Deep Reduction Research Articles

Boosting Oxygen Evolution Performance of Nickel-Iron Layered Double Hydroxides by Controlling Oxygen Vacancies and Structural Disorder via n-Butyllithium Treatment.

Nickel-iron-based layered double hydroxides (NiFe-LDHs) are promising catalysts for the oxygen evolution reaction (OER) because of their high activity, availability, and low cost. Defect engineering, particularly the formation of oxygen vacancies, can improve the catalytic activity of NiFe-LDHs. However, the controllable introduction of uniform oxygen vacancies remains challenging. Herein, an n-butyllithium treatment method is developed to tune oxygen vacancy defects and change the degree of amorphization in NiFe-LDHs via deep reduction, followed by partial oxidization at low temperatures. Interestingly, the Ni in the NiFe-LDHs is selectively reduced to the alloy state by n-butyllithium, whereas Fe is not. The different structural transformations of Ni and Fe during the treatment successfully produce an oxygen-defect-rich amorphous/crystalline electrocatalyst. Under optimal conditions, the treated NiFe-LDHs exhibit high OER activity with an overpotential of 223 mV at 10 mA cm-2 (68 mV lower than that of a commercial IrO2 electrocatalyst) and long-term stability. Notably, the n-butyllithium treatment can be applied to other electrocatalysts, such as CoFe-LDHs and IrO2 (treated IrO2 with an overpotential of 197 mV at 10 mA cm-2). This n-butyllithium reduction/partial oxidization treatment constitutes a novel top-down strategy for the controllable modification of metal oxide structures, with various energy-related applications.

Noise reduction by multiple path neural network using Attention mechanisms with an emphasis on robustness against Errors: A pilot study on brain Diffusion-Weighted images

PurposeIn deep learning-based noise reduction, larger networks offer advanced and complex functionality by utilizing its greater degree of freedom, but come with increased unpredictability, raising the potential risk of unforeseen errors. Here, we introduce a novel denoising model for diffusion-weighted images that intentionally limits the network output freedom by incorporating multiple pathways with varying degrees of freedom, with the aim of minimizing the chance of unintended alterations to the input. The purpose of this pilot study is to assess the model’s ability to perform effective denoising under the constraints. MethodsImages from 10 healthy volunteers were used. Key innovations in our model development include: (1) neural network architecture that separated the function for calculating the specific output values from the function for adjusting the calculation for each pixel and (2) training that optimised the network based on both image and secondary obtained diffusion tensor. The generated images were compared with the original ones by measuring the deviation from ground truth images (averaged across eight acquisitions). ResultsThe generated images demonstrated closer alignment with the ground truth images, both visually and statistically (Q < 0.05), compared to the original images. Furthermore, the advantage of the generated images over the original images was also found in the secondary obtained quantitative parameter maps with significance (Q < 0.05). ConclusionThe usefulness of the proposed method was suggested because it was successful in improving both the quality of the generated images and accuracy of the major diffusion parameter maps under the given restrictions.

Si-Cu contact mass for catalysis in coatings industry for oil and gas pipes

Two contact masses were obtained and analyzed for their catalytic properties in the production of hydrophobic coatings. These masses are based on the silicon-copper system and consist of 25%Cu-75%Si and 50%Cu-50%Si compositions. A standard method of copper(I) chloride reduction was optimized to obtain finely dispersed copper particles with high catalytic activity. It is shown that reduction is possible directly in contact with silicon, the resulting average diameter of copper particles is 5-10 microns in both contact masses (25%Cu-75%Si and 50%Cu-50%Si). A metallographic analysis revealed a loose morphology of the silicon-copper phase interfaces, which is necessary to enhance the catalytic activity of the contact masses. Local chemical analysis by scanning electron microscopy has established the ratio of the particle size of the initial polycrystals of copper chloride(I) and the resulting copper particles as a result of reduction on silicon. The process of deep reduction makes it possible to obtain particles up to 5 microns in size. These results provide useful insights into the formulation of coatings containing organosilicon compounds to reduce friction in hydrocarbon transport.

Propane Dehydrogenation over Cobalt Aluminates: Evaluation of Potential Catalytic Active Sites

Non-oxidative propane dehydrogenation (PDH) is becoming an increasingly important approach to propylene production, while cobalt-containing catalysts have recently demonstrated great potential for use in this reaction, providing efficiencies comparable to those of industrially employed Pt- and Cr-based catalytic systems. It is therefore essential to clarify the nature of their active sites, especially since contradictory opinions on this issue are expressed in the literature. In this study, efforts were made to determine the state of Co in cobalt aluminates (CoAl2O4-Al2O3) responsible for PDH under typical operating conditions (600 °C, 1 atm). It is shown that the catalyst with a low cobalt content (Co/Al = 0.1) ensured the highest selectivity to propylene, ca. 95%, while maintaining significant propylene conversion. The structural motifs such as cobalt oxide and metallic cobalt nanoparticles, in addition to tetrahedral Co2+ species in the CoAl2O4 spinel system, were evaluated as potential active-site ensembles based on the obtained catalytic performance data in combination with the XRD, H2-TPR, TEM and XPS characteristics of as-synthesized, spent and spent–regenerated catalysts. It is revealed that the most likely catalytic sites linked to PDH are the Co-oxide forms tightly covering alumina or embedded in the spinel structure. However, additional in situ tuning is certainly needed, probably through the formation of surface oxygen vacancies rather than through a deeper reduction in Co0 as previously thought.

Quantifying voyage optimisation with wind propulsion for short-term CO2 mitigation in shipping

The global shipping sector requires deep and rapid carbon reductions within a decade to align with Paris Agreement goals. Wind propulsion offers a retrofit option where sails provide renewable power to reduce emissions from a ship's engine. Combining wind propulsion with voyage optimisation shows synergistic potential, but the underlying technical mechanisms at play are poorly understood. Here, we model wind propulsion on globally distributed routes to show how voyage optimisation amplifies carbon saving to over 30% on ideal routes as ships exploit a critical dependency on wind speed and angle. Reduced shipping speeds further increase savings, and all technologies combine to reduce carbon by up to 60%, demonstrating the potential for substantial CO2 cuts from existing technologies. The findings challenge the sector to urgently broaden its narrow focus on alternative fuels by providing a steer on how to cut shipping's committed emissions and bring shipping closer to Paris Agreement goals.

Improving Deep Arc Reduction Parameters for Useful Elements in the Extra-Furnace Processing of Iron–Carbon Melts

Improving Deep Arc Reduction Parameters for Useful Elements in the Extra-Furnace Processing of Iron–Carbon Melts

Kinetically matched C–N coupling toward efficient urea electrosynthesis enabled on copper single-atom alloy

Chemical C–N coupling from CO2 and NO3–, driven by renewable electricity, toward urea synthesis is an appealing alternative for Bosch–Meiser urea production. However, the unmatched kinetics in CO2 and NO3– reduction reactions and the complexity of C- and N-species involved in the co-reduction render the challenge of C–N coupling, leading to the low urea yield rate and Faradaic efficiency. Here, we report a single-atom copper-alloyed Pd catalyst (Pd4Cu1) that can achieve highly efficient C–N coupling toward urea electrosynthesis. The reduction kinetics of CO2 and NO3– is regulated and matched by steering Cu doping level and Pd4Cu1/FeNi(OH)2 interface. Charge-polarized Pdδ–-Cuδ+ dual-sites stabilize the key *CO and *NH2 intermediates to promote C–N coupling. The synthesized Pd4Cu1-FeNi(OH)2 composite catalyst achieves a urea yield rate of 436.9 mmol gcat.–1 h–1 and Faradaic efficiency of 66.4%, as well as a long cycling stability of 1000 h. In-situ spectroscopic results and theoretical calculation reveal that atomically dispersed Cu in Pd lattice promotes the deep reduction of NO3– to *NH2, and the Pd-Cu dual-sites lower the energy barrier of the pivotal C–N coupling between *NH2 and *CO.

Deep learning-based noise reduction preserves quantitative MRI biomarkers in patients with brain tumors

The use of relaxometry and Diffusion-Tensor Imaging sequences for brain tumor assessment is limited by their long acquisition time. We aim to test the effect of a denoising algorithm based on a Deep Learning Reconstruction (DLR) technique on quantitative MRI parameters while reducing scan time. In 22 consecutive patients with brain tumors, DLR applied to fast and noisy MR sequences preserves the mean values of quantitative parameters (fractional anisotropy, mean diffusivity, T1 and T2-relaxation time) and produces maps with higher structural similarity compared to long duration sequences. This could promote wider use of these biomarkers in clinical setting.

Deep decarburization of molybdenum and tungsten powders by wet hydrogen

The control of residual carbon content is an urgent problem to be solved for the preparations of molybdenum and tungsten powders by method of carbothermic reduction of their oxides. In this paper, finer Mo (W) powder with a low residual carbon content was prepared by a two-step reduction process consisting of carbothermal pre-reduction of MoO3 (WO3) at 1050 °C and following deep hydrogen reduction. During the deep hydrogen reduction process, with the increases of hydrogen reduction temperature and dew point, the residual carbon contents of the finally prepared Mo and W powders gradually decreased, while the particle sizes of Mo and W powders gradually increased. The lowest residual carbon and oxygen contents of Mo and W powders obtained after hydrogen reduction at 1100 °C with the hydrogen dew point of 41 °C were 0.007 wt% (carbon content) and 0.104 wt% (oxygen content), as well as 0.010 wt% and 0.086 wt%, respectively. In addition, the mean particle sizes of them were 1032 nm and 998 nm under this condition, respectively.

Application of an intelligent evaluation model of online teaching based on improved BPNN

Online teaching quality evaluation is essential for university management, but the current evaluation model of online teaching quality has problems such as subjectivity, high randomness and slow convergence speed. Therefore, the study uses adaptive learning rate with momentum term for optimizing and improving the traditional back propagation neural network, and the improved adaptive back propagation neural network rating model has good application in small-scale low-dimensional datasets, and its computational power is weak in large-scale datasets. Therefore, the study uses deep noise reduction autoencoder to improve the model structure, and then obtains an improved backpropagation neural network. The model shows better performance in a self-made university online teaching evaluation dataset. When the quantity of hidden layers of the improved model is 2, the mean square error value of the model is 0.0026; when the quantity of hidden layer neurons is 25, the mean square error of the model is 0.0012; when the model is made comparison with the shallow model, the mean absolute percentage error, the mean square error, and the root mean square error perform the best with the values of 0.0502, 24.53, and 4.95, respectively. the experimental results verify that the experiment verified that the improved model possesses powerful computational ability, and also proved that it also possesses excellent forecasting convergence, which can better help universities to complete the evaluation of online teaching quality.

A novel method for preparing ultrafine molybdenum‑rhenium alloy powders

To address the issues of large particle size and poor particle size uniformity in the traditional two-step hydrogen reduction method for preparing molybdenum‑rhenium alloy powders, this study innovatively employed glucose as the carbon source and successfully prepared ultrafine molybdenum‑rhenium alloy powders through wet-wet mixing, evaporative crystallization, calcination, carbothermal pre-reduction, and hydrogen deep reduction steps. The results demonstrate that when the C/(Mo + Re) molar ratio is 0.7, the powders synthesized through carbon‑hydrogen synergistic reduction exhibit a spherical shape, with particle sizes distributed in the sub-micrometer range and an average particle size of 173 nm. The phase composition of the reduction powders consists predominantly of molybdenum, rhenium and χ phases, with a uniform distribution of alloy elements. In contrast, the powders synthesized using the traditional two-step hydrogen reduction method exhibit irregular, porous and loosely-packed structures, with particle sizes in tens of microns range and slightly larger than those of the precursor. This method is highly suitable for preparing high-purity, ultrafine, and highly homogeneous binary alloy powders, like MoRe, MoW, WRe, ect.

A comparative study of the reaction mechanism for deep reduction hydrogen production using two special steel solid wastes and a chemical looping hydrogen production scheme

Pickling sludge (PS) and presintered waste (PW) are two special steel solid wastes that have potential as oxygen carriers due to their Fe content. Using these raw materials to produce hydrogen allows for the resource utilization of solid waste, which has good environmental effects. Here, we studied the deep reduction-hydrogen production characteristics. Specifically, the effects of temperature, papermaking sludge (PMS) blended ratio, and reaction time on the deep reduction characteristics of Fe in pickling sludge and presintered waste were studied. On this basis, the study also aimed to investigate the effects of temperature and PMS blended ratio on hydrogen production. The results indicated that when exposed to high temperature, PMS shows the ability to effectively reduce pickling sludge and presintered waste. The maximum Fe recovery rate can reach 65.63 and 96.94 % in a fixed bed reactor operating at 700 °C and a blended ratio of approximately 10 %. Under the conditions of 900 °C and 15 %/50 % papermaking sludge addition, the Fe content within pickling sludge and presintered waste was reduced to FeCr2O4 and FeO, respectively. Subsequently, during the reaction with steam, Fe2O3 and Fe3O4 were generated in PS and PW, respectively. The hydrogen production per unit mass of Fe2O3 in pickling sludge is higher due to the existence of the FeCr2O4 spinel structure in the reduction product of pickling sludge. According to the above results, two hydrogen production strategies of PS and PW through different deep reduction paths are proposed.

A Review of the Network Arch Bridge

The network arch bridge (NAB) is a new structural form of arch bridge that was devised 60 years ago by the Norwegian engineer Per Tveit, who is now prof. dr. docent emeritus at the University of Agder, Norway. The network arch is a tied-arch (also known as a bowstring-arch) bridge that combines the benefits of tied-arch bridges and trusses in a single system. While in a classical tied arch, the hangers are vertical, in a network arch, the suspension of the deck to the arch is ensured through a network of inclined hangers that intersect each other at least twice. Thus, the core of the NAB is the hanger arrangement that minimizes the bending moment in the arch to very small values, leading to compression in the arch. Compression with only small bending leads to very slender cross-sections for the elements of the bridge, and deep reductions in terms of materials used and economic and environmental costs. This paper reviews the research into the structural form proposed by Per Tveit and extended by researchers and engineers worldwide. The research methodology included bibliometric literature research, obtained by interrogating the ISI Web of Science (WoS) database and the cited references from the articles on WoS. While the first structural form of a network arch is still in use today and it has proven to be a good idea for spans around 100–120 m, engineers worldwide devised new bridge cross-sections. A brief view of the types of bridge cross-section in use today is given, with details about the bridges chosen as representative. Using analysis of Prof. Tveit’s map, Structurae database and literature review, a database of the network arches around the world was created, emphasizing the development of network arches from the perspectives of continental distribution, opening year, number of structures in different structural forms, and bridge purposes. The structural form was assessed from the perspective of materials used for the arch and the tie, span, purpose and number of lanes, the presence/absence of upper wind-bracings and arch disposition in the vertical plane. In the last part of this review, the newest research into the development of the network arch is discussed. In the past 15 years we have seen an acceleration in network arch development from multiple perspectives: new materials used, such as glulam for the arch or carbon fiber-reinforced plastic for the hangers; span lengths of 250 m and 380 m for large bridge widths; architectural constraints that lead to the outward inclination of the arch, that is pleasing to the eye, but difficult to address from an engineering perspective; the most slender arch bridge in the world, with very slender cross-sections for the arch and the tie.

Deep learning-based photodamage reduction on harmonic generation microscope at low-level optical power.

The trade-off between high-quality images and cellular health in optical bioimaging is a crucial problem. We demonstrated a deep-learning-based power-enhancement (PE) model in a harmonic generation microscope (HGM), including second harmonic generation (SHG) and third harmonic generation (THG). Our model can predict high-power HGM images from low-power images, greatly reducing the risk of phototoxicity and photodamage. Furthermore, the PE model trained only on normal skin data can also be used to predict abnormal skin data, enabling the dermatopathologist to successfully identify and label cancer cells. The PE model shows potential for in-vivo and ex-vivo HGM imaging.

Alkaline Ionic Liquid Microphase Promotes Deep Reduction of CO2 on Copper.

Electrochemical reduction of CO2 to multicarbon (C2+) products using renewable energy sources is an important route to storing sustainable energy and achieving carbon neutrality. It remains a challenge to achieve high C2+ product faraday efficiency (FE) at ampere-level current densities. Herein, we propose the immobilization of an alkaline ionic liquid on copper for promoting the deep reduction of CO2. By this strategy, a C2+ FE of 81.4% can be achieved under a current density of 0.9 A·cm-2 with a half-cell energy conversion efficiency of 47.4% at -0.76 V vs reversible hydrogen electrode (RHE). Particularly, when the current density is as high as 1.8 A·cm-2, the C2+ FE reaches 71.6% at an applied potential of -1.31 V vs RHE. Mechanistic studies demonstrate that the alkaline ionic liquid plays multiple roles of improving the accumulation of CO2 molecules on the copper surface, promoting the activation of the adsorbed CO2, reducing the energy barrier of CO dimerization, stabilizing intermediates, and facilitating the C2+ product formation.

ON THE PATTERNS AND CAUSES OF CHRONOGRAPHIC VARIABILITY OF MORPHOLOGICAL CHARACTERISTICS IN SOME MAMMALS: THE ORDERS RODENTIA AND CARNIVORA

Chronographic variations in some craniological features in the Spotted ground squirrel, the Red fox and the Wolf in the middle of the 20th to early 21st centuries have been established. They represent their temporary deviations from the average size towards an increase which, after several generations, is replaced by a return to the original values. The influence of trophic and climatic factors, parasites, diseases and population densities during the development of young individuals is considered. Chronographic changes in the condylobasal length and zygomatic width of the skull are established as temporary and reversible, increases in their values being replaced by their decreases. Increases in the size of the skull of the Spotted ground squirrel and the Red fox occur “suddenly” in individuals of one generation, and their return to the initial values occurs gradually in individuals of several subsequent generations. Chronographic variations in the size of the skull of the Spotted ground squirrel and the Red fox correlate with changes in reproduction rates and some other varying factors. They correspond to certain phases of population abundance. Increased sizes of the Red fox and Wolf skulls are observed in the phases of quitting a depression to the beginning of a population growth. The probable causes of chronographic changes lie in somatic heterosis, caused in natural habitats by the alternation of the prevailing mating types (inbreeding or outbreeding), these being due to massive rearrangements of individuals as a result of deep population reductions from natural or anthropogenic impacts.

Tailoring ligand fields of metal-azolate frameworks for highly selective electroreduction of CO2 to hydrocarbons at industrial current density

The catalytic activity of a metal complex is often contingent upon factors such as its valence state, coordination configuration of the metal ion, and coordination ability of the ligand. Hence, revealing the influence of the ligand field of the active metal center on the selectivity of the product for electrochemical CO2 reduction is crucial. Herein, three isostructural metal-azolate frameworks (MAFs) (Cu-BTP, Cu-BTTri, and Cu-BTT) with the same cyclic tetracopper(II) cluster units, namely [Cu3(BTP)2] (Cu-BTP, H3BTP = 1,3,5-tris(1H-pyrazol-4-yl)benzene), [Cu3(BTTri)2] (Cu-BTTri, H3BTTri = 1,3,5-tris(1H-1,2,3-triazol-5-yl)benzene), and [Cu3(BTT)2] (Cu-BTT, H3BTT = 1,3,5-tris(2H-tetrazol-5-yl)benzene) were synthesized using pyrazolate-, triazolate-, and tetrazolate-based ligands, respectively. The synthesized MAFs were subjected to analysis to evaluate their electrochemical capabilities for reducing CO2 under identical reaction conditions. Among them, the pyrazolate MAF, Cu-BTP, delivers a current density of 1.25 A cm2 in a flow cell device with the highest Faradiac efficiency for hydrocarbons (CH4, 60%; C2H4, 22%). Furthermore, the system shows no obvious degradation over 60 h of continuous operation. The order of selectivity of the three MAFs for hydrocarbon production is consistent with the corresponding pKa values of the azolate ligands. Theoretical calculations show that a stronger Lewis basicity of the organic ligand, resulting in a stronger ligand field strength, is conducive to strengthening the binding of metal centers with key intermediates, such as *CO and *CHO. This ultimately leads to the deep reduction of CO2 to hydrocarbons.

Enhancing Electroreduction CO2 to Hydrocarbons via Tandem Electrocatalysis by Incorporation Cu NPs in Boron Imidazolate Frameworks.

Due to the higher value of deeply-reduced products, electrocatalytic CO2 reduction reaction (CO2 RR) to multi-electron-transfer products has received more attention. One attractive strategy is to decouple individual steps within the complicated pathway via multi-component catalysts design in the concept of tandem catalysts. Here, a composite of Cu@BIF-144(Zn) (BIF = boron imidazolate framework) is synthesized by using an anion framework BIF-144(Zn) as host to impregnate Cu2+ ions that are further reduced to Cu nanoparticles (NPs) via in situ electrochemical transformation. Due to the microenvironment modulation by functional BH(im)3 - on the pore surfaces, the Cu@BIF-144(Zn) catalyst exhibits a perfect synergetic effect between the BIF-144(Zn) host and the Cu NP guest during CO2 RR. Electrochemistry results show that Cu@BIF-144(Zn) catalysts can effectively enhance the selectivity and activity for the CO2 reduction to multi-electron-transfer products, with the maximum FECH4 value of 41.8% at -1.6V and FEC2H4 value of 12.9% at -1.5V versus RHE. The Cu@BIF-144(Zn) tandem catalyst with CO-rich microenvironment generated by the Zn catalytic center in the BIF-144(Zn) skeleton enhanced deep reduction on the incorporated Cu NPs for the CO2 RR to multi-electron-transfer products.

Influence of carbon derivatives on carbon capture investments in coal-based power sector, a China perspective

Coal-based power sector needs deep carbon emission reduction in the upcoming 20 years to fulfill China's carbon peaking and carbon neutrality pledge. Due to the low and fluctuating carbon price in the emission trading system, decarbonization projects are risky and face massive potential losses. To promote decarbonization investment, a lot of policies of subsidy have been set forth. However, market instruments, which could be efficient and motivating for market entities, should have received more attention. In this article, the influence of carbon derivatives on decarbonization investment and financing is analyzed for different technology progresses and price trajectories. Results show that carbon futures and options have de-risking ability, lowering expected variation and financial cost, and consequently, making decarbonization project feasible. For advanced technology and optimistic outlook, investment can be feasible with 42-66% debt share when options are available. For the base case and neutral price outlook, derivatives can pull subsidy down by around 1%.

Cost and life cycle analysis for deep CO2 emissions reduction of steelmaking: Blast furnace-basic oxygen furnace and electric arc furnace technologies

Iron and steel manufacturing is the largest contributor to CO2 emissions among heavy industries worldwide. This is mostly due to the use of coal in blast furnace-basic oxygen furnace (BF-BOF) process for virgin (primary) steel production. The electricity generation mix used in the electric arc furnace (EAF) process to recycle scrap steel also contributes to the CO2 emission associated with secondary steel production. To decarbonize iron and steel sector, we investigated decarbonization options for BF-BOF and EAF processes, including energy efficiency, carbon capture and storage, and the use of clean energy sources, in various BF-BOF and EAF process configurations. For each decarbonization approach, we evaluated the CO2 reduction potential via life cycle analysis (LCA) and estimated the associated cost through techno-economic analysis (TEA). A typical U.S. BF-BOF for virgin steel production has a cradle-to-gate (CTG) CO2 emissions of 1,990 kg/MT steel with a levelized cost of steel (LCOS) of $439/MT steel, while a typical U.S. EAF process for secondary steel production in the United States has a CTG CO2 emissions of 270 kg/MT steel with a LCOS of $365/MT steel. Combining renewable energy sources and carbon capture, BF-BOF CTG CO2 emissions can be reduced to 16 kg/MT steel, and EAF configurations can achieve similar deep reductions to reach 25 kg/MT steel. The corresponding LCOS with these decarbonization levels is estimated to increase to $542/MT steel and $348/MT steel, respectively. The estimated CO2 avoidance costs vary from -$90/MT CO2 to $646/MT CO2, depending on the various decarbonization technologies and energy prices.