This study systematically investigates the NH3 selective catalytic reduction (NH3-SCR) mechanism over Mnbased Zr-pillared montmorillonite (Mn/Zr-PILM) by combining experimental characterization with density functional theory (DFT) calculations. Catalytic performance evaluation shows that Mn/Zr-PILM exhibits excellent low-temperature denitrification activity, achieving a NOx conversion of 91.8 % at 473 K, which is 59.4 % higher than that of Mn-loaded sodium montmorillonite (Mn/NaMt). To elucidate the structural origins of this enhancement, detailed characterization was conducted, revealing that Zr pillaring increases surface roughness, generates a mesoporous architecture, and enriches surface acid sites, thereby facilitating reactant adsorption and activation. Subsequently, DFT calculations were employed to investigate the adsorption behaviors and reaction pathways of key species (NH3, NO, and O2) on MnO2, Mn-PILM, and MnZr-PILM surfaces. The results indicate that NH3 readily dissociates into NH2* intermediates under both aerobic and anaerobic conditions. Further energy barrier analysis demonstrates that the rate-determining steps vary significantly among the three surfaces, reflecting their distinct catalytic functions. Based on these findings, a structure-induced multi-site synergistic mechanism is proposed, in which NH3 activation, NO coupling, and final product formation proceed cooperatively on differentiated Mn sites, which provides mechanistic insight into low-temperature NH3-SCR and offers guidance for designing high-performance layered clay catalysts via pillar engineering.

Perovskite oxides show excellent catalytic performance for thermochemical CO2 splitting, with A/B-site cation substitution further enhancing redox activity. While traditional first-principles methods are computationally expensive, machine learning (ML) provides an efficient approach to perovskite optimization. In this paper, machine learning is employed to investigate and predict the performance of perovskite catalysts in CO2 decomposition reactions. Based on 227 perovskite compositions (A1A2)(B1B2)O3 curated from experimental literature, a total of five ML models are used, including Decision Tree, Bagging, Random Forest, Extra Trees, and Gradient Boosting Regression (GBR). The Random Forest model performed best. After hyperparameter optimization, the Random Forest model achieved an R2 of 0.910 and an MAE of 41.528 on an independent test set. SHAP analysis indicated that the thermal reduction temperature (T1) and the B1-site stoichiometric fraction (C_b1) are the most influential features governing the predicted CO yield. A higher CO yield is predicted when C_b1 ranges from 0.6 to 0.8, and T1 exceeds 1300 degrees C. This behavior can be attributed to the enhanced formation of oxygen vacancies at elevated temperatures and the optimized electronic structure induced by appropriate B-site stoichiometry.

The vortex feeder utilizes fluid rotation to generate a negative pressure, which entrains and rapidly disperses lightweight particles into molten slag. This process is crucial for converting acidic slag to basic slag. While labscale mechanisms are clear, scaling up faces challenges: attenuated momentum transfer and reduced heat transfer-diffusion efficiency.This investigation employs computational fluid dynamics modeling to conduct an amplification study on vortex feeders at different scales. The numerical simulation and mechanism analysis in this study only focus on simplified physical mixing, fluid flow and particle transport processes. It centers on the particle momentum transfer (e.g., particle concentration, centrifugal acceleration, axial velocity) and particle heat transfer and diffusion (e.g., particle temperature, basicity, diffusion coefficient), aiming to pinpoint the primary limiting factors in large-scale operation. Range analysis was used to evaluate factor-level impacts on the diffusion coefficient and determine the optimal combinations. The findings clarify that the key mechanism of fluid-solid coupling: the velocity difference between the fluid and the particles is the primary driving force for the drag force; equipment size and flow field controlled momentum transport, the fluid velocity affected the swirl intensity and basicity; larger equipment suffered from an inhomogeneous flow field and synergy between particle dispersion. Range analysis shows optimal combinations are A1B4 (uin= 1 m/s, Qp = 23.63 kg/s), A1B4 (uin= 1 m/s, Qp = 33.63 kg/s), A2B4 (uin= 2 m/s, Qp = 43.63 kg/s) and A2B4 (uin= 2 m/s, Qp = 53.63 kg/s). The dominant factor for diffusion coefficient shifts from particle concentration to inlet velocity. This study discusses scale-up challenges and potential solutions.

To realize the resource utilization of municipal sludge and improve the quality of gasification products, the chemical looping gasification (CLG) strategy was proposed to convert high-moisture sludge (HMS) into H2-rich syngas using a low-cost red mud-based perovskite oxygen carrier (POC), CaMn0.5Fe0.5O3-δ. H2-TPR analysis showed a high-temperature reduction peak at 651.2 °C and an H2 uptake of 3.9859 mmol/g, indicating strong reducibility and lattice oxygen mobility associated with Fe/Mn compared to conventional carriers. Under a fuel-to-oxygen-carrier ratio (C/O) of 1.75 at 800 °C in a dual-layer POC reactor, the system achieved 30.5 vol% H2 and a carbon conversion efficiency (ηC) of 90.8%, outperforming the corresponding fixed-bed configurations. Post reaction XRD and SEM-EDS analyses revealed deeper reduction of the oxygen carrier and enhanced fuel conversion, with the reduced phases such as Fe3O4 and MnFe2O4 being detected. XPS analysis further showed that the lower-layer POC contained up to 92.65% Mn2+ together with partial reduction of Fe3+ to Fe2+, supporting the active participation of lattice oxygen release and oxygen uncoupling during gas-phase reforming. These results suggest that the staged coupling strategy enhances sludge gasification and syngas upgrading through the combined effects of oxygen-carrier redox activity and improved gas-solid contact. Although the present work demonstrates the feasibility of using red mud-derived POCs for wet sewage sludge gasification, further studies are still needed on quantitative oxygen-decoupling analysis, direct tar/impurity measurement, long-term cyclic stability, and scale-up hydrodynamics.

Aiming to boost the catalytic performance and SO2 tolerance of CO oxidation catalysts employed in sintering flue gas environments, a set of Cu-Ce catalysts with varying Cu/Ce molar ratios were prepared via co-precipitation and impregnation methods. The catalytic performances for CO oxidation were systematically evaluated under both non-sulfated and sulfated atmospheres. The results show that the Cu0.15Ce0.85-CM catalyst achieves complete CO oxidation at 160 °C. Notably, after 1500 minutes of continuous operation in an atmosphere containing 100 ppm SO2, the catalyst still maintains approximately 90% CO conversion, demonstrating significantly superior sulfur resistance compared to the impregnation-derived catalysts. Structural and surface characterizations (XRD, SEM, XPS, and Raman) reveal that Cu species are effectively incorporated into the CeO2 lattice, forming a uniform solid-solution structure. The introduction of Cu into the CeO2 structure leads to the formation of abundant oxygen vacancies, promotes the migration of reactive oxygen on the surface, and effectively inhibits the formation and deposition of sulfates. DFT calculations further demonstrate that Cu doping significantly strengthens CO adsorption on the catalyst surface, transforming it from weak physisorption into strong chemisorption, with the adsorption energy shifting from -0.2 eV to -5.20 eV. In the presence of SO2, CO preferentially occupies the active sites during competitive adsorption, thereby markedly enhancing both the catalytic efficiency and sulfur tolerance. This investigation provides valuable experimental evidence and theoretical insights for developing highly efficient and sulfur-resistant catalysts for CO oxidation processes.

In the process of sewage sludge (SS) fluidized pyrolysis, clarifying the evolution items of particles fragmentation is crucial to the stability and economy of the disposal way. This study systematically investigated the effects of SS moisture content (M=19.31-40.94 wt%) and pyrolysis temperature (T=600-800 degrees C) on the deformation, fragmentation, and attrition characteristics of SS particles. Quantitative high-speed photography revealed that SS particles undergo a two-stage expansion-contraction process, with the maximum contour area expansion ratio increasing from 102.4% to 108.4% as M and T rise, and the anisotropy of the deformation (radial contraction exceeding axial contraction by 2.1-9.9%) driving the generation of circular cracks. Basket sampling experiments demonstrated that the fragmentation rate is synergistically regulated by M and T: at 600 degrees C, the 30 s fragmentation rate decreases significantly from 86.4 wt% to 27.1 wt% as M increases from similar to 20% to similar to 40%, whereas at 800 degrees C, all samples approach 100% fragmentation within 60 s, a behavior attributed to the M- and T-dependent evolution of biochar pore structure and agglomeration. Cold sieving tests showed that the intrinsic particle size distribution of SS biochar shifts toward finer sizes with the increasing T, and that the fluidized attrition rate constant of SS biochar is two orders of magnitude higher than that of typical coal ash (e.g., 10(-3) vs. 10(-5) s(-1) at U=4 m/s), indicating poor wear resistance. Consequently, conventional high fluidization velocity schemes are unsuitable for SS fluidized pyrolysis systems. The quantitative data and analytical framework established in this study provide key references for the scaled design and stable operation of SS fluidized pyrolysis processes.

Biomass plays a vital role in alleviating the energy crisis and environmental pressure through its efficient conversion and utilization. Pyrolysis technology has become one of the core pathways for biomass resource utilization. However, the complex interactions among the three core components of biomass (cellulose, hemicellulose, and lignin) during the pyrolysis process complicate the in-depth analysis of biomass pyrolysis mechanisms and the optimization of pyrolysis processes. In this study, TG-GC/MS tandem technology was employed to systematically investigate the individual and co-pyrolysis characteristics of cellulose, hemicellulose, and lignin. The interactions between components were quantitatively analyzed by comparing experimental values with calculated superimposed values, and the distribution pattern of pyrolysis products was clarified through qualitative detection via GC/MS. The key findings were as follows: (1) due to the inherent characteristics of their chemical structures, the three components exhibit obvious differences in pyrolysis properties and characteristic products; (2) specific interactions exist between components during the co-pyrolysis process. By revealing the co-pyrolysis interactions between components and the product regulation mechanism, this research not only deepens the understanding of the intrinsic nature of biomass pyrolysis but also provides key data support and theoretical references for the high-value application of biomass pyrolysis technology.

The production of municipal sludge is increasing continuously, creating an urgent need to avoid secondary pollution from traditional disposal methods while achieving resource utilization of sludge. This study prepared Mn-modified Fe based oxygen carriers (OCs) and conducted chemical looping gasification (CLG) experiments with sludge in a fixed-bed reactor to produce high quality syngas, thereby realizing the resource utilization of sludge. The effects of reaction conditions and OC characteristics on the gasification process were investigated, and the underlying reaction mechanisms of the CLG process were elucidated using various characterization techniques. Experimental results indicate that Mn doping induces a synergistic effect between Fe and Mn elements, enhancing the reactivity of the OC. Under optimal Mn/Fe ratio conditions, the formation of the FeMnO3 component significantly improves the oxygen uncoupling capacity of the OC. At a reaction temperature of 800 degrees C and an Fe/DS ratio of 3.5, the OC achieves optimal oxygen uncoupling performance, and the released gaseous molecular oxygen leads to a high carbon conversion rate of the sludge. After oxygen uncoupling, the resulting Fe2MnO4 component exhibits low reactivity with syngas components such as CO, CH4, and H2, effectively preventing over-oxidation of the syngas and ensuring its quality. The OC utilizes the reversible "FeMnO3 <-> Fe2MnO4 + MnO + O2" oxygen uncoupling reaction and regeneration cycle to achieve efficient conversion of sludge over multiple cycles while maintaining its structural stability. This study provides valuable insights for utilizing Fe-Mn based OCs to achieve efficient sludge conversion and produce high-quality syngas.

Numerical simulation of the particle motion and swirl flow characteristics in a vortex feeder was carried out using a Dense Discrete Phase Model (DDPM). The effects of the geometrical parameters of the feeder were analyzed in terms of particle trajectory, mass flow rate, particle concentration, particle velocity, etc. The results indicate that the particle trajectory, mass flow rate, and escape time increase with the increase of inlet contraction angle (alpha) and cylinder-to-cone ratio (Hcyl : Hcon), while pitch (P) is the opposite. The radial concentration of alpha = 16 degrees, Hcyl = 4Hcon, P = 300 mm increased by 3.12, 1.18, and 1.4 times, respectively, and the particle dispersion was enhanced. The variance values of particle mass flow rate were reduced by 33.3 %, 34 %, and 32.7 %, respectively, and the particle output stability was improved. With the increase of alpha and Hcyl : Hcon, the tangential velocity increases, and vortex negative pressure entrainment was enhanced. As P decreases, the axial velocity near the wall decreases significantly, and the time required for particles to completely escape was prolonged. The correlation equation about particle concentration was established using dimensional analysis. The results improve the sustainable utilization of hot copper slag and the efficiency of melt reduction and reduce the environmental pollution of copper slag stockpiling.

Blast furnace gas (BFG) is a major source of carbon emissions in the steel industry. To address this, a novel dual chemical-looping process is proposed for producing H2/CO syngas from BFG while achieving decarbonization. The process integrates an oxygen carrier cycle and a carbon carrier cycle, coupling chemical-looping technology with integrated CO2 capture and utilization (ICCU), and utilizes coke oven gas (COG) as the reducing agent to convert all carbon in BFG into CO. Oxygen carriers and carbon carriers are screened from various candidate metal oxides. Among them, NiO, Fe2O3 and CuO as oxygen carriers exhibit high melting points, high oxygen transport capacities, high equilibrium constants, and strong spontaneity in reactions with CO, while CaO as the carbon carrier shows high CO2 desorption and adsorption capacity under required reaction conditions. With these selected carriers, the carbon fixation and release processes are systematically investigated through thermodynamic analysis, focusing on the effects of temperature, pressure, and feed ratio. Under the combined action of each oxygen carrier and CaO, both carbon fixation rates and CO yields nearly reach 100%, indicating that the proposed process achieves highly efficient decarbonization of BFG. Building on these findings, energy, environmental, and economic analyses are conducted. Compared to conventional processes such as pressure swing adsorption separation and dry reforming of methane, the proposed process exhibits lower energy consumption. Furthermore, it is environmentally advantageous, achieving net-negative carbon emissions in most scenarios across the three oxygen carriers, and economically viable, with a positive net present value (NPV).

Rational design of perovskite oxygen carriers requires understanding of oxygen uncoupling kinetics, yet the relative contributions of surface reactions versus bulk diffusion remain poorly quantified. Here we establish a combined computational-experimental framework for CaFeO3-delta. DFT maps the oxygen uncoupling pathway-from vacancy diffusing to surface O2 uncoupling-with barriers of 0.50 eV (bulk) and 2.24 eV (surface). Non-isothermal thermogravimetry and an Avrami model solved via convex optimization enable robust extraction of intrinsic kinetic parameters. The macroscopic E alpha (1.14 eV) is substantially higher than the bulk diffusion barrier (0.50 eV) yet much lower than the ideal surface O2 uncoupling barrier (2.24 eV), indicating that surface uncoupling remains the rate-determining step but via defect-mediated pathways on real surfaces. This underscores the potential of surface engineering for performance optimization and provides a basis for tailoring CaFeO3-delta in chemical looping hydrogen production.

Achieving high-efficiency and low-carbon iron ore sintering has become increasingly critical under tightening energy and emission constraints in the iron and steel industry. This study develops a mechanism-informed, multiscale prediction and optimization framework that simultaneously forecasts specific fuel consumption and an overall sinter quality score, while providing process-feasible optimization strategies. First, a hybrid subjectiveobjective evaluation model is established to integrate key quality-related indicators, including FeO content, basicity, drum index, and yield, into a unified comprehensive quality score, enabling holistic characterization of sinter quality. A multi-scale ensemble learning architecture is then constructed. At the parameter scale, a multiobjective evolutionary ensemble model combines evolutionary feature selection with nonlinear modeling, achieving high predictive accuracy for fuel consumption (R2 = 0.9293) and the quality score (R2 = 0.9165). At the process-category scale, a mechanism-guided CatBoost model incorporates physicochemical knowledge through grouped features, producing interpretable and thermochemically consistent predictions. Performance-weighted fusion of the two scales further improves robustness, yielding final R2 values of 0.9501 for fuel con-sumption and 0.9273 for the quality score. Building on the predictive layer, a mechanism-constrained multi-objective optimization strategy is developed using high-quality historical operating conditions. The resulting Pareto solutions demonstrate that fuel consumption can be reduced to approximately 44.2 kg/t while increasing the quality score to about 87.5. For a sinter plant with an annual output of 3.7 million tons, this improvement corresponds to approximately 23,500 tons of fuel savings and 70,600 tons of CO2 reduction per year. Overall, the proposed framework integrates multi-scale data, mechanism knowledge, and optimization principles into a coherent and interpretable system, offering substantial potential for energy conservation and decarbonization in industrial sintering operations.

Chemical looping dry reforming of methane (CLDRM) presents a viable solution for the conversion of greenhouse gases. However, conventional external heating method suffers from various issues, such as sluggish thermal response and uneven heat and mass transfer. To enhance heating efficiency and reaction performance, this work proposes a novel Joule heating-driven CLDRM method. A LaFeO3/Ti monolithic oxygen carrier was developed for the Joule heating mode. By applying a direct current to it, a rapid temperature response was achieved. At 850 degrees C, the Joule heating mode achieved CH4 and CO2 conversions of up to 81.21% and 98.35%, respectively, significantly outperforming the conventional mode (64.74% and 83.25%). The CO selectivity reached 79.09%, which was 7.8% higher than that of the latter. Mechanistically, the additional electric effect can effectively promote the regeneration of lattice oxygen. Furthermore, the LaFeO3/Ti demonstrates superior electron transfer capability in the Joule heating mode. It also showed excellent anti-coking performance with a 41% reduction in carbon deposition and a lower graphitization degree compared to the conventional heating mode. The LaFeO3/Ti maintained excellent stability without noticeable reactivity decline over successive redox cycles. The strategy can also be powered by renewable electricity. Therefore, it holds significant potential for applications in the "Power-to-X" field.

Abstract Understanding lattice-oxygen participation and migration in LaCuO3 oxygen carriers is essential for optimizing chemical looping oxidative dehydrogenation of ethane (CL-ODHE). Herein, the lattice-oxygen migration behavior and ethylene selectivity mechanism of a LaCuO3 oxygen carrier were investigated by combining density functional theory (DFT) calculations with experimental characterization. DFT results show that oxygen-vacancy formation and bulk-to-surface lattice-oxygen migration are energetically feasible, enabling the continuous supply of reactive lattice oxygen during redox cycling. DOS and COHP analyses indicate that Cu participates in charge redistribution and regulates the metal–oxygen bonding environment, thereby facilitating lattice-oxygen activation. Adsorption and reaction-pathway calculations further suggest that La-related sites favor C2H6 adsorption and initial C–H activation, while the calculated free-energy profile indicates a thermodynamic preference for the C2H6-to-C2H4 oxidative dehydrogenation pathway. Experimentally, TGA confirms reversible oxygen release and replenishment, while XRD and XPS reveal coupled phase evolution, lattice-oxygen consumption, oxygen-vacancy formation, and reoxidation-induced oxygen replenishment. Cycling tests further demonstrate stable oxygen release/replenishment capacity, while redox-induced morphological evolution may contribute to improved gas–solid contact and redox kinetics. Overall, these results support a mechanism in which lattice oxygen in the LaCuO3 oxygen carrier is consumed, replenished, and redistributed during redox cycling, thereby promoting selective ethane-to-ethylene conversion.

Rapid, synchronous, and quantitative detection of interleukin-6 (IL-6) and procalcitonin (PCT) is of great significance for the early diagnosis of infectious diseases, yet it remains a formidable challenge for conventional lateral flow immunoassays (LFIAs) due to their inherent semi-quantitative nature and limited sensitivity. Herein, we tackle this challenge through the rational design of dumbbell-shaped Au/end-CeO2 heterojunctions, where the site-specific deposition of CeO2 on gold nanorods creates a Schottky interface that profoundly modulates carrier dynamics. This unique architecture effectively suppresses electron-hole recombination and enhances localized electromagnetic fields, yielding an exceptional photothermal conversion efficiency of 58.22%-39.3% increase over pure Au nanorods. Capitalizing on this advanced nanomaterial, we engineer a colorimetric/photothermal dual-mode LFIA platform. This platform not only achieves ultrasensitive individual detection of IL-6 and PCT with detection limits as low as 44 and 52 pg & sdot;mL- 1, respectively, but also realizes their efficient synchronous co-detection in a single, rapid chromatography step. By integrating visual colorimetric screening with quantitative photothermal readout, this work establishes a material foundation and technical pathway toward highly sensitive, quantitative LFIA systems, providing a referential approach for rapid screening in settings where access to large-scale instrumentation is limited.

This study proposes co-extraction of iron and manganese from steel slag using NH4Cl-induced chlorination, followed by the preparation of nano MnFe2O4 via the hydrothermal method. Under optimal conditions (1000 °C, 60 min, NH4Cl/steel slag 2:1 g/g, particle size 75–106 μm), the extraction rates of iron and manganese reached 85.23% and 74.39%, respectively. Characterization via XRD, SEM, XPS, TEM, FTIR, Raman, BET, and VSM confirmed that the hydrothermal product consists of nano MnFe2O4 particles with a spinel structure, exhibiting mesoporous characteristics, ferromagnetism, and soft magnetic behavior, with a specific surface area of 108.36 m2/g and a saturation magnetization of approximately 28 emu/g.

Carbon dioxide produced in aluminum electrolysis greatly affects molten electrolyte flow, inter-electrode resistance and alumina dissolution, while the sealed high-temperature cell environment hinders real-time bubble monitoring. Traditional CFD simulation of this process requires high-precision meshes and therefore entails huge computational costs. This study adopts physics-informed neural networks (PINN) to combine physical governing equations with CFD data through loss functions, realizing mechanism-data fusion to explore gas-liquid flow characteristics in electrolyte melts. Driven by randomly sampled data, the model accurately predicts full-field internal flow fields using merely 2% of the original data, achieving relative RMSE and MAE both below 10%. Its prediction efficiency is about 3000 times higher than CFD. Simulation results indicate that bubbles drive electrolyte circulation. Rising bubble flow rate raises bubble volume fraction and layer thickness with a slowing growth trend. Bubble-induced extra resistance shows a nonlinear correlation with flow rate, with a critical value of 164.54 L/min; resistance surges rapidly once exceeding this point. Elevated current density also sharply increases extra resistance. Longitudinal-slot anodes can effectively reduce bubble resistance, reaching an 85.33% reduction at 180 L/min. Larger alumina particles present lower mass transfer efficiency and poorer dissolution performance. Rational regulation of bubble flow optimizes particle dissolution. The proposed bubble dynamic optimization method provides practical guidance for energy saving and output improvement in aluminum electrolysis industry.

Lateral flow assays (LFAs) have evolved from simple qualitative tools into intelligent, multi-modal analytical platforms that integrate rationally engineered multi-metallic nanoparticles (MMNPs) with artificial intelligence (AI)-assisted data analysis to redefine the frontier of point-of-care diagnostics. This transformation has been driven by the advent of MMNPs, which couple plasmonic, catalytic, and magnetic properties within a single nano-system to achieve the tuneable synergistic enhancement of sensitivity, specificity, and dynamic range. The rational design of alloy, core-shell, hetero-structured, and hollow MMNP architectures allows simultaneous multi-signal readouts (e.g. colourimetric, fluorescence, chemiluminescence, surface-enhanced Raman scattering, photothermal, and electrochemical), thereby enabling intrinsic cross-verification and expanding diagnostic reliability. Parallel advances in AI, smartphone integration, and the Internet of Things connectivity have further elevated LFAs into digitally networked biosensors where embedded algorithms perform automated signal interpretation, error correction, and multi-mode data fusion, while cloud-linked infrastructures enable remote monitoring and epidemiological intelligence. These developments collectively reframe LFAs as integral components of data-driven, personalised, and preventive healthcare systems. Herein, we provide a unified framework that links design-on-demand MMNP synthesis, fully automated microfluidic LFA devices, AI-enhanced clinical decision support, and regulatory standardisation, and outline strategies for translating next-generation intelligent LFAs from laboratory innovation to global medical deployment.