Your new furniture probably made the air in your living room more toxic than a busy highway. That sharp, eye-watering smell from pressed-wood cabinets and laminate flooring is formaldehyde, a carcinogen the World Health Organization has flagged as a serious indoor air threat. Now, a team of French and Tunisian researchers thinks a cheap, abundant element could hold the key to scrubbing it out of the air at room temperature.
In 2005, the Nobel Prize in Physics went to a technology that measures light with a precision so extreme it can count individual cycles of a laser beam. The optical frequency comb, a spectrum of evenly spaced, perfectly synchronized lines, became the ruler that redefined how we measure time, distance, and the composition of distant stars. Since then, the same pattern has turned up in vibrating crystals, magnetic materials, ferroelectric devices, and even cosmological models of the early universe.
One place it had never appeared, until now: a bubbling dish of chemicals changing color.
Gregory Bassen had a simple question: could a large language model write a recipe for making a ceramic oxide that actually works in a real lab? Not in theory. Not in simulation. In a furnace, with real powders, producing a material you could hold in your hand.
Electrochemical impedance spectroscopy (EIS) is one of the most widely used techniques in battery labs, fuel cell research, and corrosion science. It is elegant in principle: apply a small alternating voltage across a cell, measure the resulting current, and extract a spectrum that encodes the system’s internal dynamics. In practice, however, EIS spectra are notoriously difficult to interpret at the molecular level. Researchers typically fit them to empirical circuit models — collections of imaginary resistors and capacitors — but the connection between those abstract circuit elements and what ions are actually doing inside the electrolyte remains frustratingly opaque.
A new paper from researchers at the University of Cambridge, Durham University, and Sorbonne Université, published on July 2, 2026, takes a significant step toward bridging that gap. Led by Connie Fairchild, Stephen Cox, Benjamin Rotenberg, and Thomas Sayer, the team proposes an alternative framework rooted in statistical mechanics that extracts physically meaningful parameters from impedance data — parameters that directly report on the molecular-scale motions of ions. Their approach, which combines molecular dynamics simulations with the “itinerant oscillator” model, offers a path toward rational design of next-generation electrolytes for batteries and supercapacitors.
The work tackles a fundamental problem: the standard Debye-Falkenhagen theory, which describes how ions move in response to an electric field, was developed for dilute electrolyte solutions. It assumes ions are largely independent, each surrounded by a diffuse “cloud” of counter-ions that distorts under an applied field. But modern energy storage devices increasingly rely on concentrated electrolytes — ionic liquids, which are essentially molten salts at room temperature, or highly concentrated “water-in-salt” formulations. In these systems, ions are packed so tightly that every motion is collective. There is no dilute cloud; there is a cage.
Inside every living cell, enzymes are running the chemistry of life, snipping, splicing, and assembling molecules with a precision that industrial chemists can only dream of. A single enzyme can catalyze a million reactions per second. The problem? Understanding how they do it has been painfully slow. Even with GPU-accelerated quantum chemistry, simulating a single step in an enzymatic reaction on a cluster of 300 atoms can take hours. Mapping a full reaction pathway takes days.
Now researchers at MIT have built something that changes the math entirely.
Heather Kulik’s group released Enerzyme, an open-source framework that trains neural networks to simulate enzyme catalysis with near-quantum-chemical accuracy, but at a fraction of the cost. The key breakthrough isn’t a better algorithm. It’s that Enerzyme handles everything that makes enzymes uniquely hard to model: their size, their solvent environment, and the complex charge transfers that drive their chemistry.
Every year, computational chemists publish lists of new materials that, according to their calculations, should be stable. The structures are plausible. The energies are negative. The phonon spectra show no imaginary frequencies. The papers get published. Then nothing happens.
There is a reason for that silence. The gap between a theoretically stable crystal and something you can actually synthesize in a lab — something that survives on a benchtop, in air, at room temperature — is wide enough to swallow entire classes of predicted materials. A material predicted by density functional theory may be stable at zero Kelvin in vacuum, but fall apart the moment a postdoc tries to grow it on a substrate.
Now a collaboration between Toyota Research Institute, Toyota Central R&D Labs, and the University of Tokyo has built a framework that might finally close that gap. Their approach, described in a preprint posted to arXiv on July 2, takes a list of 894 computationally stable materials and winnows it down to 25 that are actually worth trying to make in a lab.
In 1974, a group of scientists at Imperial College discovered that when you shine light on a rough silver surface, the molecules sitting on top of it scatter far more light than they should, about a million times more. That accidental finding, surface-enhanced Raman spectroscopy (SERS), gave chemists a superpower: the ability to identify individual molecules by the way they vibrate. A few years later, researchers found that infrared light, the kind your TV remote uses, could be similarly amplified on nanostructured surfaces. The catch? Nobody could get both techniques to work on the same device.
Deep in the laboratory of the University of South Bohemia, a team of scientists has done something that would have sounded like science fiction just a decade ago: they took a virus that infects bacteria, hollowed it out, stuffed it with a photosynthetic protein and an electron-carrying partner, and turned the whole assembly into a working solar-powered nanoreactor. The result, published this week on arXiv, is a modular molecular toolkit that could rewrite how we think about artificial photosynthesis — and eventually, how we produce fuels, chemicals, and medicines using nothing but sunlight.
Imagine being handed a box containing more objects than there are stars in the observable universe — and being told that somewhere inside it lies a material that could make batteries last ten times longer, or a molecule that kills antibiotic-resistant bacteria. You get to test a few hundred items before your budget runs out. Good luck.
That, roughly speaking, is the problem facing chemists who search for new molecules. The space of synthesizable chemical compounds is estimated to exceed 10⁶⁰ candidates — a number so large it mocks the very idea of systematic search. For decades, discovering a useful new molecule has been a mixture of chemical intuition, educated guesswork, and sheer persistence. Now, a team from Los Alamos National Laboratory and Georgia Tech has built an AI system that learns how to search — and it’s dramatically better than anything we’ve had before.
Proteins are often described as the body’s molecular machines, but the metaphor has always been missing something: an off switch. When a protein misfires, there is no handy dial you can turn to dial its activity back down. For decades, the dream of controlling individual proteins with the precision of a light switch — quite literally, using light — has been one of the holy grails of biochemistry. Now, a team of researchers at the Weizmann Institute of Science in Israel and Caltech has achieved exactly that. And the mechanism they uncovered is so strange that it belongs as much to quantum physics as it does to biology.