Quantum mechanics describes a microscopic world in which particles exist in a superposition of states—being in multiple places and configurations all at once, defined mathematically by what physicists call a 'wavefunction.' But this runs counter to our everyday experience of objects that are either here or there, never both at the same time. Typically, physicists manage this conflict by arguing that, when a quantum system comes into contact with a measuring device or an experimental observer, the system’s wavefunction ‘collapses’ into a single, definite state. Now, with support from the Foundational Questions Institute, FQxI, an international team of physicists has shown that a family of unconventional solutions to this measurement problem—called ‘quantum collapse models’—has far-reaching implications for the nature of time and for clock precision. They published their results suggesting a new way to distinguish these rival models from standard quantum theory, in Physical Review Research, in November 2025.

Scientists have created a fingertip‑scale spectrometer‑on‑a‑chip that brings lab‑grade hyperspectral sensing into the near‑infrared range long considered out of reach for silicon. By engineering photon‑trapping textures onto silicon photodiodes and using a neural network to decode their combined signals, the device accurately reconstructs spectra from 640 to 1100 nanometers—well beyond the limits of conventional silicon spectrometers. Despite its tiny 0.4‑mm footprint, the chip delivers ~8‑nm resolution, maintains high accuracy with fewer than 16 detectors, and remains stable even under heavy electronic noise. The team also demonstrated precise hyperspectral imaging of a butterfly dataset, highlighting the technology’s potential for compact biomedical, environmental, and remote‑sensing tools.

University of Chicago and Northwestern University researchers have turned the conditions that accidentally degrade battery components into a new, powerful technique for intentionally destroying the water pollutants known as per- and polyfluoroalkyl substances, or PFAS. Their results, published today in Nature Chemistry, showed 94% defluorination and 95% degradation in breaking down the long-chain PFAS molecule perfluorooctanoic acid (PFOA) into mineralized fluorine without forming short molecular chains that can be even tricker to remove from water. This new fluorine source can be used to create PFAS-free compounds, turning pollutants into valuable commercial products. Of 33 PFAS compounds tested, 22 demonstrated degradation amounts exceeding 70%, with some degradation up to 99%, showing remarkable promise as electrochemistry enters the fight against "forever chemicals."

SPIE, the international society for optics and photonics, is celebrating the inaugural Biophotonics Discovery's Impact of the Year Award as well as its first recipient, Stanford University Associate Professor of materials science and engineering, Guosong Hong. Hong was officially honored at the Biophotonics Focus: Light-Based Technologies for Reproductive, Maternal, and Neonatal Health plenary during SPIE Photonics West on Sunday evening.

For years, researchers have provided contradictory evidence about the “premelting film” of ice, its thickness and whether it even exists. In The Journal of Chemical Physics, Luis MacDowell sought to resolve this contention. He focused on the phase diagram of ice and, using computer simulations, visualized the movements of molecules at the surface. At the triple point, where all three phases are equally stable, a nanometer-thin film appeared. MacDowell proposes that much of the disagreement is due to experiments that unintentionally occur slightly away from equilibrium.

One of the primary challenges with prosthetic hands is the ability to properly tune the appropriate grip based on the object being handled. In Nanotechnology and Precision Engineering, researchers in China have developed an object identification system for prosthetic hands to guide appropriate grip strength decisions in real time. Their system uses an electromyography sensor at the user’s forearm to determine what the user intends to do with the object at hand.

In APL Bioengineering, researchers use a machine learning algorithm to explore whether electroencephalography could be useful for connecting brain signals with limb movements in patients who have lost some or all their limb function. In tests, the researchers equipped patients with EEG monitors and asked them to perform simple movements, using their algorithm to classify the range of possible signals. They found they could detect the difference between attempted movement and no movement but struggled to differentiate between specific signals.