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Category: nanotechnology

Quantum Light Reveals How Solvents Affect Molecules

Understanding how a molecule’s neighbors shape its behavior is essential for explaining chemical reactions and designing molecular systems. Conventional ultrafast spectroscopy can uncover these dependencies. Unfortunately, the technique requires intense laser pulses, which can perturb or damage delicate samples and complicate measurements. Now Tanvir Rajib of Texas A&M University and his colleagues have demonstrated an approach that avoids those problems by using pairs of photons instead of intense laser pulses [1]. Researchers could apply the method to a wide range of chemical, biological, and nanoscale molecular systems.

In the technique, a nonlinear optical crystal transforms laser light into pairs of entangled, identical-frequency photons through a process called spontaneous parametric down-conversion. One photon in each pair interacts with a molecular sample, whose ultrafast molecular dynamics alter that photon’s quantum state and, in turn, its indistinguishability with respect to the other, noninteracting photon. The photons then enter opposite input ports of a four-port beam splitter. How likely they are to emerge from the same output port depends on their indistinguishability through an effect called Hong-Ou-Mandel interference, providing a way to infer the molecular dynamics.

In work published earlier this year, Rajib and his colleagues used the approach to measure a molecule’s coherence time—its ability to maintain a definite phase relationship between its different energy states [2]. Now the researchers have studied how that time varies with the molecule’s neighbors. They investigated a molecular dye known as IR-797 in five solvents—which provided differing dielectric environments—and found that the coherence time ranged from 25 to 60 femtoseconds. In future work, the researchers plan to optimize the method, boosting its speed, efficiency, and sensitivity.

Bending nanoribbons tunes diamond’s light emission without doping

In a new Physical Review Letters study, researchers have demonstrated that bending diamond nanostructures can tune the light they emit without doping.

Diamond is an ultrawide bandgap semiconductor with high carrier mobility, high thermal conductivity, deep-ultraviolet light emission and stable single-photon emission. These properties make it a potential candidate for next-generation electronic and optoelectronic devices.

However, properties such as its bandgap and light emission are difficult to tune because doping in diamond poses limitations. Elastic strain engineering, which stretches or compresses a material without permanently deforming it, has emerged as an alternative.

Researchers Uncover a Hidden Molecular Machine Parasites Need To Survive

Researchers have visualized two key stages of RNA processing in parasites that cause diseases such as sleeping sickness, Chagas’ disease, and leishmaniasis.

For nearly 40 years, scientists have known that trypanosomatid parasites depend on an unusual system for processing RNA. Now researchers have captured that machinery at near-atomic resolution, showing how its components assemble and operate during a reaction the parasites need to survive.

The study, conducted by researchers at the University of Liège and Rockefeller University, reconstructed the three-dimensional architecture of the trans-spliceosome, a massive molecular machine that prepares genetic messages for use inside trypanosomatid cells. The structures also identify features that differ from the RNA processing machinery found in humans, providing a potential starting point for developing drugs that interfere selectively with the parasites.

Self-powered artificial synapse combines sensing and memory in flexible electronics

Neuromorphic devices, which are designed to emulate aspects of biological neural networks, are promising candidates for low-power, intelligent sensing technologies, including wearable applications.

Among the architectures explored for neuromorphic computing, graphene-channel ion-gel-gated transistors (g-IGTs) are attractive because of their electronic properties, flexibility, low-voltage operation and ability to modulate synaptic weights to mimic biological synapses. However, most current g-IGTs still rely on external power supplies, limiting their practical use in wearable neuromorphic systems.

To address this challenge, a research team led by professor Sejoon Lee of the Department of System Semiconductor at Dongguk University in South Korea has developed a battery-free, self-powered, flexible g-IGT device driven by a triboelectric nanogenerator (TENG). TENGs convert mechanical stimuli, such as body movement, touch or vibration, into electrical signals.

Researchers Borrow Chipmaking Technology To Build Better EV Batteries

Researchers used nanoscale manufacturing techniques from the semiconductor industry to design a copper current collector that helps lithium build up evenly during battery charging.

Electric vehicles could travel farther on a single charge if their batteries could store more energy without getting bigger or heavier. A promising alternative, known as an anode-free battery, eliminates materials such as graphite that normally store lithium during charging, saving weight and space.

Lithium instead deposits directly onto thin copper foil, where repeated charging and discharging can cause it to build up unevenly into sharp, branching spikes called dendrites. The protective layer over the lithium also becomes unstable, reducing battery performance and shortening its lifespan.

Autonomous Lipid Nanoparticle Engineering

Researchers at the Massachusetts Institute of Technology have developed an automated production system for lipid nanoparticles (LNPs) that offers unprecedented control over their size and shape, significantly accelerating the design of targeted RNA and DNA therapeutics. While LNPs serve as crucial delivery vehicles for mRNA vaccines and other nucleic acids, traditional manufacturing relies on time-consuming trial-and-error and cannot reliably control particle dimensions, which dictate their behavior and targeted organs in the body. Building on a previously developed two-step fluid mixing technique, the newly autonomous platform integrates real-time dynamic light scattering to continuously monitor particle formation and adjust parameters on the fly. By leveraging data gathered from this automated system, the team also trained a machine-learning model capable of predicting the exact manufacturing conditions required to engineer LNPs with specific sizes and non-spherical shapes. This streamlined, data-driven approach eliminates previous manufacturing guesswork, providing a powerful tool to rapidly engineer customized delivery vehicles for next-generation genetic therapies.

Lipid nanoparticles (LNPs) are the leading vehicles for encapsulating and delivering nucleic acid therapeutics. Yet, their process development remains labor-intensive and empirical, constrained by coupled quality attributes and limited mechanistic insight. We present an autonomous, pilot-scale platform for accelerating LNP process development by identifying critical process parameters (CPPs) that produce LNPs with target size attributes. The platform combines a size-control production method with inline dynamic light scattering (DLS) for real-time feedback, enabling closed-loop experimentation and accelerated optimization. With built-in automated design of experiments, dynamic parameter sweeps, and Bayesian optimization, the platform enables rapid, data-rich exploration of complex design spaces.

An RNA Language Model trained on sequence alone reveals the structural logic of Internal Ribosome Entry Sites

Teaching a computer to read rna’s hidden blueprint.

Every living thing relies on tiny molecular machines called ribosomes to build proteins — the workhorses of our cells. Normally, a ribosome latches onto a strand of RNA and starts reading it like a recipe. But some viruses, including the picornaviruses (the family behind the common cold and polio), have evolved a clever workaround. Instead of using the standard starting signal, they use special RNA structures called Internal Ribosome Entry Sites, or IRESes, to hijack our ribosomes and force them to produce viral proteins.

The problem is that these IRES structures are enormous, wildly varied between viruses, and notoriously difficult to map. Scientists have only been able to determine the detailed structures of a handful of them.

A new study tackles this challenge by training an RNA “language model” — a type of artificial intelligence similar to the models behind modern chatbots, but designed to read the genetic language of RNA. The model, named Albatross, learns only from RNA sequences, not structures, yet it can predict how these molecules fold into functional shapes with remarkable accuracy.

To test it, the team gathered real-world chemical probing data from 96 full-length IRESes taken from a diverse range of viruses. Albatross dramatically outperformed existing prediction tools, achieving 80% precision compared to 47%. The researchers then used Albatross to analyze a massive collection of 75,000 IRES structures, uncovering a previously unknown structural category (dubbed “Type II”) that they confirmed in the lab.

But what surprised the researchers most wasn’t the accuracy — it was what the model taught itself along the way. Albatross was never told anything about thermodynamics, the physical rules that govern how RNA folds. It was given only sequences. Yet it spontaneously learned to recognize alternative structures of riboswitches — RNA elements that change shape to control gene activity — and even critical loop-to-loop contacts that hold RNA molecules in precise three-dimensional arrangements.

“This is totally brain shattering,” said Dr. Silvi Rouskin, the study’s advising author (Harvard Medical School). “An LLM that was never told what thermodynamics is, was just given sequences, picks up alternative structures of riboswitches and critical tertiary loop-loop contacts.”

In other words, the model appears to have rediscovered the physics of RNA from raw data alone — much as a language model seems to pick up grammar without ever being taught linguistics. The team is now applying it to human biology, with early hints of previously unknown human riboswitches on the horizon.

Continue reading “An RNA Language Model trained on sequence alone reveals the structural logic of Internal Ribosome Entry Sites” | >

Tiny polymer devices mimic neuron firing, pointing toward energy-efficient edge computing

MIT researchers have created a computing platform that could be used to develop intelligent and adaptive next-generation electronics that can perform multiple functions, like computing and memory, within one extremely compact, energy-efficient device.

Such a platform opens opportunities for low-power edge computing applications, interactive medical and environmental monitoring systems, and smart robots.

The researchers accomplished this by leveraging the unique mechanical response of soft polymers at the nanoscale. A mechanical response is how a structure changes when a force is applied to it.

Scientists put a tiny lump of metal in two places at once in record-breaking quantum experiment

Scientists have pulled off a mind-bending quantum experiment that sounds almost impossible: they showed that tiny metal particles made of thousands of atoms can exist in multiple places at once. Using advanced laser techniques, researchers at the University of Vienna observed quantum interference in sodium nanoparticles far larger than the kinds of particles usually seen behaving this way. The finding pushes quantum mechanics into a new realm, suggesting that even surprisingly “large” objects still obey the bizarre rules of the quantum world.

Vertical quantum sensor could reveal nanoscale magnetic patterns in quantum materials

Quantum materials do things ordinary materials cannot. They carry current without any loss, or conduct only along their outer edge while the inside insulates. Future quantum computers and quantum sensors will run on materials like these. To improve them, researchers need to see exactly where currents and magnetic fields run at the nanoscale.

A SQUID, a superconducting ring that registers even the smallest magnetic changes, is sensitive enough for the task. The problem is distance. Magnetic fields weaken quickly with distance, so the sensor must get close to the material. If the ring lies flat in the plane of a chip, the rest of the chip holds it several micrometers away from the material. That is where the detail is lost. The University of Twente has worked on scanning SQUID microscopy for years, mapping the magnetism of a surface.

“That is why we put the sensor on a pyramid,” says Hans Hilgenkamp. “On top of that pyramid the sensor can be brought right up to the material we want to look at, with nothing else in the way. That lets us image magnetism at the scale where quantum materials do their work.” This opens the door to new materials with unusual functionality. Within the Gravitation program QuMat, Twente builds instruments that it and its partners can use to measure quantum materials and develop them further.

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