In a study appearing in Nature Catalysis, researchers from the Inorganic Chemistry Department of the Fritz Haber Institute reveal how structural changes on the surface and in the bulk region of the cobalt oxide catalyst Co3O4 influence its selectivity in the production of industrially relevant chemicals like acetone.
They discovered that a metastable, structurally “trapped” state exhibits the highest catalytic activity—an important finding for catalyst design.
Sum-frequency generation (SFG) is a powerful vibrational spectroscopy that can selectively probe molecular structures at surfaces and interfaces, but its spatial resolution has been limited to the micrometer scale by the diffraction limit of light.
In a study published in The Journal of Physical Chemistry C, investigators overcame this limitation by utilizing a highly confined near field within a plasmonic nanogap and successfully extended the SFG spectroscopy into a nanoscopic regime with ~10-nm spatial resolution.
The team also established a comprehensive theoretical framework that accurately describes the microscopic mechanisms of this near-field SFG process. These experimental and theoretical achievements collectively represent a groundbreaking advancement in near-field second-order nonlinear nanospectroscopy, enabling direct access to correlated chemical and topographic information of interfacial molecular systems at the nanoscale.
A collaborative team of four professors and several graduate students from the Departments of Chemistry and Biochemical Science and Technology at National Taiwan University, together with the Department of Applied Chemistry at National Chi Nan University, has achieved a long-sought breakthrough.
By combining atomic force microscopy (AFM) with a Hadamard product–based image reconstruction algorithm, the researchers successfully visualized, for the first time, the nanoscopic dynamics of membrane rafts in live cells—making visible what had long remained invisible on the cell membrane.
Membrane rafts are nanometer-scale structures rich in cholesterol and sphingolipids, believed to serve as vital platforms for cell signaling, viral entry, and cancer metastasis. Since the concept emerged in the 1990s, the existence and behavior of these lipid domains have been intensely debated.
Depression is one of the most widespread mental health disorders worldwide, affecting approximately 4% of the global population. It is characterized by a persistent low mood, disruptions in typical sleeping and/or eating habits, a lack of motivation, a loss of interest in daily activities and unhelpful thought patterns.
There are now various treatments for depression, including psychotherapy-based interventions and different types of antidepressant medications. Identifying the best treatment strategy, however, is not always easy, and many patients try different medications before they find one that works for them.
Researchers at Stanford University, Lehigh University, the University of Texas at Austin and other institutes explored the potential of machine learning techniques, computational models that can identify patterns in data, for predicting the responses of individual patients to two different antidepressants and to a placebo (i.e., a pill that contains no active chemicals).
Organic electrochemical neurons respond to brain signals in real time, firing at biologically relevant speeds. Their flexibility and low power use could enable soft, implantable systems for closed-loop neuromodulation and future brain–computer interfaces.
You have full access to this article via your institution.
During chemical reactions, atoms in the reacting substances break their bonds and re-arrange, forming different chemical products. This process entails the movement of both electrons (i.e., negatively charged particles) and nuclei (i.e., the positively charged central parts of atoms). Valence electrons are shared and re-arranged between different atoms, creating new bonds.
The movements of electrons and nuclei during chemical reactions are incredibly fast, in many cases only lasting millionths of a billionth of a second (i.e., femtoseconds). Yet reliably tracking and understanding these movements could help to shed new light on how specific molecules are formed, as well as on the underpinnings of quantum mechanical phenomena.
Researchers at Shanghai Jiao Tong University recently introduced a new approach to observe chemical reactions as they unfold, precisely tracking the movement of electrons and atomic nuclei as a molecule breaks apart. This strategy, outlined in a paper published in Physical Review Letters, was successfully used to image the photodissociation of ammonia (NH₃), the process in which a NH₃ molecule absorbs light and breaks down into smaller pieces.
Researchers at the Department of Energy’s Oak Ridge National Laboratory are pioneering the design and synthesis of quantum materials, which are central to discovery science involving synergies with quantum computation. These innovative materials, including magnetic compounds with honeycomb-patterned lattices, have the potential to host states of matter with exotic behavior.
Using theory, experimentation and computation, scientists synthesized a magnetic honeycomb of potassium cobalt arsenate and conducted the most detailed characterization of the material to date. They discovered that its honeycomb structure is slightly distorted, causing magnetic spins of charged cobalt atoms to strongly couple and align.
Tuning these interactions, such as through chemically modifying the material or applying a large magnetic field, may enable the formation of a state of matter known as a quantum spin liquid. Unlike permanent magnets, in which spins align fixedly, quantum spins do not freeze in one magnetic state.
A research project conducted by the Max-Planck-Institut für Kohlenforschung shows how biomass can be used as a raw material for chemical products instead of petroleum. The scientists have published their findings in the journal Science.
The chemical industry is facing major challenges: for reasons of CO2 neutrality, circular economy, and geopolitical instability, there is a desire to move away from petroleum and other fossil materials as raw materials for the production of high-quality chemicals. But how will molecular building blocks for essential medicines, for example, be obtained in the future?
Scientists at the X-ray free-electron laser SwissFEL have realized a long-pursued experimental goal in physics: to show how electrons dance together. The technique, known as X-ray four-wave mixing, opens a new way to see how energy and information flow within atoms and molecules. In the future, it could illuminate how quantum information is stored and lost, eventually aiding the design of more error-tolerant quantum devices. The findings are reported in Nature.
Much of the behavior of matter arises not from electrons acting alone, but from the ways they influence each other. From chemical systems to advanced materials, their interactions shape how molecules rearrange, how materials conduct or insulate and how energy flows.
In many quantum technologies —not least quantum computing—information is stored in delicate patterns of these interactions, known as coherences. When these coherences are lost, information disappears—a process known as decoherence. Learning how to understand and ultimately control such fleeting states is one of the major challenges facing quantum technologies today.
In a unique finding, researchers at Georgetown’s Lombardi Comprehensive Cancer Center discovered that when pancreatic cancer cells send out tiny particles that are packed with certain microRNA molecules, nearby immune cells called macrophages are reprogrammed to help the tumor grow instead of engaging in their regular role of fighting the tumor. This insight from cell and mouse experiments helped the scientists outline a potential way to reverse the process and possibly improve outcomes in pancreatic cancer.
“Our approach focuses on blocking adverse outcomes of microRNA-based communication between pancreatic cancer cells and immune cells,” says Amrita Cheema, Ph.D., professor, Departments of Oncology, Biochemistry, Molecular and Cellular Biology and Radiation Medicine at Georgetown and senior author of the study. “By disrupting these channels of communication, we could reprogram the immune cells and restore their ability to fight cancer, resulting in meaningful reductions in pancreatic tumor growth.”
The study appears January 16, 2026, in the journal Signal Transduction and Targeted Therapy.