The Quantitation of Aqueous Aluminum, P.R. Bloom and M.S. Erich Aqueous Equilibrium Data for Mononuclear Aluminum Species, D.K. Nordstrom and H.M. May Inorganic Aluminum Bearing Solid Phases, B.S. Hemingway and G. Sposito Aqueous Polynuclear Aluminum Species, P.M. Bertsch and D.R. Parker Environmental Chemistry of Aluminum-Organic Complexes, G.F. Vance, F.J. Stevenson, and F.J. Sikora Surface Reactions of Aqueous Aluminum Species, P.M. Jardine and L.W. Zelazny The Surface Chemistry of Aluminum Oxides and Hydroxides, S.R. Goldberg, J.A. Davis, and J.D. Hem The Solubility of Aluminum in Soils, W.L. Lindsay and P.M. Walthall The Chemistry of Aluminum in Surface Waters, C.T. Driscoll and K.M. Postek Aluminum Geochemistry at the Catchment Scale in Watersheds Influenced by Acidic Precipitation, W.H. Hendershot, F. Courchesne, and D.S. Jeffries Index
The application of the magnetic nanocatalysts is a rapidly growing field for the development of sustainable and green processes. Magnetic separation not only avoids the need for catalyst filtration or centrifugation after completion of the reaction, but also provides practical techniques for recovering these catalysts. Multicomponent reactions are recognized as very powerful tools in synthetic organic and medicinal chemistry for the synthesis of the complex products in a single step from simple starting materials. The combination of magnetic nanocatalysts and multicomponent reactions will become an emerging strategic research area and is an ideal blend for the development of sustainable methods in green synthetic chemistry. This review focuses on the synthesis and application of magnetic nanocatalysts as novel task-specific catalysts for multicomponent reactions in recent years.
Ilyes Batatia, William J. Baldwin, Domantas Kuryla
et al.
Accurate modelling of electrostatic interactions and charge transfer is fundamental to computational chemistry, yet most machine learning interatomic potentials (MLIPs) rely on local atomic descriptors that cannot capture long-range electrostatic effects. We present a new electrostatic foundation model for molecular chemistry that extends the MACE architecture with explicit treatment of long-range interactions and electrostatic induction. Our approach combines local many-body geometric features with a non-self-consistent field formalism that updates learnable charge and spin densities through polarisable iterations to model induction, followed by global charge equilibration via learnable Fukui functions to control total charge and total spin. This design enables an accurate and physical description of systems with varying charge and spin states while maintaining computational efficiency. Trained on the OMol25 dataset of 100 million hybrid DFT calculations, our models achieve chemical accuracy across diverse benchmarks, with accuracy competitive with hybrid DFT on thermochemistry, reaction barriers, conformational energies, and transition metal complexes. Notably, we demonstrate that the inclusion of long-range electrostatics leads to a large improvement in the description of non-covalent interactions and supramolecular complexes over non-electrostatic models, including sub-kcal/mol prediction of molecular crystal formation energy in the X23-DMC dataset and a fourfold improvement over short-ranged models on protein-ligand interactions. The model's ability to handle variable charge and spin states, respond to external fields, provide interpretable spin-resolved charge densities, and maintain accuracy from small molecules to protein-ligand complexes positions it as a versatile tool for computational molecular chemistry and drug discovery.
Atomistic simulations are essential tools in chemistry and materials science, accelerating the discovery of novel catalysts, energy storage materials, and pharmaceuticals. However, running these simulations remains challenging due to the wide range of computational methods, diverse software ecosystems, and the need for expert knowledge and manual effort for the setup, execution, and validation stages. In this work, we present ChemGraph, an agentic framework powered by artificial intelligence and state-of-the-art simulation tools to streamline and automate computational chemistry and materials science workflows. ChemGraph leverages graph neural network-based foundation models for accurate yet computationally efficient calculations and large language models (LLMs) for natural language understanding, task planning, and scientific reasoning to provide an intuitive and interactive interface. Users can perform tasks such as molecular structure generation, single-point energy, geometry optimization, vibrational analysis, and thermochemistry calculations with methods ranging from tight-binding and machine learning interatomic potentials to density functional theory or wave function theory-based methods. We evaluate ChemGraph across 13 benchmark tasks and demonstrate that smaller LLMs (GPT-4o-mini, Claude-3.5-haiku, Qwen2.5-14B) perform well on simple workflows, while more complex tasks benefit from using larger models like GPT-4o. Importantly, we show that decomposing complex tasks into smaller subtasks through a multi-agent framework enables smaller LLM models to match or exceed GPT-4o's performance in specific scenarios.
Johan R Johansson, T. Beke-Somfai, Anna Said Stålsmeden
et al.
The ruthenium-catalyzed azide alkyne cycloaddition (RuAAC) affords 1,5-disubstituted 1,2,3-triazoles in one step and complements the more established copper-catalyzed reaction providing the 1,4-isomer. The RuAAC reaction has quickly found its way into the organic chemistry toolbox and found applications in many different areas, such as medicinal chemistry, polymer synthesis, organocatalysis, supramolecular chemistry, and the construction of electronic devices. This Review discusses the mechanism, scope, and applications of the RuAAC reaction, covering the literature from the last 10 years.
We would like to take this opportunity to highlight the Outstanding Reviewers for Organic Chemistry Frontiers in 2023, as selected by the editorial team for their significant contribution to the journal.
Rituparno Chowdhury, Petri Murto, Naitik A. Panjwani
et al.
Optical control and read-out of the ground state spin structure has been demonstrated for defect states in crystalline semiconductors, including the diamond NV- center, and these are promising systems for quantum technologies. Molecular organic semiconductors offer synthetic control of spin placement, in contrast to current limitations in these crystalline systems. Here we report the discovery of spin-optical addressability in a diradical molecule that comprises two trityl radical groups coupled via a fluorene bridge. We demonstrate the three important properties that enable operation as a spin-photon interface: (i) triplet and singlet spin states show photoluminescence peaked at 640 and 700 nm respectively; this allows easy optical measurement of ground state spin. (ii) the ground state spin exchange is small (~60 μeV) that allows preparation of ground state spin population. This can be achieved by spin-selective excited state intersystem crossing, and we report up to 8% microwave-driven contrast in photoluminescence. (iii) both singlet and triplet manifolds have near-unity photoluminescence quantum yield, which is in contrast to the near-zero quantum yields in prior reports of molecular diradicals. Our results establish these tuneable open-shell organic molecules as a platform to engineer tailor-made spin-optical interfaces.
The combination of Al nanoparticles (ANPs) as fuel and H<sub>2</sub>O<sub>2</sub> as oxidizer is a potential green space propellant. In this research, reactive force field molecular dynamics (ReaxFF-MD) simulations were used to study the influence of water addition on the combustion of Al/H<sub>2</sub>O<sub>2</sub>. The MD results showed that as the percentage of H<sub>2</sub>O increased from 0 to 30%, the number of Al-O bonds on the ANPs decreased, the number of Al-H bonds increased, and the adiabatic flame temperature of the system decreased from 4612 K to 4380 K. Since the Al-O bond is more stable, as the simulation proceeds, the number of Al-O bonds will be significantly higher than that of Al-H and Al-OH bonds, and the Al oxides (Al[O]<sub>x</sub>) will be transformed from low to high coordination. Subsequently, the combustion mechanism of the Al/H<sub>2</sub>O<sub>2</sub>/H<sub>2</sub>O system was elaborated from an atomic perspective. Both H<sub>2</sub>O<sub>2</sub> and H<sub>2</sub>O were adsorbed and chemically activated on the surface of ANPs, resulting in molecular decomposition into free radicals, which were then captured by ANPs. H<sub>2</sub> molecules could be released from the ANPs, while O<sub>2</sub> could not be released through this pathway. Finally, it was found that the coverage of the oxide layer reduced the rate of H<sub>2</sub>O<sub>2</sub> consumption and H<sub>2</sub> production significantly, simultaneously preventing the deformation of the Al clusters’ morphology.
Alexandru Vasile Rusu, Monica Trif, João Miguel Rocha
Food supplementation formulations refer to products that are designed to provide additional nutrients to the diet. Vitamins, dietary fibers, minerals and other functional compounds (such as antioxidants) are concentrated in dietary supplements. Specific amounts of dietary compounds are given to the body through food supplements, and these include as well so-called non-essential compounds such as secondary plant bioactive components or microbial natural products in addition to nutrients in the narrower sense. A significant social challenge represents how to moderately use the natural resources in light of the growing world population. In terms of economic production of (especially natural) bioactive molecules, ways of white biotechnology production with various microorganisms have recently been intensively explored. In the current review other relevant dietary supplements and natural substances (e.g., vitamins, amino acids, antioxidants) used in production of dietary supplements formulations and their microbial natural production via fermentative biotechnological approaches are briefly reviewed. Biotechnology plays a crucial role in optimizing fermentation conditions to maximize the yield and quality of the target compounds. Advantages of microbial production include the ability to use renewable feedstocks, high production yields, and the potential for cost-effective large-scale production. Additionally, it can be more environmentally friendly compared to chemical synthesis, as it reduces the reliance on petrochemicals and minimizes waste generation. Educating consumers about the benefits, safety, and production methods of microbial products in general is crucial. Providing clear and accurate information about the science behind microbial production can help address any concerns or misconceptions consumers may have.