High-level Theory Research Articles

Chemically Transferable Electronic Coarse Graining for Polythiophenes.

Recent advances in machine-learning-based electronic coarse graining (ECG) methods have demonstrated the potential to enable electronic predictions in soft materials at mesoscopic length scales. However, previous ECG models have yet to confront the issue of chemical transferability. In this study, we develop chemically transferable ECG models for polythiophenes using graph neural networks. Our models are trained on a data set that samples over the conformational space of random polythiophene sequences generated with 15 different monomer chemistries and three different degrees of polymerization. We systematically explore the impact of coarse-grained representation on ECG accuracy, highlighting the significance of preserving the C-β coordinates in thiophene. We also find that integrating unique polymer sequences into training enhances the model performance more efficiently than augmenting conformational sampling for sequences already in the training data set. Moreover, our ECG models, developed initially for one property and one level of quantum chemical theory, can be efficiently transferred to related properties and higher levels of theory with minimal additional data. The chemically transferable ECG model introduced in this work will serve as a foundation model for new classes of chemically transferable ECG predictions across chemical space.

Theoretical and experimental study on the pyrolysis of N-methylpyrrolidone

As a widely used organic solvent, N-methylpyrrolidone (NMP) may suffer thermal decomposition during large-scale industrial usage. The complex potential energy surface and corresponding pressure-dependent rate coefficients of NMP and NMP radicals were obtained at a high level of theory. The intermediates and products of NMP pyrolysis within 900–1200 K were identified and measured by synchrotron vacuum ultraviolet photoionization mass spectrometry. Theoretical calculations show that the Keto-enol tautomerization and direct ring-opening channel yielding C2H4, CO, and CH3NCH2 exhibit the highest rate coefficient among NMP unimolecular reactions at low and high temperatures, respectively. However, the fuel decomposition process cannot be exclusively dominated by single low-barrier channel. The ring-opening reaction forming imino aldehyde radical and following decarbonylation reactions are favored channels during the NMP radical decomposition, while the formation of the dihydropyrrolidone via H or CH3 loss competes with them at higher temperatures. Various smaller heterocycle radicals can be produced and act as intermediates for the multi-step isomerization of the ring-opening products with a relatively low energy barrier, changing the position of the functional group and radical site. A variety of nitrogenous and oxygenated species were observed in the experiment, such as ketene, methyl isocyanate, pyrrole, benzene, and different kinds of imines. Among them, C2H4, HCN, and CO are the major products for NMP decomposition. Potential reaction pathways from NMP to the major experimentally observed products were inferred based on the calculated results and existing knowledge on the pyrolysis of nitrogenous and oxygenated compounds. These results shed light on the complex C/H/O/N reaction system. The calculated rate coefficients and extensive mole fractions data could provide good support for the modeling of a detailed reaction mechanism in the future.

로스쿨 이후 경찰대 존폐론 평가

This article examines the background and purpose of the establishment of the National Police University, and analyzes and evaluates the content and meaning of the argument for the existence of the National Police University. The theory of the existence of the National Police University is the theory of the existence of the undergraduate degree program of the National Police University. There is no dispute about maintaining the Graduate School of Public Safety. The theory of maintaining the National Police University is the theory of maintaining the undergraduate degree program of the National Police University. To be precise, it is a theory of introducing the police officer public recruitment exam. Regarding this, the method of the test may be controversial, it may be difficult to attract top talent, which may go against the purpose of establishing the National Police University, and there is a risk that it will further accelerate the departure of National Police University graduates to law school. The theory of abolishing the National Police University is the theory of abolishing the National Police University undergraduate program. These include the promotion backlog theory, high-level monopoly theory, preferential treatment theory, situational change theory, and organizational defection theory. However, the situation in which the majority of the National Police University graduates go on to law school could pose a crisis to the existence of the National Police University. The status of National Police University graduates leaving the police organization and the status of National Police University graduates returning to the police organization must be disclosed and analyzed, and the reasons for this must be revealed. If the National Police University undergraduate program has to be abolished, a plan must be prepared to ensure the police organization can secure excellent talent to replace it for the development of the organization. For example, a new police position is being added to the 5th level civil servant recruitment exam.

Strong Bases Design: Key Techniques and Stability Issues.

Theoretical design of molecular superbases has been attracting researchers for more than twenty years. General approaches were developed to make the bases potentially stronger, but less attention was paid to the stability of the predicted structures. Hence, only a small fraction of the theoretical research has led to positive experimental results. Possible stability issues of extremely strong bases are extensively studied in this work using quantum chemical calculations on a high level of theory. Several step-by-step design examples are discussed in detail, and general recommendations are given to avoid the most common stability problems. New potentially stable structures are theoretically studied to demonstrate the future prospects of molecular superbases design.

Femtosecond Core-Level Spectroscopy Reveals Involvement of Triplet States in the Gas-Phase Photodissociation of Fe(CO)5.

Excitation of iron pentacarbonyl [Fe(CO)5], a prototypical photocatalyst, at 266 nm causes the sequential loss of two CO ligands in the gas phase, creating catalytically active, unsaturated iron carbonyls. Despite numerous studies, major aspects of its ultrafast photochemistry remain unresolved because the early excited-state dynamics have so far eluded spectroscopic observation. This has led to the long-held assumption that ultrafast dissociation of gas-phase Fe(CO)5 proceeds exclusively on the singlet manifold. Herein, we present a combined experimental-theoretical study employing ultrafast extreme ultraviolet transient absorption spectroscopy near the Fe M2,3-edge, which features spectral evolution on 100 fs and 3 ps time scales, alongside high-level electronic structure theory, which enables characterization of the molecular geometries and electronic states involved in the ultrafast photodissociation of Fe(CO)5. We assign the 100 fs evolution to spectroscopic signatures associated with intertwined structural and electronic dynamics on the singlet metal-centered states during the first CO loss and the 3 ps evolution to the competing dissociation of Fe(CO)4 along the lowest singlet and triplet surfaces to form Fe(CO)3. Calculations of transient spectra in both singlet and triplet states as well as spin-orbit coupling constants along key structural pathways provide evidence for intersystem crossing to the triplet ground state of Fe(CO)4. Thus, our work presents the first spectroscopic detection of transient excited states during ultrafast photodissociation of gas-phase Fe(CO)5 and challenges the long-standing assumption that triplet states do not play a role in the ultrafast dynamics.

Relativistic coupled-cluster simulation of the kinetics of the hydrolysis of uranium hexafluoride

Uranium hexafluoride ( UF 6 ) is used ubiquitously in the nuclear fuel cycle. Its spontaneous hydrolysis in the presence of atmospheric moisture is well known but the associated reaction mechanism is poorly understood. Here a computational study was undertaken with the aim of characterizing the rate-limiting step of the hydrolysis of UF 6 under dry conditions. Toward this end, we began by examining the convergence of various energy contributions to the atomization enthalpy ( Δ H at ) of UF 6 and its hydrolysis product, UO 2 F 2 . This enabled a refinement of our previous composite method, resulting in a composite method with improved accuracy that also scales well by leveraging existing massively-parallel codes. We find that extremely high levels of theory are required to obtain Δ H at within experimental error bars, in agreement with the literature. Having calibrated our composite scheme, we proceed to investigate approximations appropriate for computing accurate binding energies and barrier heights on the potential energy surface governing the hydrolysis of UF 6 . In combination with high-accuracy relative energies, a master equation approach is used to generate rate constants, and comparisons are made with recent measurements [Richards et al., RSC Adv., 2020, 10, 34729]. Based on kinetic and equilibrium arguments, we conclude that the hydrolysis of U F 6 , when proceeding under very dry conditions, is unlikely to be initiated via U F 6 + H 2 O → UF 5 OH + HF type reactions. Finally, computed equilibrium constants and rate coefficients are tabulated for the temperature range 300–1800 K.

Non-adiabatic electronic relaxation of tetracene from its brightest singlet excited state.

The ultrafast relaxation dynamics of tetracene following UV excitation to the bright singlet state S6 has been studied with time-resolved photoelectron spectroscopy. With the help of high-level abinitio multireference perturbation theory calculations, we assign photoelectron signals to intermediate dark electronic states S3, S4, and S5 as well as to a low-lying electronic state S2. The energetic structure of these dark states has not been determined experimentally previously. The time-dependent photoelectron yields assigned to the states S6, S5, and S4 have been analyzed and reveal the depopulation of S6 within 60fs, while S5 and S4 are populated with delays of about 50 and 80fs. The dynamics of the lower-lying states S3 and S2 seem to agree with a delayed population coinciding with the depopulation of the higher-lying states S4-S6 but could not be elucidated in full detail due to the low signal levels of the corresponding two-photon ionization probe processes.

New rotational rate coefficients computation of the linear NaC3N(X1Σ+) by collision with He(1S) and astrophysical implication

ABSTRACT For a better understanding of the physico-chemistry in the interstellar medium, collisional data are needed. In this work, we provide rate coefficients for the recently detected by Cabezas and collaborators in 2023 sodium cyanoacetylide molecule NaC$_{3}$N($X^{1}\Sigma ^{+}$) induced by collisions with He. A new two-dimensional potential energy surface (2D-PES) is derived by adopting the high-level theory of the explicitly correlated coupled cluster with single, double, and perturbative triple excitations (CCSD(T)-F12) in conjunction with the aug-cc-pVTZ basis sets. Our 2D-PES presents two minima: the global one with a well depth of $-86.58$ cm$^{-1}$ and the second well with a $-47.01$ cm$^{-1}$ depth. This was expected by the Natural Bond Orbital analysis showing two electrophilic zones located on Na and N linked Carbon with respective atomic charges : $+0.96575$ and $+0.28910$. This PES was used to calculate inelastic cross-sections within the framework of the close-coupling theory for total energies $\le 170$ cm$^{-1}$ and $J\le 22$. Using the Maxwell–Boltzmann distribution of kinetic energies, these cross-sections were integrated to generate the collisional (de)-excitation rate coefficients for temperatures below 30 K. In order to estimate the impact of the computed collisional rates, a non-local thermodynamical equilibrium radiative transfer calculation was performed. The new collisional data can allow accurate NaC$_{3}$N abundance determination in the interstellar medium.

Ethynyl Radical Hydrogen Abstraction Energetics and Kinetics Utilizing High-Level Theory.

The ethynyl radical, C2H, is found in a variety of different environments ranging from interstellar space and planetary atmospheres to playing an important role in the combustion of various alkynes under fuel-rich conditions. Hydrogen-atom abstraction reactions are common for the ethynyl radical in these contrasting environments. In this study, the C2H + HX → C2H2 + X, where HX = HNCO, trans-HONO, cis-HONO, C2H4, and CH3OH, reactions have been investigated at rigorously high levels of theory, including CCSD(T)-F12a/cc-pVTZ-F12. For the stationary points thus located, much higher levels of theory have been used, with basis sets as large as aug-cc-pV5Z and methods up to CCSDT(Q), and core correlation was also included. These molecules were chosen because they can be found in either interstellar or combustion environments. Various additive energy corrections have been included to converge the relative enthalpies of the stationary points to subchemical accuracy (≤0.5 kcal mol-1). Barriers predicted here (2.19 kcal mol-1 for the HNCO reaction and 0.47 kcal mol-1 for C2H4) are significantly lower than previous predictions. Reliable kinetics were acquired over a wide range of temperatures (50-5000 K), which may be useful for future experimental studies of these reactions.

A familiar face and person processing area in the human temporal pole

How does the brain process the faces of familiar people? Neuropsychological studies have argued for an area of the temporal pole (TP) linking faces with person identities, but magnetic susceptibility artifacts in this region have hampered its study with fMRI. Using data acquisition and analysis methods optimized to overcome this artifact, we identify a familiar face response in TP, reliably observed in individual brains. This area responds strongly to visual images of familiar faces over unfamiliar faces, objects, and scenes. However, TP did not just respond to images of faces, but also to a variety of high-level social cognitive tasks, including semantic, episodic, and theory of mind tasks. The response profile of TP contrasted with a nearby region of the perirhinal cortex that responded specifically to faces, but not to social cognition tasks. TP was functionally connected with a distributed network in the association cortex associated with social cognition, while PR was functionally connected with face-preferring areas of the ventral visual cortex. This work identifies a missing link in the human face processing system that specifically processes familiar faces, and is well placed to integrate visual information about faces with higher-order conceptual information about other people. The results suggest that separate streams for person and face processing reach anterior temporal areas positioned at the top of the cortical hierarchy.

Evaluating Possible Formation Mechanisms of Criegee Intermediates during the Heterogeneous Autoxidation of Squalene.

Organic molecules in the environment oxidatively degrade by a variety of free radical, microbial, and biogeochemical pathways. A significant pathway is heterogeneous autoxidation, in which degradation occurs via a network of carbon and oxygen centered free radicals. Recently, we found evidence for a new heterogeneous autoxidation mechanism of squalene that is initiated by hydroxyl (OH) radical addition to a carbon-carbon double bond and apparently propagated through pathways involving Criegee Intermediates (CI) produced from β-hydroxy peroxy radicals (β-OH-RO2•). It remains unclear, however, exactly how CI are formed from β-OH-RO2•, which could occur by a unimolecular or bimolecular pathway. Combining kinetic models and multiphase OH oxidation measurements of squalene, we evaluate the kinetic viability of three mechanistic scenarios. Scenario 1 assumes that CI are formed by the unimolecular bond scission of β-OH-RO2•, whereas Scenarios 2 and 3 test bimolecular pathways of β-OH-RO2• to yield CI. Scenario 1 best replicates the entire experimental data set, which includes effective uptake coefficients vs [OH] as well as the formation kinetics of the major products (i.e., aldehydes and secondary ozonides). Although the unimolecular pathway appears to be kinetically viable, future high-level theory is needed to fully explain the mechanistic relationship between CI and β-OH-RO2• in the condensed phase.

Learning QM/MM potential using equivariant multiscale model.

The machine learning (ML) method emerges as an efficient and precise surrogate model for high-level electronic structure theory. Its application has been limited to closed chemical systems without considering external potentials from the surrounding environment. To address this limitation and incorporate the influence of external potentials, polarization effects, and long-range interactions between a chemical system and its environment, the first two terms of the Taylor expansion of an electrostatic operator have been used as extra input to the existing ML model to represent the electrostatic environments. However, high-order electrostatic interaction is often essential to account for external potentials from the environment. The existing models based only on invariant features cannot capture significant distribution patterns of the external potentials. Here, we propose a novel ML model that includes high-order terms of the Taylor expansion of an electrostatic operator and uses an equivariant model, which can generate a high-order tensor covariant with rotations as a base model. Therefore, we can use the multipole-expansion equation to derive a useful representation by accounting for polarization and intermolecular interaction. Moreover, to deal with long-range interactions, we follow the same strategy adopted to derive long-range interactions between a target system and its environment media. Our model achieves higher prediction accuracy and transferability among various environment media with these modifications.

Development of antipsychotic-induced weight gain management guidance: patient experiences and preferences - a qualitative descriptive study

Abstract Introduction It is estimated that 50% of patients with a severe mental illness (SMI) do not adhere to their medication regimen. Antipsychotic-induced weight gain (AIWG) is one of the most common reasons for patient disengagement with treatment.[1] Clinically significant weight gain has been linked to almost all antipsychotics and contributes significantly to the 2-3-fold higher rates of obesity seen in psychiatry.[2] Given the personal, social, and healthcare costs of overweight, obesity and relapse of mental illness, effectively managing AIWG is a priority for patients, clinicians, and policymakers. However, effectively managing AIWG is challenging. Sparse guidance for clinicians invariably contributes to management complexity. An important barrier to the development of high-quality AIWG management guidance is the lack of empirical research assessing patient values and preferences for management and other key guideline decision-making criteria including acceptability, feasibility, and equity considerations. Integration of patient experiences and preferences for management into guidance informing practice is essential to ensure recommendation relevance, applicability, and transferability. Aim To elicit the values and preferences of those with lived experience of AIWG regarding preferred management. Methods Qualitative descriptive methodology was used as it avoids high-level interpretation and theory development in data analysis and results in outputs that can be more readily implementable by policymakers and guideline developers. The study recruited adult outpatients attending a secondary psychiatric care setting with a diagnosis of a SMI and experience of AIWG. Purposive sampling informed recruitment whereby participants who had a diverse range of experiences of AIWG were recruited. Participants took part in online semi-structured interviews. Reflexive thematic analysis informed data analysis. Results Seventeen participants were recruited (24% male; mean age: 51.7 years [range: 26-66]). Participants conceptualised preferred AIWG management as being individualised, proactive, collaborative, and holistic. Participants were asked how the tenets of preferred management could be realised in practice and within clinician management guidance. Results are summarised in Figure 1. Conclusion Use of this research by guideline developers will provide opportunities to maximise the impact of recommendations in practice for those funding, providing, and using services. The opportunity to include a culturally diverse group was unavailable, which may limit transferability of study findings.

ExROPPP: Fast, accurate, and spin-pure calculation of the electronically excited states of organic hydrocarbon radicals.

Recent years have seen an explosion of interest in organic radicals due to their promise for highly efficient organic light-emitting diodes and molecular qubits. However, accurately and inexpensively computing their electronic structure has been challenging, especially for excited states, due to the spin-contamination problem. Furthermore, while alternacy or "pseudoparity" rules have guided the interpretation and prediction of the excited states of closed-shell hydrocarbons since the 1950s, similar general rules for hydrocarbon radicals have not to our knowledge been found yet. In this article, we present solutions to both of these challenges. First, we combine the extended configuration interaction singles method with Pariser-Parr-Pople (PPP) theory to obtain a method that we call ExROPPP (Extended Restricted Open-shell PPP) theory. We find that ExROPPP computes spin-pure excited states of hydrocarbon radicals with comparable accuracy to experiment as high-level general multi-configurational quasi-degenerate perturbation theory calculations but at a computational cost that is at least two orders of magnitude lower. We then use ExROPPP to derive widely applicable rules for the spectra of alternant hydrocarbon radicals, which are completely consistent with our computed results. These findings pave the way for highly accurate and efficient computation and prediction of the excited states of organic radicals.

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.

Dynamic and relativistic effects on Pt-Pt indirect spin-spin coupling in aqueous solution studied by abinitio molecular dynamics and two- vs four-component density functional NMR calculations.

Treating 195Pt nuclear magnetic resonance parameters in solution remains a considerable challenge from a quantum chemistry point of view, requiring a high level of theory that simultaneously takes into account the relativistic effects, the dynamic treatment of the solvent-solute system, and the dynamic electron correlation. A combination of Car-Parrinello molecular dynamics (CPMD) and relativistic calculations based on two-component zeroth order regular approximation spin-orbit Kohn-Sham (2c-ZKS) and four-component Dirac-Kohn-Sham (4c-DKS) Hamiltonians is performed to address the solvent effect (water) on the conformational changes and JPtPt1 coupling. A series of bridged PtIII dinuclear complexes [L1-Pt2(NH3)4(Am)2-L2]n+ (Am = α-pyrrolidonate and pivalamidate; L = H2O, Cl-, and Br-) are studied. The computed Pt-Pt coupling is strongly dependent on the conformational dynamics of the complexes, which, in turn, is correlated with the trans influence among axial ligands and with the angle N-C-O from the bridging ligands. The J-coupling is decomposed in terms of dynamic contributions. The decomposition reveals that the vibrational and explicit solvation contributions reduce JPtPt1 of diaquo complexes (L1 = L2 = H2O) in comparison to the static gas-phase magnitude, whereas the implicit solvation and bulk contributions correspond to an increase in JPtPt1 in dihalo (L1 = L2 = X-) and aquahalo (L1 = H2O; L2 = X-) complexes. Relativistic treatment combined with CPMD shows that the 2c-ZKS Hamiltonian performs as well as 4c-DKS for the JPtPt1 coupling.

Accelerating defect predictions in semiconductors using graph neural networks

First-principles computations reliably predict the energetics of point defects in semiconductors but are constrained by the expense of using large supercells and advanced levels of theory. Machine learning models trained on computational data, especially ones that sufficiently encode defect coordination environments, can be used to accelerate defect predictions. Here, we develop a framework for the prediction and screening of native defects and functional impurities in a chemical space of group IV, III–V, and II–VI zinc blende semiconductors, powered by crystal Graph-based Neural Networks (GNNs) trained on high-throughput density functional theory (DFT) data. Using an innovative approach of sampling partially optimized defect configurations from DFT calculations, we generate one of the largest computational defect datasets to date, containing many types of vacancies, self-interstitials, anti-site substitutions, impurity interstitials and substitutions, as well as some defect complexes. We applied three types of established GNN techniques, namely crystal graph convolutional neural network, materials graph network, and Atomistic Line Graph Neural Network (ALIGNN), to rigorously train models for predicting defect formation energy (DFE) in multiple charge states and chemical potential conditions. We find that ALIGNN yields the best DFE predictions with root mean square errors around 0.3 eV, which represents a prediction accuracy of 98% given the range of values within the dataset, improving significantly on the state-of-the-art. We further show that GNN-based defective structure optimization can take us close to DFT-optimized geometries at a fraction of the cost of full DFT. The current models are based on the semi-local generalized gradient approximation-Perdew–Burke–Ernzerhof (PBE) functional but are highly promising because of the correlation of computed energetics and defect levels with higher levels of theory and experimental data, the accuracy and necessity of discovering novel metastable and low energy defect structures at the PBE level of theory before advanced methods could be applied, and the ability to train multi-fidelity models in the future with new data from non-local functionals. The DFT-GNN models enable prediction and screening across thousands of hypothetical defects based on both unoptimized and partially optimized defective structures, helping identify electronically active defects in technologically important semiconductors.

Highly Accurate Prediction of NMR Chemical Shifts from Low-Level Quantum Mechanics Calculations Using Machine Learning.

Theoretical predictions of NMR chemical shifts from first-principles can greatly facilitate experimental interpretation and structure identification of molecules in gas, solution, and solid-state phases. However, accurate prediction of chemical shifts using the gold-standard coupled cluster with singles, doubles, and perturbative triple excitations [CCSD(T)] method with a complete basis set (CBS) can be prohibitively expensive. By contrast, machine learning (ML) methods offer inexpensive alternatives for chemical shift predictions but are hampered by generalization to molecules outside the original training set. Here, we propose several new ideas in ML of the chemical shift prediction for H, C, N, and O that first introduce a novel feature representation, based on the atomic chemical shielding tensors within a molecular environment using an inexpensive quantum mechanics (QM) method, and train it to predict NMR chemical shieldings of a high-level composite theory that approaches the accuracy of CCSD(T)/CBS. In addition, we train the ML model through a new progressive active learning workflow that reduces the total number of expensive high-level composite calculations required while allowing the model to continuously improve on unseen data. Furthermore, the algorithm provides an error estimation, signaling potential unreliability in predictions if the error is large. Finally, we introduce a novel approach to keep the rotational invariance of the features using tensor environment vectors (TEVs) that yields a ML model with the highest accuracy compared to a similar model using data augmentation. We illustrate the predictive capacity of the resulting inexpensive shift machine learning (iShiftML) models across several benchmarks, including unseen molecules in the NS372 data set, gas-phase experimental chemical shifts for small organic molecules, and much larger and more complex natural products in which we can accurately differentiate between subtle diastereomers based on chemical shift assignments.

An experimental, theoretical, and kinetic modeling study of post-flame oxidation of ammonia

The post-flame oxidation rate of ammonia was investigated in a novel atmospheric pressure flow reactor at temperatures of 1280 ± 16 K and as a function of residence time and mixture composition (1-10% O2, dry and moist). The experimental results, as well as selected data from literature, were analyzed using an updated detailed chemical kinetic model. The medium temperature, very lean conditions enhance the importance of reactions of the nitroxyl (HNO) intermediate. High-level theory was used to calculate the rate constant for HNO + NH2, indicating that this step is significantly faster than values used in literature. Furthermore, a trajectory based approach was used to determine collision efficiencies for selected bath gases for HNO + M. The experimental results show that the NH3 oxidation rate increases with temperature and O2 concentration, while the presence of water vapor slightly inhibits reaction. Formation of NO and N2O was strongly promoted at higher levels of O2. Modeling results agreed well with the measurements, except at the lowest level of O2. The predicted oxidation rate of NH3 was shown to result from a delicate balance between chain branching and terminating steps involving NH2, H2NO, and HNO. Recent theoretical work on reactions of these species by Klippenstein and coworkers and Stagni et al. was instrumental in improving modeling predictions. After initiation, NO reached a pseudo-steady-state level, where the pathways to NO were largely balanced by the NH2 + NO reaction. Nitric oxide was partly oxidized to NO2, with the NH2 + NO2 reaction responsible for most of the N2O formation. Novelty and significance statement:This study provides the first detailed kinetic analysis of the lean post-flame oxidation of ammonia, based on time-resolved flow reactor data in a novel reactor. In addition to the post-flame oxidation rate of ammonia, data for formation of NO and N2O were compared with modeling predictions. The medium temperature, very lean conditions enhance the importance of reactions of the HNO and H2NO intermediates. Inclusion in the model of results from recent high-level theoretical work, including present calculations for HNO + NH2 and HNO + M, was crucial for capturing the observed behavior. It is argued that the post-initiation steady-state NH3 oxidation rates constitute important data for model validation, along with ignition delays and laminar flame speeds.

Інноваційні аспекти багатосуб’єктного управління програмами соціального захисту

The article is devoted to the search for innovative ways of updating the management mechanisms of social protection programs. The theoretical basis for substantiating the management model as a whole is an interdisciplinary theoretical-methodological basis, founded on the fundamental tenets of the concepts of social protection, social justice, social involvement, as well as high-level management theories (governance theories). The hypothesis of the research, which became the reason for writing this article, was the assumption that the complexity and multidimensionality of the social sphere needs to be provided by an appropriate multidimensional model of management. It is this kind of multidimensional (or multi-subject) management, using modern innovative approaches, that seems to give advantages and increase the effectiveness of social programs. The purpose of this article is to develop methodological principles for building an innovative model of social program implementation, based on the principles of multi-subject management, aimed at achieving a balanced distribution of powers and responsibilities of interested subjects of various levels and their inclusive interaction. To substantiate the main theses and conclusions of the article, a complex of analytical methods was used, including synthesis and analysis of available sources of information, logical framework analysis of problems and their possible solutions, the method of comparisons and analogies for adapting existing experience to real conditions in Ukraine. As examples of successful implementation of social programs and projects based on multi-subject management, the experience of implementation of EU programs, as well as the relevant experience of some countries of North and South America, was used. The article offers the author’s interpretation of the essence of multi-subject management in the field of social protection and develops its basic three-dimensional model. The analysis of many sources of information made it possible to propose general innovative mechanisms for implementing the principles of multi-level management, namely those related to the information support of the management process. Based on the assessment of the current situation on the labor market of Ukraine, the prospects for its development, a set of expected problems that will need to be solved is formulated and a list of specific measures and mechanisms is proposed that will ensure the effective interaction of various management entities in the process of implementing support programs for the unemployed and individuals who are looking for work. Particular emphasis is placed on the aspects of inclusive management, and in this regard, proposals regarding the mechanism of involving the unemployed and employers in the management process are considered in more detail.