Researchers have developed a new computational method, DOCI-QSCI-AFQMC, which accurately simulates complex molecular systems by effectively doubling the number of orbitals considered in standard quantum simulations and overcoming limitations of existing single-reference techniques, as demonstrated through successful modelling of chemical bonds and reactions.
Additive manufacturing (AM) methods, such as 3D printing, enable the realization of objects with different geometric properties, by adding materials layer-by-layer to physically replicate a digital model. These methods are now widely used to rapidly create product prototypes, as well as components for vehicles, consumer goods and medical technologies.
A particularly effective AM technique, called direct ink writing (DIW), entails the 3D printing of objects at room temperature using inks with various formulations. Most of these inks are based on fossil-derived polymers, materials that are neither recyclable nor biodegradable. Recently introduced lignin-derived inks could be a more sustainable alternative. However, they typically need to be treated at high heat or undergo permanent chemical bonding processes to reliably support 3D printing. This prevents them from being re-utilized after objects are printed, limiting their sustainability.
A new microscale gas chromatography system integrates all fluidic components into a single chip for the first time. The design leverages three Knudsen pumps that move gas molecules using heat differentials to eliminate the need for valves, according to a new University of Michigan Engineering study published in Microsystems & Nanoengineering. The monolithic gas sampling and analysis system, or monoGSA system for short, could offer reliable, low-cost monitoring for industrial chemical or pharmaceutical synthesis, natural gas pipelines, or even at-home air quality.
Gas chromatography has long been considered the gold standard for measuring and quantifying volatile organic compounds—gases emitted from industrial processes, fuels, household products and more. Recently, micro gas chromatography miniaturized the technology to briefcase-size or smaller, bringing gas analysis from the laboratory to the source.
Most micro gas chromatography systems use pumps and valves to move gas molecules from an input port to a preconcentrator, which extracts and concentrates samples, then from the preconcentrator to a column for chemical separation, and then to the detector and finally to an exhaust port. Up to this point, pumps and valves have been fabricated and assembled separately, which increases device size, assembly cost and risk of failure at connection points.
Scientific notation is a system developed to represent extremely large and extremely small numbers in a way that is easy to read, write, and understand. In chemistry and physics, many values such as the mass of an electron are too large or too small to be written conveniently in standard notation.
In this video, you will learn:
What scientific notation is and why it is used.
How to write numbers in the form a × 10ⁿ, where a is between 1 and 10
How to convert large numbers into scientific notation.
How to convert small numbers into scientific notation.
The LARS rule:
Left → Add to the exponent.
Right → Subtract from the exponent.
We also discuss how the direction of decimal movement affects the exponent and why the same rules apply to both very large and very small numbers.
📌 At the end of the video, you’ll find practice multiple-choice questions (MCQs) to test your understanding, including a real-life chemistry example involving the mass of an electron.
Many countries worldwide are increasingly investing in new infrastructure that enables the production of electricity from renewable energy sources, particularly wind and sunlight. To make the best of these energy solutions, one should also be able to reliably store the excess energy created during periods of intense sunlight or wind, so that it can be used later in times of need.
One promising type of battery for this purpose is based on zinc-manganese (Zn-Mn) and utilizes aqueous (i.e., water-based) electrodes instead of flammable organic electrolytes. These batteries rely on processes known as electrodeposition and dissolution, via which solid materials form and dissolve on electrodes as the battery is charging and discharging.
In Zn-Mn batteries, Zn serves as the anode (i.e., the electrode that releases electrons) and manganese dioxide (MnO₂) the cathode (i.e., the electrode from which electrons are gained). A key chemical reaction prompting their functioning, known as the MnO₂/Mn²⁺ conversion reaction, typically can only occur in acidic conditions.
For the first time, physicists have developed a model that explains the origins of unusually stable magic nuclei based directly on the interactions between their protons and neutrons. Published in Physical Review Letters, the research could help scientists better understand the exotic properties of heavy atomic nuclei and the fundamental forces that hold them together.
While every chemical element is defined by a fixed number of protons in its atomic nucleus, the number of neutrons it contains is far less constrained. For almost every known element, there are at least two different nuclear configurations, or isotopes, which vary only in their number of neutrons.
However, if the number of protons and neutrons becomes too unbalanced in either direction, the nucleus becomes unstable. Since heavier elements tend to have fewer stable isotopes, these radioactive nuclei grow increasingly rare as this imbalance increases. Yet for certain specific numbers of protons and neutrons (collectively known as “nucleons”), some isotopes are found to be exceptionally stable, for reasons that physicists have struggled to fully explain.
Researchers in China have isolated the effects of electronic friction, showing for the first time how the subtle drag force it imparts at sliding interfaces can be controlled. They demonstrate that it can be tuned by applying a voltage, or switched off entirely simply by applying mechanical pressure. The results, published in Physical Review X, could inform new designs that allow engineers to fine-tune the drag forces materials experience as they slide over each other.
In engineering, friction causes materials to wear and degrade over time, and also causes useful energy to be wasted as heat. While this problem can be mitigated through lubricants and smoother surfaces, friction can also arise from deeper, more subtle effects.
Among these is an effect which can occur at metallic or chemically active surfaces as they slide past one another. In these cases, atomic nuclei in one surface can transfer some of their energy to electrons in the other surface, exciting them to higher energy levels. This lost energy produces a drag force that increases with sliding velocity: an effect known as “electronic friction.”
From table salt to snowflakes, and from gemstones to diamonds—we encounter crystals everywhere in daily life, usually cubic (table salt) or hexagonal (snowflakes). Researchers from Noushine Shahidzadeh’s group at the UvA Institute of Physics now demonstrate how mesmerizing spherical crystal shapes arise through structures called spherulites.
A new study done in Shahidzadeh’s lab at the Institute of Physics / Van der Waals Zeeman-Institute, reveals how neatly ordered (hemi-) spherical or pancake-like structures in nature can emerge from completely disordered salt solutions. Moreover, scientists can now harness these structures to create advanced materials. The work is published in the journal Communications Chemistry.
Researchers at the University of Bayreuth have developed a method using artificial intelligence that can significantly speed up the calculation of liquid properties. The AI approach predicts the chemical potential—an indispensable quantity for describing liquids in thermodynamic equilibrium. The researchers present their findings in a new study published in Physical Review Letters.
Many common AI methods are based on the principle of supervised machine learning: a model—for instance, a neural network—is specifically trained to predict a particular target quantity directly. One example that illustrates this approach is image recognition, where the AI system is shown numerous images in which it is known whether or not a cat is depicted. On this basis, the system learns to identify cats in new, previously unseen images.
“However, such a direct approach is difficult in the case of the chemical potential, because determining it usually requires computationally expensive algorithms,” says Prof. Dr. Matthias Schmidt, Chair of Theoretical Physics II at the University of Bayreuth. He and his research associate Dr. Florian Sammüller address this challenge with their newly developed AI method. It is based on a neural network that incorporates the theoretical structure of liquids—and more generally, of soft matter—allowing it to predict their properties with great accuracy.