Transition State Search Research Articles

Effect of Oxidized Tin Interfacial Atoms on Methylcyclohexane Dehydrogenation Catalyzed by γ‐Alumina Supported Platinum Clusters

AbstractTin (Sn) doped platinum (Pt) nanoparticles supported on γ‐alumina are important materials catalyzing various reactions, such as the dehydrogenation of methylcyclohexane (MCH) into toluene, involved in liquid organic hydrogen carriers process, and naphtha catalytic reforming. The role of oxidized forms of Sn, remains misunderstood. Relying on catalysts' models made of a Pt13cluster on the (100) surface of γ‐alumina, the location and effects of Sn4+and Sn2+ interfacial atoms are investigated by density functional theory (DFT) calculations. A simulated annealing approach based on ab initio molecular dynamics enables to explore the configurations of the catalytic systems. The Pt clusters' stabilities and that of the adsorbed molecular intermediates are linked to the electronic properties of the cluster and to its ductility modulated by the adsorbed species, the alumina support and the Sn atoms. The Gibbs free energy profiles related to the reaction mechanism of the MCH dehydrogenation into toluene are quantified on the basis of transition state search. The energy span concept reveals that the dual (either detrimental or beneficial) impact of Sn oxidized forms on the intrinsic reactivity of the Pt13 cluster depends on temperature. Based on this DFT analysis, we attempt to clarify the debated role of Sn oxidized species.

A theoretical simulation of carbon dioxide adsorption capture on amine-functionalized graphene oxides

Amine-functionalized graphene oxides (AFGOs) are potential candidates for CO2 capture applications, as the amine functional groups could act as effective sites, promoting CO2 adsorption strength and selectivity. However, the CO2 adsorption mechanism on AFGO appears to be ambiguous, particularly with no explicit identification of the chemically bonded species. In this work, density functional theory and microkinetic simulations were carried out to investigate the physical and chemical adsorption mechanisms and behavior between AFGO and CO2. Our findings indicate that the van der Waals and hydrogen bonding (HB) interactions constitute physical binding modes between the aminated GO and CO2. The chemical adsorption pathways on AFGO materials have been revealed by the transition state search method, in which the relevant CO2 adsorption species, including carbamic acid and carbamate, and their formation conditions were theoretically identified on AFGOs. The chemisorption mechanisms have been recognized to be the nucleophilic interaction between GO-supported amines and CO2, simultaneously assisted by the cooperative effect of multiple HB interactions. More specifically, the surface hydroxyl groups and water molecules can serve as proton transfer media and offer HB interactions, thus regulating the reaction activity and chemical adsorption species. This theoretical work presents a new insight into CO2-AFGO interaction mechanisms, which is valuable for designing aminated GO adsorbents.

Quantum chemical package Jaguar: A survey of recent developments and unique features.

This paper is dedicated to the quantum chemical package Jaguar, which is commercial software developed and distributed by Schrödinger, Inc. We discuss Jaguar's scientific features that are relevant to chemical research as well as describe those aspects of the program that are pertinent to the user interface, the organization of the computer code, and its maintenance and testing. Among the scientific topics that feature prominently in this paper are the quantum chemical methods grounded in the pseudospectral approach. A number of multistep workflows dependent on Jaguar are covered: prediction of protonation equilibria in aqueous solutions (particularly calculations of tautomeric stability and pKa), reactivity predictions based on automated transition state search, assembly of Boltzmann-averaged spectra such as vibrational and electronic circular dichroism, as well as nuclear magnetic resonance. Discussed also are quantum chemical calculations that are oriented toward materials science applications, in particular, prediction of properties of optoelectronic materials and organic semiconductors, and molecular catalyst design. The topic of treatment of conformations inevitably comes up in real world research projects and is considered as part of all the workflows mentioned above. In addition, we examine the role of machine learning methods in quantum chemical calculations performed by Jaguar, from auxiliary functions that return the approximate calculation runtime in a user interface, to prediction of actual molecular properties. The current work is second in a series of reviews of Jaguar, the first having been published more than ten years ago. Thus, this paper serves as a rare milestone on the path that is being traversed by Jaguar's development in more than thirty years of its existence.

Interfacial adsorption and catalysis of single-atom Pt-loaded ZrO2 surfaces for enhancing formaldehyde oxidation

The emerging transition metal single-atom catalysts (SACs) are expected to have high activity for converting detrimental formaldehyde (HCHO) under mild conditions. Currently, the exploitation of novel SACs with designed or modified support surfaces remains largely imperative. Herein, we used density functional theory to explore the catalytic performance for HCHO oxidation on various single-atom Pt-loaded ZrO2 surfaces, in which oxygen vacancy and lattice hydroxyl group were included on the surfaces. The surface modifications could enhance the co-adsorption of HCHO and O2 molecules and provide more active sites due to interfacial electron redistribution. The transition state search approach was applied to investigate the oxidation pathways on various ZrO2-supported SAC surfaces, wherein distinct dehydrogenation mechanisms and intermediates were clarified. The oxygen vacancy defects and hydroxyl groups can enhance the catalytic activity for HCHO oxidation by providing active receptor sites for the H-transfer and facilitating the CH bond breakage. Microkinetic modeling was further used to characterize the reaction rates and kinetics behavior of HCHO oxidation on the SAC surfaces. Our simulation demonstrates the excellent catalytic performance of the modified ZrO2-supported SAC in the HCHO oxidation. Meanwhile, the mechanism is also an important implication for the synthesis and design of novel SACs.

Theoretical insight into the sulfurated poisoning process of rhenium-doped nickel catalyst

Three rhenium-doped nickel catalyst surfaces including Re-Ni(111), Re-Ni(200) and Re-Ni(220) were built via Re atom substituting one atom of upper layer Ni surfaces. The sulfurated poisoning process involving H2S adsorption and subsequent splitting steps (H2S→HS+H, HS→S+H) on the three surfaces was investigated and further compared using density functional theory (DFT) to understand the crystal-plane effect. For H2S adsorption, the H2S molecule is preferentially adsorbed on the Re top and bridge ReNi1 sites. Re-Ni(200) surface possessed lower H2S adsorption energy compared to Re-Ni(111) and Re-Ni(220) surfaces, due to its smaller coordination number and higher d-orbital intensity at Fermi level. For splitting steps, transition state search showed that the two processes on the three surfaces are all calculated as exothermic while the energy barrier is basically in the range of 0.23 eV∼0.44 eV, which signifies that the poisoning process is favorable both kinetically and thermodynamically. The splitting steps can be described as a dynamic procedure that contains intimate HS fragment activation, S–H bond stretching accompanied by H atom departure and final dissociation into HS+H or S+H co-intermediates. The valence bond changes during dissociation deduced from projected density of states (PDOS) analysis also proved the above procedure.

Computational Investigation about the Effects of Solvent Polarity and Chalcogen Element Electronegativity on ESIPT Behaviors for the Et2N-Substituted Flavonoid.

Inspired by the outstanding nature of flavonoid derivatives in the fields of chemistry and medicine, in this work we mainly focus on exploring the photo-induced properties of the novel Et2N-substituted flavonoid (ENF) fluorophore theoretically. Considering the potential photo-induced properties in different solvents and the chalcogen atomic electronegativity-associated photoexcitation, by time-dependent density functional theory (TDDFT) methods we primarily explore the intramolecular hydrogen bonding interactions and photo-induced charge redistribution behaviors. Via comparing geometrical data and the infrared (IR) spectral shifts-associated hydroxy moiety of ENF, we confirm that the intramolecular hydrogen bond O-H···O should be enhanced with facilitating an excited-state intramolecular proton-transfer (ESIPT) reaction. Particularly, the charge reorganization around hydrogen bonding moieties further reveals the tendency of ESIPT behavior. Combined with the construction of the potential energy surface and the search for reaction transition states, we finally confirmed the solvent-polarity-regulated behaviors as well as the chalcogen elements' electronegativity-dependent ESIPT mechanisms for the ENF fluorophore. We sincerely wish our work could accelerate the further development and applications of flavonoid derivatives.

Exploring potential energy surfaces to reach saddle points above convex regions.

Saddle points on high-dimensional potential energy surfaces (PES) play a determining role in the activated dynamics of molecules and materials. Building on approaches dating back more than 50 years, many open-ended transition-state search methods have been developed to follow the direction of negative curvature from a local minimum to an adjacent first-order saddle point. Despite the mathematical justification, these methods can display a high failure rate: using small deformation steps, up to 80% of the explorations can end up in a convex region of the PES, where all directions of negative curvature vanish, while if the deformation is aggressive, a similar fraction of attempts lead to saddle points that are not directly connected to the initial minimum. In high-dimension PES, these reproducible failures were thought to only increase the overall computational cost, without having any effect on the methods' capacity to find all saddle points surrounding a minimum. Using activation-relaxation technique nouveau (ARTn), we characterize the nature of the PES around minima, considerably expanding on previous knowledge. We show that convex regions can lie on activation pathways and that not exploring beyond them can introduce significant bias in the saddle-point search. We introduce an efficient approach for traversing the convex regions, almost eliminating exploration failures, while multiplying by almost 10 the number of identified unique and connected saddle points as compared to the standard ARTn, thus underlining the importance of correctly handling convex regions for completeness of saddle point explorations.

TS-tools: Rapid and automated localization of transition states based on a textual reaction SMILES input.

Here, TS-tools is presented, a Python package facilitating the automated localization of transition states (TS) based on a textual reaction SMILES input. TS searches can either be performed at xTB or DFT level of theory, with the former yielding guesses at marginal computational cost, and the latter directly yielding accurate structures at greater expense. On a benchmarking dataset of mono- and bimolecular reactions, TS-tools reaches an excellent success rate of 95% already at xTB level of theory. For tri- and multimolecular reaction pathways - which are typically not benchmarked when developing new automated TS search approaches, yet are relevant for various types of reactivity, cf. solvent- and autocatalysis and enzymatic reactivity - TS-tools retains its ability to identify TS geometries, though a DFT treatment becomes essential in many cases. Throughout the presented applications, a particular emphasis is placed on solvation-induced mechanistic changes, another issue that received limited attention in the automated TS search literature so far.

Picturing the Gap Between the Performance and US-DOE's Hydrogen Storage Target: A Data-Driven Model for MgH2 Dehydrogenation.

Developing solid-state hydrogen storage materials requires a comprehensive understanding of the dehydrogenation chemistry of a solid-state hydride. Transition state search and kinetics calculations are essential to understanding and designing high-performance solid-state hydrogen storage materials by filling in the knowledge gap that current experimentscannot measure. However, the ab initio analysis of these processes is expensive and time-consuming. Searching for descriptors to accurately predict the energy barrier is urgently needed, to accelerate the prediction of hydrogen storage material properties and identify the opportunities and challenges. Herein, we develop a data-driven model to describe and predict the dehydrogenation barriers of a typical solid-state hydrogen storage material, MgH2, based on the combination of the crystal Hamilton population orbital of Mg-H bond and the distance between atomic hydrogen.All the parameters in this model can be directly calculated with significantly less computational cost than conventional transition state search, so that the dehydrogenation performance of hydrogen storage materials can be predicted efficiently. Finally, we found that this model leads to excellent agreement with typical experimental measurements reported to date and provides clear design guidelines on how to propel the performance of MgH2 closer to the target set by the United States Department of Energy (US-DOE).

Picturing the Gap Between the Performance and US‐DOE's Hydrogen Storage Target: A Data‐Driven Model for MgH2 Dehydrogenation

AbstractDeveloping solid‐state hydrogen storage materials is as pressing as ever, which requires a comprehensive understanding of the dehydrogenation chemistry of a solid‐state hydride. Transition state search and kinetics calculations are essential to understanding and designing high‐performance solid‐state hydrogen storage materials by filling in the knowledge gap that current experimental techniques cannot measure. However, the ab initio analysis of these processes is computationally expensive and time‐consuming. Searching for descriptors to accurately predict the energy barrier is urgently needed, to accelerate the prediction of hydrogen storage material properties and identify the opportunities and challenges in this field. Herein, we develop a data‐driven model to describe and predict the dehydrogenation barriers of a typical solid‐state hydrogen storage material, magnesium hydride (MgH2), based on the combination of the crystal Hamilton population orbital of Mg−H bond and the distance between atomic hydrogen. By deriving the distance energy ratio, this model elucidates the key chemistry of the reaction kinetics. All the parameters in this model can be directly calculated with significantly less computational cost than conventional transition state search, so that the dehydrogenation performance of hydrogen storage materials can be predicted efficiently. Finally, we found that this model leads to excellent agreement with typical experimental measurements reported to date and provides clear design guidelines on how to propel the performance of MgH2 closer to the target set by the United States Department of Energy (US‐DOE).

First-principles study of hydrogen sulfide decomposition on Sc-Ti3C2O2 single-atom catalyst.

Hydrogen sulfide gas poses significant risks to both human health and the environment, with the potential to induce respiratory and neurological effects, and a heightened fatality risk at elevated concentrations. This article investigates the catalytic decomposition of H2S on a Sc-Ti3C2O2 single-atom catalyst(SAC) using the density functional theory-based first-principles calculation approach. Initially, the adsorption behavior of H2S on Ti3C2O2-MXene was examined, revealing weak physical adsorption between them. Subsequently, the transition metal atom Sc was introduced to the Ti3C2O2 surface, and its stability was studied, demonstrating high stability. Further exploration of H2S adsorption on Sc-Ti3C2O2 revealed direct dissociation of H2S gas molecules into HS* and H*, with HS* binding to Sc and H* binding to O on the Ti3C2O2 surface, resulting in OH groups. Using the transition state search method, the dissociation of H2S molecules on the SAC's surface was investigated, revealing a potential barrier of 2.45eV for HS* dissociation. This indicates that the H2S molecule can be dissociated into H2 and S with the action of the Sc-Ti3C2O2 SAC. Moreover, the S atom left on the catalyst surface can aggregate to produce elemental S8, desorbing on the catalyst surface, completing the catalytic cycle. Consequently, the Sc-Ti3C2O2 SAC is poised to be an efficient catalyst for the catalytic decomposition of H2S. The Dmol3 module in Materials Studio software based on density functional theory is used in this study. The generalized gradient approximation method GGA-PBE is used for the exchange-correlation function. The complete LST/QST and the NEB methods in the Dmol3 module were used to study the minimum energy path of the dissociation of hydrogen sulfide molecules on the catalyst surface.

Effective electro-removal of glyphosate and glufosinate from aqueous solutions via capacitive deionization

Both glyphosate (Gly) and glufosinate (Glu) are commonly used as herbicides, which has caused rising concerns about their impact on the environment and human health. Capacitive deionization (CDI) is a newly developed energy-efficient water treatment technique that effectively removes inorganic and small organic molecules under the electric field. In this work, a carbon electrode-based CDI device was used in the electro-removal of Gly and Glu in an aqueous solution with an excellent removal rate of up to 95 % under various conditions. Furthermore, maximum electro-removal rates of 1.99 μmol∙cm−2∙min−1 for Gly and 1.33 μmol∙cm−2∙min−1 for Glu were also achieved, except with super-high removal rates of 100 % (Gly) and 97.6 % (Glu) in actual Gly and Glu-containing lake water, demonstrating the feasibility of the CDI device in real aqueous systems. In-situ electro-oxidation that occurred during the electrosorption process was confirmed by analyzing the three-electrode system-based electrochemical behaviors and tracing the time-dependent changes of the suspicious degradation products. Transition state (TS) search and molecular dynamics (MD) simulations were employed to study the microscopic processes theoretically, revealing the degradation pathways and thermodynamic characteristics of Gly and Glu. This research presents a novel avenue for the decomposition and removal of herbicides from water, leveraging the inherent side reactions of CDI systems as an advantage to decontaminate organic pollutants.

Development of analytic gradients for the Huzinaga quantum embedding method and its applications to large-scale hybrid and double hybrid DFT forces.

The theory of analytic gradients is presented for the projector-based density functional theory (DFT) embedding approach utilizing the Huzinaga-equation. The advantages of the Huzinaga-equation-based formulation are demonstrated. In particular, it is shown that the projector employed does not appear in the Lagrangian, and the potential risk of numerical problems is avoided at the evaluation of the gradients. The efficient implementation of the analytic gradient theory is presented for approaches where hybrid DFT, second-order Møller-Plesset perturbation theory, or double hybrid DFT are embedded in lower-level DFT environments. To demonstrate the applicability of the method and to gain insight into its accuracy, it is applied to equilibrium geometry optimizations, transition state searches, and potential energy surface scans. Our results show that bond lengths and angles converge rapidly with the size of the embedded system. While providing structural parameters close to high-level quality for the embedded atoms, the embedding approach has the potential to relax the coordinates of the environment as well. Our demonstrations on a 171-atom zeolite and a 570-atom protein system show that the Huzinaga-equation-based embedding can accelerate (double) hybrid gradient computations by an order of magnitude with sufficient active regions and enables affordable force evaluations or geometry optimizations for molecules of hundreds of atoms.

Graph to Activation Energy Models Easily Reach Irreducible Errors but Show Limited Transferability.

Activation energy characterization of competing reactions is a costly but crucial step for understanding the kinetic relevance of distinct reaction pathways, product yields, and myriad other properties of reacting systems. The standard methodology for activation energy characterization has historically been a transition state search using the highest level of theory that can be afforded. However, recently, several groups have popularized the idea of predicting activation energies directly based on nothing more than the reactant and product graphs, a sufficiently complex neural network, and a broad enough data set. Here, we have revisited this task using the recently developed Reaction Graph Depth 1 (RGD1) transition state data set and several newly developed graph attention architectures. All of these new architectures achieve similar state-of-the-art results of ∼4 kcal/mol mean absolute error on withheld testing sets of reactions but poor performance on external testing sets composed of reactions with differing mechanisms, reaction molecularity, or reactant size distribution. Limited transferability is also shown to be shared by other contemporary graph to activation energy architectures through a series of case studies. We conclude that an array of standard graph architectures can already achieve results comparable to the irreducible error of available reaction data sets but that out-of-distribution performance remains poor.

Locating Transition States by Variational Reaction Path Optimization with an Energy-Derivative-Free Objective Function.

Locating transition states is essential for understanding molecular reactions. We propose a double-ended transition state search method by revisiting a variational reaction path optimization method known as the MaxFlux method. Although its original purpose is to add temperature effects to reaction paths, we conversely let the temperature approach zero to obtain an asymptotically exact minimum energy path and its corresponding transition state in variational formalism with an energy-derivative-free objective function. Using several numerical techniques to directly optimize the objective function, the present method reliably finds transition states with low computational cost. In particular, only three force evaluations per iteration are sufficient. This is confirmed on a variety of molecular reactions where the nudged elastic band method often fails. The present method is implemented in Python using the Atomic Simulation Environment and is available on GitHub.

Discovering SnB7-: a half-sandwich structure with double aromaticity and pathways to molecular machines.

Numerous boron-based molecular fluxional models, such as the Wankel motor, tank treads B11- and B10C, and the Earth-Moon system Be6B11-, have been widely recognized for their potential to develop molecular machines. From a series of tin-doped boron clusters SnBn- (n = 5-14), the half-sandwich structure SnB7- is found to possess high relative energy stability, and a HOMO-LUMO gap of 4.33 eV. This structure exhibits valence electron orbitals reminiscent of σ-π double aromatic compounds. The incorporation of tin effectively fills the doubly vacant π orbitals of its parent triplet B7-, thereby enhancing both magnetic shielding capabilities and range. Thermal bath tests demonstrate its significant dynamic stability, as the kinetic energy provided by thermal baths below 3800 K remains insufficient to disrupt its inherent elasticity. Additionally, transition state searches and intrinsic reaction coordinate analyses confirm that the tin atom migrates from the centre to the edge of the boron ligand surface, a phenomenon that can be observed in high-temperature thermal bath simulations. This fluxional behaviour provides insights for constructing novel molecular machine models.

Cost-effective approach for atmospheric accretion reactions: a case of peroxy radical addition to isoprene.

We present an accurate and cost-effective method for investigating the accretion reactions between unsaturated hydrocarbons and oxidized organic radicals. We use accretion between isoprene and primary, secondary and tertiary alkyl peroxy radicals as model reactions. We show that a systematic semiempirical transition state search can lead to better transition state structures than relaxed scanning with density functional theory with a significant gain in computational efficiency. Additionally, we suggest accurate and effective quantum chemical methods to study accretion reactions between large unsaturated hydrocarbons and oxidized organic radicals. Furthermore, we examine the atmospheric relevance of these types of reactions by calculating the bimolecular reaction rate coefficients and formation rates under atmospheric conditions from the quantum chemical reaction energy barriers.

First-Principles Calculations on Diffusion and Association Behavior of Fluoride Ions in Ba-Doped LaF3 Solid Electrolyte

Fluoride ion batteries, which generate electrical energy through the fluorination/de-fluorination reaction of electrode active materials, are expected to be the next-generation storage batteries with high energy density. LaF3 with a tysonite-type structure is known as one of the representative solid-state fluoride ion conductors. Substitutional solid solution of divalent cations such as Ba or Sr ions at La sites forms fluoride ion vacancies as charge compensation, resulting in fluoride ion conductivity. The main objective of this study is to analyze the formation behavior of point defects in LaF3 through quantitative evaluation of their formation energies using first-principles calculations, and to elucidate the conduction mechanism of fluoride ions in LaF3.The plane-wave basis PAW method implemented in VASP code was used for the electronic structure calculations. The electron correlation treatment uses the PBEsol-type potential based on the generalized gradient approximation. Calculations for given point defects were performed. The cutoff energy of the plane wave basis sets was set to 420 eV, and the structure optimization calculations were performed until the Hellmann-Feynman force acting on each atom was less than 0.02 eV/Å.There are three fluoride ion sites (12g, 4d, and 2a) in the unit cell of LaF3. Using the supercell model, we calculated the defect formation energies of fluoride ion vacancies at each site. The formation energies at the 12g, 4d, and 2a sites are 0.17 eV, 0.52 eV, and 0.52 eV, respectively. According to the defect formation energies, fluoride ion vacancies are most likely to form at the 12g sites.To elucidate the elementary processes of fluoride ion transfer between 12g sites by the vacancy mechanism, we performed first-principles calculations and transition state search using the Nudged Elastic Band method. From symmetry, four different pathways exist in the lattice of undoped LaF3. The pathway with the highest energy barrier is shown to have very low transfer energies, 0.17 eV, and the energy barriers of the other pathways are less than 0.1 eV. These results suggest that LaF3 has a potential for very fast fluoride ionic conductivity by the vacancy mechanism. In the presentation, the mechanism of fluoride ion conduction in Ba-doped LaF3 will also be reported. This work was supported by the RISING2 (JPNP16001) and the RISING3 (JPNP21006) projects from the New Energy and Industrial Technology Development Organization (NEDO), Japan.

Accurate transition state generation with an object-aware equivariant elementary reaction diffusion model.

Transition state search is key in chemistry for elucidating reaction mechanisms and exploring reaction networks. The search for accurate 3D transition state structures, however, requires numerous computationally intensive quantum chemistry calculations due to the complexity of potential energy surfaces. Here we developed an object-aware SE(3) equivariant diffusion model that satisfies all physical symmetries and constraints for generating sets of structures-reactant, transition state and product-in an elementary reaction. Provided reactant and product, this model generates a transition state structure in seconds instead of hours, which is typically required when performing quantum-chemistry-based optimizations. The generated transition state structures achieve a median of 0.08 Å root mean square deviation compared to the true transition state. With a confidence scoring model for uncertainty quantification, we approach an accuracy required for reaction barrier estimation (2.6 kcal mol-1) by only performing quantum chemistry-based optimizations on 14% of the most challenging reactions. We envision usefulness for our approach in constructing large reaction networks with unknown mechanisms.

Large Computational Survey of Intrinsic Reactivity of Aromatic Carbon Atoms with Respect to a Model Aldehyde Oxidase.

Aldehyde oxidase (AOX) and other related molybdenum-containing enzymes are known to oxidize the C-H bonds of aromatic rings. This process contributes to the metabolism of pharmaceutical compounds and, therefore, is of vital importance to drug pharmacokinetics. The present work describes an automated computational workflow and its use for the prediction of intrinsic reactivity of small aromatic molecules toward a minimal model of the active site of AOX. The workflow is based on quantum chemical transition state searches for the underlying single-step oxidation reaction, where the automated protocol includes identification of unique aromatic C-H bonds, creation of three-dimensional reactant and product complex geometries via a templating approach, search for a transition state, and validation of reaction end points. Conformational search on the reactants, products, and the transition states is performed. The automated procedure has been validated on previously reported transition state barriers and was used to evaluate the intrinsic reactivity of nearly three hundred heterocycles commonly found in approved drug molecules. The intrinsic reactivity of more than 1000 individual aromatic carbon sites is reported. Stereochemical and conformational aspects of the oxidation reaction, which have not been discussed in previous studies, are shown to play important roles in accurate modeling of the oxidation reaction. Observations on structural trends that determine the reactivity are provided and rationalized.