Minerals Engineering最新文献

{"title":"Hydrothermal synthesis of aragonite from acid mine drainage (AMD) of the Witwatersrand basin in Gauteng, South Africa","authors":"R.D.S. Khumalo,&nbsp;H.G. Brink,&nbsp;E.M.N. Chirwa","doi":"10.1016/j.mineng.2025.109745","DOIUrl":"10.1016/j.mineng.2025.109745","url":null,"abstract":"<div><div>Hydrothermal urea hydrolysis has been extensively used for homogenous precipitation processes mainly because the resulting products are generally of high crystallinity, uniform particle size and shape, as well as not generating waste brine. In this study, acid mine drainage water samples from the three Witwatersrand goldfields basins (Eastern, Central and Western, Gauteng, South Africa) were subjected to hydrothermal urea hydrolysis to investigate if any mineral(s) could be recovered. In these experiments, three urea concentrations (3.3, 4.0 and 10.0 [urea]/[total metal] ratio) were used while the reaction time (3 h) and temperature (80 °C) were kept constant. The resulting materials were characterised to reveal their chemical compositions, crystalline phases and morphologies. The bulk properties as determined using the Fourier Transform Infrared Spectroscopy, Thermogravimetric Analysis and X-ray Diffraction Spectroscopy showed that the obtained products were predominantly calcium carbonate, the aragonite polymorph, for all three basins. The particles obtained from the polluted mine water samples displayed different morphologies, while mostly were characterised by needle and/or rod-like morphologies with varying lengths and diameters in nanometre range (average aspect ratios ranged from 3.1 to 13.2) as shown by the Scanning Electron Microscope images. Other morphologies, cauliflower-, bouquet- and urchin-like particles were obtained without the use of organic additives. The method was demonstrated to be effective in the removal of calcium (more than 98 % on average) and some evidence of heavy metals, manganese in particular, also being removed from the polluted water. The findings highlighted a possibility of a single method that can be adopted for the remediation of acid mine drainage of the three basins to recover aragonite calcium carbonate, an industrially valuable mineral.</div></div>","PeriodicalId":18594,"journal":{"name":"Minerals Engineering","volume":"234 ","pages":"Article 109745"},"PeriodicalIF":5.0,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144911932","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Coffee shell as a green reductant for iron recovery from pyrite cinder: synergy of biomass-driven reduction and acid leaching","authors":"Xiaojiao Li ,&nbsp;Ruiquan Ran ,&nbsp;Wei Zeng ,&nbsp;Shan Ren ,&nbsp;Yibin Wang ,&nbsp;Aibin Zhu ,&nbsp;Chunli Zheng","doi":"10.1016/j.mineng.2025.109747","DOIUrl":"10.1016/j.mineng.2025.109747","url":null,"abstract":"<div><div>The inefficient recovery of iron from pyrite cinder (PC), a hazardous iron-rich byproduct of sulfuric acid production, remains a critical challenge due to chemical inertness of hematite and risks of toxic H₂S generation. This study proposes a sustainable strategy integrating biomass reductive roasting with oxalic acid-assisted leaching to achieve high-efficiency iron recovery. Using coffee shell (CS) as a renewable reductant, PC was converted into reduced pyrite cinder (RPC) at 700 ℃ for 3 h (mass ratio PC:CS = 1:1), where hematite (Fe₂O₃) was stepwise reduced to reactive Fe₃O₄, FeO, and Fe(0), while simultaneously removing 35.78 % sulfur. The resultant RPC exhibited a porous structure with 21.4-fold increased surface area versus PC, facilitating rapid iron dissolution. Coupled with oxalic acid leaching (<em>n</em>(H₂C₂O₄)/<em>n</em>(Fe) = 1:65), over 97 % iron extraction was achieved within 20 min under mild conditions (60 °C, 20 % H₂SO₄, L/S = 5:1 mL·g⁻¹), outperforming untreated PC (&lt;27 %) and yielding leachates with Fe(Ⅱ) concentrations up to 2.14 mol·L⁻¹. Thermodynamic and kinetic analyses revealed that the process shifted from interfacial reaction control (PC) to diffusion-dominated mechanisms (RPC), driven by enhanced reducibility and microstructural modification. High-purity ferrous oxalate dihydrate was recovered at 85.6 % efficiency from RPC leachate via direct precipitation, avoiding the energy-intensive photoreduction required for Fe(Ⅲ)-rich PC leachate. This strategy achieved an overall iron recovery rate of 83.7 % from raw PC to battery-grade ferrous oxalate dihydrate, synchronizing hazardous tailings utilization, critical metal recovery, and agricultural waste upcycling while offering a sustainable blueprint for circular resource economies.</div></div>","PeriodicalId":18594,"journal":{"name":"Minerals Engineering","volume":"234 ","pages":"Article 109747"},"PeriodicalIF":5.0,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144911934","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Flotation separation of hemimorphite and quartz using potassium dodecyl hydrogen phosphate as a high efficiency collector: experimental and DFT simulation","authors":"Songyu Yang ,&nbsp;Shuming Wen ,&nbsp;Runpeng Liao ,&nbsp;Qicheng Feng ,&nbsp;Jing Cao ,&nbsp;Zhenhao Guan","doi":"10.1016/j.mineng.2025.109748","DOIUrl":"10.1016/j.mineng.2025.109748","url":null,"abstract":"<div><div>The flotation separation of hemimorphite and quartz presents challenges due to their analogous surface characteristics when conventional collectors are used. In this study, potassium dodecyl hydrogen phosphate (PDHP) was systematically evaluated as a highly efficient collector for the selective flotation of hemimorphite and quartz. Micro-flotation experiments demonstrated that PDHP displayed remarkably enhanced selectivity for hemimorphite compared to the conventional collector dodecylamine (DDA). Under neutral pH and in the absence of depressants, effective separation of artificially mixed minerals was achieved at a PDHP concentration of 4 × 10⁻⁴ mol/L. The concentrate exhibited a zinc grade and recovery of 48.26 % and 86.38 %, respectively, while the SiO₂ grade was merely 27.29 %. A battery of characterization techniques elucidated the selective adsorption behavior of PDHP on hemimorphite, revealing robust chemisorption between the phosphonic acid groups of PDHP and the Zn active sites of hemimorphite. Density functional theory (DFT) simulation calculations further elucidated that stable six-membered rings were formed through bidentate bonding structures, where the O and H atoms of the phosphonic acid groups were bonded to the Zn and O atoms of hemimorphite by chemical bonds and hydrogen bonds, respectively, with a calculated adsorption energy of −343.55 kJ/mol, ultimately achieving efficient flotation of hemimorphite.</div></div>","PeriodicalId":18594,"journal":{"name":"Minerals Engineering","volume":"234 ","pages":"Article 109748"},"PeriodicalIF":5.0,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144911930","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automated ore texture classification using µ-XRF imaging and unsupervised machine learning: Correlation with surface hardness","authors":"Aghata Zarelli Viana ,&nbsp;Carolina Månbro ,&nbsp;Mohammad Jooshaki ,&nbsp;Mehdi Parian","doi":"10.1016/j.mineng.2025.109744","DOIUrl":"10.1016/j.mineng.2025.109744","url":null,"abstract":"<div><div>As the geometallurgy concept gains more visibility, the importance of parameters, in particular, ore texture, in downstream processing performance is increasingly recognized, yet methodologies for fast, unbiased, and automated texture classification remain limited, particularly for complex and low-grade deposits. This study proposes an alternative approach with potential for automated ore texture classification by combining micro-X-ray fluorescence (μ-XRF) imaging with unsupervised machine learning. Drill core samples from northern Sweden iron ore deposits were analyzed using μ-XRF to produce high-resolution mineral and X-ray intensity maps, which were converted to grayscale, divided into patches, and processed with Gray Level Co-Occurrence Matrix (GLCM) for feature extraction. Additionally, Principal Component Analysis (PCA) was applied for dimensionality reduction and k-means clustering for textural classification. The integration of mineral and X-ray maps improved classification accuracy, with clustering results effectively distinguishing major textural groups, despite some misclassifications attributed to pixel intensity variations. Evaluation of possible correlation between the classified textures and Leeb hardness measurements was carried out. Promising results were obtained, however, future advancements, such as the application of deep learning and alternative clustering algorithms, could further enhance the accuracy and applicability of this technique.</div></div>","PeriodicalId":18594,"journal":{"name":"Minerals Engineering","volume":"234 ","pages":"Article 109744"},"PeriodicalIF":5.0,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144908248","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A full-scale study on the recovery of ultra-fine and low-grade barite and pyrite using an agitated reflux classifier","authors":"Zhenqiang Liu ,&nbsp;Dongfang Lu ,&nbsp;Yuhua Wang ,&nbsp;Xiayu Zheng ,&nbsp;Zhenhua Su ,&nbsp;Bing Liu ,&nbsp;Yangge Zhu","doi":"10.1016/j.mineng.2025.109700","DOIUrl":"10.1016/j.mineng.2025.109700","url":null,"abstract":"<div><div>The agitated reflux classifier (ARC) is capable of efficiently separating particles based on density differences. This device has been demonstrated to be effective in the separation of various minerals with density contrasts and has already undergone successful pilot-scale testing for the recovery of ultra-fine and low-grade barite and pyrite. Building upon the pilot-scale experiments, this study presents the full-scale structural design and operational study of the ARC. Results indicate that for a feed material with BaSO₄ grade of 22.62 % and an Fe grade of 9.27 %, a single-stage enrichment using the full-scale ARC yields a concentrate with an average BaSO₄ grade of 44.16 % and an Fe grade of 18.28 %. The corresponding BaSO₄ and Fe recoveries reach 83.16 % and 84.02 %, respectively. Sieve analysis of the products reveals that the full-scale ARC performs well in separating material coarser than 0.016 mm, while losses in the − 0.016 mm fraction are the primary cause of reduced overall recovery. These findings confirm that the full-scale ARC enables efficient co-enrichment of ultra-fine and low-grade barite and pyrite.</div></div>","PeriodicalId":18594,"journal":{"name":"Minerals Engineering","volume":"234 ","pages":"Article 109700"},"PeriodicalIF":5.0,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144911935","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Purification of fluorite concentrate via stepwise magnetic separation: An experimental and simulation study","authors":"Wenqiang Zhu ,&nbsp;Jieliang Wang ,&nbsp;Zhao Cao ,&nbsp;Jianguo Cui ,&nbsp;Meng Xu ,&nbsp;Xianbo Fang ,&nbsp;Yiwen Hu ,&nbsp;Xu Wu","doi":"10.1016/j.mineng.2025.109732","DOIUrl":"10.1016/j.mineng.2025.109732","url":null,"abstract":"<div><div>The fluorite concentrate from the Bayan Obo ore exhibits low grade with significant impurities including iron, rare earth minerals, and calcium-bearing gangue minerals. This study developed a magnetic purification process by combining high-gradient magnetic separation (HGMS) and superconducting magnetic separation (SCMS) technologies, leveraging magnetic susceptibility differences between fluorite and gangue minerals. The optimized process achieved high-quality fluorite concentrate with 97.20 % grade and 82.14 % recovery. Modern analytical techniques, including Automated Mineral Identification and Characterization System (AMICS), Vibrating Sample Magnetometry (VSM), X-ray Diffraction (XRD), and computational simulations, were employed to investigate the separation mechanisms between fluorite and associated iron, rare earth, and calcium-bearing minerals. Results demonstrate that conventional HGMS effectively removes strongly iron minerals but exhibits limited effectiveness in separating fluorite from weakly magnetic rare earth minerals and calcium- bearing gangue. Under superconducting high-intensity magnetic fields of 5 T, optimized parameters with 0.12 mm magnetic matrix wires and slurry flow rate of 0.09 m/s enabled efficient capture of residual weakly magnetic iron mineral hematite, rare earth minerals bastnaesite and monazite, carbonate minerals dolomite, and phosphate minerals apatite while minimizing fluorite entrainment. This selective magnetic adsorption, attributed to the differential magnetic susceptibility of fluorite and gangue minerals, constitutes the critical mechanism for advanced fluorite purification.</div></div>","PeriodicalId":18594,"journal":{"name":"Minerals Engineering","volume":"234 ","pages":"Article 109732"},"PeriodicalIF":5.0,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144911929","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sustainable exploitation of siderite ore using fluidization roasting technology without reductant","authors":"Yuchao Qiu ,&nbsp;Xinran Zhu ,&nbsp;Jianping Jin ,&nbsp;Yuexin Han ,&nbsp;Yuwen Tao ,&nbsp;Chenhao Lu","doi":"10.1016/j.mineng.2025.109749","DOIUrl":"10.1016/j.mineng.2025.109749","url":null,"abstract":"<div><div>The development of efficient and environmentally friendly technologies for processing low-grade iron ores is essential to address the depletion of high-grade resources. In this study, a phase transformation process was proposed for the direct conversion of siderite into magnetite under an inert nitrogen atmosphere, without the use of any external reductant. The optimal fluidized roasting condition was identified as 700 °C for 30 min under a nitrogen flow rate of 200 mL/min. The final magnetic concentrate exhibited a total iron content of 60.60 %, with a corresponding iron recovery rate of 89.54 %. The phase transformation mechanism was investigated using XRD, SEM-EDS, and VSM techniques. The results reveal that siderite decomposes at elevated temperatures to form FeO and CO₂, while the generation of surface cracks enhances gas–solid interaction. Subsequently, FeO reacts with CO₂ to form magnetite, and the in-situ generated CO further reduces hematite present in the raw ore, also contributing to magnetite formation. These coupled reactions result in a substantial increase in saturation magnetization from 1.94 Am²/kg to 29.61Am²/kg, confirming the successful transformation into strongly magnetic phases. This additive-free process enables efficient phase transformation and separation, providing a clean and scalable pathway for the sustainable utilization of low-grade siderite resources.</div></div>","PeriodicalId":18594,"journal":{"name":"Minerals Engineering","volume":"234 ","pages":"Article 109749"},"PeriodicalIF":5.0,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144911931","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Basalt visible light image recognition optimization algorithm based on YOLOv8","authors":"Fen Jiao ,&nbsp;Yu Yin ,&nbsp;Xiangchuan Min ,&nbsp;Congren Yang ,&nbsp;Junwei Han ,&nbsp;Qian Wei ,&nbsp;Limin Tang ,&nbsp;Ying Huang ,&nbsp;Wenqing Qin","doi":"10.1016/j.mineng.2025.109735","DOIUrl":"10.1016/j.mineng.2025.109735","url":null,"abstract":"<div><div>With the rapid advancement of photoelectric intelligent sorting technology, YOLOv8-based algorithms for scheelite recognition have been successfully optimized. However, the development of scheelite resources is often accompanied by substantial waste rock accumulation and tailings discharge, which pose significant environmental challenges. This study focuses on the waste rocks obtained after the pre-concentration stage of scheelite processing. Due to the complex surface features of basalt and its high visual similarity to non-target gangue in visible light images, accurate identification remains difficult. To address this issue, we propose the YOLOv8-Basalt algorithm, which integrates multiple optimization strategies including HSV-based image enhancement, Inner-SIoU loss function, ECA attention mechanism, TSiLU activation function, Optuna-based hyperparameter tuning, and GhostConv lightweight convolution.Ablation and comparative experiments were conducted to evaluate the independent contributions of each module to recognition accuracy and inference efficiency. As a result, the average precision for basalt identification increased from 0.922 to 0.979, and the F1-score improved from 0.90 to 0.93, while maintaining a low inference time of only 2.60 ms. This demonstrates the algorithm’s ability to achieve accurate and rapid recognition of basalt in visible light images.Considering the complexity of deploying YOLO-based models on AI accelerators and their limited compatibility with industrial color sorters, we introduce a simulation-based waste rejection prediction method as an alternative. This approach enables rapid evaluation of sorting performance without relying on specific hardware. Based on quantitative simulation experiments, the predicted waste rejection rate for the test set samples reached 66.46%. The findings of this study provide theoretical support and technical guidance for the industrial deployment of intelligent ore recognition algorithms.</div></div>","PeriodicalId":18594,"journal":{"name":"Minerals Engineering","volume":"234 ","pages":"Article 109735"},"PeriodicalIF":5.0,"publicationDate":"2025-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144903293","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Source control of acid mine drainage production by sulfate reducing bacteria community: Effect and mechanism","authors":"Qi Jin,&nbsp;Tianyu Zhi,&nbsp;Hai Lin","doi":"10.1016/j.mineng.2025.109746","DOIUrl":"10.1016/j.mineng.2025.109746","url":null,"abstract":"<div><div>The application of the sulfate-reducing bacteria community (SRBC) for acid mine drainage (AMD) bioremediation has attracted growing interest. However, its potential for in situ suppression of acid generation from mine waste rocks remains unexplored. This study investigates the oxygen-inhibition and acid-control capabilities of SRBC using a low-cost carbon source (extract LP and alkali-pretreated peanut shells) through a series of static experiments. Results demonstrate that SRBC effectively suppresses acid production in waste rocks, achieving a 92 % inhibition rate for pyrite oxidation and maintaining pH above 6 for over 35 days. Even in the presence of <em>Acidithiobacillus ferrooxidans</em> (<em>At.f</em>), SRBC keep the pH above 6 and the oxidation–reduction potential (ORP) below −150 mV, confirming its robust inhibitory effect. Microbial community analysis revealed enhanced richness and diversity following pyrite exposure. X-ray diffraction (XRD) and scanning electron microscopy (SEM) analyses further indicate that SRBC mitigates acid generation by reducing SO₄²⁻ to S²⁻, which subsequently precipitates with Fe ions as metal sulfides. This study highlights SRBC’s cost-effective and sustainable potential for in situ waste rock stabilization, providing a foundation for its practical implementation in mine remediation.</div></div>","PeriodicalId":18594,"journal":{"name":"Minerals Engineering","volume":"234 ","pages":"Article 109746"},"PeriodicalIF":5.0,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144896020","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial intelligence transforming minerals engineering: Key trends in literature and applications","authors":"Hang Yang ,&nbsp;Wei Feng ,&nbsp;Hongli Diao ,&nbsp;Shibin Xia","doi":"10.1016/j.mineng.2025.109741","DOIUrl":"10.1016/j.mineng.2025.109741","url":null,"abstract":"<div><div>Artificial Intelligence (AI) is progressively reshaping the landscape of minerals engineering, driving advancements across exploration, mining, and processing. This review systematically examines the current applications of AI in these domains, highlighting its role in optimizing resource estimation, enhancing safety, and improving operational efficiency. Through a bibliometric analysis, trends, key contributors, and geographical distributions in AI-related research within minerals engineering are explored, revealing a significant rise in AI-focused studies and a global shift towards integrating these technologies. In exploration, AI techniques such as machine learning (ML) and data analytics are utilized for mineral prospectivity mapping (MPM) and anomaly detection, facilitating more precise resource identification. In mining operations, AI aids in optimizing extraction processes, predicting equipment failures, and enabling autonomous systems for increased safety. Within mineral processing, AI contributes to real-time monitoring, process optimization, and product quality improvement through advanced modeling and control systems. Despite these advancements, challenges persist, including data quality, integration complexities, and the need for interdisciplinary expertise. This review underscores AI’s transformative impact on the sector and outlines the need for continued research and collaboration to overcome existing barriers and unlock AI’s full potential in minerals engineering.</div></div>","PeriodicalId":18594,"journal":{"name":"Minerals Engineering","volume":"234 ","pages":"Article 109741"},"PeriodicalIF":5.0,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144902821","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}