Chemical Engineering Journal Advances最新文献

Engineered oligomeric-phase carbon nitride/WO3 Z-scheme photocatalysts for enhanced degradation of pharmaceutical contaminants in natural water under visible light 工程低聚相氮化碳/WO3 z -方案光催化剂在可见光下增强天然水中药物污染物的降解

IF 7.1 Q1 ENGINEERING, CHEMICAL

Chemical Engineering Journal Advances

Pub Date : 2025-12-07 DOI: 10.1016/j.ceja.2025.100989

Chee-Hun Han, Yongju Choi, Jong Kwon Choe

In this study, we have synthesized oligomeric-phase carbon nitride/WO₃ (o-WCN) Z-scheme photocatalysts to enhance reactive species (RS) generation and photocatalytic efficiency under visible-light irradiation compared to more traditional graphitic-phase (g-WCN) and polymer-phase (p-WCN) catalysts. X-ray diffraction (XRD), fourier transform infrared spectroscopy (FT-IR), scanning electron microscope (SEM), and transmission electron microscope (TEM) were used for structural analysis of photocatalyst confirming that o-WCN contained a higher proportion of terminal amine-rich oligomeric carbon nitride (o-CN) structures, leading to a more negative conduction band minimum (CBM) compared to other WCNs. Although charge transfer rate was fastest in g-WCN followed by p-WCN and o-WCN, o-WCN demonstrated superior overall photocatalytic performance (i.e., 4.82-fold) due to enhanced generation of the superoxide anion radicals (O₂^•−), driven by its highly negative CBM. In water sampled from a water treatment facility, o-WCN exhibited remarkable efficiency in removing pharmaceutical contaminants, with a 25-fold reduction in half-life (t_1/2) compared to WO₃ and notable advantages over other reported photocatalysts. These findings highlight the importance of carbon nitride (CN) phase tuning when developing Z-scheme carbon nitride/WO₃ (WCN) photocatalysts for environmental applications and confirm the practical applicability of o-WCN for efficient water treatment.

在这项研究中，我们合成了寡聚相氮化碳/WO3 (o-WCN) Z-scheme光催化剂，与传统的石墨相（g-WCN）和聚合物相（p-WCN）催化剂相比，在可见光照射下提高了反应物质（RS）的生成和光催化效率。利用x射线衍射（XRD）、傅里叶变换红外光谱（FT-IR）、扫描电镜（SEM）和透射电镜（TEM）对光催化剂进行结构分析，证实o-WCN含有更高比例的末端富胺低聚氮化碳（o-CN）结构，导致其负导带最小值（CBM）高于其他wcn。虽然g-WCN中的电荷转移速率最快，其次是p-WCN和o-WCN，但o-WCN由于其高度负的CBM驱动超氧阴离子自由基（O2•−）的生成增强，因此表现出更好的整体光催化性能（即4.82倍）。在从水处理设施取样的水中，o-WCN在去除药物污染物方面表现出显着的效率，与WO3相比，半衰期（t1/2）减少了25倍，并且比其他报道的光催化剂具有显着的优势。这些发现强调了氮化碳（CN）相调整在开发环境应用的z型氮化碳/WO3 （WCN）光催化剂时的重要性，并证实了o-WCN在高效水处理方面的实用性。

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引用次数: 0

Machine learning-assisted ultrafiltration for sustainable sub-20 nm nanoparticle removal in chip production 机器学习辅助超滤在芯片生产中可持续去除20纳米以下的纳米颗粒

IF 7.1 Q1 ENGINEERING, CHEMICAL

Chemical Engineering Journal Advances

Pub Date : 2025-12-06 DOI: 10.1016/j.ceja.2025.100988

Jie McAtee , Genhui Jing , Tianwei Liu , Wilson Poon , Da-Ren Chen , Chuen-Jinn Tsai , Sheng-Chieh Chen

The presence of particles in processing liquids, such as ultrapure water (UPW), isopropyl alcohol (IPA), and sulfuric acid (H₂SO₄) for wafer cleaning, up to 800 times in advanced semiconductor chip production, can cause defects and yield loss. Size-exclusion nanofiltration (NF) membranes with extremely small pores of 1–10 nm are widely used to remove sub-20 nm NPs during chip production, ensuring the cleanliness of these liquids, as commercial liquid particle detectors cannot quantitatively measure these tiny NPs. However, NF membrane’s small pores lead to high energy consumption. To achieve more sustainable chip production, the research team reported that ultrafiltration (UF) membranes with 20–100 nm pores can effectively capture sub-20 nm nanoparticles (NPs) by adsorption when favorable filtration conditions are met. However, due to the complex mechanisms underlying UF against sub-20 nm NPs, sustainable UF systems can be identified only with the aid of machine learning (ML). Therefore, a homemade electrospray aerosolization and particle classification system was developed to generate additional retention data for UF against 3–20 nm NPs in water and in >96 % concentrated H₂SO₄. A ML model was developed using high-quality data and theoretical retentions derived from the xDLVO theory to identify optimal filtration conditions for achieving sustainable UF. Results showed that 3 and 5 nm NPs could be retained at 99.9 % efficiency by a ∼50 nm-rated UF membrane in UPW, proving energy-efficient, high-NP retention by UF is feasible. In 96 % H₂SO_4, however, low experimental retention of ∼5 % for 20 nm SiO₂ NPs by both 70 nm and 100 nm rated PTFE membranes was observed. The current successful ML model for UPW will be extended to H₂SO₄ when more retention data is available for smaller NPs (e.g., 5 and 10 nm) in H₂SO₄.

在先进的半导体芯片生产中，用于晶圆清洗的超纯水（UPW）、异丙醇（IPA）和硫酸（H2SO4）等加工液体中颗粒的存在高达800次，可能导致缺陷和产量损失。由于商用液体粒子探测器无法定量测量这些微小的纳米粒子，因此在芯片生产过程中，具有1 - 10nm极小孔的纳米滤膜被广泛用于去除20 nm以下的纳米粒子，以确保这些液体的清洁度。但由于纳滤膜孔小，能耗高。为了实现更可持续的芯片生产，研究小组报告说，具有20-100 nm孔的超滤（UF）膜在满足有利过滤条件的情况下，可以通过吸附有效捕获20 nm以下的纳米颗粒（NPs）。然而，由于UF对抗亚-20 nm NPs的复杂机制，可持续UF系统只能借助机器学习（ML）来识别。因此，开发了自制的电喷雾雾化和颗粒分类系统，以获得UF在水中和96%浓度H2SO4中对3-20 nm NPs的保留数据。利用xDLVO理论衍生的高质量数据和理论保留，开发了ML模型，以确定实现可持续UF的最佳过滤条件。结果表明，在UPW中，约50 nm的UF膜可以以99.9%的效率保留3 nm和5 nm的np，证明UF高效、高np保留是可行的。然而，在96% H2SO4中，70 nm和100 nm额定PTFE膜对20 nm SiO2 NPs的实验保留率都很低~ 5%。目前成功的UPW ML模型将扩展到H2SO4，当更多的保留数据可用于H2SO4中较小的NPs（例如5和10 nm）时。

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引用次数: 0

Morphological evolution and recovery of high-purity ZnO from zinc roasting dust via pyrometallurgical reduction 从锌焙烧粉尘中提取高纯氧化锌的形态演变及火法还原

IF 7.1 Q1 ENGINEERING, CHEMICAL

Chemical Engineering Journal Advances

Pub Date : 2025-12-05 DOI: 10.1016/j.ceja.2025.100974

Seon Ho Sim , Ki Min Roh , Tae Jun Park

Zinc oxide (ZnO) is a versatile functional material with widespread applications in ceramics, coatings, catalysts, and optoelectronics, where high-purity ZnO is required. In this study, zinc roaster fume dust, a major metallurgical byproduct, was utilized as an alternative feedstock to produce ZnO through a dry reduction–reoxidation process. The reduction conditions, such as the reduction temperature (1100–1400 °C), reductant content (10–20 wt% coke with respect to the feedstock), and reaction time (30–90 min), were systematically explored to maximize ZnO recovery. The best-performing condition was 1150 °C for 60 min with 15 wt% coke, yielding ZnO of ∼99.5 wt% purity, as verified by inductively coupled plasma optical emission spectrometry and X-ray diffraction. Morphological characterization of the recovered ZnO powder revealed a distinct temperature-dependent phase transition; at ≤1150 °C, hexagonal prismatic crystals dominated due to anisotropic growth along [0001], whereas at higher temperatures spherical particles prevailed, consistent with supersaturation-driven nucleation of Zn vapor followed by rapid reoxidation. The particle-size distribution narrowed with increasing temperature, while agglomeration above ∼1300 °C broadened the mean size, indicating an upper thermal limit for uniform powders. these results demonstrate that dry reduction–reoxidation offers an effective and environmentally benign pathway to recover industrial-grade, high-purity ZnO from zinc fume dust while enabling morphology control via thermally tunable nucleation–growth regimes.

氧化锌是一种用途广泛的功能材料，在陶瓷、涂料、催化剂、光电等领域有着广泛的应用。在本研究中，锌焙烧烟气粉尘是一种主要的冶金副产物，作为替代原料，通过干燥还原-再氧化工艺生产ZnO。系统地探讨了还原温度（1100 ~ 1400℃）、还原剂含量（焦炭与原料的比例为10 ~ 20 wt%）和反应时间（30 ~ 90 min）等还原条件，以最大限度地提高ZnO的回收率。通过电感耦合等离子体发射光谱和x射线衍射验证，最佳条件为1150°C， 60 min，焦炭质量为15 wt%， ZnO纯度为~ 99.5 wt%。形貌表征表明ZnO粉体具有明显的温度依赖性相变；在≤1150°C时，由于沿[0001]的各向异性生长，六方棱柱状晶体占主导地位，而在更高温度下，球形颗粒占主导地位，与过饱和驱动的Zn蒸汽成核以及随后的快速再氧化相一致。随着温度的升高，颗粒尺寸分布变窄，而在~ 1300°C以上的团聚使平均尺寸变宽，表明均匀粉末的热上限。这些结果表明，干还原-再氧化为从锌烟尘中回收工业级高纯度ZnO提供了一种有效且环保的途径，同时通过热可调的成核生长机制实现形貌控制。

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引用次数: 0

Modeling of the Ni(II) removal from aqueous solutions by ion exchange resin: Comparison of various machine learning approaches 离子交换树脂去除水溶液中Ni（II）的建模：各种机器学习方法的比较

IF 7.1 Q1 ENGINEERING, CHEMICAL

Chemical Engineering Journal Advances

Pub Date : 2025-12-03 DOI: 10.1016/j.ceja.2025.100987

Shahrzad Maleki , Maryam Mousavifard , Ayoub Karimi-Jashni

This study aims to investigate the removal of Ni(II) ions from aqueous solutions using an ion exchange resin, focusing on various machine learning approaches to predict the process. The research highlights the efficiency of Amberlite IR120 Na as a strong acidic cation exchange resin, examining its adsorption capacity under varying conditions, including resin dose, initial Ni(II) concentration, solution pH, temperature, and contact time. The adsorption kinetics were accurately described by the pseudo-second-order kinetic model. Additionally, both surface adsorption and intra-particle diffusion played roles in the steps of the adsorption rate. The adsorption isotherm data fitted well with the Langmuir model, indicating a maximum adsorption capacity of 134.8 mg/g. Moreover, machine learning techniques were utilized to predict the resin’s performance, evaluating five diverse models: Support Vector Regression (SVR), Random Forest, Decision Tree, Multi-Layer Perceptron (MLP), and Polynomial Regression. The results showed that the SVR model performed better than the others, with a training R² of 0.990 and testing R² of 0.973, along with the lowest mean absolute error and mean squared error. These findings demonstrate the effectiveness of machine learning in accurately modeling the complex relationships within the adsorption process, thus offering valuable insights for optimizing heavy metal removal from wastewater.

本研究旨在研究使用离子交换树脂从水溶液中去除Ni（II）离子，重点关注各种机器学习方法来预测这一过程。本研究强调了Amberlite IR120 Na作为强酸性阳离子交换树脂的效率，考察了其在不同条件下的吸附能力，包括树脂剂量、初始Ni（II）浓度、溶液pH、温度和接触时间。拟二级动力学模型准确地描述了吸附动力学。此外，表面吸附和颗粒内扩散对吸附速率的变化都有影响。吸附等温线数据与Langmuir模型拟合良好，最大吸附量为134.8 mg/g。此外，利用机器学习技术来预测树脂的性能，评估五种不同的模型：支持向量回归（SVR）、随机森林、决策树、多层感知器（MLP）和多项式回归。结果表明，该SVR模型的训练R²为0.990，检验R²为0.973，具有最低的平均绝对误差和均方误差。这些发现证明了机器学习在准确模拟吸附过程中的复杂关系方面的有效性，从而为优化废水中重金属的去除提供了有价值的见解。

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引用次数: 0

Mitigating ammonia emissions for a sustainable livestock farming by advances in membrane technology and modelling tools 通过膜技术和建模工具的进步，减少可持续畜牧业的氨排放

IF 7.1 Q1 ENGINEERING, CHEMICAL

Chemical Engineering Journal Advances

Pub Date : 2025-12-03 DOI: 10.1016/j.ceja.2025.100983

Paula Calvo-de Diego, María Cruz García-González, Mercedes Sánchez-Báscones, Beatriz Molinuevo-Salces

With the agricultural sector contributing to 93 % of total ammonia emissions, the development of mitigation technologies is imperative for livestock farming. This study compared the nitrogen recovery performance of two novel gas-permeable membrane configurations: System 1 (S1), with external gas flow, and System 2 (S2), with internal gas flow. The influence of initial N concentration and exposure time on N recovery rates was investigated. The results established the markedly superior performance of S2, which achieved a N recovery rate of 237 g m⁻² d⁻¹, outperforming the 154 g m⁻² d⁻¹ rate of S1. This peak rate represents a 7-fold increase when compared to previous results. Mathematical models derived from regression analysis were developed for S1 and S2 and indicating that the theoretical maximum performance of S2 was 1.8-fold higher than that of S1 (Maximum N recovery rates of 155.65 and 281.2 g N m⁻² d⁻¹ for S1 and S2, respectively). The enhanced efficiency of S2 is ascribed to its internal flow configuration, which promotes a superior nitrogen mass transfer rate across the membrane. This design demonstrated greater robustness in managing high nitrogen loads, positioning it as a highly promising technology for practical implementation in livestock operations.

由于农业部门占氨排放总量的93%，因此开发减缓技术对畜牧业至关重要。本研究比较了系统1 （S1）和系统2 （S2）两种新型透膜配置的氮气回收性能，系统1 （S1）为外部气流，系统2 （S2）为内部气流。研究了初始氮浓度和暴露时间对氮素回收率的影响。结果表明，S2的表现明显优于S1，它的N的回收率为237 g m⁻²d⁻¹，优于S1的154 g m⁻²d⁻¹。与之前的结果相比，这个峰值率增加了7倍。通过对S1和S2进行回归分析得出的数学模型表明，S2的理论最大性能比S1高1.8倍（S1和S2的最大N回收率分别为155.65和281.2 g N m⁻²d⁻¹）。S2的效率提高是由于其内部流动结构，促进了优异的氮在膜上的传质速率。该设计在管理高氮负荷方面表现出更强的稳健性，使其成为一项非常有前途的技术，可在畜牧业中实际实施。

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引用次数: 0

Hybrid capacitive deionization using MgAl-LDHs-coated graphite felt electrodes for phosphate removal 用mal - ldhs包覆石墨毡电极进行杂化电容去离子去除磷酸盐

IF 7.1 Q1 ENGINEERING, CHEMICAL

Chemical Engineering Journal Advances

Pub Date : 2025-12-03 DOI: 10.1016/j.ceja.2025.100985

Tanzila Sharker , Xinxin Xiao , Jens Muff

Capacitive deionization (CDI) is a promising technology for selective phosphate removal, though its performance is often constrained by electrode materials. This study developed composite electrodes by integrating MgAl-layered double hydroxides (LDHs) onto conductive oxidized graphite felt (OGF) to improve charge storage and ion selectivity. Two types were tested: CLGF prepared with commercial nitrate-intercalated LDHs, and LLGF synthesized with chloride-intercalated LDHs. Phosphate removal performance was evaluated in synthetic mixed ion solutions as well as in real lake sediment dewatering reject water. The novelty of this work lies in both the electrode fabrication method and the integration of MgAl LDHs with oxidized graphite felt. This combination provides dual functionality with enhanced phosphate selectivity and improved charge storage for practical CDI based phosphorus recovery. Kinetic modeling identified chemisorption as the main mechanism, with both LDH-coated electrodes outperforming bare OGF in adsorption and capacitance. LLGF and CLGF showed maximum phosphate removal capacities of ∼60 mg/g, while pristine GF and OGF showed negligible ion adsorption capacity. CDI based steady state adsorption capacities stabilized at ∼10 mg/g over 5 cycles during phosphate removal from 1.0 mM mixed anions solution. Phosphate-to-sulphate selectivity coefficients were highly time dependent, reaching 2.0 (CLGF) and 4.3 (LLGF) under +1.0 V applied voltage. CLGF removed over 80 % of phosphate in reject water at both +1.0 V and open circuit (OC), while LLGF achieved moderate phosphate removal of about 57 % with better selectivity. Energy consumption for the CDI system ranged from 0.03 – 0.25 kWh/m³, within reported CDI benchmarks. Statistical analysis revealed that removal performance was significantly influenced by electrode-time and electrode-voltage interactions rather than individual factors. Overall, this study demonstrates MgAl-LDHs-OGF electrodes as a feasible electrode for lake water P removal with high selectivity towards phosphate over other competing anions.

电容去离子（CDI）是一种很有前途的选择性除磷技术，但其性能往往受到电极材料的限制。本研究通过将mgal层状双氢氧化物（LDHs）集成到导电氧化石墨毡（OGF）上，开发了复合电极，以提高电荷存储和离子选择性。测试了两种类型的LLGF：用商业硝酸盐插层LDHs制备的CLGF和用氯化物插层LDHs合成的LLGF。对合成混合离子溶液和真实湖泊沉积物脱水废水的除磷性能进行了评价。这项工作的新颖之处在于电极制作方法和MgAl ldh与氧化石墨毡的集成。这种组合提供了双重功能，具有增强的磷酸盐选择性和改进的电荷存储，用于实际的基于CDI的磷回收。动力学模型确定了化学吸附是主要机制，ldh涂层电极在吸附和电容方面都优于裸OGF。LLGF和CLGF的最大磷酸盐去除能力为~ 60 mg/g，而原始GF和OGF的离子吸附能力可以忽略不计。在从1.0 mM混合阴离子溶液中去除磷酸盐的过程中，基于CDI的稳态吸附能力在5个循环中稳定在~ 10 mg/g。在+1.0 V电压下，磷酸盐对硫酸盐的选择性系数分别达到2.0 （CLGF）和4.3 （LLGF）。在+1.0 V和开路（OC）条件下，CLGF对废水中磷酸盐的去除率超过80%，而LLGF对磷酸盐的去除率约为57%，选择性更好。CDI系统的能耗范围为0.03 - 0.25 kWh/m3，在报告的CDI基准范围内。统计分析表明，电极-时间和电极-电压相互作用显著影响去除性能，而不是单个因素。总的来说，本研究表明MgAl-LDHs-OGF电极是一种可行的湖边除磷电极，对磷酸盐的选择性高于其他竞争阴离子。

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引用次数: 0

Environment-, process-, and energy-specific multi-objective optimization of the industrial large-scale natural gas dehydration process 工业大规模天然气脱水过程的环境、工艺和能源多目标优化

IF 7.1 Q1 ENGINEERING, CHEMICAL

Chemical Engineering Journal Advances

Pub Date : 2025-12-03 DOI: 10.1016/j.ceja.2025.100980

Swaprabha P. Patel, Mustafa Al Salmi, Ashish M. Gujarathi

The industrial natural gas dehydration process using triethylene glycol (TEG) is characterized by fundamental conflicts between environmental goals and energy consumption. The study employs a multi-objective optimization (MOO) framework to systematically map these trade-offs by simultaneously optimizing six conflicting objectives: to minimize energy consumption, water content in dry gas, BTEX emissions, global warming potential (GWP), and TEG makeup, while maximizing hydrocarbon recovery. The optimization study showed that achieving a drier gas (lower water content) invariably demands higher energy. While lower regeneration temperatures reduce energy use and GWP, they simultaneously increase BTEX emissions and compromise dehydration efficiency. Pareto ranking analysis using the TOPSIS method was employed to identify optimal solutions, confirming that while energy and water content are dominant drivers, explicitly prioritizing environmental objectives significantly shifts the optimal conditions toward lower-emission operations. This work provides insights for designing sustainable and efficient natural gas dehydration processes that navigate the inherent conflicts between environmental responsibility and operational performance.

采用三甘醇（TEG）的工业天然气脱水工艺具有环境目标与能源消耗之间的根本冲突。该研究采用了多目标优化（MOO）框架，通过同时优化六个相互冲突的目标来系统地绘制这些权衡图：最小化能源消耗、干气含水量、BTEX排放、全球变暖潜能值（GWP）和TEG组成，同时最大化油气采收率。优化研究表明，获得更干燥的气体（含水量更低）总是需要更高的能量。虽然较低的再生温度降低了能源消耗和全球变暖潜能值，但同时也增加了BTEX的排放，降低了脱水效率。使用TOPSIS方法的帕累托排名分析确定了最优解决方案，确认了虽然能源和水含量是主要驱动因素，但明确优先考虑环境目标显着将最优条件转向低排放操作。这项工作为设计可持续和高效的天然气脱水工艺提供了见解，该工艺可以解决环境责任和运营绩效之间的内在冲突。

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引用次数: 0

Novel axial-tangential corrugated inner cylinders in Taylor-Couette reactors: CFD analysis of Taylor vortex modulation, turbulence, and mixing efficiency Taylor- couette反应器中新型轴向-切向波纹内柱：Taylor涡调制、湍流和混合效率的CFD分析

IF 7.1 Q1 ENGINEERING, CHEMICAL

Chemical Engineering Journal Advances

Pub Date : 2025-12-03 DOI: 10.1016/j.ceja.2025.100976

Bin Li , Guang Li , Xiaogang Yang , Shanshan Long , Yanqing Guo

Three novel corrugated cylinders with three-dimensional (3D) roughness elements, featuring periodic axial-tangential wave structures, were developed for Taylor-Couette (TC) reactors to investigate their regulation on Taylor vortex dynamics. CFD modelling coupled with the Reynolds stress model was employed to analyze hydrodynamics, turbulence characteristics and mixing process in TC reactors equipped with these novel cylinders, alongside a classical smooth cylinder for baseline comparison. Simulation results show the corrugated surfaces significantly affect Taylor vortex dynamic, NZ20 exerts the strongest influence on velocity fields, while NZ80 shows the weakest effect, with its flow field closely resembling that of a smooth cylinder. All novel cylinders generate higher flow strain rates compared to the smooth cylinder (e.g., NZ20 achieves a ∼40 % increase), while improving strain rate uniformity (reducing the coefficient of variance by 10–15 %). NZ20 further outperforms others by exhibiting the highest turbulent kinetic energy dissipation rate (∼60% higher than the smooth cylinder), effectively reducing micro-mixing time. At Reynolds number exceeding 1249, NZ20 and NZ40 display steeper concentration-response curves and achieve 10–25% shorter macro-mixing times than NZ80 and the smooth cylinder. This discrepancy arises from the enhanced axial flow induced by their larger corrugation wavelengths; in contrast, NZ80 and the smooth cylinder show comparable mixing times due to NZ80’s high roughness density mitigating such effects. To facilitate practical applications, an empirical correlation for predicting macro-mixing time in TC reactors with various rotating cylinders has been developed. These findings provide critical insights for optimizing TC reactor performance through purposeful surface modification strategies.

针对Taylor- couette （TC）反应器，设计了三种具有轴向-切向周期性波结构的三维粗糙度单元，研究了其对Taylor涡动力学的调节。采用CFD模型与雷诺应力模型相结合的方法，对装有这些新型圆柱体的TC反应器的流体力学、湍流特性和混合过程进行了分析，并与经典光滑圆柱体进行了基线比较。仿真结果表明，波纹表面对泰勒涡动态影响显著，NZ20对速度场的影响最大，而NZ80对速度场的影响最弱，其流场与光滑圆柱体的流场相似。与光滑圆柱体相比，所有新型圆柱体产生更高的流动应变率（例如，NZ20实现了~ 40%的增长），同时改善了应变率均匀性（将方差系数降低了10 - 15%）。NZ20表现出最高的湍流动能耗散率（比光滑圆柱体高约60%），有效地缩短了微混合时间，从而进一步优于其他材料。在雷诺数超过1249时，NZ20和NZ40的浓度-响应曲线比NZ80和光滑圆柱体更陡峭，宏观混合时间缩短10-25%。这种差异是由于波纹波长较大引起的轴向流增强所致；相比之下，由于NZ80的高粗糙度密度减轻了这种影响，NZ80和光滑的圆柱体显示出相当的混合时间。为了便于实际应用，本文提出了一种预测具有不同旋转气缸的TC反应器宏观混合时间的经验关联方法。这些发现为通过有目的的表面改性策略优化TC反应器性能提供了重要的见解。

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引用次数: 0

Synergistic effects of a zirconium doped stannate-carbon nitride nanocomposite on design of electrochemical sensor for sensitive detection of the antiandrogen drug flutamide 锆掺杂锡-氮化碳纳米复合材料对抗雄激素药物氟他胺灵敏检测电化学传感器设计的协同效应

IF 7.1 Q1 ENGINEERING, CHEMICAL

Chemical Engineering Journal Advances

Pub Date : 2025-12-02 DOI: 10.1016/j.ceja.2025.100981

Chandran Bhuvaneswari , Ponnaiah Sathish Kumar , Arumugam Elangovan , Ganesh Arivazhagan , Young-Ki Kim

Flutamide (FLT), an anti-androgen drug widely used in anti-cancer therapy, can cause serious hepatotoxic side effects at high doses. However, released from industrial and hospital effluents, FLT is not fully removed by conventional water treatments, resulting in contamination of water sources that threatens aquatic ecosystems and thus human health. Therefore, there has been a pressing need for sensitive and reliable FLT monitoring, but existing techniques are often costly, labor-intensive, and complex, limiting their practicality. To address this challenge, herein we report the novel design of electrochemical sensor based on a new class of electrode modifier, ZS-CN nanocomposite, synthesized by integrating zirconium (Zr)-doped SnO₂ nanoparticles (ZS) onto 2D graphitic carbon nitride nanosheets (CN). Especially, we introduce Zr as a new dopant in SnO₂ and reveal that the correlative coupling of ZS with porous CN allows for their intimate interfacial contact that promotes efficient electron transfer and electrocatalytic activity, while keeping the functional groups of both components active. As a result, the electrochemical sensor designed by the ZS-CN nanocomposite coated glassy carbon electrode demonstrates an outstanding level of selectivity and sensitivity (1.6553 µA µM⁻¹ cm⁻²) for FLT with a low detection limit (0.009 µM) and a wide detection linear range (0.04–1166 µM), alongside robust reproducibility, stability, and applicability in real-world samples (e.g., human urine and river water). Furthermore, its cyclic voltammetric responses provide mechanistic insights into the correlation between multi-electron redox process and FLT transformation pathways, informing future interfacial engineering strategies for designing versatile electrochemical systems for pharmaceutical pollutant monitoring.

氟他胺（FLT）是一种广泛用于抗癌治疗的抗雄激素药物，大剂量时可引起严重的肝毒性副作用。然而，从工业和医院的废水中释放出来的浮油不能通过常规水处理完全去除，导致水源受到污染，威胁到水生生态系统，从而威胁到人类健康。因此，迫切需要对FLT进行敏感和可靠的监测，但是现有的技术往往成本高、劳动密集且复杂，限制了它们的实用性。为了解决这一挑战，本文报道了一种基于新型电极改性剂ZS-CN纳米复合材料的电化学传感器的新设计，该复合材料是通过将锆（Zr）掺杂的SnO2纳米颗粒（ZS）集成到二维石墨氮化碳纳米片（CN）上合成的。特别是，我们在SnO2中引入了Zr作为新的掺杂剂，并发现ZS与多孔CN的相关耦合允许它们之间的密切界面接触，促进有效的电子转移和电催化活性，同时保持两组分的官能团的活性。因此，由ZS-CN纳米复合涂层玻碳电极设计的电化学传感器对FLT具有出色的选择性和灵敏度（1.6553 μ a μ M−1 cm−2），具有低检测限（0.009 μ M）和宽检测线性范围（0.04-1166 μ M），同时具有强大的再现性，稳定性和在实际样品（例如人类尿液和河水）中的适用性。此外，它的循环伏安响应为多电子氧化还原过程和FLT转化途径之间的相关性提供了机制见解，为未来设计用于药物污染物监测的多功能电化学系统提供了界面工程策略。

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引用次数: 0

Leveraging molecular simulations and machine learning to assess CO2, O2, and N2 adsorption and separation performances of diverse MOF databases 利用分子模拟和机器学习来评估不同MOF数据库对CO2、O2和N2的吸附和分离性能

IF 7.1 Q1 ENGINEERING, CHEMICAL

Chemical Engineering Journal Advances

Pub Date : 2025-12-02 DOI: 10.1016/j.ceja.2025.100984

Hasan Can Gulbalkan, Seda Keskin

We integrated molecular simulations and machine learning (ML) to comprehensively explore the gas adsorption and separation performances of both synthesized and hypothetical metal-organic frameworks (MOFs) available in five different MOF databases. Following the generation of CO₂, O₂, and N₂ adsorption data for synthesized MOFs at varying pressures through grand canonical Monte Carlo (GCMC) simulations, we developed ML models that can swiftly and accurately predict the gas adsorption properties of any MOF based on its structural, chemical, and energetic characteristics. These ML models were then transferred to four distinct hypothetical MOF databases consisting of nearly 130,000 structures to assess their CO₂, O₂, and N₂ adsorption properties in addition to CO₂/N₂ and O₂/N₂ separation performances as a very efficient alternative to computationally time and resource demanding molecular simulations. We identified the top-performing materials from each database to uncover their structural, chemical, and topological properties leading to high selectivities and concluded that synthesized MOFs with narrow pores, lanthanide metals, and linkers featuring oxalate, pyridine dicarboxylate, and fumarate offer the highest CO₂/N₂ selectivities. Our work presents the most extensive dataset produced for CO₂, O₂, and N₂ gas adsorption in MOFs, composed of ∼3.9 million data points for materials’ structural, chemical, and energetic features, gas adsorption properties, and selectivities computed at different pressures to accelerate the materials design and discovery for CO₂, O₂, and N₂ adsorption and separation.

我们将分子模拟和机器学习（ML）相结合，全面探索了5个不同金属有机框架（MOF）数据库中合成的和假设的金属有机框架（MOF）的气体吸附和分离性能。通过大规范蒙特卡罗（GCMC）模拟生成不同压力下合成MOF的CO2， O2和N2吸附数据，我们开发了ML模型，可以根据其结构，化学和能量特征快速准确地预测任何MOF的气体吸附特性。然后将这些ML模型转移到四个不同的假设MOF数据库中，该数据库包含近13万个结构，以评估它们的CO2， O2和N2吸附性能以及CO2/N2和O2/N2分离性能，作为一种非常有效的替代计算时间和资源要求高的分子模拟。我们从每个数据库中确定了性能最好的材料，揭示了它们的结构、化学和拓扑特性，从而获得了高选择性，并得出结论：具有窄孔、镧系金属和以草酸盐、二羧酸吡啶和富马酸盐为连接物的合成mof具有最高的CO2/N2选择性。我们的工作展示了mof中CO2， O2和N2气体吸附的最广泛的数据集，由~ 390万个数据点组成，用于材料的结构，化学和能量特征，气体吸附特性和在不同压力下计算的选择性，以加速CO2， O2和N2吸附和分离的材料设计和发现。

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引用次数: 0