Japanese chemists conducted a detailed study on the mechanism of hydrogen diffusion at temperatures below 70 Kelvin in vanadium—one of the most promising metals for hydrogen storage. They found that the behavior of diffusion critically depends on the metal's crystal structure.
In the cubic α-phase, hydrogen atoms are capable of tunneling between tetrahedral lattice positions, exhibiting a diffusion coefficient of at least 10–12 square centimeters per second even at 35 Kelvin. However, in the uniaxially deformed β-phase, the tunneling process is suppressed, and diffusion occurs via thermal jumps between octahedral positions with an activation energy of 148 milli-electronvolts.
Vanadium is valued for its ability to exothermically absorb and store hydrogen in a larger volume compared to other analogues. Since hydrogen has the smallest mass among elements, at low temperatures, when the thermal barrier for an atom in the lattice is overcome, its movement in metals is determined by quantum phenomena such as zero-point vibrations and quantum tunneling.
Tunneling is a physical phenomenon that allows a particle to pass through a potential barrier that is classically considered insurmountable; the probability of such a transition increases with a decrease in the particle's mass and a reduction in the barrier height. Theoretically, this phenomenon should be most pronounced in metals with a body-centered cubic lattice, where the distance between neighboring interstitial sites is smaller than in a face-centered cubic lattice. Nevertheless, there was very little experimental data on the mechanism of hydrogen tunneling at low temperatures.
Takahiro Ozawa from the Institute of Industrial Science at the University of Tokyo, together with a team of researchers, began studying the influence of crystal symmetry on proton tunneling in vanadium. For this purpose, an epitaxial film of vanadium with a thickness of 40 nanometers was grown on a magnesium oxide substrate. Then, hydrogen ions were implanted into this film at a temperature of 35 Kelvin with an energy of 500 electronvolts, creating a non-uniform distribution.
At low concentrations, hydrogen penetrates into lattice vacancies, maintaining the cubic α-phase. But at higher concentrations, around 0.3–0.6 atoms per vanadium atom, the metal transforms into a tetragonal β-phase with uniaxial lattice distortion. This change is caused by the hydrogen atoms aligning along the Oz axis, pushing the vanadium atoms and stretching the structure. As a result, the unit cell acquires 12 inequivalent tetrahedral and 6 octahedral positions.
The authors used the nuclear reaction method, bombarding the sample with a beam of $^{15} ext{N}$ ions to determine the hydrogen concentration gradient through emitted gamma radiation. It was found that at 35 Kelvin, hydrogen concentrates in a thin layer near the surface, whereas at 70 and 200 Kelvin, it is distributed uniformly throughout the entire film thickness. Thus, diffusion in the β-phase is activated approximately at 70 Kelvin, while in the α-phase, hydrogen remains mobile at 35 Kelvin and below.
Analysis of the time dynamics of electrical resistance, where non-uniform hydrogen distribution leads to lower resistance than uniform distribution, combined with random walk modeling, allowed the calculation of the diffusion coefficient in the β-phase as $148 ext{ ± } 20$ milli-electronvolts. Furthermore, signs of tunneling were not detected because the Arrhenius plot used to determine the diffusion coefficient did not have the characteristic upward bend at the end, indicating classical, thermally dependent diffusion in this phase.
Subsequently, the group of scientists performed calculations of hydrogen quantum states and explained the observed differences. In the cubic α-phase, all 12 tetrahedral positions are equivalent, and the wave function of the ground state is distributed across all of them, confirming the possibility of tunneling. The calculated tunneling matrix element between nearest neighbors was $-0.61$ milli-electronvolts, corresponding to a transition frequency of about $3 imes 10^{11}$ per second and a diffusion coefficient of $5.5 imes 10^{-6}$ square centimeters per second. The negative sign indicates an energetic advantage of tunneling for hydrogen atoms.
In the deformed β-phase, the reduction in symmetry leads to the localization of the wave function around the octahedral Oz positions, the splitting of levels between them disappears, and the direct Oz–Oz path has a barrier of $1.025$ electronvolts and is geometrically $2 ext{√}2$ times longer than the T–T path, making tunneling practically impossible. Although indirect routes through tetrahedral positions have barriers of $0.228$ and $0.376$ electronvolts, the mismatch of energy levels at intermediate nodes further reduces the probability of quantum tunneling.
In conclusion, the authors succeeded in detailing how changes in the crystal structure of vanadium affect the diffusion of hydrogen atoms in this metal, which will aid in further understanding processes related to hydrogen storage in similar materials.
WhatsApp is implementing a widget in its beta version 2.26.302 for Android, allowing users to record voice messages without needing to start a conversation. This feature enables the user to record the audio and subsequently choose one or more recipients, eliminating the need to create broadcast lists.
This functionality is being progressively released to participants in the WhatsApp beta program on Android. The new feature was detected via the specialized website WABetaInfo, which monitors tests on the Google Play Store.
When interacting with the widget, WhatsApp displays a specific interface for audio recording, offering usual controls such as record, pause, resume, discard, or confirm the message before sending, similar to what occurs within conventional chats.
After finishing the recording, the application presents a screen where the user selects the contacts to send the same message to. Although this capability already exists through broadcast lists, the new feature may be particularly useful for those who need to send quick announcements to several conversations without relying on a pre-configured list.
Additionally, this tool eliminates the current process of recording in a chat, sending it, and only then forwarding it to other individuals. Furthermore, it simplifies the creation of voice notes, allowing the user to start speaking just by unlocking the smartphone.
Besides the audio widget, WhatsApp has recently been testing and releasing several other modifications. The company announced comprehensive improvements for platforms such as iPad, CarPlay, Android Auto, Web, and Desktop. These improvements include direct account registration on iPad, the ability to view PDFs in chats with integration to Adobe Acrobat, and sharing music from Apple Music and Spotify in Status.
It is important to note that the voice message widget is still in a gradual release phase only for participants in the Android beta program, and there is no confirmation of an analogous tool for iPhone devices. Meta has not yet announced a date for this feature to arrive in the stable version of the application, but its presence in the testing channel suggests a more advanced stage of development.
The largest technology corporations based in the United States are allocating billions of dollars to artificial intelligence, expecting this technology to revolutionize both business and the economy. However, the magnitude of these expenditures has generated apprehension among investors, who question when this capital will begin to generate profits.
As reported by The Washington Post, giants such as Google, Microsoft, Meta, Amazon, and Oracle have intensified their investments in AI infrastructure, while the market watches for the potential risks of a speculative bubble.
Historically, companies like Google and Facebook were recognized for their remarkable revenue generation capacity, driven by digital products that reached millions of users and established these companies as market leaders. Currently, a significant portion of this revenue is being redirected to a new technological phase, focused on large data centers, advanced processors, and equipment necessary to support artificial intelligence models.
While proponents of this approach argue that the investment could usher in a new era of productivity and growth, critics express doubts about whether the return will be sufficient to justify the billions invested. Torsten Slok, chief economist at Apollo Global Management, commented to The Washington Post that the functionality of AI is crucial, as its failure would represent a serious problem.
The main vectors of this dispute include the construction of vast data centers to operate AI systems, the acquisition of specialized chips and equipment, the expansion of AI-based software and services, the increase in operational costs of this infrastructure, and the growing pressure for more agile financial results.
Projections indicate a change in the financial profile of these technology companies. Due to the costs associated with AI infrastructure, Google, Amazon, Microsoft, Meta, and Oracle may show negative free cash flow in 2026.
Google serves as a prominent example: in the last three analyzed months, the company spent $1.15 for every dollar earned in cash, mostly allocated to purchasing chips, equipment, and areas reserved for new data centers. Despite this financial pressure, the companies maintain profitability under conventional accounting models, which allow these costs to be distributed over multiple years.
Andy Jassy, CEO of Amazon, defended the investments, assuring that the demand for AI solutions remains robust. He told investors that the company has a clear outlook for achieving strong financial returns.
Competition for essential components for artificial intelligence is already impacting other segments of the market. The increased demand for chips has raised costs for companies that rely on these inputs, pressuring the prices of devices such as smartphones, laptops, and consoles.
Additionally, the expansion of data centers has intensified concerns about energy consumption in certain locations in the United States. Another point of attention lies in the distribution of economic benefits. According to Barbara Denham, chief economist at Oxford Economics, regions that already have concentrated technology and wealth, such as Silicon Valley, New York, Seattle, and Washington, tend to reap the greatest benefits.
Denham emphasized that the economic gains resulting from AI advancement are a self-reinforcing cycle. Although artificial intelligence continues to attract massive investments, the market is now monitoring a fundamental question: whether the technological promises will be able to keep pace with the speed at which expenses for its development are being incurred.
Astronomers, led by Carlos Marrero-de la Rosa from the Canary Islands Institute of Astrophysics, have for the first time established the outer boundaries of IC 1101, one of the largest galactic structures ever observed. The research results, published in July of this year, confirmed that this object is the largest known galaxy.
Ultra-deep images obtained using the Isaac Newton Telescope in the UK were used to define the boundaries. These images allowed for the identification of the region where the galaxy's light merges with the surrounding background. The data obtained showed an extent of about 520 kiloparsecs, equivalent to approximately 1.7 million light-years. This discovery contributes to the understanding of the processes of formation and growth of the largest galaxies in the Universe over billions of years. Galaxy IC 1101 is located at the center of the Abell 2029 galaxy cluster and retains signs of accreting surrounding matter.
Galaxies that are the brightest in clusters and known as BCGs are among the largest stellar systems. They increase in size throughout cosmic history by accreting smaller galaxies, accumulating stars, and forming vast luminous halos. The complexity of measuring these structures lies in defining the point where the galaxy itself ends and the diffuse material of the surrounding cluster begins. The outer regions of BCGs often blend with the diffuse light present between galaxies, which makes identifying their boundary difficult.
IC 1101 was already considered a candidate for the title of the largest known galaxy. Before the new survey, its size was estimated at approximately 607 kiloparsecs. To refine this measurement, researchers applied deeper observations and detailed image processing. One of the main obstacles was separating the actual light of the galaxy from the scattered light of nearby stars. This effect, caused by light scattering within the telescope's optical system, could obscure the faintest regions of the structure.
To solve this problem, the team developed a specific model of the image behavior created by the telescope, using dozens of calibration stars with varying light intensities. This model was then applied to eliminate interference from more than 250 stars present in the images. After correcting the data, researchers were able to observe a gradual decrease in the brightness of IC 1101 as it moved away from the center. This analysis showed that the galaxy's boundary is located approximately 260 kiloparsecs from the core, resulting in a total diameter of about 520 kiloparsecs. Furthermore, the estimate indicates that the galaxy contains about 3.4 trillion solar masses in stars alone. For comparison, the Milky Way spans approximately 30 kiloparsecs, and its total mass, including dark matter, is calculated to be between 1 and 1.5 trillion solar masses.
Despite its extraordinary size, IC 1101 does not appear to be a fully completed structure. During observations, scientists discovered eight extensive and asymmetric stellar formations in its outer regions. Two of these formations coincide with disturbances previously observed in the cluster's hot gas via X-ray emission. This connection suggests that the galaxy continues to accumulate matter and undergo growth processes. According to the research team, the peripheral regions of IC 1101 show clear signs of continuous mass assembly. This interpretation supports the idea that the galaxy is still evolving, rather than being a completely stable system.
The results are also consistent with previous research based on X-ray observations, which identified similar disturbances as possible traces of a collision that occurred between 2 and 3 billion years ago with another group of galaxies. The analysis of IC 1101 provides a new perspective on the evolution of the largest galaxies in the Universe. By determining its size with greater accuracy, the study shows that these cosmic giants hold records of their own formation and continue to change over time.
