Selective Reduction Research Articles

CO2 and O2 Separation Dual-Phase Membranes for Diesel Heavy-Duty Vehicles Applications

Diesel-engine Heavy-Duty Vehicle (HDV) exhaust gas mixture contains pollutants including unburned hydrocarbons, carbon monoxide, nitrogen oxides, and particulate matter. A catalyst-based emission control system is commonly used to eliminate the above pollutants. However, the excess of oxygen that exists in the exhaust gasses of diesel engines hinders the efficient and selective reduction of nitrogen oxides over conventional catalytic converters. The AdBlue® solution, which is currently used to eliminate nitrogen oxides, is based on ammonia. The latter is toxic in high concentrations. The aim of this work is to develop an Oxygen Reduction System (ORS) to remove oxygen from the exhaust gas of diesel engines, allowing the successful catalytic reduction of nitrogen oxides on a reduction catalyst without the need for ammonia. The ORS device consists of dense composite dual-phase membranes that allow the permeation of oxygen and carbon dioxide. Even though the oxygen concentration gradient across the membranes favors oxygen spontaneous diffusion from the atmosphere to the exhaust gas, the carbonate ion-based technology proposed herein utilizes the big difference in the concentration of carbon dioxide across the membrane to remove oxygen without any power consumption requirement. The results of this study are promising for the application of O2 reduction in diesel HDVs.

Advances in the Energy‐Saving Electro‐Oxidation of 5‐Hydroxymethylfurfural to 2,5‐Furandicarboxylic Acid

AbstractAs a pivotal bio‐based building block, 2,5‐furandicarboxylic acid (FDCA) holds immense and broad application potential in the chemistry industry. Its polymeric derivative, polyethylene furandicarboxylate (PEF), emerges as an appealing alternative to conventional petroleum‐based polyethylene terephthalate (PET). The electrochemical route for oxidizing 5‐hydroxymethylfurfural (HMF) into FDCA presents significant advantages over the thermochemical processes, without the requirements of high temperature, high pressure, chemical oxidants, and precious metal catalysts, featuring higher energy efficiency. Furthermore, the electrosynthesis of FDCA at the anode can be synergistically integrated with selective reduction reactions at the cathode, enabling the simultaneous production of two desirable value‐added products and further enhancing overall energy utilization efficiency. This work reviews the advancements in electrocatalytic HMF to FDCA (EHTF), encompassing catalyst design, reaction mechanisms, coupling strategies, and reactor configurations. It also indicates the challenges and opportunities of EHTF and provides insights into the future development directions.

Unleashing Selective Reduction and Reductive Methylation of N-Heterocycles Using Methanol via Strategic Reaction Condition Modulation

Unleashing Selective Reduction and Reductive Methylation of N-Heterocycles Using Methanol via Strategic Reaction Condition Modulation

Prediction of Pellet Durability Index (PDI) in a Commercial Feed Mill Using Multiple Linear Regression with Variable Selection and Dimensionality Reduction.

Pellet quality, measured as Pellet Durability Index (PDI), is an important key performance indicator (KPI) for commercial feed manufacturing, as it can impact both mill efficiency and downstream performance of animals fed the manufactured diets. However, it is an ongoing challenge for the feed industry to control pellet quality, due to the complexity of feed manufacturing and the large number of variables influencing the process. Previous studies have explored prediction of pellet quality using either simple empirical models with a few variables or machine learning models with many variables. The objective of the current study was to develop statistical regression models to predict PDI, and to describe the relationship between pellet quality and 55 available variables based on a dataset with 2691 observations collected from a commercial feed mill. In the current study, the response variable (PDI) was transformed using the Box-Cox approach into the transformed response variable (tPDI), that was more normally distributed. Three multiple regression models were developed based on subsets of variables processed by variable selection and dimensionality reduction methods: Forward Selection, Principal Component Analysis, and Partial Least Squares. The results indicated that Model 1 (Forward Selection with manual removal of sparse variables), built on 9 variables, performed better than the other two models. It exhibited consistent model prediction performance on the training data and testing data, in terms of MAE (1.93 ± 0.063 versus 1.96), RMSPE (2.45 ± 0.079 versus 2.45), and CCC (0.549 ± 0.0273 versus 0.550), with a better prediction precision based on the fit plot. Expanding Temperature (℃), Fat Content (%), and ADF Content (%), and Indoor Humidity (Pelletizer) (%) were identified as more influential than other variables on the transformed response variable (tPDI) in Model 1, based on a behavior analysis. The models developed in the current study can be helpful to feed mills for predicting and comprehending the effect of a number of commonly measured variables on pellet quality in the commercial setting.

Chemo-Selective Electrochemical Pinacol Coupling of Aldehydes and Ketones Using TMSN3 as a Promoter.

The pinacol coupling is a significant method for carbon-carbon bond formation. Here, we present an efficient electrochemical approach for pinacol coupling of aldehydes and ketones using TMSN3 as a sacrificial reagent. This method exhibits broad applicability to aryl, heteroaryl, and alkyl aldehydes/ketones with excellent chemo-selectivity and high yields (40 examples, up to 99% yield) under mild conditions. This method can also be applied in the selective reduction of phthalimides to hydroxyl lactams in good yields. The proposed mechanism was elucidated by control experiments and cyclic voltammetry.

Sustained, Selective, and Efficient Photochemical CO2 Reduction to Formate by Electron-Deficient Ruthenium Polypyridyl Complexes.

Metal hydrides play a significant role in a variety of reactions, including chemical, electrochemical, and photochemical CO2 reduction. Molecular metal hydrides have the distinct advantage of allowing tunability of their hydricities by rational ligand modifications, with more electron-rich metal hydrides being in general more hydridic. We report here a new approach to generate highly hydridic metal hydrides of the type [Ru(tpy)(LL)(H)]n+ by introducing electron-withdrawing substituents into the backbone of the bidentate LL ligand. This strategy enables the generation of the metal hydride [Ru(tpy)(LL)(H)]+ at mild negative potentials and further one-electron reduction to the more hydridic [Ru(tpy)(LL)(H)]0 at a potential window that is redox silent for the more electron-rich metal hydride analogue [Ru(tpy)(bpy)(H)]+. In addition, formate release takes place from the hydride transfer adducts [Ru---HCOO)(tpy)(LL)]0 rather than from the corresponding formato complexes [Ru(tpy)(LL)(OCHO)]0, which would require further reduction to [Ru(tpy)(LL)(OCHO)]- as demonstrated by IR spectroelectrochemistry. The parent [Ru(tpy)(LL)(CH3CN)]n+ solvento complexes were then tested as catalysts for the reduction of CO2 to formate in a four-component homogeneous photochemical approach driven by a Ru(II) sensitizer. The results showed selective (>88%) formate production with a record turnover number of ∼50,000 and record turnover frequency of 4.4 s-1 when compared to other molecular catalysts.

Highly Selective Acidic CO2 Electroreduction with Large Current on Polypyrrole‐Modified Ag Catalyst by Local Microenvironment Modulation

AbstractElectrocatalytic carbon dioxide reduction (CO2RR) holds great promise for carbon capture and utilization. In acidic media, CO2RR enables efficient CO2 conversion, but with low selectivity due to the competitive hydrogen evolution reaction (HER) and catalyst corrosion. Herein, conductive polymer polypyrrole (PPy) coated Ag nanoparticles (NPs) catalysts (Ag@PPy) with different thicknesses are designed and synthesized, which could create a hydrophobic environment that reduces the accessibility of H2O to the Ag NPs thereby inhibiting HER. The coating of the PPy layer also protects the catalysts from corrosion and improves the stability of the system. Among them, Ag@PPy‐2 with the appropriate thickness showed up to 91.7% for the electrocatalytic reduction of CO2 to CO and high durability in acidic electrolyte at −300 mA cm−2. Density functional theory (DFT) calculation shows that Ag nanoparticles coated with PPy not only inhibit the competitive HER, but also reduce the CO2RR energy barrier, and improve the efficiency of CO2 to CO. This study may provide some new ideas for the design of advanced selective electrocatalytic CO2 reduction catalysts by local microenvironmental engineering.

MXene-based materials for enhanced water quality: Advances in remediation strategies.

Two-dimensional MXenes are promising candidates for water treatment because of their large surface area (e.g., exceeding 1000 m²/g for certain structures), high electrical conductivity (e.g., >1000 S/m), hydrophilicity, and chemical stability. Their strong sorption selectivity and effective reduction capacity, exemplified by heavy metal adsorption efficiencies exceeding 95 % in several studies, coupled with facile surface modification, make them suitable for removing diverse contaminants. Applications include the removal of heavy metals (e.g., achieving >90 % removal of Pb(II)), dye removal (e.g., demonstrating >80 % removal of methylene blue), and radioactive waste elimination. Furthermore, 3D MXene architecture exhibit enhanced performance in antibacterial activities (e.g., against bacteria), desalination rejection percentage, and photocatalytic degradation of organic contaminants. However, several challenges have remained, which necessitate further investigation into toxicity (e.g., assessing effects on aquatic organisms), scalability, and cost-effectiveness of large-scale production. This review summarizes recent advancements in 3D MXene-based functional materials for wastewater treatment and water remediation, critically analyzing their both potential and limitations.

Radiation-synthesis of covalent bonding heterojunctions for selective solar-driven CO2 reduction

Radiation-synthesis of covalent bonding heterojunctions for selective solar-driven CO2 reduction

The Proximal Protonation Source in Cu−NHx−C Single Atom Catalysts Selectively Boosts CO2 to Methane Electroreduction

AbstractRegulating the coordination environment of active sites has proved powerful for tapping into their catalytic activity and selectivity in homogeneous catalysis, yet the heterogeneous nature of copper single‐atom catalysts (SACs) makes it challenging. This work reports a bottom‐up approach to construct a SAC (rGO@Cu−N(Hx)−C) by inlaying preformed amine coordinated Cu2+ units into reduced graphene oxide (rGO), permitting molecular level revelation on how the proximal N‐site functional groups (N−H or N−CH3) impact on the carbon dioxide reduction reaction (CO2RR). It is demonstrated that the N−H moiety of rGO@Cu−NHx−C can serve as an in situ protonation agent to accelerate the CO2‐to‐methane reduction kinetics, delivering a methane current density (163 mA/cm2) 2.42‐times that with the ‐CH3 substituted counterpart rGO@Cu−N−C. Operando spectroscopic studies and theoretical calculations elucidate that the high methane faradaic efficiency (77.1 %) achieved here is enabled by opening up the energetically favorable formyl pathway (*OCHO pathway) against the traditional *CO pathway that normally leads to various CO2RR products other than methane. Our strategy sets the stage to precisely modulate single‐atom catalysts for efficient and selective electrochemical CO2 reduction.

Comparison of Hydrogen Bonded Organic Framework with Reduced Graphene Oxide-Pd Based Nanocatalyst: Which One Is More Efficient for Entrapment of Nitrophenol Pollutants?

In this study, a Pd nanoparticles@hydrogen-bonded organic framework (Pd NPs@HOF) thin film was fabricated at the toluene-water interface. The HOF was formed through the interaction of trimesic acid (TMA) and melamine (Mel) in the water phase, while Pd(0) was produced from the reduction of [PdCl2(cod)] in the organic phase. The as-synthesized Pd NPs@HOF thin film was demonstrated to be an effective catalyst for the selective reduction of p-nitrophenol and o-nitrophenol to p-aminophenol and o-aminophenol. The porous network of the Pd NPs@HOF introduced strong active sites between Mel, TMA, and Pd(0). Kinetic studies showed that the Pd NPs@HOF catalyst exhibited an enhanced rate of p-nitrophenol and o-nitrophenol reduction in comparison with Pd@reduced-graphene oxide (r-GO) with rates that were 1.7 times faster for p-nitrophenol and 1.5 times faster for o-nitrophenol or even 10 times faster than some Pd-based catalysts, with a maximum conversion of 97.1% which was attributed to the higher porosity and greater surface-to-volume ratio of the Pd NPs@HOF material. Furthermore, π-π stacking interactions enhance the catalytic activity of the Pd NPs@HOF catalyst by increasing the active sites, stabilizing the NPs and trapping the nitrophenols, facilitating the electron transfer, and providing the synergistic effect. Also, contributions of hydrogen bonding, van der Waals forces, electrostatic interactions, and π-σ noncovalent interactions are reasons for better performance of Pd NPs@HOF than Pd/r-GO catalyst with the reduced functional groups.

Nickel-Catalyzed Selective Reduction of Carbon Monoxide with Magnesium Alkyl Compounds.

Research on CO activation and homologation is pivotal for promoting sustainable chemistry and the construction of Cn molecular blocks. This work reports the nickel-catalyzed reduction of CO by magnesium alkyl compounds utilizing a bimetallic Mg/Ni synergistic strategy. The exposure of β-diketiminato ligand-supported magnesium monoalkyl compounds LMgR (L = [(DippNCMe)2CH]-, Dipp = 2,6-iPr2C6H3; R = nBu, CH3, C5H9) to 1 bar of CO in the presence of 10 mol% Ni(COD)2 (COD: 1,5-cyclooctadiene) selectively afforded the CO single-insertion product [LMg(CHO)C5H8], the dimerization product [(LMg)2(μ-C2O2)(CH3)2], and the linear trimerization product [(LMg)2(μ-C3O3)(nBu)2], respectively, depending on the R group. In addition, transition metal-stabilized carbene species resulted from CO activation were successfully isolated through stoichiometric control experiments. The profiles of CO trimerization and dimerization were further elucidated by density functional theory calculations, which confirmed the crucial roles of Mg/Ni cooperation and carbene species in the current reaction.

Amelioration of a von Willebrand disease type 2B phenotype invivo upon treatment with allele-selective siRNAs.

Treatment options for the bleeding disorder von Willebrand disease type 2B (VWD2B) are insufficient and fail to address the negative effects of circulating mutant von Willebrand factor (VWF). The dominant-negative nature of VWD2B makes functionally defective VWF an interesting therapeutic target. Previous invitro studies have demonstrated the feasibility of allele-selective silencing of mutant VWF using small interfering RNAs (siRNAs) targeting common single nucleotide polymorphisms (SNPs) in the human VWF gene, an approach that can be applied irrespective of the disease-causing VWF mutation. This study aims to extend this concept to a heterozygous VWD2B mouse model (c.3946G>A; p.Val1316Met) here using mouse strain-specific genetic differences as proxy for human SNPs. Homozygous VWD2B C57BL/6J (2B-B6) mice were crossed with homozygous wild-type 129S1/SvImJ (129S) mice to create heterozygous 2B-B6.129S F1 offspring. These 2B-B6.129S mice were intravenously injected with endothelial-specific lipid nanoparticles loaded with the allele-selective siVwf.B6 or control and 96 hours later, lung Vwf messenger RNA, plasma VWF levels, and phenotypic characteristics were evaluated. Treatment with siVwf.B6 reduced total VWF levels by 50%, with an expected selective reduction in mutant VWF protein. This coincided with normalization of multimeric structure, improved VWF collagen binding/VWF antigen ratio, and normalized bleeding times in two-thirds of heterozygous 2B-B6.129S mice. Being a novel approach in the field of hemostasis, we proved, for VWD, in mice, the concept of selectively inhibiting a mutant dominant-negative allele with siRNAs targeting a single nucleotide variation rather than the disease-causing mutation. For dominant-negative VWD, this offers potential for a customized therapeutic strategy.

The Proximal Protonation Source in Cu-NHx-C Single Atom Catalysts Selectively Boosts CO2 to Methane Electroreduction.

Regulating the coordination environment of active sites has proved powerful for tapping into their catalytic activity and selectivity in homogeneous catalysis, yet the heterogeneous nature of copper single-atom catalysts (SACs) makes it challenging. This work reports a bottom-up approach to construct a SAC (rGO@Cu-N(Hx)-C) by inlaying preformed amine coordinated Cu2+ units into reduced graphene oxide (rGO), permitting molecular level revelation on how the proximal N-site functional groups (N-H or N-CH3) impact on the carbon dioxide reduction reaction (CO2RR). It is demonstrated that the N-H moiety of rGO@Cu-NHx-C can serve as an in-situ protonation agent to accelerate the CO2-to-methane reduction kinetics, delivering a methane current density (163 mA/cm2) 2.42-times that with the -CH3 substituted counterpart rGO@Cu-N-C. Operando spectroscopic studies and theoretical calculations elucidate that the high methane faradaic efficiency (77.1%) achieved here is enabled by opening up the energetically favorable formyl pathway (*OCHO pathway) against the traditional *CO pathway that normally leads to various CO2RR products other than methane. Our strategy sets the stage to precisely modulate single-atom catalysts for efficient and selective electrochemical CO2 reduction.

Effect of Selective Non-Catalytic Reduction Reaction on the Combustion and Emission Performance of In-Cylinder Direct Injection Diesel/Ammonia Dual Fuel Engines

Ammonia, as a hydrogen carrier and an ideal zero-carbon fuel, can be liquefied and stored under ambient temperature and pressure. Its application in internal combustion engines holds significant potential for promoting low-carbon emissions. However, due to its unique physicochemical properties, ammonia faces challenges in achieving ignition and combustion when used as a single fuel. Additionally, the presence of nitrogen atoms in ammonia results in increased NOx emissions in the exhaust. High-temperature selective non-catalytic reduction (SNCR) is an effective method for controlling flue gas emissions in engineering applications. By injecting ammonia as a NOx-reducing agent into exhaust gases at specific temperatures, NOx can be reduced to N2, thereby directly lowering NOx concentrations within the cylinder. Based on this principle, a numerical simulation study was conducted to investigate two high-pressure injection strategies for sequential diesel/ammonia dual-fuel injection. By varying fuel spray orientations and injection durations, and adjusting the energy ratio between diesel and ammonia under different operating conditions, the combustion and emission characteristics of the engine were numerically analyzed. The results indicate that using in-cylinder high-pressure direct injection can maintain a constant total energy output while significantly reducing NOx emissions under high ammonia substitution ratios. This reduction is primarily attributed to the role of ammonia in forming NH2, NH, and N radicals, which effectively reduce the dominant NO species in NOx. As the ammonia substitution ratio increases, CO2 emissions are further reduced due to the absence of carbon atoms in ammonia. By adjusting the timing and duration of diesel and ammonia injection, tailpipe emissions can be effectively controlled, providing valuable insights into the development of diesel substitution fuels and exhaust emission control strategies.

Porous 3d-4f Coordination Clusters for Selective Visible-Light Photocatalytic CO2 Reduction to CO.

We report herein two families of porous coordination clusters (PCCs) with 216 nuclearity (M120RE96 or PCC-216MR) and 300 nuclearity (Co144Gd156 or PCC-300CG). For the first family M could be either nickel or cobalt, and RE = Pr, Nd, Sm, Eu, and Gd; while the latter features the highest nuclearity of transition-rare earth metal clusters. Characterized by their cube-like, hollow structures, these clusters exhibit the ability to absorb N2 and CO2. Besides, these clusters can be dissolved in both aqueous and acetonitrile/methanol solutions, and capable of acting as homogeneous catalysts for converting CO2 to CO under visible light. The gadolinium analogues of these clusters all show turnover numbers over 10000 and turnover frequencies over 1 s-1. In particular, the nickel based bimetallic cluster (PCC-216NG) demonstrates near ly 100% selectivity for the reduction product, which may open a new direction for the design and development of PCCs based catalysts.

Hierarchically Porous SnO2/Cu Composites via Freeze Casting and Selective Cu Reduction

Hierarchically Porous SnO₂/Cu Composites via Freeze Casting and Selective Cu Reduction

Gaussian process latent variable models-ANN based method for automatic features selection and dimensionality reduction for control of EMG-driven systems.

Electromyography (EMG) signals have gained significant attention due to their potential applications in prosthetics, rehabilitation, and human-computer interfaces. However, the dimensionality of EMG signal features poses challenges in achieving accurate classification and reducing computational complexity. To overcome such issues, this paper proposes a novel approach that integrates feature reduction techniques with an artificial neural network (ANN) classifier to enhance the accuracy of high-dimensional EMG classification. This approach aims to improve the classification accuracy of EMG signals while substantially reducing computational costs, offering valuable implications for all EMG-related processes on such data. The proposed methodology involves extracting time and frequency domain features from twelve channels of EMG signals, followed by dimensionality reduction using techniques such as PCA, LDA, PPCA, Lasso and GPLVM, and classification using an ANN. Our investigation revealed that LDA is not appropriate for this dataset. The dimensionality reduction models did not have any significant effect on the accuracy, but the computational cost decreased significantly. In individual comparisons, GPLVM had the shortest computational time (29 s), which was significantly less than that of all the other models (p < 0.05), with PCA following at approximately 35 s and Relief at approximately 57 s, while PPCA took approximately 69 s, and Lasso exhibited higher computational costs than all the models but lower computational costs than did the original set. Using the best-performing features, all possible sets of 2, 3, 4 and 5 features were tested, and the 5-feature set exhibited the best performance. This research demonstrates the effectiveness of dimensionality reduction and feature selection in improving the accuracy of movement recognition in myoelectric control.

Enzyme‐Inspired Single Selenium Site for Selective Oxygen Reduction

Enzyme‐Inspired Single Selenium Site for Selective Oxygen Reduction

Enzyme-Inspired Single Selenium Site for Selective Oxygen Reduction.

Learning from nature has garnered significant attention in the scientific community for its potential to inspire creative solutions in material or catalyst design. The study reports a biomimetic single selenium (Se) site-modified carbon (C) moiety that retains the unique reactivity of selenoenzyme with peroxides, aiming to selectively catalyze the oxygen reduction reaction (ORR). The as-designed Se-C demonstrates nearly 100% 4-electron selectivity, evidenced by 0.039% of H2O2 yield at 0.5 V versus reversible hydrogen electrode, outperforming commercial platinum (Pt) by 65 times. In-situ X-ray absorption spectroscopy and theoretical calculations attribute this exceptional selectivity to the enzyme-like behaviors of the Se site to steal an O atom from peroxide intermediates. The second achievement is the significantly increased consecutive 2+2 electron selectivity. Benefiting from the enzyme-like H2O2 reduction activity with a higher onset potential of 0.915 V compared to Pt at 0.875 V, the Se-C as a secondary catalytic site reduced the H2O2 yields of the Co-N-C, Fe-N-C, and N-C catalysts by 96%, 67%, and 98%, respectively, via a consecutive 2+2 electron pathway. This also leads to more stable catalysts via protecting the active sites from oxidative attacks. This work establishes new pathways for precise tuning of reaction selectivity in ORR and beyond.