This book explores the mechanisms of enzyme catalysis and specificity, from both the theoretical and experimental points of view. The emphasis is on kinetics and chemical thermodynamics as methods for establishing mechanisms at the atomic level for enzymes whose tertiary structure has already been solved by X-ray crystallography. Those with a good grasp of first-year organic chemistry, physical chemistry, and introductory biochemistry will find that this book starts where their basic courses left off, with a review of catalytic mechanisms and the equations of enzyme kinetics, and then moves into such topics as pre-steady-state kinetics and experimental methods, pH dependence, enzyme-substrate complementarity, and specificity. One chapter is devoted to explaining in detail how kinetic experiments have helped elucidate the mechanisms of selected enzymes by means of the detection of intermediates; the author is careful to discuss what constitutes proof that a certain substance is an intermediate, and reviews sources of possible experimental error. Although the book contains little on multi-substrate systems or on the methods of structure determination themselves, it is nevertheless a well-written, compact introduction to an important field.
Since two-dimensional boron sheet (borophene) synthesized on Ag substrates in 2015, research on borophene has grown fast in the fields of condensed matter physics, chemistry, material science, and nanotechnology. Due to the unique physical and chemical properties, borophene has various potential applications. In this review, we summarize the progress on borophene with a particular emphasis on the recent advances. First, we introduce the phases of borophene by experimental synthesis and theoretical predictions. Then, the physical and chemical properties, such as mechanical, thermal, electronic, optical and superconducting properties are summarized. We also discuss in detail the utilization of the borophene for wide ranges of potential application among the alkali metal ion batteries, Li-S batteries, hydrogen storage, supercapacitor, sensor and catalytic in hydrogen evolution, oxygen reduction, oxygen evolution, and CO2 electroreduction reaction. Finally, the challenges and outlooks in this promising field are featured on the basis of its current development.
Deaglan J. Bartlett, Harry Desmond, Pedro G. Ferreira
et al.
Symbolic regression (SR) has emerged as a powerful method for uncovering interpretable mathematical relationships from data, offering a novel route to both scientific discovery and efficient empirical modelling. This article introduces the Special Issue on Symbolic Regression for the Physical Sciences, motivated by the Royal Society discussion meeting held in April 2025. The contributions collected here span applications from automated equation discovery and emergent-phenomena modelling to the construction of compact emulators for computationally expensive simulations. The introductory review outlines the conceptual foundations of SR, contrasts it with conventional regression approaches, and surveys its main use cases in the physical sciences, including the derivation of effective theories, empirical functional forms and surrogate models. We summarise methodological considerations such as search-space design, operator selection, complexity control, feature selection, and integration with modern AI approaches. We also highlight ongoing challenges, including scalability, robustness to noise, overfitting and computational complexity. Finally we emphasise emerging directions, particularly the incorporation of symmetry constraints, asymptotic behaviour and other theoretical information. Taken together, the papers in this Special Issue illustrate the accelerating progress of SR and its growing relevance across the physical sciences.
Leonardo S. G. Leite, Swarup Banerjee, Yihui Wei
et al.
Graph theory has a long history in chemistry. Yet as the breadth and variety of chemical data is rapidly changing, so too do graph encoding methods and analyses that yield qualitative and quantitative insights. Using illustrative cases within a basic mathematical framework, we showcase modern chemical graph theory's utility in Chemists' analysis and model development toolkit. The encoding of both experimental and simulation data is discussed at various levels of granularity of information. This is followed by a discussion of the two major classes of graph theoretical analyses: identifying connectivity patterns and partitioning methods. Measures, metrics, descriptors, and topological indices are then introduced with an emphasis upon enhancing interpretability and incorporation into physical models. Challenging data cases are described that include strategies for studying time dependence. Throughout, we incorporate recent advancements in computer science and applied mathematics that are propelling chemical graph theory into new domains of chemical study.
AbstractIn this study, the density functional M06‐2X/6‐311++G(3df,3pd) method was employed to investigate the mutual isomerization reaction mechanism of 5‐chlorouracil from diketone to diol under the catalysis of water, methanol, formic acid, and an electric field. Parameters such as reaction enthalpy, activation energy, activation Gibbs free energy, and proton transfer reaction rate were obtained. The computational results show that under the same conditions, formic acid demonstrates the best catalytic effect, while the influence of electric field catalysis on the reaction barrier is minimal.
INADEQUATE (Incredible Natural Abundance DoublE QUAntum Transfer Experiment) is one of the most important techniques in revealing the carbon skeleton of organic solids in solid-state NMR spectroscopy. Nevertheless, its use for structural analysis is quite limited due to the low natural abundance of 13C–13C connectivity (∼0.01%) and thus low sensitivity. Particularly, in semi-solids like rubbers, the sensitivity will be further significantly reduced by the inefficient cross polarization from 1H to 13C due to molecular motions induced averaging of 1H–13C dipolar couplings. Herein, in this study, we demonstrate that transient nuclear Overhauser effect (NOE) can be used to efficiently enhance 13C signals, and thus enable rapid acquisition of two-dimensional (2D) 13C INADEQUATE spectra of rubbers. Using chlorobutyl rubber as the model system, it is found that an overall signal-to-noise ratio (SNR) enhancement about 22% can be achieved, leading to significant time-saving by about 33% as compared to the direct polarization-based INADEQUATE experiment. Further experimental results on natural rubber and ethylene propylene diene monomer (EPDM) rubber are also shown to demonstrate the robust performance of transient NOE enhanced INADEQUATE experiment.
Physical and theoretical chemistry, Analytical chemistry
Interactive notebooks are a precious tool for creating graphical user interfaces and teaching materials. Python and Jupyter are becoming increasingly popular in this context, with Jupyter widgets at the core of the interactive functionalities. However, while packages and libraries which offer a broad range of general-purpose widgets exist, there is limited development of specialized widgets for computational physics, chemistry and materials science. This deficiency implies significant time investments for the development of effective Jupyter notebooks for research and education in these domains. Here, we present custom Jupyter widgets that we have developed to target the needs of these communities. These widgets constitute high-quality interactive graphical components and can be employed, for example, to visualize and manipulate data, or to explore different visual representations of concepts, clarifying the relationships existing between them. In addition, we discuss with one example how similar functionality can be exposed in the form of JupyterLab extensions, modifying the JupyterLab interface for an enhanced user experience when working with applications within the targeted scientific domains.
Microseismic can build a bridge between engineering operations such as drilling and fracturing and stratum evaluation such as earthquake, geology, and logging by fully excavating fracture time, spatial development characteristics, and focal information. For the postseismic evaluation of microseisms, a comprehensive evaluation system integrating microseisms and multidisciplines is established in this paper: through deep excavation of microseismic information such as the time and space distribution of microseismic events, quantitative statistics, magnitude-frequency gradient (B-value) and S-P wave energy ratio (Es/Ep), the effective identification of dry faults, wet faults, cracks, joints, sweet spots and nonsweet spots is realized, combined with seismology and geology. The engineering problems (casing change, pressure change, fracturing barrier, etc.) are analyzed accordingly, which enhances the comprehensive evaluation function of microseismic and multidiscipline.
Spruce bark, a waste from forestry and wood industry, represent a valuable resource. Subjected to biorefinery, this feedstock leads to separation of extractibles and final conversion products. The main final product is biochar, a rich carbon material that could be efficient in environmental remediation. This study assessed the lead uptake capacity of biochar. The total Pb uptake was a quite fast process (120 min), for a sorbent dose of 0.25 g/L and pH 5. The kinetic was properly described by pseudo-second-order model. Further developments are open considering activation/functionalization of biochar as well as other hazardous pollutants and competitors’ uptake.
Due to a special nature of the Landau problem, in which the magnetic field is uniformly spreading over the whole two-dimensional plane, there necessarily exist three conserved quantities, i.e. two conserved momenta and one conserved orbital angular momentum for the electron, independently of the choice of the gauge potential. Accordingly, the quantum eigen-functions of the Landau problem can be obtained by diagonalizing the Landau Hamiltonian together with one of the above three conserved operators with the result that the quantum mechanical eigen-functions of the Landau problem can be written down for arbitrary gauge potential. The purpose of the present paper is to clarify the meaning of gauge choice in the Landau problem based on this gauge-potential-independent formulation, with a particular intention of unraveling the physical significance of the concept of gauge-invariant-extension of the canonical orbital angular momentum advocated in recent literature on the nucleon spin decomposition problem. At the end, our analysis is shown to disclose a physically vacuous side face of the gauge symmetry.
Molecules in space are synthesized via a large variety of gas-phase reactions, and reactions on dust-grain surfaces, where the surface acts as a catalyst. Especially, saturated, hydrogen-rich molecules are formed through surface chemistry. Astrochemical models have developed over the decades to understand the molecular processes in the interstellar medium, taking into account grain surface chemistry. However, essential input information for gas-grain models, such as binding energies of molecules to the surface, have been derived experimentally only for a handful of species, leaving hundreds of species with highly uncertain estimates. Moreover, some fundamental processes are not well enough constrained to implement these into the models. The proceedings gives three examples how computational chemistry techniques can help answer fundamental questions regarding grain surface chemistry.
A curved hole surface can be formed by a rotating contour tool. Owing to the shape and the material hardness, traditional cutting methods suffer from significant tool wear and poor machining efficiency. In this research we used the electrochemical machining method (ECM), in which there is no wear of tool cathode, no stress on the workpiece, high processing efficiency, and high quality machining of the curved hole is achieved. The machining gap is the key factor determining the effectiveness of ECM and the machining accuracy. In this study, numerical simulations of the ECM process were carried out. Five factors and five levels of orthogonal experiments were carried out on the main parameters affecting the machining balance gap. The optimal technological parameters consisting of the applied voltage, feed speed, initial gap, pulse duty cycle, and pulse frequency were obtained using the range analysis method. The optimized parameters were verified experimentally, and the surface roughness of the sample reached 0.613 μm, which meets the requirements of engineering applications.
Industrial electrochemistry, Physical and theoretical chemistry
Moustafa Essam B., Melaibari A., Alsoruji Ghazi
et al.
The strength and wear resistance of aluminium alloys must be improved to enhance their usage in lightweight constructions. Thus, in this study, graphene nanoplates (GNPs) and boron nitride (BN) nanoparticles were reinforced into the Al 5251 aluminium alloy by friction stir processing (FSP). The Al 5251 aluminum alloy sheets were patterned with holes and filled by mono GNPs, mono BN nanoparticles and a hybrid of BN nanoparticles and GNPs. The microstructure, wear, and mechanical properties of the as-received, after FSP, and the manufactured surface nanocomposites were analysed. Wear tests were performed using two methods: weight loss and volume loss methods. FSP led to four times grain refinement. Due to the Zener pinning effect, the reinforcement nanoparticles improved the grain refinement effect by seven times decrease in the mean grain size. The wear rate by volume and weight loss with reinforcing BN nanoparticles decreased by 160 and 1,340%, respectively. Note that the GNP reinforcement insignificantly improved the wear resistance and hardness compared with the BN nanoparticles. The hardness was increased by 50, 120, and 80% by reinforcing the Al 5251 alloy with GNPs, BN, and a hybrid of BN nanoparticles and GNPs, respectively. The nanocomposite reinforced with GNPs exhibited superior mechanical properties compared to the other nanocomposites.
Varun Shankar, Gavin D. Portwood, Arvind T. Mohan
et al.
In fluid physics, data-driven models to enhance or accelerate solution methods are becoming increasingly popular for many application domains, such as alternatives to turbulence closures, system surrogates, or for new physics discovery. In the context of reduced order models of high-dimensional time-dependent fluid systems, machine learning methods grant the benefit of automated learning from data, but the burden of a model lies on its reduced-order representation of both the fluid state and physical dynamics. In this work, we build a physics-constrained, data-driven reduced order model for the Navier-Stokes equations to approximate spatio-temporal turbulent fluid dynamics. The model design choices mimic numerical and physical constraints by, for example, implicitly enforcing the incompressibility constraint and utilizing continuous Neural Ordinary Differential Equations for tracking the evolution of the differential equation. We demonstrate this technique on three-dimensional, moderate Reynolds number turbulent fluid flow. In assessing the statistical quality and characteristics of the machine-learned model through rigorous diagnostic tests, we find that our model is capable of reconstructing the dynamics of the flow over large integral timescales, favoring accuracy at the larger length scales. More significantly, comprehensive diagnostics suggest that physically-interpretable model parameters, corresponding to the representations of the fluid state and dynamics, have attributable and quantifiable impact on the quality of the model predictions and computational complexity.