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New strategy for designing ultra-fast charging batteries could prevent hazardous lithium plating

A redesigned lithium-ion anode retained 86% of its initial capacity at a demanding 10C charge rate and stayed stable for more than 250 cycles, while aiming to reduce hazardous lithium plating during fast charging.

The rapid progress in electric vehicles and high-power electronics has increased the demand for ultra-fast-charging lithium-ion (Li-ion) batteries. However, during fast charging, current Li-ion rechargeable batteries suffer from severe degradation in power and potential catastrophic failure, increasing safety risks. This is mainly due to electrochemical instability at the anode–electrolyte interface, causing hazardous Li metal plating on their surface and poor thermal stability.

Recently, high-voltage anode materials have emerged as promising alternatives because they prevent excessive lithium plating and the formation of unstable solid-electrolyte interface layers. Despite these advantages, current state-of-the-art materials are limited by poor ionic conductivity and thermal stability, reducing power output and long-term reliability.

To address these issues, a research team led by associate professor Dongwook Han from Seoul National University of Science and Technology in South Korea developed a novel strategy.

🏰 The Only Moat That Survives AI

Christian Catalini’s central argument is that AI is commoditizing intelligence itself, so the traditional sources of competitive advantage (“moats”) are becoming much less durable. Instead, the scarce resource shifts from *generating answers* to *verifying which answers are actually correct*.

So what is actually left? Today’s guest has spent his career answering that question with models rather than vibes. Christian Catalini co-founded Lightspark, co-created Libra at Meta, founded the MIT Cryptoeconomics Lab, and is a Research Associate and Senior Lecturer at MIT. His answer is uncomfortable and, I think, correct: most of the network effects we treat as permanent are far weaker than they look, and exactly one kind gets stronger every time the models improve. He calls it a verification-grade network effect, and if he is right about it, the money in this industry lands somewhere very different from where most of us are pointing.

I will stay out of the way and let the work speak for itself.

If you also think you have an exciting contribution, apply at the following link: [CLICK THIS].

Texture Analysis of 68GaDOTATOC PET/CT Images for the Prediction of Outcome in Patients with Neuroendocrine Tumors

Objectives: The aim of our study is to evaluate whether texture analysis of 68Ga-DOTATOC PET/CT images can predict clinical outcome in patients with neuroendocrine tumors (NET). Methods: Forty-seven NET patients who had undergone 68Ga-DOTATOC PET/CT were studied. Primary tumors were localized in the gastroenteropancreatic (n = 35), bronchopulmonary (n = 8), and other (n = 4) districts. NET lesions were segmented using an automated contouring program and subjected to texture analysis, thus obtaining the conventional parameters SUVmax and SUVmean, volumetric parameters of the primary lesion, such as Receptor-Expressing Tumor Volume (RETV) and Total Lesion Receptor Expression (TLRE), volumetric parameters of the lesions in the whole-body, such as wbRETV and wbTLRE, and texture features such as Coefficient of Variation (CoV), HISTO Skewness, HISTO Kurtosis, HISTO Entropy-log10, GLCM Entropy-log10, GLCM Dissimilarity, and NGLDM Coarseness. Patients were subjected to a mean follow-up period of 17 months, and survival analysis was performed using the Kaplan–Meier method and log-rank tests. Results: Forty-seven primary lesions were analyzed. Survival analysis was performed, including clinical variables along with conventional, volumetric, and texture imaging features. At univariate analysis, overall survival (OS) was predicted by age (p = 0.0079), grading (p = 0.0130), SUVmax (p = 0.0017), SUVmean (p = 0.0011), CoV (p = 0.0037), HISTO Entropy-log10 (p = 0.0039), GLCM Entropy-log10 (p = 0.0044), and GLCM Dissimilarity (p = 0.0063). At multivariate analysis, only GLCM Entropy-log10 was retained in the model (χ2 = 7.7120, p = 0.0055). Kaplan–Meier curves showed that patients with GLCM Entropy-log10 >1.28 had a significantly better OS than patients with GLCM Entropy-log10 ≤1.28 (χ2 = 10.6063, p = 0.0011). Conclusions: Texture analysis of 68Ga-DOTATOC PET/CT images, by revealing the heterogeneity of somatostatin receptor expression, can predict the clinical outcome of NET patients.

Elevated phagocytic capacity directs innate spinal cord repair

Klatt Shaw et al. report that transient activation of zebrafish macrophages directs spontaneous regeneration after spinal cord injury. By comparing regenerative and non-regenerative vertebrates, they identify Tcim as a central regulator of lipid metabolism and phagocytosis following injury. Tcim expression enhances lipid and debris clearance in zebrafish and mouse macrophages.

Rare footage of humpback whale birth captured on drone video: “A remarkable moment”

Forrest was joined by members of ORRCA’s team, who continued to monitor the mother and calf over several hours, “collecting observations during the critical first hours of the calf’s life,” the organization said on social media.

The footage is believed to be only the fourth complete humpback whale birth ever recorded on film anywhere in the world and the first-ever captured by drone.

“Every observation like this has the potential to expand our understanding of humpback whale reproduction, maternal behaviour and the earliest stages of a calf’s life,” ORRCA said.

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