Functional groups regulate ion concentration and pH in nanopores

 

To understand the chemical reactions occurring inside the nanopores of nanostructured materials—whether synthetic or natural, such as those found in membranes or ion channels in biological systems—it is essential to determine the ion concentration within them. For this purpose, nanopores are functionalized with specific chemical groups.

Until now, it had not been possible to determine how functional groups influence ion concentration inside nanopores.

In this study, a group of researchers from the United States reported the development of a core–shell-type plasmonic nanosensor, consisting of a gold nanorod coated with mesoporous silica functionalized with phenyl and methyl groups. This nanosensor can measure the local concentration of protons, anions (such as phosphates, nitrates, sulfates, and arsenates), as well as cations (such as mercury, lead, and copper) in functionalized nanopores. The measurements were performed using Surface-Enhanced Raman Spectroscopy (SERS), applied in situ.

The obtained values were compared with those of bulk silica. Moreover, results indicated that ion concentrations differ in pristine and hydrophobic nanopores compared with those functionalized with phenyl and methyl radicals. In the latter, an increase in anion concentration and a concurrent decrease in cation concentration were reported. Additionally, the pH within the nanopores was found to depend on the composition of the solution. In some cases, the pH inside the nanopores decreased by as much as 2.5 units compared to the bulk value.

These findings provide insight into ion–nanopore chemical interactions and enable precise and selective control of contaminants, with direct applications in water chemistry for membrane-based desalination processes, CO₂ storage, and catalysis in porous materials.

More information at: ACS Applied Materials and Interfaces

Ab initio structural solutions from nanocrystal powder diffraction using diffusion models

 

Over the past century, the development of materials science has increasingly relied on the precise determination of atomic arrangements—that is, the crystal structure and its properties. To this end, X-ray diffraction (XRD) is commonly applied, with the sine qua non being the availability of a single crystal or monocrystal. However, this is not always feasible, especially with atomic clusters of nanometric size (smaller than 1000 Å), known as the nanostructure problem. In such cases, powder X-ray diffraction (PXRD) patterns are degraded due to peak broadening, intensity loss, and Bragg peak overlap.

Researchers from the United States and Germany have proposed a procedure that uses a generative machine learning model* based on diffusion processes, trained on 45,229 known structures. The model, called PXRDnet, conditioned solely on the compound's chemical formula, can solve simulated nanocrystals up to 10 Å in 200 materials with various symmetries and complexities, including all seven crystal systems.

PXRDnet correctly identifies structural candidates in 4 out of 5 cases, with an average error of just 7% in the Rietveld refinement factor R. Moreover, it is capable of resolving structures from noisy experimentally obtained diffraction patterns.

The authors argue that this data-driven, theoretically bootstrapped approach opens new avenues for determining previously unsolved nanomaterial structures. However, the model has limitations: it requires prior knowledge of the chemical formula and is restricted to structures with fewer than 20 atoms per unit cell.

*The term “generative” refers to a class of statistical models as opposed to discriminative models. Generative models can generate new data instances, while discriminative models distinguish between different types of data instances.

The work was published by Nature Materials

Synthesis of 2-Dimensional Metals via van der Waals (vdW) Compression

Since the discovery of graphene in 2004, two-dimensional (2D) materials have attracted considerable attention from the scientific community. To date, a wide variety of 2D materials are known, such as MXenes and transition metal dichalcogenides, as well as monolayers composed of a single type of atom from elements such as carbon (C), silicon (Si), germanium (Ge), and phosphorus (P). Most of these materials grow in three dimensions, forming structures stabilized by van der Waals (vdW) forces, which makes it relatively easy to exfoliate atomically thin layers.

However, this is not the case for metals, which grow three-dimensionally through strong chemical bonding. Until recently, it was believed that obtaining an atomically thin metal layer was practically impossible, as such structures would also be thermodynamically unstable.

Recently, a group of researchers in China succeeded in producing two-dimensional metals with thicknesses on the order of angstroms using a technique known as van der Waals compression. To carry out this process, they first grew a monolayer of molybdenum disulfide (MoS₂) on a sapphire substrate. This bilayer serves as a base or bottom anvil. A small amount of metal was then placed on the MoS₂ monolayer and heated until it formed a molten droplet. A second MoS₂/sapphire layer was placed on top, with the MoS₂ in direct contact with the molten metal. A pressure of 200 MPa was applied and maintained until both anvils returned to room temperature. The 2D metal was then obtained via a cleaving process that separated the MoS₂/2D-metal/MoS₂ sandwich from the sapphire substrates.

Using this simple and effective technique, two-dimensional metals have been synthesized from bismuth (Bi), tin (Sn), lead (Pb), indium (In), and gallium (Ga). Transport properties measured via Raman spectroscopy on 2D bismuth revealed enhanced electrical conductivity, improved field-effect performance, and increased conductivity due to a nonlinear Hall effect.

This paves the way to a new line of research focused on exploring metals, alloys, and non-layered materials at the 2D scale, along with the investigation of their properties and potential implementation in various technological devices.

For further information go to: NATURE

Gold Nanoparticles Conjugated with Aptamers for Targeted microRNA Delivery Promote Dystrophic Muscle Regeneration

 

Duchenne muscular dystrophy is a genetic disorder characterized by the progressive loss of muscle mass due to mutations in the gene coding for dystrophin, a protein essential for muscle stability. In the absence of this functional protein, muscles cannot operate or repair properly, leading to deterioration of skeletal, cardiac, and pulmonary muscles.

Under normal conditions, when a healthy muscle is damaged, satellite cells are activated, differentiate, and contribute to muscle regeneration. This process helps maintain muscle integrity. However, in Duchenne muscular dystrophy, dystrophin is defective, making muscle fibers more vulnerable to damage. As a result, satellite cells remain continuously activated, leading to inflammation and, eventually, their exhaustion and death. Since satellite cells fail to differentiate, the muscle loses its ability to regenerate and, over time, is replaced by fibrotic and adipose tissue, contributing to the progressive deterioration of muscle tissue characteristic of the disease.

MicroRNAs are a class of RNA molecules involved in gene regulation that play crucial roles in the post-transcriptional regulation of genes. They inhibit messenger RNA (mRNA), preventing the production of defective proteins.  However, delivering them through the bloodstream is challenging due to their low stability and poor cellular penetration.

 A research team designed a strategy to treat muscular dystrophy using gold nanoparticles (AuNPs) as vehicles to transport therapeutic microRNAs into muscle cells. To specifically recognize these cells, the nanoparticles were functionalized with molecules called aptamers, which identify the α7/β1 integrin, a highly specific surface receptor expressed by muscle progenitors and differentiated myofibers, but virtually absent in other organs or tissues.

Once the system enters muscle stem cells, the nanoparticles release the microRNAs, which inhibit mRNA translation, preventing the synthesis of mutated (defective) dystrophin. As a result, satellite cells are not excessively activated but instead function in a regulated manner. 

The study investigated the system's activity in cellular and animal models, where muscle regeneration was observed at the cellular level, as well as functional recovery. The muscles of the treated mice improved and strengthened after treatment, increasing the functional capacity of the animals.

For further information, refer to: Nature Communications

Design of a Low-Temperature Solar Heat Concentrator for the synthesis of ZnO nanostructures

 

Diagram and image of the CPC Solar Collector and SEM image of the synthesized ZnO

It has been widely reported that heat obtained from solar radiation can be applied to the synthesis of nanomaterials. In the case of zinc oxide (ZnO), the nanomaterial of interest in this study, it has previously been synthesized using high-temperature solar heat through Physical Vapor Deposition (PVD) methods.

In this work, a group of researchers from Mexico (CNyN-UNAM, CICESE, and UABC) propose evaluating a new approach for ZnO production through the design of a compound parabolic concentrator (CPC) with a cylindrical (tubular) receiver that generates heat at low temperatures.

The authors placed the precursors, zinc nitrate Zn(NO₃)₂ and sodium hydroxide (NaOH), inside the reactor where ZnO is produced. The reactor is in turn located within the CPC. In this setup, the collector serves both as a heat generator and as a reactor for synthesis.

The synthesis temperatures ranged from 50°C to 70°C. Using solar heat, pure ZnO crystalline clusters were obtained, with sizes ranging from 40.4 nm to 55.7 nm, and a band gap of 3.27 eV, slightly lower than that obtained by other methods at 50°C. The absorbance of the synthesized ZnO was 90%, regardless of the synthesis temperature.

This study confirmed that high-quality ZnO can be feasibly produced using low-temperature solar heat. This constitutes a new "green chemistry" approach and a renewable energy source for nanomaterial synthesis.

For more details, consult: Journal of Nanotechnology

NiFe Sulfide and Ti3C2 MXene Nanocomposites for High-Performance Seawater Electrolysis

 

Clean and sustainable energy sources are essential to address energy shortage and reduce carbon emissions caused by fossil fuels. One promising solution is the production of "green" hydrogen through water electrolysis using renewable energy sources such as solar and wind power.

The use of anion exchange membranes (AEM) has gained attention because it combines the low cost and high efficiency of alkaline water electrolysis with the benefits of proton exchange membrane (PEM) electrolysis, which is compatible with renewable energy sources.

However, freshwater is a limited resource, making direct seawater electrolysis an attractive alternative for future large-scale hydrogen production.  To date, satisfactory progress has not been made in obtaining systems based on anion exchange membranes. One major challenge is the development of highly stable electrocatalysts that can withstand both oxygen evolution reaction (OER) conditions and chloride-induced corrosion.

A research team in China developed a robust nanocomposite electrocatalyst by integrating MXene (Ti3C2) with Ni-Fe sulfides ((Ni,Fe)S2@Ti3C2). Using various characterizations and theoretical (DFT) calculations, they demonstrated that the strong interaction between (Ni,Fe)S2 and Ti3C2 regulates electron distribution, activating the OER. Additionally, the formation of Ti-O-Fe bonds prevents the loss of Fe species during the process, improving long-term stability. Additionally, the material effectively retains sulfates and features Ti3C2 groups that shield against chloride corrosion.

As a result, the (Ni,Fe)S2@Ti3C2 nanocomposite achieves high OER activity (1.598 V at 2 A cm-2) and remains stable for over 1,000 hours in seawater electrolysis. Moreover, when used as an anode along with a Ni Raney cathode (a nickel-based material with a porous structure that improves hydrogen production), the system operates at industrial current density (0.5 A cm-2) with 500 hours of durability, 70% efficiency, and an energy consumption of 48.4 kWh per kg of H2.

This study provides an effective methodology to address seawater electrolysis based on AEM technology, solving the challenges of catalyst degradation and chloride corrosion. 

More information in Nature Communications

Machine Learning Algorithms for Calculating the Electronic Structure of Molecules

 

Advances in nanotechnology largely rely on computational models that help us understand how materials behave at the atomic level. These tools are essential in physics, chemistry, and materials science, as they allow researchers to uncover new mechanisms and accelerate the design of innovative materials. However, one major challenge in this field is calculating the electronic structure of molecules—a process that is often slow and requires significant computing power.

This is where machine learning (ML) comes into play. ML, a branch of artificial intelligence, enables computers to learn from data and improve their performance over time. ML has become a promising alternative for studying molecules more quickly and efficiently thanks to its ability to identify patterns and make predictions.

In recent years, scientists have combined ML with density functional theory (DFT), a widely used method in computational chemistry. However, DFT can introduce errors in the results. To address this issue, researchers at the Massachusetts Institute of Technology developed an ML-based approach incorporating a more precise method called coupled-cluster singles, doubles, and perturbative triples or CCSD(T). While CCSD(T) is known for its accuracy, it is also computationally expensive, especially for larger molecules.

The researchers used data from 70 molecules with 7,440 different atomic configurations to train their model. The results were promising: they successfully calculated molecular formation enthalpies with a high degree of accuracy (showing differences of just 0.1-0.2 Kcal/mol compared with experimental data) and simulated infrared spectra that matched real measurements in peak position and intensity.

Although this technique has not yet been applied to crystalline materials, the authors believe this will be possible in the future. If so, it could transform how new materials are designed, paving the way for more advanced technologies.

For more information see: Nature Computational Science