Artificial solid electrolyte interphase offers a promising route to improve the lifespan and safety of quasi-solid-state sodium-metal batteries (QSMBs), yet its limited ion conductivity and the low liquidity of the polymer electrolyte often result in unstable Na plating/stripping kinetics and poor interfacial durability. Herein, we propose a surface-induced “top” Na deposition mechanism facilitated by an ultra-sodiophilic ionic/electronic mixed conductor interphase exhibiting strong Na⁺ adsorption affinity, which ensures fast and dendrite-free Na anode operation. The embedded Na₃Sb alloy phase, featuring high electronic conductivity and strong Na⁺ adsorption energy, significantly accelerates interfacial ion diffusion and nucleation kinetics, forming a smooth and compact Na deposition layer that facilitates stable solid electrolyte interphase formation and preserves interfacial integrity. Consequently, Na||Na symmetric cells employing a 1, 3-dioxolane-based gel polymer electrolyte deliver an ultra-long-cycling lifespan of 1000 h at 0.5 mA cm⁻² with a low overpotential of 40 mV. Moreover, QSMBs incorporating the modified Na anode and Na₃V₂(PO₄)₃ cathode demonstrate outstanding cycling stability (74.1% capacity retention after 9000 cycles at 2C) and superior rate capability (91.7 mAh g⁻¹ at 5C). The work provides mechanistic insights and practical strategies for regulating Na deposition, paving the way toward high-performance QSMBs.

Researchers from the Mark and Mary Stevens Neuroimaging and Informatics Institute (Stevens INI) at the Keck School of Medicine of USC have developed an automated system that uses MRI to measure brain tissue changes in animal models for large, multicenter studies of experimental stroke treatments. By reducing variability among thousands of preclinical research scans from different sources, the automated system has the potential to improve the early-stage evaluation of new therapies. The system was created for the National Institutes of Health-sponsored Stroke Preclinical Assessment Network (SPAN), which tests potential treatments for acute ischemic stroke in coordinated animal studies before the most promising therapies advance toward clinical trials. The Stevens INI serves as the informatics and data core for SPAN. The open-source image analysis pipeline, described in a new study published in Imaging Neuroscience, was tested on scans from more than 2,000 mice and rats at six academic research centers. Its measurements closely matched those made by human experts and helped reduce variability associated with differences in MRI scanners and acquisition environments. Scientists have traditionally measured damage after an experimental stroke by removing the brain, staining tissue sections, and manually outlining injured areas. That process can distort tissue, depends partly on individual judgment, and captures only one point in time. MRI allows researchers to scan the same animal more than once, tracking early injury and swelling as well as tissue loss that develops later. But analyzing thousands of scans collected on different equipment creates another challenge. The scale of SPAN required a method that could process thousands of scans while applying the same rules to every image, regardless of where the data were collected. The pipeline performed about as consistently with a human reviewer as two human reviewers did with each other. The system also reduced variation linked to different scanners and imaging environments, allowing the same analysis approach to work across all six research centers.

 

 

 

 

For dogs with heart disease, a fraction of a centimeter can make a life-changing difference.

Veterinarians often rely on chest radiographs to monitor subtle changes in heart size that help determine when treatment should begin. But those measurements can vary from one doctor to the next, making it difficult to know whether a dog’s condition is truly changing or whether the difference is simply the result of human interpretation.

Now, researchers at the Texas A&M College of Veterinary Medicine and Biomedical Sciences (VMBS) have shown that an artificial intelligence-powered tool can measure canine heart size on radiographs with accuracy comparable to trained clinicians, potentially helping veterinarians identify disease progression with greater consistency and confidence.

The study, recently published in PLOS ONE, evaluated RadAnalyzer, a web- and smartphone-based application that automatically measures vertebral heart size (VHS) and vertebral left atrial size (VLAS) — two metrics veterinarians use to assess heart enlargement and monitor disease progression.

A cancerous tumor is not a single enemy. It is made up of many different cell populations that can behave in very different ways. Among them, even at the time of diagnosis, there may already be cells that months or years later could give rise to metastases or become resistant to treatment. The challenge is that these critical cell populations are extremely difficult to detect in time. Researchers in Szeged, working together with Swiss and Swedish partners, have achieved an important breakthrough in this field.

Little red dots have puzzled astronomers since their discovery in JWST data from the Universe’s deep past. Their ‘powering engines’ might resemble a newly discovered phenomenon dubbed a “black hole star”—an early, rapidly growing black hole wrapped in dense gas. This object, described in a study published today in Nature by researchers at the Institute of Science and Technology Austria (ISTA) and international collaborators, may help explain how billion-solar-mass black holes formed so soon after the Big Bang.