Nitrogen Oxides Research Articles

Enhancing catalytic activity with less metal resource by regulating zeolite topology

Big-ring topology of zeolite is crucial to the catalytic activity of selective catalytic reduction (SCR) of nitrogen oxide (NOx). More works should explore how to increase the number of the big-ring topology. This work regulated ZSM-5 topologies by different acids and amino acids, exchanged Cu species into the regulated zeolites, and compared their SCR performances. L-aspartate- and L-cysteine-regulated ZSM-5 removed 67 ∼ 73 % of NO (1000 ppm, 15,000 h−1) at 150 °C. Their Cu contents were only 0.04 wt%. Under the same catalytic conditions, citric acid-regulated ZSM-5 only reached a conversion of 19 % with a Cu content of 0.15 wt%. After detailed characterizations, primary 10-membered ring (MR) on (200) plane of ZSM-5 was retained after mild treatment of the amino acids. At the same time, 5-MR on (351) and (352) planes were opened to form more 10-MR. 10-MR along (501) plane was reinforced. As a result, the 10-MR content was increased from 22 % (citric acid) to 32 % (L-aspartate and L-cysteine). The 10-MR-localized Cu was more effective than other species. Above results are in favor of understanding topology-activity relationship in zeolite. Synthesizing a more effective catalyst with less active metal is also helpful to resource saving.

Optimization of isolated copper species on the NH3-SCR performance over Cu/SSZ-39 modified by ammonia water

To further improve the activity of Selective Catalytic Reduction of nitrogen oxide (NOx) with Ammonia (NH3-SCR) and the hydrothermal stability at 900 °C on Cu-based small-pore zeolite catalyst, the interaction between ammonia water and Cu2+ is used to regulate the interaction between Cu2+ and Si-O-Al structures of the zeolite, and affect the migration of Cu2+ in the channels of zeolite. The characterization results show that the microchemical environment of isolated Cu2+ species is optimized, and more isolated Cu2+ species can stably exist over the surface of Cu/SSZ-39, exhibiting a strong redox property. Besides, the ability of acid sites to adsorb and activate NH3 is also enhanced, and the amount of H2O adsorbed in the pores of zeolite is reduced due to the stable surface isolated Cu2+ species, which is conducive to the stability of the catalyst after hydrothermal aging at 900 °C. When the mole ratio of ammonia and copper is 3, the NH3-SCR performance and the hydrothermal stability at 900 °C are both optimized on the modified catalyst. The temperature window that the NOx conversion is higher than 90% (ΔT90) on the unmodified catalyst is 200–475 °C, but the ΔT90 on the modified catalyst is broadened to 178–544 °C. More importantly, after hydrothermal aging at 900 °C, the ΔT90 on the modified catalyst is shifted to the higher temperature by 48 °C, with the maximum NOx conversion of 96%. This work would provide a new strategy for further optimizing Cu/SSZ-39 catalyst for deNOx emitted from diesel engines to meet higher emission standards in future.

Electrosynthesis of nitrate by FeS2-TiO2 heterogeneous nanoparticles supported on 2-methylimidazolium functionalized polypyrrole/graphene oxide

Electrochemical nitrogen oxidation reaction (NOR) acts as a clean and sustainable approach for promisingly replacing the conventional industrial method for producing nitrate. Designing and preparing the effective electrocatalysts for NOR has aroused great research interests. Herein, FeS2-TiO2 heterostructures are well-dispersed on the surface of 2-methylimidazolium functionalized polypyrrole/graphene oxide (2-MeIm/PPy/GO) by the in situ growth due to the ion-exchange and the coordination between the 2-MeIm groups and the metal precursors, which exhibit the excellent NOR electroactivity. The highest NO3– yield by the obtained FeS2-TiO2@2-MeIm/PPy/GO reaches to 137.04 μg h−1 mg-1act. with the maximum Faradic efficiency (FE) of 4.38 % at 2.04 V (vs. RHE), better than most reported electrocatalysts. The good selectivity for producing nitrate is also achieved, but the stability of both the NO3– yield and FE present a trend of rising and then declining in the cyclic test of FeS2-TiO2@2-MeIm/PPy/GO. The irreversible conversion of the electrocatalysts can be confirmed through detailed characterization towards the crystal structures and chemical states of FeS2-TiO2@2-MeIm/PPy/GO after long term NOR test. The electrochemical step of NOR mainly occurs at FeS2, converting the inert N2 to active *NO intermediates. SO42- generated at high NOR potential gradually increase by the oxidation of S element, which can cause the promotion of the NOR performance before the fourth cycle. But along with the disappearance of FeS2 phase, FeS2-TiO2 as the catalytic center is finally irreversibly converted to Fe and S ions doped TiO2, resulting in the sharp drop of both the NO3– yield and FE at the sixth cycle. This work will provide valuable guidance for the designing and preparing NOR electrocatalysts.

Technical Note: A technique to convert NO2 to NO2− with S(IV) and its application to measuring nitrate photolysis

Abstract. Nitrate photolysis is a potentially significant mechanism for “renoxifying” the atmosphere, i.e., converting nitrate into nitrogen oxides – nitrogen dioxide (NO2) and nitric oxide (NO) – and nitrous acid (HONO). Nitrate photolysis in the environment occurs through two channels which produce (1) NO2 and hydroxyl radical (⚫OH) and (2) nitrite (NO2-) and an oxygen atom (O(3P)). Although the aqueous quantum yields and photolysis rate constants of both channels have been established, field observations suggest that nitrate photolysis is enhanced in the environment. Laboratory studies investigating these enhancements typically only measure one of the two photo-channels, since measuring both channels generally requires separate analytical methods and instrumentation. However, measuring only one channel makes it difficult to assess whether secondary chemistry is enhancing one channel at the expense of the other or if there is an overall enhancement of nitrate photochemistry. Here, we show that the addition of S(IV), i.e., bisulfite and sulfite, can convert NO2 to NO2-, allowing for measurement of both nitrate photolysis channels with the same equipment. By varying the concentration of S(IV) and exploring method parameters, we determine the experimental conditions that quantitatively convert NO2 and accurately quantify the resulting NO2-. We then apply the method to a test case, showing how an ⚫OH scavenger in solution prevents the oxidation of NO2- to NO2 but does not enhance the overall photolysis efficiency of nitrate.

How we deal with transition planning as we approach the e-bus era

With the rapid development of new energy, the widespread adoption of electric buses is significant. This paper delves into the environmental impact, economic implications, and strategic planning solutions associated with the proliferation of electric buses. We initiate this process by using bus data from Qingdao, China. Subsequently, using the fuel life cycle method, we carefully examined CO and the pollutant emission characteristics of Qingdao bus fleet before and after electrification. The research results reveal that CO, nitrogen oxides, and VOC emissions have all increased. An economic analysis of electric vehicle adoption in public transportation was conducted in response to the second question. Considering government subsidies, a cost-saving model for electric vehicles per 100 kilometers was constructed. Furthermore, the study found that as the electrification rate increases, the operating costs of electric buses in Qingdao gradually decrease. We developed a comprehensive linear integer programming model to address the third question. The model, aimed at achieving the lowest comprehensive cost while considering various constraints, was solved through the branch and bound method. We applied this model to analyze the optimal plans for completing the conversion of electric buses in Qingdao, Zhengzhou, and Xi’an within a 10-year timeframe. For the fourth question, we have put forward policy recommendations.

Experimental Study of the Process of Cleaning Flue Gases from Harmful Components Using Granulated Blast-furnace Slag

An experimental study of the adsorption process of carbon and nitrogen oxides was carried out at various flue gas flow rates and concentrations of harmful components. The optimal conditions for the adsorption process were determined at various flue gas flow rates. It has been demonstrated that the efficiency of adsorption of carbon and nitrogen oxides depends on the flue gas flow rate, the concentration of these components and the process temperature. A design has been developed for an environmentally friendly nozzle for a chimney using granulated blast furnace slag as an adsorbent, which allows achieving a degree of gas purification of 50–55% or higher under optimal operating conditions.

Mitigating air pollution benefits multiple sustainable development goals in China

Achieving the United nations 2030 Sustainable Development Goals (SDGs) remains a significant challenge, necessitating urgent and prioritized strategies. Among the various challenges, air pollution continues to pose one of the most substantial threats to the SDGs due to its widespread adverse effects on human health and ecosystems. However, the connections between air pollution and the SDGs have often been overlooked. This study reveals that out of the 169 SDG targets, 71 are adversely impacted by air pollution, while only 6 show potential positive effects. In China, two major atmospheric nitrogen pollutants, ammonia and nitrogen oxides, resulted in an economic loss of 400 billion United States Dollar (USD) in 2020, which could be reduced by 33% and 34% by 2030, respectively. It would enhance the progress towards SDGs in China by 14%, directly contributing to the achievement of SDGs 1 to 6 and 11 to 15. This improvement is estimated to yield overall benefits totaling 119 billion USD, exceeded the total implementation cost of 82 billion USD with ammonia as the preferential mitigation target. This study underscores the importance of robust scientific evidence in integrated policies aimed at aligning improvements in environmental quality with the priorities of sustainable development.

A Comparative Study of SOx, NOx, PM2.5 and PM10 in the UK and Poland from 1970 to 2020

Presently, there is growing discourse surrounding climate change, global warming, and the possibility of urban smog. Daily, we encounter air pollutants unwittingly, often oblivious to the substantial adverse impacts they may impart on our health. This manuscript furnishes a thorough examination of the shifts in the concentrations of distinct air pollutants, namely, sulphur oxides (SOx), nitrogen oxides (NOx), and particulate matter PM2.5 and PM10, within the contexts of the United Kingdom (a country progressively transitioning to alternative energy sources) and Poland (a country in the EU with the second lowest climate policy evaluation in the climate change performance index or CCPI) from 1970 to 2020. This comparative study between the two countries clearly showed the importance of the transition to clean energy sources. The UK’s efforts towards cleaner energy sources have led to reduced emissions of sulphur oxides (SOx) and particulate matter. In contrast, Poland’s heavy dependence on energy primarily from coal over the years has resulted in elevated pollution levels, notably in the concentration of pollutants such as particulate matter. This study also revealed that changes in emissions predominantly stem from technological advancements and economic activities. Additionally, political regulations and the gradual phase-out of specific fuels or energy-rich substances and their substitution with alternative energy sources have also imparted considerable influence.

A convolutional neural network prediction model for aviation nitrogen oxides emissions throughout all flight phases

In recent years, there has been an increasing amount of research on nitrogen oxides (NOx) emissions, and the environmental impact of aviation NOx emissions at cruising altitudes has received widespread attention. NOx may play a crucial role in altering the composition of the atmosphere, particularly regarding ozone formation in the upper troposphere. At present, the ground emission database based on the landing and takeoff (LTO) cycle is more comprehensive, while high-altitude emission data is scarce due to the prohibitively high cost and the inevitable measurement uncertainty associated with in-flight sampling. Therefore, it is necessary to establish a comprehensive NOx emission database for the entire flight envelope, encompassing both ground and cruise phases. This will enable a thorough assessment of the impact of aviation NOx emissions on climate and air quality. In this study, a prediction model has been developed via convolutional neural network (CNN) technology. This model can predict the ground and cruise NOx emission index for turbofan engines and mixed turbofan engines fueled by either conventional aviation kerosene or sustainable aviation fuels (SAFs). The model utilizes data from the engine emission database (EEDB) released by the International Civil Aviation Organization (ICAO) and results obtained from several in-situ emission measurements conducted during ground and cruise phases. The model has been validated by comparing measured and predicted data, and the results demonstrate its high prediction accuracy for both the ground (R2 > 0.95) and cruise phases (R2 > 0.9). This surpasses traditional prediction models that rely on fuel flow rate, such as the Boeing Fuel Flow Method 2 (BFFM2). Furthermore, the model can predict NOx emissions from aircrafts burning SAFs with satisfactory accuracy, facilitating the development of a more complete and accurate aviation NOx emission inventory, which can serve as a basis for aviation environmental and climatic research. SynopsisThe utilization of the ANOEPM-CNN offers a foundation for establishing more precise emission inventories, thereby reducing inaccuracies in assessing the impact of aviation NOx emissions on climate and air quality.

A calibration methodology for the novel radioluminescence detector systems

Radioluminescence mapping using well-characterized and optimized optical systems is an effective method for localizing contamination with alpha-emitting radionuclides. To apply this novel approach to radiological emergency management, nuclear safeguards, nuclear decommissioning, and nuclear forensics, an established traceability chain is required. This work presents the development and implementation of a novel calibration methodology to provide valuable information about, and confidence in, the performance of radioluminescence detection systems. The proposed calibration methodology is based on two complementary approaches: (a) application of well-characterized activity standards to establish a traceable relationship between radioluminescence intensity and alpha activity, and (b) use of all optical radiation-based devices that, when calibrated against an alpha activity standard, simulate the radioluminescence induced in nitrogen (N2) and nitric oxide (NO) gases by alpha particles in specific spectral regions. A dedicated 210Po alpha activity standard with a narrow peak of less than 32keV FWHM at 5.3MeV has been developed and used to characterize a lens-based radioluminescence detection system in terms of its sensitivity to alpha-induced radioluminescence in different atmospheres (air, N2, N2 ＋ NO mixture) in the UV-A and UV-C (solar blind) spectral regions. The characterized lens system was then used as an intermediary transfer device to cross-calibrate two portable integrating sphere-based radiance standards designed to simulate radioluminescence in the UV-A and UV-C spectral regions. Both radiance standards can be used as transfer standards simulating up to 510MBq when used in the UV-A and up to 8.7GBq when used in the UV-C spectral regions. They substantially simplify routine quality control of radioluminescence detection systems by eliminating the need for open alpha sources, which are always associated with strict radiation safety precautions. Furthermore, since radiance standards can operate in a wide range of intensities, linearity and detection limits of radioluminescence detectors can be readily determined. The design, construction, radiometric characterization, and calibration of dedicated transfer standards, as well as the development of new calibration procedures for radiometric traceability of radioluminescence detection systems, will enable appropriate accident and post-accident radiation measurements that will lead to more effective countermeasures and better protection of people, wildlife, and the environment.

Pollution characteristics and transformation mechanisms of secondary inorganic components during winter heavy pollution in Handan city in 2018–2020

The mechanism of secondary transformation of nitrate and sulfate under different relative humidity (RH), and different atmospheric oxidizing conditions is still controversial. This study focuses on the winter heavy pollution process in Handan city from 2018 to 2020, and the following results were obtained: The average concentration of PM2.5 showed a decreasing trend from year to year, but the SNA (SO42−, NO3− and NH4+) showed an increasing trend from year to year, and the concentration of nitrate exceeded that of sulfate. During the haze formation period from 2018 to 2020, a significant disparity was observed in NO2 and SO2 emissions, with NO2 emissions surpassing those of SO2. Mobile sources were found to contribute more emissions than fixed sources, particularly during the haze persistence period (HP). However, sulfate conversion outweighed nitrate conversion. HighRH and photochemical reactions induced by ozone favored the formation of nitrate and sulfate, which formated heavy haze. When 40% ≤ RH ≤ 70%, atmospheric oxidants (Ox = O3 + NO2) concentration was the highest, nitrogen oxidation ratio (NOR) and sulfur oxidation ratio (SOR) increased rapidly, and nitrate was mainly generated by photochemical reactions during daytime. When RH > 70% and the concentration of Ox was low, RH will weaken the photochemical intensity. Therefore, the liquid-phase oxidation reaction of N2O5 contributed significantly to atmospheric nitrate at night, and the liquid-phase oxidation reaction of sulfate was faster than the gas-phase one. Local emissions contributed significantly to particulate matter pollution during heavy pollution in 2018–2020, and the main sources were secondary conversion, coal combustion, biomass burning, and road dust.

Long-term exposure to ambient air pollution and risk of microvascular complications among patients with type 2 diabetes: a prospective study.

Patients with type 2 diabetes (T2D) may disproportionately suffer the adverse cardiovascular effects of air pollution, but relevant evidence on microvascular outcome is lacking. We aimed to examine the association between air pollution exposure and the risk of microvascular complications among patients with T2D. This prospective study included 17 995 participants with T2D who were free of macro- and micro-vascular complications at baseline from the UK Biobank. Annual average concentrations of particulate matter (PM) with diameters <2.5 μm (PM2.5), <10 μm (PM10), nitrogen dioxide (NO2) and nitrogen oxides (NOx) were assessed using land use regression models. Cox proportional hazards regression was used to estimate the associations of air pollution exposure with incident diabetic microvascular complications. The joint effects of the air pollutant mixture were examined using quantile-based g-computation in a survival setting. In single-pollutant models, the adjusted hazard ratios (95% confidence intervals) for composite diabetic microvascular complications per interquartile range increase in PM2.5, PM10, NO2 and NOx were 1.09 (1.04-1.14), 1.06 (1.01-1.11), 1.07 (1.02-1.12) and 1.04 (1.00-1.08), respectively. Similar significant results were found for diabetic nephropathy and diabetic neuropathy, but not for diabetic retinopathy. The associations of certain air pollutants with composite microvascular complications and diabetic nephropathy were present even at concentrations below the World Health Organization limit values. Multi-pollutant analyses demonstrated that PM2.5 contributed most to the elevated risk associated with the air pollutant mixture. In addition, we found no interactions between air pollution and metabolic risk factor control on the risk of diabetic microvascular complications. Long-term individual and joint exposure to PM2.5, PM10, NO2 and NOx, even at low levels, was associated with an increased risk of diabetic microvascular complications, with PM2.5 potentially being the main contributor.

Enhanced atmospheric oxidation toward carbon neutrality reduces methane’s climate forcing

The hydroxyl radical (OH), as the central atmospheric oxidant, controls the removal rates of methane, a powerful greenhouse gas. It is being suggested that OH levels would decrease with reductions of nitrogen oxides and ozone levels by climate polices, but this remains unsettled. Here, we show that driven by the carbon neutrality pledge, the global-mean OH concentration, derived from multiple chemistry-climate model simulations, is projected to be significantly increasing with a trend of 0.071‒0.16% per year during 2015–2100. The leading cause of this OH enhancement is dramatic decreases in carbon monoxide and methane concentrations, which together reduce OH sinks. The OH increase shortens methane’s lifetime by 0.19‒1.1 years across models and subsequently diminishes methane’s radiative forcing. If following a largely unmitigated scenario, the global OH exhibits a significant decrease that would exacerbate methane’s radiative forcing. Thus, we highlight that targeted emission abatement strategies for sustained oxidation capacity can benefit climate change mitigation in the Anthropocene.

Remote Sensing and GIS in Air Pollution Mitigation: A Bibliometric Review of Chhattisgarh, India

Remote sensing and GIS serve as indispensable tools in the ongoing battle against air pollution and in the quest to improve air quality. These technologies, including satellite imagery and aerial mapping, provide a wealth of data pertaining to atmospheric conditions and the presence of pollutants. Through the integration of this data within Geographic Information Systems (GIS) software, researchers and policymakers gain valuable insights into the distribution and concentration of pollutants such as sulphur dioxide, nitrogen oxides, and particulate matter, all of which have significant implications for human health and the environment. The utilization of remote sensing allows for the monitoring of pollutant movement over large geographical areas, enabling the identification of pollution sources and the assessment of their impact on surrounding regions. GIS, on the other hand, facilitates the visualization of this data through the creation of maps and spatial analyses, offering a comprehensive understanding of air pollution patterns and their correlations with demographic characteristics and land use practices. By leveraging these technologies, decision-makers can pinpoint areas with heightened pollution levels, such as urban centers or regions with extensive industrial activity. Armed with this information, targeted mitigation strategies can be devised and implemented to alleviate pollution burdens and safeguard public health. Furthermore, the integrated approach provided by remote sensing and GIS enables the monitoring of the effectiveness of these strategies over time, allowing for adjustments and refinements as needed. In the context of Chhattisgarh, India, where air pollution poses a significant challenge, the application of remote sensing and GIS holds particular promise. By highlighting the advancements and innovations in these fields within the state, we aim to underscore the potential for informed decision-making and proactive measures to combat air pollution and enhance air quality for the benefit of all residents. In this review paper we aim to highlight the advancements in this field that has occurred in controlling air pollution in the state of Chhattisgarh, India.

Comprehensive stratified K-fold machine learning for surface roughness prediction with 3D printed electrodes: Analyzing dielectric variability and heat treatment effects

Electrical discharge machining (EDM) is popular for its accuracy in cutting difficult materials. Material removal rate (MRR), electrode wear ratio (EWR) and surface roughness (SR) are deemed as the success indicators in EDM. The positive attributes of sustainable manufacturing make it a suitable choice for diverse applications. EDM electrodes used in this work are prepared by 3D printing and further subjected to deep cryogenic treatment (DCT) to investigate the influence of heat treatment on EDM performance. In the second instance, the experiments are conducted with different dielectric fluids viz. conventional dielectric (CD) and lemon peel biodiesel dielectric (LPD) to analyze the impact of dielectric change. When the dielectric is changed to lemon peel biodiesel, the roughness of the electro-discharged machined surface is reduced to a maximum of about 33%. A further improvement of about 11% is possible in the surface finish of machined surfaces when the EDM electrodes are subjected to DCT under an identical operating condition. LPD as dielectric also helps in attaining the primary objective of green manufacturing by reducing specific evaporative emissions. During the EDM process, LPD adoption causes a reduction of 96% in nitrogen oxide (NO) emissions and 86% in hydrocarbon (HC) emissions. A modified adaptive neuro fuzzy inference system (ANFIS) with a stratified k-fold machine learning approach with a prediction accuracy of about 95% is also developed in this work to envisage the surface roughness of electro-discharge machined surfaces.

Combining the Emission Preprocessor HERMES with the Chemical Transport Model TM5-MP

Emission inventories (EIs) are vital for air quality modeling. Specific research goals often require modifying EIs from diverse data sources, demanding significant code development. In this study, we utilized and further developed the High Elective Resolution Modeling Emission System version three for Global and Regional domains (HERMESv3_gr). This user-friendly processing system was adapted for generating EIs compatible with the Chemistry Transport Model Tracel Model 5 Massive Parallel (TM5-MP). The results indicate that HERMESv3_gr is capable of generating EIs with negligible biases (10−7 relative differences) for TM5-MP, showcasing its effectiveness. We applied HERMESv3_gr to integrate the EI Regional Emission inventory in Asia (REAS) into the global EI Community Emission Data System (CEDS). Comparison of model results using CEDS alone and the integrated EI against measurement data for various pollutants globally revealed small improvements for carbon monoxide (1%) ethane (1–2%), and nitrogen oxide (2%) and larger for propane (5–7%). Ozone in the northern hemisphere improved by about 2% while in the southern hemisphere improvements of 5% could be observed. Our findings highlight the importance of carefully considering the effects of EI integration for accurate air quality modeling.

Dissecting Drivers of Ozone Pollution during the 2022 Multicity Lockdowns in China Sheds Light on Future Control Direction.

In 2022, many Chinese cities experienced lockdowns and heatwaves. We analyzed ground and satellite data using machine learning to elucidate chemical and meteorological drivers of changes in O3 pollution in 27 major Chinese cities during lockdowns. We found that there was an increase in O3 concentrations in 23 out of 27 cities compared with the corresponding period in 2021. Random forest modeling indicates that emission reductions in transportation and other sectors, as well as the changes in meteorology, increased the level of O3 in most cities. In cities with over 80% transportation reductions and temperature fluctuations within -2 to 2 °C, the increases in O3 concentrations were mainly attributable to reductions in nitrogen oxide (NOx) emissions. In cities that experienced heatwaves and droughts, increases in the O3 concentrations were primarily driven by increases in temperature and volatile organic compound (VOC) emissions, and reductions in NOx concentrations from ground transport were offset by increases in emissions from coal-fired power generation. Despite 3-99% reduction in passenger volume, most cities remained VOC-limited during lockdowns. These findings demonstrate that to alleviate urban O3 pollution, it will be necessary to further reduce industrial emissions along with transportation sources and to take into account the climate penalty and the impact of heatwaves on O3 pollution.

Highly Efficient One-pot Electrosynthesis of Oxime Ethers from NOx over Ultrafine MgO Nanoparticles Derived from Mg-based Metal-Organic Frameworks.

Oxime ethers are attractive compounds in medicinal scaffolds due to the biological and pharmaceutical properties, however, the crucial and widespread step of industrial oxime formation using explosive hydroxylamine (NH2OH) is insecure and troublesome. Herein, we present a convenient method of oxime ether synthesis in a one-pot tandem electrochemical system using magnesium based metal-organic frameworks-derived magnesium oxide anchoring in self-supporting carbon nanofiber membrane catalyst (MgO-SCM), the in situ NH2OH from nitrogen oxides electrocatalytic reduction couples with aldehyde to produce 4-cyanobenzaldoxime with a selectivity of 93% and Faraday efficiency up to 65.1%, which further reacted with benzyl bromide to directly give oxime ether precipitate with a purity of 97% by convenient filtering separation. The high efficiency was attributed to the ultrafine MgO nanoparticles in MgO-SCM, effectively inhibiting hydrogen evolution reaction and accelerating the production of NH2OH, which rapidly attacked carbonyl of aldehydes to form oximes, but hardly crossed the hydrogenation barrier of forming amines, thus leading to a high yield of oxime ether when coupling benzyl bromide nucleophilic reaction. This work highlights the importance of kinetic control in complex electrosynthetic organonitrogen system and demonstrates a green and safe alternative method for synthesis of organic nitrogen drug molecules.

Highly Efficient One‐pot Electrosynthesis of Oxime Ethers from NOx over Ultrafine MgO Nanoparticles Derived from Mg‐based Metal–Organic Frameworks

Oxime ethers are attractive compounds in medicinal scaffolds due to the biological and pharmaceutical properties, however, the crucial and widespread step of industrial oxime formation using explosive hydroxylamine (NH2OH) is insecure and troublesome. Herein, we present a convenient method of oxime ether synthesis in a one‐pot tandem electrochemical system using magnesium based metal‐organic frameworks‐derived magnesium oxide anchoring in self‐supporting carbon nanofiber membrane catalyst (MgO‐SCM), the in situ NH2OH from nitrogen oxides electrocatalytic reduction couples with aldehyde to produce 4‐cyanobenzaldoxime with a selectivity of 93% and Faraday efficiency up to 65.1%, which further reacted with benzyl bromide to directly give oxime ether precipitate with a purity of 97% by convenient filtering separation. The high efficiency was attributed to the ultrafine MgO nanoparticles in MgO‐SCM, effectively inhibiting hydrogen evolution reaction and accelerating the production of NH2OH, which rapidly attacked carbonyl of aldehydes to form oximes, but hardly crossed the hydrogenation barrier of forming amines, thus leading to a high yield of oxime ether when coupling benzyl bromide nucleophilic reaction. This work highlights the importance of kinetic control in complex electrosynthetic organonitrogen system and demonstrates a green and safe alternative method for synthesis of organic nitrogen drug molecules.

A Hybrid Neural Network‐Based Improved PSO Algorithm for Gas Turbine Emissions Prediction

AbstractIn gas‐fired power plants, emissions may reduce turbine blade rotation, thus decreasing power output. This study proposes a hybrid model integrating the Feed forward Neural Network (FFNN) model and Particle Swarm Optimization (PSO) algorithm to predict gas emissions from natural gas power plants. The FFNN predicts gas turbine nitrogen oxides (NOx) and carbon monoxide (CO) emissions, while the PSO optimizes FFNN weights, improving prediction accuracy. The PSO adopts a unique random number selection strategy, incorporating the K‐Nearest Neighbor (KNN) algorithm to reduce prediction errors. Neighbor Component Analysis (NCA) selects parameters most correlated with CO and NOx emissions. The hybrid model is constructed, trained, and testedusing publicly available datasets, evaluating performance with statistical metrics like Mean Square Error (MSE), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE). Results show significant improvement in FFNN training with the PSO algorithm, boosting CO and NOx prediction accuracy by 99.18% and 82.11%, respectively. The model achieves the lowest MSE, MAE, and RMSE values for CO and NOx emissions. Overall, the hybrid model achieves high prediction accuracy, particularly with optimized PSO parameter selection using seed random generators.