Two-step Method Research Articles

Functionalized Thienopyrazines on NiOx Film as Self-Assembled Monolayer for Efficient Tin-Perovskite Solar Cells Using a Two-Step Method.

Three functionalized thienopyrazines (TPs), TP-MN (1), TP-CA (2), and TPT-MN (3) were designed and synthesized as self-assembled monolayers (SAMs) deposited on the NiOx film for tin-perovskite solar cells (TPSCs). Thermal, optical, electrochemical, morphological, crystallinity, hole mobility, and charge recombination properties, as well as DFT-derived energy levels with electrostatic surface potential mapping of these SAMs, have been thoroughly investigated and discussed. The structure of the TP-MN (1) single crystal was successfully grown and analyzed to support the uniform SAM produced on the ITO/NiOx substrate. When we used NiOx as HTM in TPSC, the device showed poor performance. To improve the efficiency of TPSC, we utilized a combination of new organic SAMs with NiOx HTM, theTPSC device exhibited the highest PCE of 7.7% for TP-MN (1).Hence, the designed NiOx/TP-MN (1) acts as a new model system for the development of efficient SAM-based TPSC. To the best of our knowledge, the combination of organic SAMs with anchoring CN/CN or CN/COOH groups, and NiOx HTM for TPSC has never been reported elsewhere. The TPSC device based on the NiOx/TP-MN bilayer exhibits great enduring stability for performance, retaining ~80% of its original value for shelf storage over 4000h.

On Implementing a Two-Step Interior Point Method for Solving Linear Programs

A new two-step interior point method for solving linear programs is presented. The technique uses a convex combination of the auxiliary and central points to compute the search direction. To update the central point, we find the best value for step size such that the feasibility condition is held. Since we use the information from the previous iteration to find the search direction, the inverse of the system is evaluated only once every iteration. A detailed empirical evaluation is performed on NETLIB instances, which compares two variants of the approach to the primal-dual log barrier interior point method. Results show that the proposed method is faster. The method reduces the number of iterations and CPU time(s) by 27% and 18%, respectively, on NETLIB instances tested compared to the classical interior point algorithm.

An LSTM-based optimization algorithm for enhancing quantitative arbitrage trading

Arbitrage trading is a common quantitative trading strategy that leverages the long-term cointegration relationships between multiple related assets to conduct spread trading for profit. Specifically, when the cointegration relationship between two or more related series holds, it utilizes the stability and mean-reverting characteristics of their cointegration relationship for spread trading. However, in real quantitative trading, determining the cointegration relationship based on the Engle-Granger two-step method imposes stringent conditions for the cointegration to hold, which can easily be disrupted by price fluctuations or trend characteristics presented by the linear combination, leading to the failure of the arbitrage strategy and significant losses. To address this issue, this article proposes an optimized strategy based on long-short-term memory (LSTM), termed Dynamic-LSTM Arb (DLA), which can classify the trend movements of linear combinations between multiple assets. It assists the Engle-Granger two-step method in determining cointegration relationships when clear upward or downward non-stationary trend characteristics emerge, avoiding frequent strategy switches that lead to losses and the invalidation of arbitrage strategies due to obvious trend characteristics. Additionally, in mean-reversion arbitrage trading, to determine the optimal trading boundary, we have designed an optimized algorithm that dynamically updates the trading boundaries. Training results indicate that our proposed optimization model can successfully filter out unprofitable trades. Through trading tests on a backtesting platform, a theoretical return of 23% was achieved over a 10-day futures trading period at a 1-min level, significantly outperforming the benchmark strategy and the returns of the CSI 300 Index during the same period.

An LSTM-based optimization algorithm for enhancing quantitative arbitrage trading

Arbitrage trading is a common quantitative trading strategy that leverages the long-term cointegration relationships between multiple related assets to conduct spread trading for profit. Specifically, when the cointegration relationship between two or more related series holds, it utilizes the stability and mean-reverting characteristics of their cointegration relationship for spread trading. However, in real quantitative trading, determining the cointegration relationship based on the Engle-Granger two-step method imposes stringent conditions for the cointegration to hold, which can easily be disrupted by price fluctuations or trend characteristics presented by the linear combination, leading to the failure of the arbitrage strategy and significant losses. To address this issue, this article proposes an optimized strategy based on long-short-term memory (LSTM), termed Dynamic-LSTM Arb (DLA), which can classify the trend movements of linear combinations between multiple assets. It assists the Engle-Granger two-step method in determining cointegration relationships when clear upward or downward non-stationary trend characteristics emerge, avoiding frequent strategy switches that lead to losses and the invalidation of arbitrage strategies due to obvious trend characteristics. Additionally, in mean-reversion arbitrage trading, to determine the optimal trading boundary, we have designed an optimized algorithm that dynamically updates the trading boundaries. Training results indicate that our proposed optimization model can successfully filter out unprofitable trades. Through trading tests on a backtesting platform, a theoretical return of 23% was achieved over a 10-day futures trading period at a 1-min level, significantly outperforming the benchmark strategy and the returns of the CSI 300 Index during the same period.

High-performance photocatalytic hydrogen evolution in a Zn0.5Cd0.5S/MoS2 p–n heterojunction

With respect to solid-solution photocatalysts for water splitting, researchers have focused on improving the loading and stability of catalysts, which have been major concerns and research interests. Herein, the fabrication of Zn0.5Cd0.5S/MoS2 nanocomposites involved a two-step method that encompassed hydrothermal synthesis and physical mixing. Experimental results confirmed that the Zn0.5Cd0.5S/MoS2 nanocomposites exhibited outstanding photocatalytic performance. In particular, they exhibited a notable hydrogen precipitation rate of 19.41 mmol/(g*h). The hydrogen production performance of Zn0.5Cd0.5S/MoS2 was approximately nine times greater than that of pure Zn0.5Cd0.5S, indicating a considerable increase. The observed enhancement in photocatalytic efficiency can be attributed to the formation of a p–n heterojunction between Zn0.5Cd0.5S and MoS2. This particular junction helped in separating and migrating photoinduced charge carriers, ultimately improving the photocatalytic performance. Furthermore, the addition of excess thiourea in MoS2 preparation produced Mo–O defects and increased the layer spacing of the involved material, improving the electron transfer and visible-light utilization of the material. The inhibition of carrier recombination was beneficial for synthesizing efficient and long-lasting photocatalysts.

In2O3[sbnd]Co3O4 p-n heterojunction sensors: Comparison of efficient interfaces and gas sensing enhancement mechanism through two strategies

The fabrication of heterostructure has been considered as a great potential to improve the gas sensing performances due to its flexible features at the interface. The p-n heterojunction composites with Co3O4 modified In2O3 were synthesized through physical mixing calcination process and hydrothermal method for the effectiveness comparison as gas sensors for detecting of ethanol. The morphology, microstructure, and surface element state of the heterojunction materials were investigated. Furthermore, the forbidden band width and the active oxygen species were analyzed on account of the efficient interface for the heterojunction materials. The gas response of heterojunction sensor by the physical mixing calcination method exhibits an outstanding response of 5585 towards 100 ppm of ethanol gas, which was about 10 times higher than that by two-step hydrothermal method. Moreover, it shows improved adsorption and desorption kinetics with response time of 8 s and 15 s, respectively at the operating temperature of 280 °C. It also reveals a distinct selectivity to ethanol with better reproducibility, the humidity-dependent stability and ambient retention over a period of 30 days with rendering 94 %. Combined with the in-situ infrared diffuse reflection spectrum analysis and Hall effect test from the perspective of catalytic and electrical properties, we inferred the gas sensing enhancement mechanism involved in In2O3Co3O4 p-n heterojunction with the design of proof-of-concept demonstration by surface modification, which could provide an approach to explore the effect of heterojunction interface through the variable method.

Generating utilities for the CS-Base: a novel, generic, and patient-centered health-outcome measure

ObjectivesWe have developed a new patient-centered, preference-based generic health-outcome measure, CS-Base, which is based on a novel multi-attribute preference response (MAPR) measurement framework. This study aimed to generate a first utility set for the CS-Base, making it suitable for use in health-economic evaluations. MethodsCS-Base comprises 12 health attributes: mobility, vision, hearing, cognition, mood, anxiety, pain, fatigue, social functioning, daily activities, self-esteem, and independence, each with four levels. Our methodology to generate utilities for the CS-Base was twofold. First, we derived coefficients from patient MAPR data to calculate CS-Base values. Subsequently, these were normalized to a 0.0–1.0 utility scale, where 0.0 signifies “dead”. The “dead” position was estimated using general population data from a discrete choice experiment (DCE+Dead), using a “division value” strategy which localize the position of states better or worse than dead. ResultsWe analyzed MAPR data from 3,222 patients and DCE+Dead data from 1,995 respondents. All MAPR coefficients were negative, logically ordered, and significantly different from the reference level. The “dead” position was denoted by a division value of -148.385. Utility values spanned from -0.071 to 1.0, and only 53 of 16,777,216 states were deemed worse than dead. ConclusionThis study introduced the first CS-Base utility set, underlining a two-step utility derivation method. This method, blending societal and patient views, surpasses traditional preference-based approaches, yielding firmer results. However, improvement of the normalization procedure is expected. Estimating CS-Base utilities is an ongoing process that gains precision over time.

The growth evolution of SnSe-doped SnTe alloy by in-situ selenization substitution method

In the era of increasing environmental pollution and energy crisis, the development of thermoelectric materials to achieve the reuse of waste heat energy is of great significance. SnTe, as an environmentally friendly thermoelectric material, its high electronic-thermal conductivity and coupled high electrical conductivity originated from intrinsic high carrier hole concentration leads to the unsatisfactory thermoelectric properties. With a lot of research going on to optimize thermoelectric performance by decoupling the thermo-electrical contradiction, the entropy engineering strategy, which can independently modulate lattice thermal conductivity is proved to be feasible by increasing the internal configurational entropy through doping. However, due to the uncontrollable growth microscopic process and the perturbation of film quality, the desired precise phase doping remains a challenge. In this work, we report an instantiation of the entropy increase of SnSe-doped SnTe alloy thin films prepared by two-step magnetron sputtering and selenization method. The microstructure and physical properties of thin films were investigated for replaying and controlling the growth evolution of in-situ selenization substitution. When the reaction temperature and duration were adjusted to 300℃ and 30 mins respectively, the desired pure phase doping was obtained. This work provides a new method of doping modification of SnTe materials, and the product as a new functional material expands the thermoelectric material library. In particular, it has positive reference significance for the development of synthesis controllability and structure establishment of complex polycrystalline materials.

Dually engineered CdSe/CdTe nanorod heterojunction enables efficient, sacrificial agent-free photoremoval of U(VI)

The pollution of uranium (U)-containing radioactive wastewater has been long regarded as a serious threat to the ecosystems and the health of human beings. Herein, we report a CdSe/CdTe heterojunction that shows efficient U(VI) removal viable under the simulated/real solar light. Up to 92 % of total U(VI) (C0 = 30 mg/L) can be rapidly removed within 5 min without using any sacrificial agent or protective gas, this high removal efficiency remains stable in the presence of various interfering ions. Detailed analyses indicate that multiple interfaces can be constructed in the heterojunction using a facile two-step hydrothermal method. These interfaces can accommodate photocatalysis and yield a built-in electric field to separate the photogenerated electron/hole pairs better. Meanwhile, the ingeniously anchored Te2- in the heterojunction, which concurrently acts as an endogenous hole scavenger and a hydroxyl radical sacrifice, also makes a positive contribution. Benefiting from the above synergy, removal rate of U(VI) can be optimized to as high as 14.4 mmol/(g·h). This work offers a general strategy for the rational design of high-performance photocatalysts, potentially paving the way for cleaner and safer nuclear power use.

Development and validation of a nomogram for premature coronary artery disease patients in Guangzhou

BackgroundData regarding risk factors for premature coronary artery disease (PCAD) is scarce given that few research focus on it. This study aimed to develop and validate a clinical nomogram for PCAD patients in Guangzhou. MethodsWe recruited 108 PCAD patients (female ≤65 years old and male ≤55 years old) and 96 healthy controls from Sun Yat-sen Memorial Hospital of Sun Yat-sen University between 01/01/2021 and 31/12/2022. Twenty potentially relevant indicators of PCAD were extracted. Next, the least absolute shrinkage and selection operator (LASSO) regression analysis was used to optimize variable selection. The nomogram was developed based on the selected variables visually. ResultsIndependent risk factors, including body mass index (BMI), history of PCAD, glucose, Apolipoprotein A1(ApoA1), high density lipoprotein 2-cholesterol (HDL2-C), total cholesterol and triglyceride, were identified by LASSO and logistic regression analysis. The nomogram showed accurate discrimination (area under the receiver operator characteristic curve, ROC, 87.45 %, 95 % CI: 82.58 %-92.32 %). Decision curve analysis (DCA) suggested that the nomogram was clinical beneficial. HDL2, one risk factor, was isolated by a two-step discontinuous density-gradient ultracentrifugation method. And HDL2 from PCAD patients exhibited less 3H-cholesterol efflux (22.17 % vs 26.64 %, P < 0.05) and less delivery of NBD-cholesterol detecting by confocal microscope compared with healthy controls. ConclusionsIn conclusion, the seven-factor nomogram can achieve a reasonable relationship with PCAD, and a large cohort were needed to enhance the credibility and effectiveness of our model in future.

Mapping the intellectual structure and evolution of information technology and auditing: a bibliometric review

The study presents a thorough bibliometric analysis of articles on information technology (IT) and auditing in the Web of Science Core Collection from 1978 to 2022. The study involves examining 2,991 articles out of a total of 7,738. It uses a two-step method that includes descriptive performance measures and co-word analysis. The descriptive metrics reveal publishing patterns, productive nations and authors, their impact on specific areas and journals, citation counts by country, and highly cited publications. Co-occurrence analysis maps connections between concepts, uncovering the intricate interplay within the field. The results show six main trends: more and more technology is being used in internal auditing; more people need to know about information security; the need for transparency in the digital age; how technology is making ethical auditing easier; progress in cloud-based auditing with blockchain; and the rise of post-COVID IT continuous auditing standards. These trends highlight the necessity for auditors to navigate evolving technological landscapes while prioritizing ethical considerations, data security, and privacy. Furthermore, the study identifies key authors, publications, and influential nations in this domain, offering valuable insights for industry stakeholders and fostering potential avenues for collaborative research. This study’s novelty lies in its systematic analysis of IT and auditing literature, illuminating significant trends and providing actionable insights for both industry leaders and researchers seeking to contribute to this evolving field.

Accurately Computing the Interacted Volume of Molecules over Their 3D Mesh Models.

For quickly predicting the rational arrangement of catalysts and substrates, we previously proposed a method to calculate the interacted volumes of molecules over their 3D point cloud models. However, the nonuniform density in molecular point clouds may lead to incomplete contours in some slices, reducing the accuracy of the previous method. In this paper, we propose a two-step method for more accurately computing molecular interacted volumes. First, by employing a prematched mesh slicing method, we layer the 3D triangular mesh models of the electrostatic potential isosurfaces of two molecules globally, transforming the volume calculation into finding the intersecting areas in each layer. Next, by subdividing polygonal edges, we accurately identify intersecting parts within each layer, ensuring precise calculation of interacted volumes. In addition, we present a concise overview for computing intersecting areas in cases of multiple contour intersections and for improving computational efficiency by incorporating bounding boxes at three stages. Experimental results demonstrate that our method maintains high accuracy in different experimental data sets, with an average relative error of 0.16%. On the same experimental setup, our average relative error is 0.07%, which is lower than the previous algorithm's 1.73%, improving the accuracy and stability in calculating interacted volumes.

Preparation and Electrochromic Properties of MnO2/PPy Composite Films with Coral-like Structures.

MnO2/polypyrrole (PPy) composite films were deposited on fluorine-doped tin oxide (FTO) conductive glasses by a two-step wet-chemical method, including electrochemical deposition and chemical bath deposition (CBD). The porous MnO2 films were first grown on FTO glasses by an electrodeposition method. Second, polypyrrole nanoparticles were polymerized by the oxidation-reduction reaction between MnO2 and pyrrole, using the presynthesized MnO2 as the skeleton. Then, MnO2/PPy composite films with coral-like structures were obtained. The electrochemical and electrochromic (EC) properties of the prepared films were investigated. The results show that, compared to the single MnO2 or PPy film, the MnO2/PPy composite film has a larger optical modulation (67.3% at a wavelength of 900 nm), faster response times (4 s for coloration and 3 s for bleaching), and a higher coloration efficiency (218.16 cm2·C-1). The high coloration efficiency attests to the exceptional performance of the composite film in converting electrical signals into vivid color changes. The electrochemical stability test results show that the composite film maintains a stable EC performance after 200 coloration/bleaching cycles. The coral-like structures of the composite film are responsible for the better EC properties.

A Two-Step Grid–Coordinate Optimization Method for a Wind Farm with a Regular Layout Using a Genetic Algorithm

Currently, most studies on the optimization of wind farm layouts on flat terrain employ a discrete grid-based arrangement method and result in irregular layouts that may damage the visual appeal of wind farms. To meet the practical requirements of wind farms, a two-step optimization method called “grid–coordinate” based on a genetic algorithm is proposed in this paper. The core idea is to initially determine the number of wind turbines and their initial positions using a grid-based approach, followed by a fine-tuning of the wind farm layout by moving the turbines in a row/column manner. This two-step process not only achieves an aesthetically pleasing arrangement but also maximizes power generation. This algorithm is conducted to optimize a 2 km × 2 km wind farm under three classic wind conditions, one improved wind condition, and a real wind condition employing both the Jensen and Gaussian wake models. To validate the effectiveness of the proposed method, the optimization of configurations based on different wake models was conducted, yielding results including the efficiency, total power output, number of wind turbines, and unit cost of electricity generation. These results were compared and analyzed against the classical literature. The findings indicate that the unit cost of electricity generation using the two-step optimization approach with the Gaussian wake model is higher than that of the initial grid optimization method. Additionally, varying the number of wind turbines can lead to instances of high power generation coupled with low efficiency. This phenomenon should be carefully considered in the wind farm layout optimization process.

Experimental and statistical investigation on the dielectric breakdown of magneto nanofluids for power applications

The insulating oil serves the dual purpose of providing insulation and cooling within transformers. This investigation aims to explore the impact of various nanoparticles on the dielectric breakdown voltage (BDV) of dielectric oils. The study examines the effect of the concentration of magnetic nanoparticles on the dielectric breakdown voltage of insulating oils. Nanoparticles such as iron (II, III) oxide (Fe3O4), cobalt (II, III) oxide (CO3O4), and ferrous phosphide (Fe3P) were utilized to create nanofluids with carrier mediums consisting of mineral oil and synthetic ester oil. BDV determination was conducted using a VDE and S–S electrode system according to IEC 60156 standards. Nanofluid were prepared using a two-step method, and their concentrations ranged from 0.01 g/L, 0.02 g/L, and 0.04 g/L in base oils. Twelve iterations were conducted for each prepared nanofluid, and breakdown voltage measurements were recorded. The results indicate a noteworthy enhancement in the breakdown voltage of nanofluids. The statistical analysis was performed on the dielectric property of nanofluid samples for better breakdown accuracy. The maximum enhancement at specific nanoparticle concentrations was shown by each nanofluid. The results show that under the S–S electrode configuration, the greatest overall enhancement was observed for Fe3P in mineral oil, with an enhancement of 70.05%, and Fe3O4 in synthetic ester oil, with an enhancement of 46.29%.

Hybrid nanocomposites of Fe3O4/SiO2-EDTA: Holistic comparison of one-step and two-step modification methods

Fe3O4/SiO2-EDTA materials have been used for diverse applications and they have been modification by different methods, but a comparison is lacking between their physicochemical characteristics. This work aimed to compare the physicochemical characteristics of the Fe3O4/SiO2-EDTA nanocomposite obtained by four methods, whose modification of the Fe3O4 surface with SiO2-EDTA occurs in one or two steps, with not hazardous chemicals. In the one-step methods (M1 and M2), Fe3O4 particles were coated and functionalized in consecutive steps (by sonication in M1 and mechanical stirring in M2). In the two-step methods (M3 and M4), the Fe3O4 particles were primarily coated with silica and then functionalized with TMS-EDTA (by sonication in M3 and mechanical stirring in M4). The Fe3O4/SiO2-EDTA nanocomposites were characterized by XRD, TEM, EDX, BET, SQUID magnetometry, FTIR, XPS, and zeta potential. The TEM highlighted that the nanocomposites from methods M1 and M2 there is the simultaneous presence of magnetite particles coated and uncoated with silica, while those from M3 and M4 were totally coated, besides the nanocomposites from M2 and M3 showed to be more aggregated than those from the other methods. The presence of EDTA on the surface of the nanocomposites was evidenced by the carboxyl groups in the FTIR spectra and by the nitrogen in the XPS spectra. The nanocomposites from M3 and M4 presented a higher content of nitrogen than those from M1 and M2, and those from M4 also presented a higher content of carbon than those from M3. Overall, the Fe3O4/SiO2-EDTA nanocomposites from two-step methods showed better SiO2 coating and EDTA functionalization than those from one-step methods, and those from M4 presented the best physicochemical characteristics, being this method recommended for their modification for future applications.

Recycling and reuse of waste agricultural plastics with hydrothermal pretreatment and low-temperature pyrolysis method

Recycling and reuse of agricultural plastics is an urgent worldwide issue. In this work, it is shown that low-density polyethylene (PE) typically used in mulch films can be converted into high-capacity P and N adsorbents through a two-step method that uses hydrothermal pretreatment (180 °C, 24 h) followed by pyrolysis at 500 °C with Ca(OH)2 additive. CaPE@HC500 materials prepared with the proposed two-step method were found to have high adsorption capacities for phosphate (263.6 mg/g) and nitrogen (200.7 mg/g) over wide ranges of pH (3–11). Dynamic adsorption of phosphate by CaPE@HC500 material in a packed-bed had a half-time breakthrough of 210 min indicating the feasibility of continuous systems. Material stability, cost, environmental-friendliness, and recyclability of the CaPE@HC500 material were determined to be superior to literature-proposed Ca-containing adsorbents. The two-step method for converting waste agricultural plastic mulch films into adsorbents is robust and highly-applicable to industrial settings.

High internal phase Pickering emulsions via complexation of cellulose nanofibrils and nanochitin: Enhanced interfacial adsorption and structured aqueous network

High internal phase emulsion is a novel emulsion type for a range of applications, while its preparation is limited by low stability and the selection of proper emulsifiers. Herein, an edible sunflower oil-in-water high internal phase Pickering emulsion (HIPPE) stabilized by complexation of cellulose nanofibrils (CNF) and nanochitin (NCh) is prepared using a two-step method. The stabilization capacity of the CNF/NCh complexes stems from restricted coalescence, droplet breakage, and coarsening upon HIPPE formation, showing the minimal droplet size at 80 μm. This is the result of a dual stabilization mechanism including: enhanced adsorption of the complexes at the oil-water interfaces by existence of the acetyl groups in the complexes and structuring the aqueous phase to form networking structures within HIPPEs. The CNF/NCh complex suspensions exhibit tolerance to changes in pH (3–5) and salinity (0–500 mM), showing capability of forming and stabilizing HIPPEs with minimal alteration to droplet size and morphology. The complex-stabilized HIPPEs remain stability after at least 2-year storage at ambient condition. Overall, complexation of CNF and NCh greatly extend the conditions on forming highly stable, tuneable HIPPEs, offering potential on engineering applications relevant to clean-label, green foodstuffs.

An asymptotically consistent morphoelastic shell model for compressible biological structures with finite-strain deformations

We derive an asymptotically consistent morphoelastic shell model to describe the finite deformations of biological tissues using an energy approach. Biological materials may exhibit remarkable compressibility when under large deformations, and we take this factor into account for accurate predictions of their morphoelastic changes. The morphoelastic shell model combines the growth model of Rodriguez et al. and a novel shell model developed by us. We start from the three-dimensional (3D) morphoelastic model and construct the optimal shell energy based on a series expansion around the middle surface. A two-step variational method is applied that retains the leading-order expansion coefficient while eliminating the higher-order ones. The main outcome is a two-dimensional (2D) shell energy depending on the stretching and bending strains of the middle surface. The derived morphoelastic shell model is asymptotically consistent with three-dimensional morphoelasticity and can recover various shell models in literature. Several examples are shown for the verification and illustration.

Promoting effects of cerium oxide on catalysts performance for selectivity catalytic oxidation of NH3 over RuOx/TiO2

Cerium oxide (CeO2) was added to modified Ru/TiO2 catalyst by a two-step impregnation method for selective catalytic oxidation of ammonia (NH3-SCO). The low-temperature NH3 oxidation activity and N2 selectivity of the 1Ru/xCe-TiO2 catalyst were investigated with the result that both SCO activity and N2 selectivity were influenced by the amount of Ce. Specifically, the presence of 5 wt% CeO2 demonstrated a great promoting effect on both parameters, reaching an NH3 conversion and N2 selectivity of 97 % and 93 % at 180 ℃, respectively. Various characterization methods indicated that the presence of CeO2 regulates the dispersion of the 1Ru/xCe-TiO2 catalysts, changes the amount, strength and ratio of strong and weak acid sites on the catalyst surface, and promotes the formation of Ru-O-Ce bonds through the interaction between Ce and Ru species, which facilitates electron transfer and oxygen vacancy formation, thereby enhancing the oxidation ability of the catalysts. Furthermore, the results of in situ diffuse reflectance infrared Fourier transform spectroscopy indicated that the NH3-SCO reaction over the 1Ru/xCe-TiO2 catalysts follows the “internal” selective catalytic reduction (i-SCR) mechanism, involving the oxidation of NH3 into nitric oxide (NO) species and the reaction of -NH2 with in situ-formed NO to form N2.