surface60. Under FW conditions RDF of CaCo3–Mg2+ the inner‐sphere peak shifts outward to ≈ 5 Å with markedly lower intensity (g(r) ≈ 1.5), indicating that elevated Ca²⁺ and Na⁺ concentrations compete for adsorption sites and promote Mg²⁺ outer‐sphere, water‐mediated binding60. In Fig. 8B, Mg²⁺ in RDF of SiO2–Mg2+ interacts far more weakly with quartz: the primary g(r) peak appears at ≈ 9 Å in SW and shifts slightly to ≈ 10 Å in FW, with peak heights of ~ 3.5 SW and ~ 2.2 FW, confirm that increased ionic strength in FW significantly screens electrostatic interactions, thereby reducing the affinity of Mg²⁺ for the hydrophilic SiO2 surface.
Fig. 8
Radial distribution functions (g(r)) of Mg²⁺ relative to surface oxygen atoms on (A) calcite surface and (B) quartz surface under (SW, black) and (FW, red) systems.
Hence, in composite calcite–quartz systems, Mg²⁺ exhibits a salinity-dependent, ion‐specific adsorption behavior: under SW, Mg²⁺ preferentially forms inner‐sphere complexes at calcite’s specific binding sites, while on quartz it remains entirely in outer‐sphere hydration shells. However, in FW, elevated ionic strength and competitive adsorption by monovalent ions disrupt Mg²⁺ inner‐sphere coordination on calcite, shifting it into outer‐sphere configurations on both minerals. This dual influence of mineral heterogeneity and ionic competition critically shapes the electric double layer in carbonate reservoir rocks.
In Fig. 9A, the RDF of CaCo3–Ca2+, Ca²⁺ ions in SW exhibit a weak interaction with the surface, with the first RDF peak appearing at ~ 7.5 Å and an intensity of 0.2. In FW, this peak shifts to g(r) ≈ 9 Å and increases to g(r) ≈ 0.6. These low-intensity peaks in both cases suggest that Ca²⁺ does not strongly bind to the calcite surface, resulting in a relatively diffuse and unstable EDL, with limited impact on wettility alteration under both brine types. In contrast, near the quartz surface, Ca²⁺ shows a much stronger interaction. In sw, the first peak occurs at g(r) ≈ 7.5 Å with a high intensity of g(r) ≈ 1.75, suggesting strong adsorption onto the negatively charged quartz surface. In FW the peak shifts to a longer distance ~ 11 Å, indicating more distant accumulation. This behavior confirms that Ca²⁺ ions preferentially interact with quartz, especially under SW conditions.
Fig. 9
Radial distribution functions g(r) of Ca²⁺ relative to surface oxygens on (A) calcite and (B) quartz facets under (SW, black) and (FW, red) salinities.
From a practical standpoint, these findings indicate that in quartz-dominated rocks, the presence of divalent cations like Ca²⁺ (especially in FW) can lead to EDL disruption and stronger charge inversion, which may drive wettability alteration toward oil-wet conditions. These insights are crucial when designing low-salinity waterflooding strategies or interpreting ion-specific effects in carbonate reservoirs.
Figure 10 presents the RDF between the carbon atoms of the oil molecules and the oxygen atoms of the carbonate rock surface, evaluated at a cut-off radius of 12 Å from the rock. This analysis aimed to assess the adhesion behavior of oil molecules to the rock surface under three different brine conditions. The results reveal that increasing salinity leads to a greater number of oil molecules interacting with the carbonate surface, which is consistent with previous studie18,61 the DW system exhibited the fewest interactions with the rock from the beginning. This suggests that DW injecting in carbonate rock exhibits the highest and most persistent degree of surface hydrophilicity.
Fig. 10
Radial distribution function plots between the carbon of the oil molecules and the oxygen of the carbonate reservoir rock in the cut-off radius of 12 Å for DW, SW, and FW systems.
MSD analysis in Fig. 11a was also used to check the movement of atoms during the simulation, which showed that Na + ions are the most mobile in the case of the SW system due to the most significant number of ions that interacted with the oxygen atoms of the rock surface at the beginning (approximately the first third) of the simulation. However, in the case of FW, due to the increase in the number of atoms in the porous medium, the movement of Na+ ions was reduced by half, which is due to the occupation of the oxygen atoms of the rock surface in a faster time frame and at the very beginning of the simulation, which is due to the increase in the number of sodium atoms. In the case of the DW system, its mobility was the least due to the stable distribution of the limited number of Na+ ions in the position of the oxygen atoms of the rock surface. Figure 11b, related to the MSD of Cl− ions, provides that in all systems, Cl− ions first show a small mobility, which is due to the adsorption of Cl− into the quartz tissue; then, after the occupation of Silicon of quartz atoms by Cl− ions, other Cl− ions begin to move more freely (approximately in three-quarters of the simulation interval). An increase in salinity leads to a decrease in the movement of Cl− ions because by interacting with each other, they create a mass of atoms of the same type, which play the role of a bridge between the oil and the reservoir rock, which, in turn, increases the adhesion of oil to the carbonate rock and reduces the EOR. The MSD diagram of the oil in Fig. 11c clearly shows that the decrease in salinity reduces the mobility of the oil and, as a result, reduces the adhesion of the oil to the carbonate rock surface and improves the EOR.
Fig. 11
Mean Squared Displacement for each (a) Na + ions, (b) Cl- ions and (c) oil atoms for DW, SW, and FW systems.
Figure 12A, corresponding to the DW system, shows the upward migration of the oil phase away from the surface of the composite rock by the end of the simulation. This behavior is attributed to the disruption of ionic bridges between the oil and brine phases, which facilitates the detachment of oil from the rock surface and its movement toward production wells. Such detachment indicates a reduction in oil-wet, a condition favorable for EOR mechanism.
Fig. 12
Two-dimensional density maps in the two-layer plane (ZY) for composite rock composed of calcite and quartz for three system states at the beginning of the simulation (left) and at the end of the simulation (right) from the production stage of the simulations.
Figure 12B shows the SW system, with the morphology of oil and brine clearly distinguished at the final simulation stage. Here, ion clusters—particularly divalent cations (Ca²⁺-Mg²⁺) form bridging structures that interconnect oil masses with the carbonate surface. These cation-mediated bridges promote oil adhesion to the rock, which is unfavorable for EOR mechanism.
Similarly, Fig. 12C depicts the FW system, where abundant divalent ions in the high-salinity brine create strong ionic bridges at the oil–rock interface. This results in enhanced adhesion of oil to the carbonate reservoir rock and reduced oil mobility. The dominant role of Ca²⁺ and Mg²⁺ ions in governing electrostatic interactions and wettability alteration is thus clearly demonstrated across the different brine compositions. To provide further clarification, the initial and final atomic configurations for all systems are presented in figure.S9 of the Supplementary Information.
On a quiet mountaintop in southern Arizona, 5,000 robotic eyes scan the sky every night, each locking onto a galaxy billions of light-years away. This interesting instrument, called DESI, has now delivered a surprise big enough to unsettle decades of assumptions in physics.
For years, scientists believed dark energy, the invisible force that accelerates the universe’s expansion, was constant. However, a new study based on DESI’s latest map of the cosmos suggests otherwise.
According to the study authors, dark energy’s effect on the universe isn’t fixed, but appears to have shifted over the eons. The finding comes with an even intriguing twist. The team’s analysis hints that black holes may be quietly turning dead stars into dark energy.
For physicists who have been chasing dark energy for more than two decades, the idea that it could be tied to star death and black hole birth isn’t just unexpected, it could be transformative.
The DESI telescope uses thousands of robotic eyes, each targeting a different galaxy every 15 minutes. Night after night, it has built the most detailed map yet of the universe, tracking millions of galaxies and ancient glowing objects, some dating back to when the universe was less than half its current age.
The study authors focused on an unconventional idea called the cosmologically coupled black hole (CCBH) hypothesis. Instead of picturing black holes only as cosmic traps that consume matter, the CCBH model describes them as tiny droplets of dark energy.
According to this view, when a massive star dies and collapses into a black hole, part of its ordinary matter is transformed into dark energy. If that’s true, the amount of dark energy in the universe should rise in step with the history of star formation, a rate astronomers have measured for decades using the Hubble and James Webb space telescopes.
When the team applied this model to DESI’s three years of precise data, the results were striking. The dark energy density appeared to track the star formation rate, just as predicted by the CCBH hypothesis. “This paper is fitting the data to a particular physical model for the first time, and it works well,” Gregory Tarlé, one of the researchers, said.
This is not it. The CCBH model also solved another long-standing puzzle, i.e., the mass of neutrinos. Scientists know neutrinos must have mass greater than zero, but pinning down the exact value has been extremely hard. Using traditional interpretations of DESI’s data, the numbers looked unphysical, even hinting at negative masses.
With our model, “you find that the neutrino mass probability distribution points to not only a positive number, but a number that’s entirely in line with ground-based experiments. I find this very exciting,” Rogier Windhorst, another study author, added.
This gave the CCBH hypothesis a major boost in credibility. Moreover, the study also helped ease another tension in cosmology, which is the so-called Hubble rate problem.
Different methods of measuring how fast the universe is expanding today give slightly different answers. However, if some matter is steadily being turned into dark energy, as the CCBH model suggests, the universe’s expansion would have sped up earlier than expected, bringing the numbers closer together.
Time to make some changes
If black holes really are factories of dark energy, physics textbooks may need a rewrite. The CCBH model smoothly ties together various phenomena that once seemed unrelated: the deaths of stars, the behavior of black holes, the growth of the universe, and the mysterious properties of neutrinos.
It also offers a natural explanation for why dark energy appeared only after stars formed, instead of being a magic number fixed at the birth of the cosmos. However, the model is still very far from being considered an unshakable cosmic truth.
While DESI’s large-scale galaxy maps support the CCBH interpretation, detailed studies of individual black holes do not yet provide equally strong evidence.
The study authors also suggest that more data, sharper measurements, and independent tests are needed to validate this approach.
“It will take more data, rigorous analysis, and broader scrutiny to determine whether it can become a new paradigm for explaining our universe. Of course, it could also be ruled out as new data emerges,” Gustavo Niz, one of the study authors, said.
The study is published in the journal Physical Review Letters.
Research on lunar soil from China”s Chang’e 6 mission has identified the sources of young volcanism on the moon and proposed a thermodynamic mechanism for its activity, offering insights into volcanic processes on other small, atmosphere-less celestial bodies. The study was published Saturday in the journal Science Advances.
It was previously believed that lunar volcanic activity ceased 3 billion years ago. But a research team led by Wang Chengyuan and Academician Xu Yigang of the Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, discovered two types of basalts in the Chang’e 6 samples. These basalts, formed around 2.8 billion and 2.9 billion years ago, have different compositions and originated from different source depths.
One type is very-low-titanium basalt sourced from the deeper lunar mantle at depths greater than 120 kilometers, while the other is low-titanium basalt from a shallower part of the mantle, ranging from 60 to 80 kilometers.
By simulating the moon’s high-temperature, high-pressure interior, researchers found the two types of basalts came from distinct rock layers created during crystallization of the moon’s early magma ocean: the pyroxenite layer and the ilmenite-bearing pyroxenite layer.
The traditional view suggested that young volcanic activity on the moon might be tied to source regions rich in water or radioactive heat-generating elements. But samples from both the Chang’e 5 and Chang’e 6 missions refuted this, showing their source regions were dry and depleted in radiogenic isotopes.
Based on comparisons of the two basalt types from Chang’e 6, the team proposed a new thermodynamic mechanism. As the moon cooled, its lithosphere thickened, making it difficult for deep magma to erupt directly and causing it to be trapped at the bottom of shallow pyroxenite layers in the mantle. These trapped magmas could conduct heat upward, triggering partial melting of the shallow mantle and producing volcanic eruptions.
To test the mechanism, researchers analyzed global lunar remote sensing data and identified a significant shift in volcanic activity around 3 billion years ago. Before the shift, heat sources were varied — including radiogenic heat, tidal forces and meteorite impacts. Afterward, a single bottom-up heat transfer process dominated, concentrating younger volcanic activity in the shallow mantle.
Further analysis of remote sensing data showed that the chemical traits of younger volcanic rocks on the moon’s near side closely matched basalts collected by Chang’e 5, while those on the far side more closely resembled ultra-low-titanium basalts from Chang’e 6.
This suggests differences in mantle composition between the near and far sides of the moon. The shallow mantle on the near side may contain more ilmenite, while that on the far side may have less — offering new clues to the moon’s asymmetry.
In June 2024, Chang’e 6 returned with 1,935.3 grams of soil from the far side of the moon, a historic first. The samples were collected from the South Pole-Aitken Basin, the largest, deepest and oldest impact crater on the moon.
Previous studies of the Chang’e 6 samples have already yielded major breakthroughs. They revealed volcanic activity on the far side around 4.2 billion and 2.8 billion years ago, showing that volcanism persisted for at least 1.4 billion years. Researchers also measured the water content of the far-side mantle for the first time — less than 2 micrograms per gram — indicating it is extremely dry.
For centuries, humanity relied on coastal landmarks and tide gauges to understand sea levels. But satellites changed everything. Since the early 1990s, orbiting instruments have provided precise, global records of ocean surface height.
These data revealed not only how seas are rising but also how predictions from decades ago were impressively accurate.
Satellites changed sea-level tracking
Study lead author Torbjörn Törnqvist is a professor in the Department of Earth and Environmental Sciences at Tulane University.
“The ultimate test of climate projections is to compare them with what has played out since they were made, but this requires patience – it takes decades of observations,” said Törnqvist.
He noted that the team was quite amazed at how good those early projections were, especially considering how crude the models were back then, compared to what is available now.
“For anyone who questions the role of humans in changing our climate, here is some of the best proof that we have understood for decades what is really happening, and that we can make credible projections.”
Sea-level rise differs across regions
Professor Sönke Dangendorf emphasized the importance of translating global patterns into regional forecasts.
“Sea level doesn’t rise uniformly – it varies widely. Our recent study of this regional variability and the processes behind it relies heavily on data from NASA’s satellite missions and NOAA’s ocean monitoring programs,” he said.
“Continuing these efforts is more important than ever, and essential for informed decision-making to benefit the people living along the coast.”
Satellites confirm acceleration
When satellites first began tracking sea levels in the early 1990s, they showed an average increase of about one eighth of an inch per year. It was only later that scientists confirmed this pace was speeding up.
By October 2024, NASA researchers announced that the rate of rise had doubled over three decades. That moment offered the perfect opportunity to compare real-world changes against projections made nearly thirty years earlier.
Close call with predictions
In 1996, the Intergovernmental Panel on Climate Change (IPCC) released its assessment report, just as satellite monitoring began.
The report projected about 8 centimeters of sea-level rise over 30 years. The actual outcome was 9 centimeters, nearly identical.
However, the models underestimated melting ice sheets by over 2 centimeters. Back then, the destabilizing effects of warming ocean waters on Antarctic ice were poorly understood. Greenland’s ice was also flowing into the ocean faster than anticipated.
Components of sea-level rise
The study shows that thermal expansion of seawater and melting of smaller glaciers were predicted fairly well. But contributions from Greenland and Antarctica were treated as negligible.
In reality, these ice sheets accounted for nearly a quarter of observed sea-level rise. Another overlooked factor was groundwater depletion, which transferred more water to oceans than expected.
Ignoring ice-sheets caused errors
Early IPCC reports assumed that the dynamic behavior of ice sheets could be ignored for decades. This assumption proved incorrect. Later assessments that excluded dynamic ice flow produced unrealistically low projections.
Once dynamic ice loss was included, estimates increased significantly. Modern assessments now highlight the “deep uncertainty” surrounding possible ice-sheet disintegration, which could drive sea levels far higher than expected.
Past models were surprisingly accurate
Thermal expansion was slightly overestimated, which balanced out the underestimated role of ice sheets.
This created projections that, by chance, matched reality more closely than their flawed assumptions should have allowed.
Still, the overall accuracy offers confidence in today’s more advanced models, especially since early reports successfully predicted atmospheric carbon dioxide levels.
Future climate projections
Predictions made in the 1990s have proven largely accurate, but the greatest challenges still lie ahead.
Ongoing uncertainty about ice sheets and human emissions makes continuous monitoring vital for helping coastal societies prepare for what lies ahead.
“Given the advances in both resolution and process understanding since the 1990s, the early success of the IPCC-SAR projection gives considerable confidence to climate projections for the future,” wrote the researchers.
“Meanwhile, the importance of continued monitoring of all relevant components of the climate system by key agencies cannot be understated.”
The study is published in the journal Earth’s Future.
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Mimicking the way experienced human observers track growth over time, 3D-NOD integrates novel labeling, registration, and data augmentation strategies to boost sensitivity and accuracy. Tested across multiple crop species, the system achieved an impressive mean F1-score of 88.13% and IoU of 80.68%, offering a powerful tool for real-time, organ-level plant phenotyping.
Accurate plant growth monitoring underpins modern agriculture, enabling yield prediction, stress detection, and precise phenotyping. Traditional methods—measuring plant height, canopy volume, or detecting diseases—often rely on 2D images, which cannot fully capture depth or resolve self-occluded structures. Spatiotemporal phenotyping, which tracks individual organs over time, offers richer insights but faces challenges in detecting small, newly emerged organs and handling complex plant architectures. 3D sensing technology addresses these limitations by capturing depth information, but current 3D approaches are either computationally heavy or require organs to be large enough for tracking. Based on these challenges, the researchers developed a method to improve early-stage detection of plant growth events from time-series 3D data.
A study (DOI: 10.1016/j.plaphe.2025.100002) published in Plant Phenomicson 22 February 2025 by Dawei Li’s team, Donghua University, presents a highly sensitive 3D deep learning framework for detecting new plant organs, enabling more accurate and real-time growth monitoring to advance precision agriculture and phenotyping.
The researchers evaluated the 3D-NOD framework using a high-precision 3D plant dataset containing tobacco, tomato, and sorghum. They constructed a spatiotemporal dataset of 37 growth sequences, comprising 468 point clouds, each with over ten growth stages. Since most sequences captured seedlings, the primary growth events were budding. Using the Semantic Segmentation Editor under Ubuntu, they annotated all points under the Backward & Forward Labeling (BFL) strategy into two semantic classes—“old organ” and “new organ.” The training set included 25 sequences and the test set 12 sequences. To enhance learning, each mixed point cloud underwent Humanoid Data Augmentation (HDA) to generate ten variants for training the DGCNN backbone. Performance was assessed with Precision, Recall, F1-score, and Intersection over Union (IoU). Compared to PointNet, PointNet++, DGCNN, and PAConv, 3D-NOD achieved superior sensitivity in new organ detection, with F1 and IoU for new organs reaching 76.65% and 62.14%, respectively, despite many buds being too small for human identification. Qualitative results confirmed accurate detection of tiny buds across all three species with low false alarms. Ablation studies demonstrated that removing any key component—BFL, Registration & Mix-up (RMU), or parts of HDA—caused noticeable performance declines, underscoring their combined importance. Interestingly, detection was best in sorghum, likely due to its faster bud growth. To meet real-time phenotyping needs, the team adapted the pipeline for single point cloud testing by creating pseudo-temporal inputs, enabling inference without multiple growth stages. Comparative analyses on tomato, tobacco, and sorghum sequences showed only minor accuracy reductions compared to standard spatiotemporal testing, highlighting the framework’s versatility for both multi-stage and single-stage growth monitoring scenarios.
3D-NOD offers a robust, scalable solution for precision agriculture and breeding programs. By accurately detecting organ-level growth events early, it enables researchers and growers to monitor crop development more closely, improve trait measurements, and optimize resource use. This capability is vital for high-throughput phenotyping, where rapid and non-invasive measurement of plant traits drives selection efficiency. The framework’s adaptability to different species suggests broad potential in monitoring diverse crops under field or greenhouse conditions. Beyond agriculture, the method could inform botanical research, forestry, and ecological studies where fine-scale structural changes in plants are critical indicators of health and development.
###
References
DOI
10.1016/j.plaphe.2025.100002
Original Source URL
https://doi.org/10.1016/j.plaphe.2025.100002
Funding information
This work was supported by the self-collected funds from Dawei Li.
About Plant Phenomics
Science Partner Journal Plant Phenomics is an online-only Open Access journal published in affiliation with the State Key Laboratory of Crop Genetics & Germplasm Enhancement, Nanjing Agricultural University (NAU) and distributed by the American Association for the Advancement of Science (AAAS). Like all partners participating in the Science Partner Journal program, Plant Phenomics is editorially independent from the Science family of journals. Editorial decisions and scientific activities pursued by the journal’s Editorial Board are made independently, based on scientific merit and adhering to the highest standards for accurate and ethical promotion of science. These decisions and activities are in no way influenced by the financial support of NAU, NAU administration, or any other institutions and sponsors. The Editorial Board is solely responsible for all content published in the journal. To learn more about the Science Partner Journal program, visit the SPJ program homepage.
Researchers confirmed active flat bands in a kagome superconductor, opening new possibilities for designing quantum materials and future electronic technologies. (Artist’s concept). Credit: SciTechDaily.com
A research team has provided the first experimental proof that flat electronic bands in a kagome superconductor are active and directly shape electronic and magnetic behaviors.
Researchers from Rice University, working with international partners, have found the first clear evidence of active flat electronic bands within a kagome superconductor. The discovery marks an important step toward creating new strategies for designing quantum materials, including superconductors, topological insulators, and spin-based electronics, which could play a central role in advancing future electronics and computing.
The findings, published on August 14 in Nature Communications, focus on the chromium-based kagome metal CsCr₃Sb₅, a material that becomes superconducting when placed under pressure.
Kagome metals are defined by their unique two-dimensional lattice of corner-sharing triangles. Recent theories have suggested that these structures can host compact molecular orbitals, or standing-wave patterns of electrons, which may enable unconventional superconductivity and unusual magnetic states driven by electron correlation effects.
In most known materials, such flat bands are positioned too far from the relevant energy levels to influence behavior. In CsCr₃Sb₅, however, they play an active role and directly shape the properties of the material.
Pengcheng Dai, Ming Yi, and Qimiao Si of Rice’s Department of Physics and Astronomy and Smalley-Curl Institute, along with Di-Jing Huang of Taiwan’s National Synchrotron Radiation Research Center, led the study.
Ming Yi. Credit: Jeff Fitlow/Rice University
“Our results confirm a surprising theoretical prediction and establish a pathway for engineering exotic superconductivity through chemical and structural control,” said Dai, the Sam and Helen Worden Professor of Physics and Astronomy.
The finding provides experimental proof for ideas that had only existed in theoretical models. It also shows how the intricate geometry of kagome lattices can be used as a design tool for controlling the behavior of electrons in solids.
“By identifying active flat bands, we’ve demonstrated a direct connection between lattice geometry and emergent quantum states,” said Yi, an associate professor of physics and astronomy.
Experimental Techniques and Findings
The research team employed two advanced synchrotron techniques alongside theoretical modeling to investigate the presence of active standing-wave electron modes. They used angle-resolved photoemission spectroscopy (ARPES) to map electrons emitted under synchrotron light, revealing distinct signatures associated with compact molecular orbitals. Resonant inelastic X-ray scattering (RIXS) measured magnetic excitations linked to these electronic modes.
“The ARPES and RIXS results of our collaborative team give a consistent picture that flat bands here are not passive spectators but active participants in shaping the magnetic and electronic landscape,” said Si, the Harry C. and Olga K. Wiess Professor of Physics and Astronomy, “This is amazing to see given that, until now, we were only able to see such features in abstract theoretical models.”
Theoretical support was provided by analyzing the effect of strong correlations starting from a custom-built electronic lattice model, which replicated the observed features and guided the interpretation of results. Fang Xie, a Rice Academy Junior Fellow and co-first author, led that portion of the study.
Obtaining such precise data required unusually large and pure crystals of CsCr₃Sb₅, synthesized using a refined method that produced samples 100 times larger than previous efforts, said Zehao Wang, a Rice graduate student and co-first author.
The work underscores the potential of interdisciplinary research across fields of study, said Yucheng Guo, a Rice graduate student and co-first author who led the ARPES work.
“This work was possible due to the collaboration that consisted of materials design, synthesis, electron and magnetic spectroscopy characterization, and theory,” Guo said.
Reference: “Spin excitations and flat electronic bands in a Cr-based kagome superconductor” by Zehao Wang, Yucheng Guo, Hsiao-Yu Huang, Fang Xie, Yuefei Huang, Bin Gao, Ji Seop Oh, Han Wu, Jun Okamoto, Ganesha Channagowdra, Chien-Te Chen, Feng Ye, Xingye Lu, Zhaoyu Liu, Zheng Ren, Yuan Fang, Yiming Wang, Ananya Biswas, Yichen Zhang, Ziqin Yue, Cheng Hu, Chris Jozwiak, Aaron Bostwick, Eli Rotenberg, Makoto Hashimoto, Donghui Lu, Junichiro Kono, Jiun-Haw Chu, Boris I. Yakobson, Robert J. Birgeneau, Guang-Han Cao, Atsushi Fujimori, Di-Jing Huang, Qimiao Si, Ming Yi and Pengcheng Dai, 14 August 2025, Nature Communications. DOI: 10.1038/s41467-025-62298-5
Funding: U.S. Department of Energy, Welch Foundation, Gordon and Betty Moore Foundation, Air Force Office of Scientific Research, U.S. National Science Foundation
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