Complex Reaction Systems Research Articles

Plasma-driven synthesis of nitrogen-doped graphene: unveiling the reaction mechanism and kinetic insights.

The rotating arc plasma technique for the synthesis of nitrogen-doped graphene capitalizes on the distinctive attributes of plasma, presenting a straightforward, efficient, and catalyst-free strategy for the production of nitrogen-doped graphene. However, experimental outcomes generally fail to elucidate the atomic-level mechanism behind this process. Our research utilizes molecular dynamics simulations to explore theoretically the formation of radicals during the plasma-driven reaction between methane (CH₄) and nitrogen (N₂). The simulations present a complex reaction system comprising nine principal species: CH₄, CH₃, CN, CH₂, HCN, CH, N₂, H₂ and H. Notably, HCN and CN emerge as pivotal precursors for nitrogen doping. Optimal nitrogen concentrations enhance the synthesis of these precursors, whereas excessive nitrogen suppresses the formation of C₂ species, impacting the yield of nitrogen-doped graphene. Conversely, higher methane concentrations stimulate the generation of carbon radicals, augmenting the production of HCN and CN and thus, influencing the properties of the synthesized material. This work is expected to lay a theoretical foundation for the refinement of nitrogen-doped graphene synthesis processes. In this investigation, we employed the LAMMPS software package to explore the formation of free radicals during the methane-nitrogen reaction via molecular dynamics (MD) simulations. These simulations were conducted under an NVT ensemble, maintaining a constant temperature of 3500K with a time step of 0.1fs over a duration of 1000ps. To reduce the variability and enhance the reliability of the simulation outcomes, each simulation was meticulously conducted three times under identical parameters for subsequent statistical analysis.

Enhancing Activation Energy Predictions under Data Constraints Using Graph Neural Networks.

Accurately predicting activation energies is crucial for understanding chemical reactions and modeling complex reaction systems. However, the high computational cost of quantum chemistry methods often limits the feasibility of large-scale studies, leading to a scarcity of high-quality activation energy data. In this work, we explore and compare three innovative approaches (transfer learning, delta learning, and feature engineering) to enhance the accuracy of activation energy predictions using graph neural networks, specifically focusing on methods that incorporate low-cost, low-level computational data. Using the Chemprop model, we systematically evaluated how these methods leverage data from semiempirical quantum mechanics (SQM) calculations to improve predictions. Delta learning, which adjusts low-level SQM activation energies to align with high-level CCSD(T)-F12a targets, emerged as the most effective method, achieving high accuracy with substantially reduced data requirements. Notably, delta learning trained with just 20-30% of high-level data matched or exceeded the performance of other methods trained with full data sets, making it advantageous in data-scarce scenarios. However, its reliance on transition state searches imposes significant computational demands during model application. Transfer learning, which pretrains models on large data sets of low-level data, provided mixed results, particularly when there was a mismatch in the reaction distributions between the training and target data sets. Feature engineering, which involves adding computed molecular properties as input features, showed modest gains, particularly in thermodynamic properties. Our study highlights the trade-offs between accuracy and computational demand in selecting the best approach for enhancing activation energy predictions. These insights provide valuable guidelines for researchers aiming to apply machine learning in chemical reaction engineering, helping to balance accuracy with resource constraints.

Mathematical modeling of complex reaction systems in the oil and gas industry

Mathematical modeling of complex reaction systems in the oil and gas industry

Integrating machine learning to uncover homogeneous catalytic mechanisms in N‐vinyl‐pyrrolidone synthesis

AbstractCombining machine learning, density functional theory (DFT) calculation, and kinetic modeling offers significant advantages in studying reactions under complex conditions, enabling a detailed exploration of reaction mechanisms. Artificial neural network (ANN) models accurately predict and analyze the impact of reaction conditions, overcoming the limitations of traditional kinetic studies in complex systems. Specifically, the homogeneous catalytic reaction for synthesizing N‐vinylpyrrolidone from pyrrolidone and acetylene, which has stringent requirements, was selected. The ANN model effectively captured nonlinear relationships between residence time, moisture content, reaction temperature, catalyst concentration, reaction pressure, and catalyst preparation temperature. DFT calculations revealed the reaction pathways, leading to the development of a high‐precision predictive model. This approach not only uncovers potential reaction pathways and intermediates in complex reaction systems but also provides a new avenue for optimizing reaction conditions, resulting in more efficient catalyst design and process development in intricate reaction environments.

(Invited) Optimizing CO2 Reduction in Aprotic Media: Insights Via Kinetic Modeling of Bimetallic CuxZn1-X Electrocatalysts

Bimetallic CuxZn1-x electrocatalysts, as an earth-abundant material, have emerged as promising candidates for improving the efficiency and selectivity of CO2 reduction in aqueous electrolytes.[1] However, the use of aqueous electrolytes has several drawbacks, including limited CO2 transport due to its low solubility in the electrolyte solution and, most importantly, the competing hydrogen evolution reaction (HER).[2] These challenges can be effectively circumvented by using aprotic organic electrolytes instead, which offer higher CO2 solubility and minimize HER.[3] Aprotic electrolytes (e.g., acetonitrile) show distinct differences in the reaction pathway, yielding either CO or oxalate. Studies under these conditions are scarce, and fundamental knowledge of the performance-limiting processes at the electrode-electrolyte interface is lacking. This knowledge gap hinders further development and process design for efficient CO2R in aprotic media.In this contribution, we present a comprehensive analysis of CO2R on Cu and CuxZn1-x electrocatalysts in acetonitrile. By combining standard electrochemical methods such as cyclic voltammetry, polarization curve, and electrochemical impedance spectroscopy, with mathematical modeling that couples microkinetics with continuum transport, we elucidate reaction and transport phenomena and reveal the underlying performance limitations. Based on this approach, we provide insights into the local reaction environment and discuss in detail the rates, surface coverages of adsorbed species, and local concentration profiles at the electrode. We investigate Cu and α-brass at different CuxZn1-x ratios as planar as well as nanoparticle coated electrodes. For Cu, we find that adsorbed CO2 is being consumed faster than it replenishes, suggesting that CO2 adsorption is the bottleneck in aprotic electrolytes,[4] as opposed to CO2 transport as in alkaline solution[5]. With increasing Zn content of the α-brass, higher CO2R performance is achieved. According to our simulations this can be attributed to i) higher utilization of the electrocatalyst and ii) improved CO2 adsorption capability compared to pure Cu.The gained insights allow for a better understanding of the effects of the catalyst composition on the performance and highlight the importance of CO2 adsorption on the performance of CO2 electroreduction in organic electrolytes. This enables knowledge-based optimization of the electrocatalysts and tailoring of reaction conditions. Overall, our results provide valuable insights into the poorly understood organic electrolytes for CO2R as a viable alternative to aqueous electrolytes and pave the way for the analysis of more complex reaction systems such as electro-organic syntheses or CO2R with protic additives.

Thiol‐Ene Coupling Reaction for Functionalization of Non‐Activated Alkenes: An Experimental Study on the Chemistry of the Methyl Oleate Amination

AbstractThis work investigates the functionalization of molecules with non‐activated internal double bonds via thiol‐ene coupling (TEC) reaction. A comprehensive study of the reaction between cysteamine hydrochloride (CAHC) and methyl oleate (MO) was carried out. A series of reactions were conducted under different operating conditions in order to assess their effect on conversion and selectivity. Different conditions were considered like the type of atmospheres and solvents, a portionwise addition of CAHC, reagents concentrations, and excess of CAHC. Maximum conversion and selectivity were reached under inert atmosphere, diluted conditions, ethanol as solvent, and the addition of CAHC at the beginning of the reaction with a molar ratio of CAHC/MO of 3/1. This provides an approach for analyzing more complex reaction systems.

Research on Non-Equilibrium Dynamics Theory in Chemical Thermodynamics

Non-equilibrium dynamics theory is an important branch of chemical thermodynamics, widely applied in describing complex chemical reaction systems far from equilibrium. With the advancement of modern science and technology, an increasing number of chemical reactions exhibit unique behaviors under non-equilibrium conditions, such as self-organization and periodic oscillations—nonlinear phenomena. This paper systematically explores the distinctions between non-equilibrium and equilibrium states, introduces the fundamental principles of non-equilibrium thermodynamics, and analyzes cutting-edge issues such as nonlinear phenomena and multi-scale dynamics. Additionally, it discusses the specific applications of non-equilibrium dynamics in far-from-equilibrium reactions, heterogeneous catalysis, and industrial chemical processes, addressing both theoretical and experimental challenges. Through the combination of theoretical analysis and practical application, this paper aims to provide new insights and methodologies for research on non-equilibrium dynamics in the field of chemistry.

Mathematical modeling and kinetics of batch and continuous electro-catalytic oxidation of pharmaceutical-contaminated wastewater

An accurate yet simple model is the key to the design and control of intricate electro-catalytic oxidation of pharmaceutical contaminated wastewater. For both batch and unsteady-state continuous flow stirred tank reactors (CSTR), batch reactor models have been used earlier. Further, these models do not correlate rate to the operating conditions, and consider pseudo-first/second-order kinetics. Here, first-principles models are proposed by formulating unsteady-state mass balances, modifying them to attain realistic final conditions, and incorporating fractional variable-order kinetics. Following integral analysis, analytical solutions are obtained. These are independently applicable to design, unlike a numerical solution. Nonlinear regression is performed to estimate the model parameters from the transient experimental data. The simulations yield markedly accurate model parameters together with a better fit to the experimental data of Ti/RuO2-mediated amoxicillin-trihydrate electro-oxidation, for CSTR and batch reactors. For the batch reactor, the operating conditions are varied one at a time. Their effects on the model parameters are elucidated based on the oxidant and transformation species formed. The computed optimum model parameters are: rate constant 3.318 × 10−3 mg−0.092 m1.276 min−1, order 1.092, initial rate 4.032 × 102 mg m−2 min−1, and final conversion 90.6% in 180 min. The corresponding operating conditions are: pH 2.0, feed 50 mg L−1, electrolyte 2 g L−1, and current 1 A. A simple generalized power-law correlation, associating rate to the operating conditions, is then estimated. Statistical analysis of these models using central composite design delivers R2 0.99, predicted R2 0.96, and optimum set close to the above. The corresponding sensitivity analysis and generalized correlation, both show applied current to be the most significant operating condition. The dynamic modeling approaches proposed here can be extended to model, control, and scale-up complex reaction systems.

Paving the way to transfer hydrogenation of CO2 with bio-derived glycerol over Ni supported zeolite catalysts

The CO2 transfer hydrogenation with bio-glycerol over a Ni-zeolite was systematically studied to produce formic and lactic acids. The alkaline hydrothermal reactions without a catalyst and with Ni-zeolite heterogeneous catalyst were explored, focusing on the effects of base types and concentrations, reaction atmosphere and temperature. In alkaline hydrothermal reactions without catalyst, NaOH demonstrated superior performance at 1 M. A Ni/NaZSM-5 catalyst showed astounding performance giving 9.3 mol-L−1-g−1 lactic acid and 6.5 mol-L−1-g−1 formic acid at 250 °C after 2 h. Notably, the zeolite showed resistance to the highly basic conditions of the reaction medium. For the first time, CO2 conversion in aqueous phase was reported addressing the complexity of CO2 solubility. A reaction network was proposed including the diverse glycerol transformations not yet studied for this system. Overall, this study sheds light on the understanding of this complex reaction system and the potential of Ni-supported zeolites for sustainable CO2 utilisation.

Modeling and optimization for the continuous catalytic reforming process based on the hybrid surrogate optimization model

To address the modeling and optimization challenges of the complex reaction system in the continuous catalytic reforming process, a new integrated simulation and optimization framework is presented. First, a detailed mechanism model is established based on a reaction network involving 32 components and 50 reactions, coupled with mass transfer, heat transfer, pressure drop, and catalyst deactivation equations. Then, to solve the differential-algebraic equations in the mechanism model, a multi-objective hybrid optimization method with the adaptive infill strategy is introduced. GAMS and MATLAB are integrated to perform a joint iterative solution. Finally, two cases are conducted with the proposed algorithm. Results show that the mechanism model calculation deviations are below 4 % of reactor temperature, pressure, and composition distribution, and the Pareto front of various production plans is obtained. The accurate simulation and rapid trade-off optimization among the key goals can be achieved to provide scientific decision support for enterprise production.

Benchmarking various nonadiabatic semiclassical mapping dynamics methods with tensor-train thermo-field dynamics.

Accurate quantum dynamics simulations of nonadiabatic processes are important for studies of electron transfer, energy transfer, and photochemical reactions in complex systems. In this comparative study, we benchmark various approximate nonadiabatic dynamics methods with mapping variables against numerically exact calculations based on the tensor-train (TT) representation of high-dimensional arrays, including TT-KSL for zero-temperature dynamics and TT-thermofield dynamics for finite-temperature dynamics. The approximate nonadiabatic dynamics methods investigated include mixed quantum-classical Ehrenfest mean-field and fewest-switches surface hopping, linearized semiclassical mapping dynamics, symmetrized quasiclassical dynamics, the spin-mapping method, and extended classical mapping models. Different model systems were evaluated, including the spin-boson model for nonadiabatic dynamics in the condensed phase, the linear vibronic coupling model for electronic transition through conical intersections, the photoisomerization model of retinal, and Tully's one-dimensional scattering models. Our calculations show that the optimal choice of approximate dynamical method is system-specific, and the accuracy is sensitively dependent on the zero-point-energy parameter and the initial sampling strategy for the mapping variables.

Intrinsic kinetics of steam methane reforming over a monolithic nickel catalyst in a fixed bed reactor system

The intrinsic kinetics of steam methane reforming were experimentally investigated using a monolithic nickel-based catalyst in a specially designed fixed bed system. All kinetic tests were performed under various operating conditions: steam to carbon ratio of 2 to 5, methane volumetric concentration in feed gas of 3 % to 9 %, temperature of 500 to 650 °C, and typical biogas compositions with N2 dilution (carbon dioxide 35.7–55.6 %, methane 44.4–64.3 %, with 86–91 % nitrogen dilution). A deconvolution process was applied during the rate calculation and the reaction orders with respect to different gases were determined, coupled with thermodynamic system analysis. The reaction mechanism was then proposed and a Langmuir-Hinshelwood-Hougen-Watson (LHHW) kinetic model was established, followed by kinetic parameter estimations for the model and a further model validation with experimental data. Based on the model, the intrinsic kinetic of SMR reaction using monolithic catalysts was described. The validity and feasibility of applying the faster approach for kinetic parameter estimation were demonstrated, and this work is the first demonstration of a complex reaction system. Also, the reaction mechanism appeared to show a suitably high correlation with alternative and more sustainable methane sources (i.e. biogas).

Evidence for Dearomatizing Spirocyclization and Dynamic Effects in the Quasi-stereospecific Nitrogen Deletion of Tetrahydroisoquinolines.

Selectivity in organic chemistry is generally presumed to arise from energy differences between competing selectivity-determining transition states. However, in cases where static density functional theory (DFT) fails to reproduce experimental product distributions, dynamic effects can be examined to understand the behavior of more complex reaction systems. Previously, we reported a method for nitrogen deletion of secondary amines which relies on the formation of isodiazene intermediates that subsequently extrude dinitrogen with concomitant C-C bond formation via a caged diradical. Herein, a detailed mechanistic analysis of the nitrogen deletion of 1-aryl-tetrahydroisoquinolines is presented, suggesting that in this system the previously determined diradical mechanism undergoes dynamically controlled partitioning to both the normal 1,5-coupling product and an unexpected spirocyclic dearomatized intermediate, which converges to the expected indane by an unusually facile 1,3-sigmatropic rearrangement. This mechanism is not reproduced by static DFT but is supported by quasi-classical molecular dynamics calculations and unifies several unusual observations in this system, including partial chirality transfer, nonstatistical isotopic scrambling at the ethylene bridge, the isolation of spirocyclic dearomatized species in a related heterocyclic series, and the observation that introduction of an 8-substituent dramatically improves enantiospecificity.

Predicting Long-Time-Scale Kinetics under Variable Experimental Conditions with Kinetica.jl.

Predicting the degradation processes of molecules over long time scales is a key aspect of industrial materials design. However, it is made computationally challenging by the need to construct large networks of chemical reactions that are relevant to the experimental conditions that kinetic models must mirror, with every reaction requiring accurate kinetic data. Here, we showcase Kinetica.jl, a new software package for constructing large-scale chemical reaction networks in a fully automated fashion by exploring chemical reaction space with a kinetics-driven algorithm; coupled to efficient machine-learning models of activation energies for sampled elementary reactions, we show how this approach readily enables generation and kinetic characterization of networks containing ∼103 chemical species and ≃104-105 reactions. Symbolic-numeric modeling of the generated reaction networks is used to allow for flexible, efficient computation of kinetic profiles under experimentally realizable conditions such as continuously variable temperature regimes, enabling direct connection between bottom-up reaction networks and experimental observations. Highly efficient propagation of long-time-scale kinetic profiles is required for automated reaction network refinement and is enabled here by a new discrete kinetic approximation. The resulting Kinetica.jl simulation package therefore enables automated generation, characterization, and long-time-scale modeling of complex chemical reaction systems. We demonstrate this for hydrocarbon pyrolysis simulated over time scales of seconds, using transient temperature profiles representing those of tubular flow reactor experiments.

Unveiling kinetics post rate-determining step in Brønsted acid-catalyzed reactions of fructose: A strategy for 5-hydroxymethylfurfural production from concentrated feedstock

The development of high-yielding catalytic systems is pivotal in biomass upgrading. However, the investigation of kinetics in these complex reaction systems is often highly challenging, especially when reactions leading to byproduct formation are obscured by a preceding, slow rate-determining step. In this work, we introduce a unique approach to kinetic analysis that enables the investigation of reactions occurring post rate-determining step. The technique was applied to the conversion of sugars to 5-hydroxymethylfurfural (HMF), an important platform molecule for sustainable chemical production. First, screening experiments were carried out to identify a catalytic solvent system that achieves a balance between solvent stability, HMF yield, and cost-effectiveness. Our kinetic analysis on the identified system reveals that the fructose reactant undergoes a rate-determining dehydration step to form an unstable reactive intermediate. This intermediate further reacts competitively with other reactive species, such as the hexose reactants or the HMF product, ultimately lowering the HMF selectivity. The calculated relative activation energies of these reactions post rate-determining step provide intuitive insights into the effects of temperature, co-existing reactive species, as well as the previously unexplained effect of feed concentration on HMF selectivity. Additionally, we propose a novel reaction strategy that significantly enhances HMF yield when compared to a traditional batch synthesis. Notably, a high HMF yield of 74 % was achieved with an extreme 55 wt% fructose loading on an aqueous basis (20 wt% loading on a total weight basis) in a low-boiling 80 wt% dioxane/water mixture using an HCl catalyst at 393 K.

Deciphering complexity in Pd–catalyzed cross-couplings

Understanding complex reaction systems is critical in chemistry. While synthetic methods for selective formation of products are sought after, oftentimes it is the full reaction signature, i.e., complete profile of products/side-products, that informs mechanistic rationale and accelerates discovery chemistry. Here, we report a methodology using high-throughput experimentation and multivariate data analysis to examine the full signature of one of the most complicated chemical reactions catalyzed by palladium known in the chemical literature. A model Pd-catalyzed reaction was selected involving functionalization of 2-bromo-N-phenylbenzamide and multiple bond activation pathways. Principal component analysis, correspondence analysis and heatmaps with hierarchical clustering reveal the factors contributing to the variance in product distributions and show associations between solvents and reaction products. Using robust data from experiments performed with eight solvents, for four different reaction times at five different temperatures, we correlate side-products to a major dominant N-phenyl phenanthridinone product, and many other side products.

The computational molecular technology for complex reaction systems: The Red Moon approach

AbstractFor dealing with complex reaction (CR) systems that show typical chemical phenomena in molecular aggregation states, the Red Moon (RM) approach is introduced based on a new efficient and systematic RM methodology. First, the theoretical background with my motivation to develop the RM approach is presented from the recent necessity to perform ‘atomistic’ molecular simulation of large‐scale and long‐term phenomena of (i) complex chemical reactions, (ii) stereospecificity, and (iii) aggregation structures. The RM methodology uses both the molecular dynamics (MD) method for molecular motions (translation, rotation, and vibration of molecules) that frequently occur on a short‐time scale and the Monte Carlo (MC) method for rare events such as chemical reactions that hardly do on that time scale. Then, under the transition rate using both the potential energy difference before and after a rare event trial and its chemical kinetic probability, it is tested and judged by the MC method whether the trial is possible (Metropolis method). Next, typical applications of the RM approach are reviewed in two main research fields, (i) polymerization and (ii) storage battery (rechargeable battery or secondary cell), with various examples of our successful studies. Finally, we conclude that the RM approach using the RM methodology should become an efficient new‐generation approach as one promising computational molecular strategy (CMT). We believe it will play an essential role in surveying, at the multilevel resolution, various specificities of CR systems in molecular aggregation states.This article is categorized under: Theoretical and Physical Chemistry > Reaction Dynamics and Kinetics Structure and Mechanism > Reaction Mechanisms and Catalysis Molecular and Statistical Mechanics > Molecular Dynamics and Monte‐Carlo Methods

Modeling the alkylation of amines with alkyl bromides: explaining the low selectivity due to multiple alkylation.

Nucleophilic substitution reactions of aliphatic amines with alkyl halides represent a simple and direct mechanism for obtaining higher-order aliphatic amines. However, it is well known that these reactions suffer from low selectivity due to multiple alkylations, which is attributed to the higher reactivity of the newly formed amine. In order to provide a detailed explanation for this kind of system, we have investigated the reactivity of primary and secondary amines with 1-bromopropane and 2-bromopropane. The free energy profile in acetonitrile solution was obtained and a detailed microkinetic analysis was needed to analyze this complex reaction system. We have found that the product of the first alkylation is an ion pair corresponding to the protonated secondary amine and the bromide ion, which can transfer the proton to the reactant primary amine. Then, the newly formed secondary amine can also react, leading to a second alkylation to produce a tertiary protonated amine. Our modeling points out that both the proton transfer equilibria and the similar reactivity of the primary and secondary amines produce reduced selectivity. The proton transfer equilibria also contribute to slowing down the kinetics of the first alkylation. The exploration of the mechanism was done by geometry optimization using the CPCM/X3LYP/ma-def2-SVP method, followed by harmonic frequency calculation at this same level of theory. A composite approach was used to obtain the free energy profile, using the more accurate ωB97X-D3/ma-def2-TZVPP level of theory for electronic energy and the SMD model for the solvation free energy. These calculations were performed with the ORCA 4 program. The detailed microkinetic analysis was done using the Kintecus program.

Uncertainty quantified discovery of chemical reaction systems via Bayesian scientific machine learning

The recently proposed Chemical Reaction Neural Network (CRNN) discovers chemical reaction pathways from time resolved species concentration data in a deterministic manner. Since the weights and biases of a CRNN are physically interpretable, the CRNN acts as a digital twin of a classical chemical reaction network. In this study, we employ a Bayesian inference analysis coupled with neural ordinary differential equations (ODEs) on this digital twin to discover chemical reaction pathways in a probabilistic manner. This allows for estimation of the uncertainty surrounding the learned reaction network. To achieve this, we propose an algorithm which combines neural ODEs with a preconditioned stochastic gradient langevin descent (pSGLD) Bayesian framework, and ultimately performs posterior sampling on the neural network weights. We demonstrate the successful implementation of this algorithm on several reaction systems by not only recovering the chemical reaction pathways but also estimating the uncertainty in our predictions. We compare the results of the pSGLD with that of the standard SGLD and show that this optimizer more efficiently and accurately estimates the posterior of the reaction network parameters. Additionally, we demonstrate how the embedding of scientific knowledge improves extrapolation accuracy by comparing results to purely data-driven machine learning methods. Together, this provides a new framework for robust, autonomous Bayesian inference on unknown or complex chemical and biological reaction systems.

Study on Adsorption of Diesel Molecules on MoS<sub>2</sub> and NiMoS Catalysts

For deep insight into complex reaction system of diesel hydrotreating, the monolayer adsorption and competitive adsorption of typical reactant molecules (phenanthrene, naphthalene, acridine, quinoline, dibenzothiophene, 4,6-dimethyldibenzothiophene and H2) on MoS2 and NiMoS catalyst models with different structures were investigated. The basal plane is discovered to be the best physical adsorption position for all molecules in MoS2 series catalysts. Following saturation of the basal plane, reactant molecules will be adsorbed at Mo edge first, and Mo edge is more prone to bimolecular or multimolecular adsorption than S-edge, implying that Mo edge active sites play an important role in diesel hydrotreating. Naphthalene has a higher adsorption capacity in the partial pressure system that simulates the actual reaction atmosphere, and it is the most likely reactant molecule to predominately occupy active sites, but 4,6-dimethyl dibenzothiophene still exhibits good competition adsorption performance due to its high adsorption capacity and heat release. Interestingly, after phenanthrene adsorption, the secondary adsorption of hydrogen decreases in all of the catalyst models studied, indicating that phenanthrene is one of the most important molecules influencing hydrogen adsorption. Furthermore, the secondary adsorption of hydrogen after phenanthrene adsorption decreased the most on Tri-S50 catalyst. It shed light on that the activity and stability of Tri-S50 catalyst was most likely to decrease during diesel hydrotreating because of the notable inhibition on adsorption of hydrogen molecules brought by phenanthrene adsorption. It presents a theoretical basis for the design and development of highly efficient diesel hydrotreating catalysts.