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Charging electric-vehicle batteries in Ithaca’s frigid winter can be tough, and freezing temperatures also decrease the driving range. Hot weather can be just as challenging, leading to decomposition of battery materials and, possibly, catastrophic failure.

For (EVs) to be widely accepted, safe and fast-charging lithium-ion batteries need to be able to operate in extreme temperatures. But to achieve this, scientists need to understand how materials used in EVs change during temperature-related chemical reactions, a so-far elusive goal.

Now, Cornell chemists led by Yao Yang, Ph.D. ‘21, assistant professor of chemistry and chemical biology in the College of Arts and Sciences, have developed a way to diagnose the mechanisms behind battery failure in extreme climates using electron microscopy. Their first-of-its-kind operando (“operating”) electrochemical transmission electron microscopy (TEM) enables them to watch chemistry in action and collect real-time movies showing what happens to energy materials during temperature changes.

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Diamond is one of the most prized materials in advanced technologies due to its unmatched hardness, ability to conduct heat and capacity to host quantum-friendly defects. The same qualities that make diamond useful also make it difficult to process.

Engineers and researchers who work with diamond for quantum sensors, or thermal management technologies need it in ultrathin, ultrasmooth layers. But traditional techniques, like laser cutting and polishing, often damage the material or create surface defects.

Ion implantation and lift-off is a way to separate a thin layer of diamond from a larger crystal by bombarding a diamond with high-energy carbon ions, which penetrate to a specific depth below the surface. The process creates a buried layer in the diamond substrate where the crystalline lattice has been disrupted. That damaged layer effectively acts like a seam: Through high-temperature annealing, it turns into smooth graphite, allowing for the diamond layer above it to be lifted off in one uniform, ultrathin wafer.

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Scientists in China have developed contact lenses that let wearers see light normally invisible to the human eye. Cooler still, the lenses work better through closed eyelids, and other versions could help correct color blindness.

The human eye can see a relatively limited range of colors – light with wavelengths of between about 400 and 700 nanometers. In typical human-centric fashion, we call that the ‘visible’ part of the spectrum, even though other animals can see beyond it.

In a new study, scientists have helped humans catch a glimpse of light between 800 and 1,600 nanometers in length, a range we normally can’t see known as infrared. The trick is to pop in a pair of contact lenses embedded with nanoparticles that convert the infrared wavelengths into visible ones.

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View recent discussion. Abstract: Modern Vision-Language Models (VLMs) can solve a wide range of tasks requiring visual reasoning. In real-world scenarios, desirable properties for VLMs include fast inference and controllable generation (e.g., constraining outputs to adhere to a desired format). However, existing autoregressive (AR) VLMs like LLaVA struggle in these aspects. Discrete diffusion models (DMs) offer a promising alternative, enabling parallel decoding for faster inference and bidirectional context for controllable generation through text-infilling. While effective in language-only settings, DMs’ potential for multimodal tasks is underexplored. We introduce LaViDa, a family of VLMs built on DMs. We build LaViDa by equipping DMs with a vision encoder and jointly fine-tune the combined parts for multimodal instruction following.

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