Deep Reduction Research Articles

High-selectivity CO2-to-CH4 electrochemical reduction on copper trimer: A theoretical insight

The pursuit of enhanced catalysts represents a pivotal yet challenging undertaking in the realm of CO2 reduction to CH4 powered by renewable electricity. The development of high-performance catalysts has been constrained by the scaling between *CO and *CHO, coupled with inadequate selectivity. Here, we report a design strategy that addresses the limitation by formulating Cu trimer-anchored MXene-based catalysts using density functional theory studies. This advancement was achieved by enhancing the selectivity of *OCHO via constructing oxyphilic sites, introducing a hydrogen bond promoter for *HCOOH deep reduction, and a multi-site synergy strategy for stabilizing complex intermediates (e.g., *H2COOH), and therefore reshaping the linear relationship between adsorbates. Consequently, a novel volcano model was constructed using the adsorption free energy of *OCHO as an activity descriptor. Based on the microkinetic analysis, it is predicted that a high current density could potentially be achieved on Cu3@V2NO2 at an applied potential of −1.10 V vs. reversible hydrogen electrode. This study proposes new catalyst design approaches for CO2 conversion, accompanied by a thorough exploration of the thermodynamic and kinetic aspects of the reduction process.

Improving Automated Hemorrhage Detection at Sparse-View CT via U-Net-based Artifact Reduction.

Purpose To explore the potential benefits of deep learning-based artifact reduction in sparse-view cranial CT scans and its impact on automated hemorrhage detection. Materials and Methods In this retrospective study, a U-Net was trained for artifact reduction on simulated sparse-view cranial CT scans in 3000 patients, obtained from a public dataset and reconstructed with varying sparse-view levels. Additionally, EfficientNet-B2 was trained on full-view CT data from 17 545 patients for automated hemorrhage detection. Detection performance was evaluated using the area under the receiver operating characteristic curve (AUC), with differences assessed using the DeLong test, along with confusion matrices. A total variation (TV) postprocessing approach, commonly applied to sparse-view CT, served as the basis for comparison. A Bonferroni-corrected significance level of .001/6 = .00017 was used to accommodate for multiple hypotheses testing. Results Images with U-Net postprocessing were better than unprocessed and TV-processed images with respect to image quality and automated hemorrhage detection. With U-Net postprocessing, the number of views could be reduced from 4096 (AUC: 0.97 [95% CI: 0.97, 0.98]) to 512 (0.97 [95% CI: 0.97, 0.98], P < .00017) and to 256 views (0.97 [95% CI: 0.96, 0.97], P < .00017) with a minimal decrease in hemorrhage detection performance. This was accompanied by mean structural similarity index measure increases of 0.0210 (95% CI: 0.0210, 0.0211) and 0.0560 (95% CI: 0.0559, 0.0560) relative to unprocessed images. Conclusion U-Net-based artifact reduction substantially enhanced automated hemorrhage detection in sparse-view cranial CT scans. Keywords: CT, Head/Neck, Hemorrhage, Diagnosis, Supervised Learning Supplemental material is available for this article. © RSNA, 2024.

Deep Dimension Reduction for Supervised Representation Learning

Deep Dimension Reduction for Supervised Representation Learning

NiCuAg: An electrochemically-synthesised trimetallic stack for CO2 reduction

Electrochemical CO2 reduction is a promising technique for the production of desirable hydrocarbons without the need to resort to fossil resources. However, high overpotentials and poor selectivity remain a challenge for CO2 electro-reduction, especially for deep reduction by more than two electrons. One apparently attractive approach for breaking the scaling relations caused by simultaneous CO2 reduction pathways and for achieving deeper reduction is the use of multi-metallic electrodes, where several promising metal catalysts are present in close proximity. Herein, noting the activity shown by Ni, Cu and Ag for CO2 electroreduction when used individually, we set out to synthesise a tri-metallic “stack” catalyst, NiCuAg, and then to test this for electrochemical CO2 reduction. The stack architecture was successfully generated and the trimetallic NiCuAg system did show improved Faradaic efficiency for the reduction of CO2 to formic acid when compared to the bare Ni and bimetallic NiCu controls under some select conditions. However, the two-layer NiCu stack and bare Ni exhibited consistently higher Faradaic efficiencies than NiCuAg for deeper CO2 electroreduction to methanol and ethanol, indicating that the combination of three individually promising metals does not necessarily translate into superior catalytic performance for deep carbon dioxide reduction. [Display omitted]

Enhanced CO adsorption and *CO hydrogenation for efficient CO2 deep reduction on MnCu-NC

In CO2RR, enhancing CO adsorption could suppress desorption and increase *CO concentration for deep reduction. An underlying concern is that strong adsorption might result in high thermodynamic barrier for *CO hydrogenation, however, kinetic barrier firstly dominates the reaction pathway and determines the products selectivity. Directed by above idea, CO2RR performance on Mn, Cu doped nitrogenated carbon (MnCu-NC) is comprehensively studied using density functional theory (DFT). MnCu-NC shows a mild limiting potential of −0.59 V (*CO → *CHO) for CH3OH and CH4 products. CO poisoning is likely to cause catalyst deactivation, while CO modified MnCu-NC shows a slightly increased limiting potential of −0.63 V (*COCO → *COCHO) for CH3OH and CH4, and hydrogen evolution is significantly suppressed. Further comparative studies indicate that kinetic barrier for *CO hydrogenation is crucial for deep reduction, and C–O activation, as well as CO adsorption is enhanced by Π-backbonding. Besides, introduction Cu to dual-atom catalyst (DAC) could help retain more electrons and Mn has strong magnetization for charge transfer, both of which improve the charge densities and Π-backbonding strength, thus boosting the remarkable CO2RR performance toward CH4 and CH3OH on MnCu-NC.

Exploring hydrogen metallurgy to CO2 emissions reduction in China’s iron and steel production: An analysis based on the life cycle CO2 emissions – LEAP model

Previous research has primarily assessed the environmental impact of hydrogen metallurgy from technical or project perspectives, lacking a life cycle perspective. The impact of continued cleanliness of the hydrogen production mix and power generation structure on carbon dioxide (CO2) emissions from the hydrogen metallurgy production process has not been investigated on a medium to long-term scale, which does not provide an adequate and comprehensive view of the contribution of hydrogen metallurgy to China’s emissions reduction, resulting in short-sighted technology choices. To address this lack, we construct a bottom-up energy system model that combines life cycle CO2 emissions with long-range energy alternatives planning (LEAP), including iron and steel (IS) production, power generation, and hydrogen production. Setting development paths of two typical hydrogen metallurgy technologies, including business-as-usual (BAU) and hydrogen metallurgy (HM) scenarios, we carefully evaluate the CO2 emissions impact of hydrogen metallurgy from a whole industry perspective and in each IS production stage. The results suggest that the hydrogen metallurgy transition will help the iron and steel industry (ISI) to achieve peak and deep carbon reduction. Under the HM scenario, CO2 emissions from the ISI would decrease to 816.28 million tons (Mt) in 2050, representing an additional 281.13 Mt reduction over the BAU scenario. Hydrogen metallurgy can accelerate CO2 reduction in raw material acquisition and material processing phases but may increase CO2 emissions in manufacturing and recycling phases. In 2050, hydrogen metallurgy will contribute an additional 12.49 Mt and 300.95 Mt of CO2 reduction to the raw material acquisition and material processing phases, respectively. However, CO2 emissions in the manufacturing and recycling phases would be 32.15 Mt and 0.22 Mt more than in the BAU scenario due to the increased electricity consumption and scrap demand of electric arc furnace (EAF) steelmaking, respectively. Over time, the optimization of hydrogen production and power supply mix will gradually reduce CO2 emissions in each IS production process. In 2050, the CO2 intensity of the hydrogen-rich shaft furnace direct reduced iron (H2-DRI)-EAF process will drop to 753.41 kgCO2/t, which is lower than all other processes except the 100 % scrap-based EAF process. Advancing the development of the H2-DRI-EAF process can effectively achieve CO2 emissions reduction while alleviating dependence on scrap resources. The results are expected to inform government policy on the development of hydrogen metallurgy in China and other countries.

Dependence of Initial Capacity Irreversibility on Oxygen Framework Chemistry in Li‐Rich Layered Cathode Oxides

The undesirable capacity loss after first cycle is universal among layered cathode materials, which results in the capacity and energy decay. The key to resolving this obstacle lies in understanding the effect and origin of specific active Li sites during discharge process. In this study, focusing on Ah‐level pouch cells for reliability, an ultrahigh initial Coulombic efficiency (96.1%) is achieved in an archetypical Li‐rich layered oxide material. Combining the structure and electrochemistry analysis, we demonstrate that the achievement of high‐capacity reversibility is a kinetic effect, primarily related to the sluggish Li mobility during oxygen reduction. Activating oxygen reduction through small density would induce the oxygen framework contraction, which, according to Pauli repulsion, imposes a great repulsive force to hinder the transport of tetrahedral Li. The tetrahedral Li storage upon deep oxygen reduction is experimentally visualized and, more importantly, contributes to 6% Coulombic efficiency enhancement as well as 10% energy density improvement for pouch cells, which shows great potentials breaking through the capacity and energy limitation imposed by intercalation chemistry.

Recent Progress on Copper-Based Bimetallic Heterojunction Catalysts for CO2 Electrocatalysis: Unlocking the Mystery of Product Selectivity.

Copper-based bimetallic heterojunction catalysts facilitate the deep electrochemical reduction of CO2 (eCO2RR) to produce high-value-added organic compounds, which hold significant promise. Understanding the influence of copper interactions with other metals on the adsorption strength of various intermediates is crucial as it directly impacts the reaction selectivity. In this review, an overview of the formation mechanism of various catalytic products in eCO2RR is provided and highlight the uniqueness of copper-based catalysts. By considering the different metals' adsorption tendencies toward various reaction intermediates, metals are classified, including copper, into four categories. The significance and advantages of constructing bimetallic heterojunction catalysts are then discussed and delve into the research findings and current development status of different types of copper-based bimetallic heterojunction catalysts. Finally, insights are offered into the design strategies for future high-performance electrocatalysts, aiming to contribute to the development of eCO2RR to multi-carbon fuels with high selectivity.

Regulating the Selectivity of Nitrate Photoreduction for Purification or Ammonia Production by Cooperating Oxidative Half-Reactions.

The removal and conversion of nitrate (NO3-) from wastewater has become an important environmental and health topic. The NO3- can be reduced to nontoxic nitrogen (N2) for environmental remediation or ammonia (NH3) for recovery, in which the tailoring of the selectivity is greatly challenging. Here, by construction of the CuOx@TiO2 photocatalyst, the NO3- conversion efficiency is enhanced to ∼100%. Moreover, the precise regulation of selectivity to NH3 (∼100%) or N2 (92.67%) is accomplished by the synergy of cooperative redox reactions. It is identified that the selectivity of the NO3- photoreduction is determined by the combination of different oxidative reactions. The key roles of intermediates and reactive radicals are revealed by comprehensive in situ characterizations, providing direct evidence for the regulated selectivity of the NO3- photoreduction. Different active radicals are produced by the interaction of oxidative reactants and light-generated holes. Specifically, the introduction of CH3CHO as the oxidative reactant results in the generation of formate radicals, which drives selective NO3- reduction into N2 for its remediation. The alkyl radicals, contributed to by the (CH2OH)2 oxidation, facilitate the deep reduction of NO3- to NH3 for its upcycling. This work provides a technological basis for radical-directed NO3- reduction for its purification and resource recovery.

An active learning method using deep adversarial autoencoder-based sufficient dimension reduction neural network for high-dimensional reliability analysis

Reliability analysis often requires time-consuming evaluations, especially when dealing with high-dimensional and nonlinear problems. To address this challenge, surrogate model methods are frequently employed. One way to improve the efficiency of surrogate model methods involves selecting informative samples that significantly enhance the accuracy of the surrogate model. This paper introduces a novel approach to facilitate the construction of surrogate models and selection of informative samples in high-dimensional reliability analysis, through an active learning method based on a deep adversarial autoencoder-based sufficient dimension reduction (AAE-SDR) neural network. The AAE-SDR neural network serves as a surrogate model, transforming complex high-dimensional variables into tractable, low-dimensional embeddings relevant to the target. These embeddings are Gaussian-distributed with a distinct latent limit state boundary. A new sampling strategy is proposed to select informative misclassified samples by iteratively identifying candidate samples near the latent limit state boundary and uniformly sampling from the candidate sample dataset based on the latent Gaussian distribution. The effectiveness of the proposed approach is demonstrated through two high-dimensional numerical examples and a cable-stayed bridge case study. Results show that the proposed method simplifies complex high-dimensional reliability problems and provides a relatively accurate estimated failure probability with a limited number of samples.

Assessment of Hydrogen Energy Industry Chain Based on Hydrogen Production Methods, Storage, and Utilization

To reach climate neutrality by 2050, a goal that the European Union set itself, it is necessary to change and modify the whole EU’s energy system through deep decarbonization and reduction of greenhouse-gas emissions. The study presents a current insight into the global energy-transition pathway based on the hydrogen energy industry chain. The paper provides a critical analysis of the role of clean hydrogen based on renewable energy sources (green hydrogen) and fossil-fuels-based hydrogen (blue hydrogen) in the development of a new hydrogen-based economy and the reduction of greenhouse-gas emissions. The actual status, costs, future directions, and recommendations for low-carbon hydrogen development and commercial deployment are addressed. Additionally, the integration of hydrogen production with CCUS technologies is presented.

Energy landscapes of homopolymeric RNAs revealed by deep unsupervised learning

Conformational dynamics of RNA plays important roles in a variety of cellular functions such as transcriptional regulation, catalysis, scaffolding, and sensing. Recently, RNAs with low-complexity sequences have been shown to phase separate and form condensate phases similar to lowcomplexity protein domains. The affinity for phase separation and the material characteristics of RNA condensates are strongly dependent on sequence composition and patterning. We hypothesize that differences in the affinities for RNA phase separation can be uncovered by studying sequence-dependent conformational dynamics of single RNA chains. To this end, we have employed atomistic simulations and deep dimensionality reduction techniques to map temperature-dependent conformational free energy landscapes for 20 base-long homopolymeric RNA sequences: poly(U), poly(G), poly(C), and poly(A). The energy landscapes of homopolymeric RNAs reveal a plethora of metastable states with qualitatively different populations stemming from differences in base chemistry. Through detailed analysis of base, phosphate, and sugar interactions, we show that experimentally observed temperature-driven shifts in metastable state populations align with experiments on RNA phase transitions. Specifically, we find that the thermodynamics of unfolding of homopolymeric RNA follows the poly(G) > poly(A) > poly(C) > poly(U) order of stability, mirroring the propensity of RNA to form condensates. To conclude, this work shows that at least for homopolymeric RNA sequences the single-chain conformational dynamics contains sufficient information for predicting and quantifying condensate forming affinities of RNAs. Thus, we anticipate that atomically detailed studies of temeprature -dependent energy landscapes of RNAs will be a useful guide for understanding the propensity of various RNA molecules to form condensates.

Synergistic effect between transition metal single atom and SnS2 toward deep CO2 reduction

The electrochemical reduction of CO2 is an efficient channel to facilitate energy conversion, but the rapid design and rational screening of high-performance catalysts remain a great challenge. In this work, we investigated the relationships between the configuration, energy, and electronic properties of SnS2 loaded with transition metal single atom (TM@SnS2) and analyzed the mechanism of CO2 activation and reduction by using density functional theory. The "charge transfer bridge" promoted the adsorption of CO2 on TM@SnS2, thus enhancing the binding of HCOOH∗ to the catalyst for further hydrogenation and reduction to high-value CH4. The research revealed that the binding free energy of COOH∗ on TM@SnS2 formed a "volcano curve" with the limiting potential of CO2 reduction to CH4, and the TM@SnS2 (TM= Cr, Ru, Os, and Pt) at the "volcano top" exhibited a high CH4 activity.

Image quality and metal artifact reduction in total hip arthroplasty CT: deep learning-based algorithm versus virtual monoenergetic imaging and orthopedic metal artifact reduction

BackgroundTo compare image quality, metal artifacts, and diagnostic confidence of conventional computed tomography (CT) images of unilateral total hip arthroplasty patients (THA) with deep learning-based metal artifact reduction (DL-MAR) to conventional CT and 130-keV monoenergetic images with and without orthopedic metal artifact reduction (O-MAR).MethodsConventional CT and 130-keV monoenergetic images with and without O-MAR and DL-MAR images of 28 unilateral THA patients were reconstructed. Image quality, metal artifacts, and diagnostic confidence in bone, pelvic organs, and soft tissue adjacent to the prosthesis were jointly scored by two experienced musculoskeletal radiologists. Contrast-to-noise ratios (CNR) between bladder and fat and muscle and fat were measured. Wilcoxon signed-rank tests with Holm-Bonferroni correction were used.ResultsSignificantly higher image quality, higher diagnostic confidence, and less severe metal artifacts were observed on DL-MAR and images with O-MAR compared to images without O-MAR (p < 0.001 for all comparisons). Higher image quality, higher diagnostic confidence for bone and soft tissue adjacent to the prosthesis, and less severe metal artifacts were observed on DL-MAR when compared to conventional images and 130-keV monoenergetic images with O-MAR (p ≤ 0.014). CNRs were higher for DL-MAR and images with O-MAR compared to images without O-MAR (p < 0.001). Higher CNRs were observed on DL-MAR images compared to conventional images and 130-keV monoenergetic images with O-MAR (p ≤ 0.010).ConclusionsDL-MAR showed higher image quality, diagnostic confidence, and superior metal artifact reduction compared to conventional CT images and 130-keV monoenergetic images with and without O-MAR in unilateral THA patients.Relevance statementDL-MAR resulted into improved image quality, stronger reduction of metal artifacts, and improved diagnostic confidence compared to conventional and virtual monoenergetic images with and without metal artifact reduction, bringing DL-based metal artifact reduction closer to clinical application.Key points• Metal artifacts introduced by total hip arthroplasty hamper radiologic assessment on CT.• A deep-learning algorithm (DL-MAR) was compared to dual-layer CT images with O-MAR.• DL-MAR showed best image quality and diagnostic confidence.• Highest contrast-to-noise ratios were observed on the DL-MAR images.Graphical

Inexpensive composite copper ore/red mud oxygen carrier: Industrial granulation via hydroforming and multiple-cycle evaluation in chemical looping combustion

The industrial granulation of low-cost and high-performance oxygen carriers (OCs) is critical to developing MW or larger-scale chemical looping combustion (CLC) demonstration units. In this work, an inexpensive composite OC, Cu10.9Red89.1@C (8.72 wt% copper ore and 71.28 wt% red mud bonded by 20 wt% cement), is innovatively prepared at a large scale by the hydroforming method. Its long-period performance is comprehensively evaluated in a thermogravimetric analyzer (TGA) and a batch fluidized bed, and TGA results show that Cu10.9Red89.1@C exhibits excellent stability and reactivity even in the case of deep reduction by H2 in TGA. In addition, an obvious activation of Cu10.9Red89.1@C is observed during the CH4 CLC test, indicated by the increased CH4 conversion and CO2 selectivity from 81.58 % and 74.65 % in the 1st cycle to 97.53 % and 98.24 % after activation, respectively. With Cu20Fe80@C (16 wt% copper ore and 64 wt% iron ore bonded by 20 wt% cement) as a reference, Cu10.9Red89.1@C presents a higher CH4 conversion, CO2 selectivity, and faster oxygen transfer rate due to its larger specific surface area and alkali metals in it. Finally, the cost analysis concludes that its production cost is only $0.95/kg for Cu10.9Red89.1@C, much cheaper than other large-scale prepared OCs. This work provides theoretical support for the applicability of the hydroforming method and the oxygen carrier Cu10.9Red89.1@C.

The cooperative effect of mechanical dewatering and thermal drying for activated sludge deep reduction

Activated sludge is one of the most difficult sludges to dewatering, reduction of moisture content (MC) was very important for the treatment and disposal. In this paper, multiple advanced detecting and analysis methods were used to explore the cooperative mechanism and technology of mechanical dewatering (MDW) and thermal drying (TD) of activated sludge, including moisture distribution, pore structure, fractal dimension and thermal conductivity. Relationship between energy consumption and treatment efficiency during the drying process with different combination modes and parameters of dewatering were also studied. The results indicated that MDW can remove most of the free water (FW) in sludge, and the proportion of binding water (BW) after constant voltage gradient mode (G-PEDW) treatment is 36.64% less than that of MDW, which can reduce energy consumption during the subsequent TD progress. The factors including MC, moisture distribution, pore structure, fractal dimension etc., of the dewatered sludge have important influences on the energy consumption and treatment efficiency of the subsequent TD. Taking into account energy consumption and treatment efficiency comprehensively, the optimal process combination of MDW—TD is that MC of sludge is first reduced to about 60% by MDW, then to about 30% by TD, the whole energy consumption and treatment time can be reduced by 75.94%, 18.75% compared with the single TD mode. This work is helpful to understand the cooperative relationship of the dewatering-drying.

Direct characterization of deep soil water depletion reveals hydraulic adjustment of apple trees to edaphic changes

Deep soil water is important for trees to survive droughts in arid and semiarid regions, but often irreversibly decrease as tree age due to limitations in soil infiltration rates and precipitation. How adaptable is the hydraulic system of trees undergoing the depletion of deep soil moisture reserves? To answer this question, we examined 12 traits characterizing the hydraulic structure of apple tree branches along a plantation age gradient (7 - 26 years) under uniform soil and climatic conditions. We found that almost all deep soil available water was consumed (reduced by up to 98%) as apple trees aged, as a result, the fraction of roots located in water deficit layers increased by up to 70%. Reduced water availability in deep soil significantly increased xylem embolism resistance (P50) by 8%, without altering xylem-specific hydraulic conductivity, vessel diameter, or vessel density. A distinct adaptive response to permanently reduced deep soil water availability seems to occur primarily by increasing tree hydraulic safety with no reductions in hydraulic efficiency, through which mechanism trees may acclimatize to deep soil water reduction.

Evaluating Real-World Benefits of Hearing Aids With Deep Neural Network-Based Noise Reduction: An Ecological Momentary Assessment Study.

Noise reduction technologies in hearing aids provide benefits under controlled conditions. However, differences in their real-life effectiveness are not established. We propose that a deep neural network (DNN)-based noise reduction system trained on naturalistic sound environments will provide different real-life benefits compared to traditional systems. Real-life listening experiences collected with Ecological Momentary Assessments (EMAs) of participants who used two premium models of hearing aid are compared. One hearing aid model (HA1) used traditional noise reduction; the other hearing aid model (HA2) used DNN-based noise reduction. Participants reported listening experiences several times a day while ambient SPL, SNR, and hearing aid volume adjustments were recorded. Forty experienced hearing aid users completed a total of 3,614 EMAs and recorded 6,812 hr of sound data across two 14-day wear periods. Linear mixed-effects analysis document that participants' assessments of ambient noisiness were positively associated with SPL and negatively associated with SNR but were not otherwise affected by hearing aid model. Likewise, mean satisfaction with the two models did not differ. However, individual satisfaction ratings for HA1 were dependent on ambient SNR, which was not the case for HA2. Hearing aids with DNN-based noise reduction resulted in consistent sound satisfaction regardless of the level of background noise compared to hearing aids implementing noise reduction based on traditional statistical models. While the two hearing aid models also differed on other parameters (e.g., shape), these differences are unlikely to explain the difference in how background noise impacts sound satisfaction with the aids. https://doi.org/10.23641/asha.25114526.

Hydrogen-based direct reduction of combusted iron powder: Deep pre-oxidation, reduction kinetics and microstructural analysis

Iron powder can be a sustainable alternative to fossil fuels in power supply due to its high energy density and abundance. Iron powder releases energy through exothermic oxidation (combustion), and stores back energy through its subsequent hydrogen-based reduction, establishing a circular loop for renewable energy supply. Hydrogen-based direct reduction is also gaining global momentum as possible future backbone technology for sustainable iron and steel production, with the aim to replace blast furnaces. Here, we investigate the microstructural formation mechanisms and reduction kinetics behind hydrogen-based direct reduction of combusted iron powder at moderate temperatures (400–500 °C) using thermogravimetry, ex-situ X-ray diffraction, scanning electron microscopy coupled with energy dispersive spectroscopy and electron backscatter diffraction, as well as in-situ high-energy X-ray diffraction. The influence of pre-oxidation treatment was studied by reducing both as-combusted iron powder (50 % magnetite and 50 % hematite) and the same powder after pre-oxidation (100 % hematite). A gas diffusion-limited reaction was obtained during the in-situ high-energy X-ray diffraction experiment, with successive hematite and magnetite reduction, and a strong increase in reduction kinetics with initial hematite content. Faster reduction kinetics were obtained during the thermogravimetry experiment, with simultaneous hematite and magnetite reduction. In this case, the reduction reaction was limited by a mix of phase boundary and nucleation and growth models, as analyzed by multi-step model fitting methods as well as by microstructural investigation. When not limited by gas diffusion, the pre-oxidation treatment showed almost no influence on the reduction time but a strong effect on the final microstructure of the reduced powder.

Oxygen-enriched vacancy spinel Mn-Co oxides by deep thermal reduction for enhanced antibiotics degradation efficiency

In this research work, flower-like MnCo2O4.5 was synthesized by introducing a short-chain surfactant as a surface-active agent. This structure was utilized as a precursor, and a gas atmosphere pyrolytic deep reduction strategy was employed to construct a sea urchin-like catalyst (DTR-Vo-MCO) with abundant oxygen vacancies. A series of characterizations were conducted to reveal the catalyst's microstructure and crystal phase composition. DTR-Vo-MCO achieved more than 90% degradation of OTC within 10 min, and the efficiency was 1.5 times that of Vp-MCO. The substantial presence of oxygen defects on the DTR-Vo-MCO surface expedited charge transfer processes, resulting in a remarkable enhancement of PMS activation efficiency. Electron paramagnetic resonance spectroscopy (EPR) and radical quenching experiments indicated that the contributions of radicals to oxytetracycline (OTC) degradation were in the order of 1O2 > SO4•– > O2•– > •OH. 1O2 and SO4•– played dominant roles in OTC degradation. The possible oxytetracycline degradation pathways were proposed based on LC-MS analysis. In summary, this work presents an efficient method for a highly oxygen-deficient catalyst, expanding the application of binary transition metal materials in environmental remediation.