Zhansheng Wu | Enzyme immobilization | Best Researcher Award

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Prof. Zhansheng Wu | Enzyme immobilization | Best Researcher Award

Professor at  Xi’an Polytechnic University, China

🌟 Dr. Zhansheng Wu is a Vice President of the School of Environmental and Chemical Engineering at Xi’an Polytechnic University. 📚 A third-level professor, doctoral supervisor, and renowned scientist, he has led prestigious projects under China’s National Natural Science Foundation and the National Key R&D Program. 🌏 Recognized globally, he is among the top 2% of scientists worldwide and serves as an editorial board member of Biochar and Carbon Research. His contributions center around clean ecological dyeing, biological and environmental chemical industries, and material sciences.

Professional Profiles:

orcid

Education🎓 

2017.4–2017.5: University of California, Los Angeles – Study. 2015.12–2016.5: University of Turin – Visiting Scholar. 2008.8–2011.6: Beijing Institute of Technology – Doctorate in Biochemistry  2003.8–2006.6: Shihezi University – Master’s in Food Science & Engineering  1999.8–2003.6: Shihezi University – Bachelor’s in Food Science & Engineering.

Experience🛠️ 

Vice President and Professor, Xi’an Polytechnic University.  Chief Scientist of Shaanxi Province’s “Qin Chuangyuan” team  Project Leader for National Key Research & Development Plan (2021–2024). Editorial Board Member for Biochar and Carbon Research. Visiting Scholar, University of Turin (2015–2016).

Awards and Honors🏅

Approved by National Natural Science Foundation of China – Young Talents Fund.  Listed in the Top 100,000 Scientists and Top 2% globally.  Leader of Shaanxi’s “Qin Chuangyuan” Scientist + Engineer Team. Published in top journals like Chemical Engineering Journal (IF > 16.7).

Research Focus🔍

Clean ecological dyeing and finishing technologies.  Development of biochar-based bactericide systems for soil improvement. Photocatalysis for environmental remediation and water treatment. Sustainable agricultural practices with biochar innovations. Exploring chemical-material industry advancements.

✍️Publications Top Note :

  • Biochar and Environmental Applications:
    • Prediction of biochar yield and specific surface area using advanced algorithms.
    • Multi-functional biochar composites for pollution control and fertilizer applications.
  • Metal-Organic Frameworks (MOFs):
    • Amino-functionalized MOFs for enzyme stability and organic pollutant degradation.
    • Hollow MOFs designed for enzyme immobilization and rare ginsenoside synthesis.
  • Photocatalysis and Functional Materials:
    • Development of heterojunction photocatalysts for efficient degradation of pollutants.
    • N-doped Ti3C2Tx-MXene-modified photocatalysts for enhanced photocatalytic ammonia synthesis.
  • Biocontrol and Environmental Microbiology:
    • Identification and genetic characterization of biocontrol strains with siderophilic properties.
    • Bioreduction of hexavalent chromium using Bacillus subtilis enhanced with humic acid.
  • Innovative Enzyme Immobilization:
    • Enhancements in enzyme loading and activity for industrial pollutant degradation.
  • Nanomaterials and Wastewater Treatment:
    • Strategies leveraging BaTiO3 piezocatalysis for vibration energy harvesting and water purification.
    • Functionalized ZnO/ZnSe composites for organic dye wastewater treatment.
  • Agricultural and Environmental Stress:
    • Applications of microcapsules for Capsicum growth under salt stress.

Conclusion

Zhansheng Wu stands as a stellar candidate for the Best Researcher Award due to his groundbreaking work in environmental chemical engineering and materials science. His extensive contributions to sustainable technologies, particularly in photocatalysis and biochar systems, have significantly advanced global environmental goals. While there is room to enhance the societal impact and commercialization aspects of his research, his academic excellence, leadership in high-value projects, and international recognition firmly establish him as a deserving contender for this prestigious award.

Xiaolin Yang | CImage analysis | Best Researcher Award

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Dr. Xiaolin Yang | Image analysis | Best Researcher Award

Dr at China university of mining and technology, China

Xiaolin Yang is a skilled Business Analyst and Postdoctoral Researcher at Henan Investment Group. With a solid background in mineral process engineering, his expertise spans industry research, project management, and production optimization. Xiaolin holds a Bachelor’s and a Ph.D. in Mineral Process Engineering from the China University of Mining and Technology, specializing in mineral processing, machine learning, and image analysis. His dedication to academic excellence and practical application makes him a valuable asset in the mineral industry.

Publication Profile

scopus

Education🎓 

.Bachelor of Mineral Process Engineering | China University of Mining and Technology, 2015–2019 | Focus: Mineral separation methods and equipment. Doctor of Mineral Process Engineering | China University of Mining and Technology, 2019–2024 | Research areas: Mineral processing, machine learning, image analysis. Xiaolin’s academic journey emphasized innovation in mineral separation, blending engineering with data science to improve mineral processing efficiency and accuracy.

Experience💼 

Postdoctoral Researcher | Henan Investment Group, 2024–Present | Xiaolin’s role involves comprehensive industry research, preparing assessment reports, and offering investment insights and recommendations. His project management tasks focus on feasibility assessments and evaluating the effectiveness of production processes, aiming to optimize industrial production and implement innovative solutions in mineral processing.

Awards and Honors🏆 

Published Author | Xiaolin has authored notable academic articles, such as in Journal of Materials Research and Technology (2021), Energy (2022), and Expert Systems with Applications (2024). His work, recognized for its significance in mineral processing and machine learning, highlights his expertise in utilizing advanced algorithms for practical industry challenges.

Research Focus🔍

Research Interests | Xiaolin’s research delves into mineral processing, machine learning applications, and image analysis. His studies, including deep learning for ash determination in coal flotation, explore novel algorithms to enhance mineral processing accuracy, bridging engineering and artificial intelligence for industrial optimization.

Publication  Top Notes

Multi-scale neural network for accurate determination of ash content in coal flotation concentrate

Authors: Yang, X., Zhang, K., Thé, J., Tan, Z., Yu, H.

Journal: Expert Systems with Applications, 2025, 262, 125614

Description: This paper presents a multi-scale neural network model that accurately determines ash content in coal flotation concentrate using froth images, leveraging deep learning to enhance mineral processing efficiency.

STATNet: One-stage coal-gangue detector for real industrial applications

Authors: Zhang, K., Wang, T., Yang, X., Tan, Z., Yu, H.

Journal: Energy and AI, 2024, 17, 100388

Description: The STATNet model is introduced as a coal-gangue detection system using a one-stage deep learning algorithm, tailored for industrial application with a focus on real-time processing.

COFNet: Predicting surface area of covalent-organic frameworks

Authors: Wang, T., Yang, X., Zhang, K., Tan, Z., Yu, H.

Journal: Chemical Physics Letters, 2024, 847, 141383

Description: COFNet utilizes deep learning to predict the specific surface area of covalent-organic frameworks, combining structural image analysis with statistical features for accurate predictions.

Enhancing coal-gangue detection with GAN-based data augmentation

Authors: Zhang, K., Yang, X., Xu, L., Tan, Z., Yu, H.

Journal: Energy, 2024, 287, 129654

Description: This study employs GAN-based data augmentation and a dual attention mechanism to improve coal-gangue object detection, aiming to refine accuracy in complex industrial environments.

Multi-step carbon price forecasting using hybrid deep learning models

Authors: Zhang, K., Yang, X., Wang, T., Tan, Z., Yu, H.

Journal: Journal of Cleaner Production, 2023, 405, 136959

Description: A hybrid deep learning model for multi-step forecasting of carbon prices is proposed, integrating multivariate decomposition to enhance predictive reliability.

PM2.5 and PM10 concentration forecasting with spatial–temporal attention networks

Authors: Zhang, K., Yang, X., Cao, H., Tan, Z., Yu, H.

Journal: Environment International, 2023, 171, 107691

Description: This article introduces a spatial–temporal attention mechanism for PM2.5 and PM10 forecasting, using convolutional neural networks with residual learning to tackle air quality predictions.

Ash determination of coal flotation concentrate using hybrid deep learning model

Authors: Yang, X., Zhang, K., Ni, C., Tan, Z., Yu, H.

Journal: Energy, 2022, 260, 125027

Description: This work features a hybrid model that utilizes deep learning and attention mechanisms to determine ash content in coal flotation, contributing to process optimization.

Influence of cation valency on flotation of chalcopyrite and pyrite

Authors: Yang, X., Bu, X., Xie, G., Chehreh Chelgani, S.

Journal: Journal of Materials Research and Technology, 2021, 11, pp. 1112–1122

Description: This comparative study explores how different cation valencies affect chalcopyrite and pyrite flotation, contributing to better separation techniques in mineral processing.

Conclusion

Xiaolin Yang is a compelling candidate for the Best Researcher Award. His strengths in applying AI and image analysis to mineral processing reflect a unique skill set that is highly relevant for advancing research and industry practices. With further interdisciplinary work and expanded research visibility, Xiaolin is well-positioned to make impactful contributions and earn recognition in his field.