Wentao Shang | Green Technology | Best Researcher Award

Published on by Technology Scientists

Best Researcher Award

Wentao Shang
Affiliation Jinan University
Country China
Scopus ID 57604364900
Documents 34
Citations 812
h-index 15
Subject Area Green Technology
Event Technology Scientists Awards
ORCID 0000-0002-5168-7696

Wentao Shang is affiliated with Jinan University, China, and works across membrane science, separation technologies, computational prediction, imaging, and advanced materials. His recent scholarly record includes research on membrane distillation, nanofiltration fouling prediction, and supramolecular materials, providing a multidisciplinary basis for consideration within the field of green technology. [1] [2] [3]

Abstract

Wentao Shang is a researcher at Jinan University whose documented work connects membrane science, green technology, computational modeling, imaging, and advanced materials. His recent publications examine surface patterning for membrane distillation, multimodal convolutional neural networks for dynamic nanofiltration fouling prediction, and solution-sheared supramolecular oligomers with improved thermal-resistant adhesion. These studies demonstrate an interdisciplinary approach combining materials engineering, separation processes, experimental characterization, and data-driven analysis. With 34 documented publications, 812 citations, and an h-index of 15, his profile indicates sustained scholarly activity and measurable research visibility. The breadth and environmental relevance of these themes support consideration for a Best Researcher Award.

Keywords

Keywords: Green Technology, Membrane Distillation, Nanofiltration, Membrane Fouling, Optical Coherence Tomography, Convolutional Neural Networks, Surface Patterning, Advanced Materials, Supramolecular Oligomers, Sustainable Engineering.

Introduction

Wentao Shang’s research profile at Jinan University reflects an interdisciplinary focus connecting membrane processes, nanofiltration, imaging-based analysis, advanced materials, and sustainable engineering. His recent publications address membrane distillation, fouling prediction, and thermally resistant supramolecular materials, indicating a research trajectory relevant to emerging green technology and resource-efficient engineering. [1] [2] [3]

Research Profile

Shang is associated with research spanning membrane science, separation technologies, computational prediction, and functional materials. His publication record includes studies using surface patterning to improve membrane distillation and multimodal convolutional neural networks to model nanofiltration fouling. These themes connect experimental characterization, materials engineering, and data-driven methods for environmental applications. [1] [2]

Research Contributions

Shang’s contributions can be viewed through three complementary areas: engineering membrane surfaces for improved separation performance, applying in-situ optical coherence tomography and multimodal neural networks to characterize fouling dynamics, and investigating supramolecular materials with enhanced thermal and adhesive properties. Together, these studies demonstrate integration of experimental methods, computational analysis, and materials design. [1] [2] [3]

Publications

The documented publications associated with Shang include a 2026 review of surface patterning in membrane distillation, a 2026 Desalination article on multimodal convolutional neural networks for nanofiltration fouling prediction, and a Nature Communications study on solution-sheared supramolecular oligomers. The works collectively cover membrane engineering, machine learning, imaging, adhesion, and advanced materials. [1] [2] [3]

Research Impact

The research has potential relevance to green technology through improved membrane efficiency, fouling management, and durable functional materials. Surface-engineered membranes may support cleaner separation processes, while predictive imaging models can improve understanding of fouling development. Work on thermally resistant adhesives further broadens the profile toward resource-conscious and performance-oriented materials engineering. [1] [2] [3]

Award Suitability

The Best Researcher Award profile is supported by a combination of publication activity, citation indicators, interdisciplinary research themes, and alignment with green technology. The reported record of 34 documents, 812 citations, and an h-index of 15 provides quantitative evidence of scholarly visibility, while recent publications demonstrate continuing research activity. [1] [2] [3]

Conclusion

Wentao Shang presents a research profile combining membrane technology, computational modeling, imaging, and advanced materials. His recent work addresses practical challenges in separation efficiency, fouling prediction, and material durability. The combination of documented scholarly output and green-technology relevance provides a reasonable academic basis for consideration under the Best Researcher Award. [1] [2] [3]

References

  1. Zhang, C., Lin, Y., Lu, G., Yuan, B., Chen, P., Farid, M. U., Lee, V. P. H., Shang, W., Li, W., & An, A. K. (2026). Surface patterning in membrane distillation: Fabrication, mechanism, and performance enhancement. Separation and Purification Technology, 394(Part 3), Article 137561.
    https://www.sciencedirect.com/science/article/abs/pii/S1383586626008270
  2. Shang, W., Zeng, Y., Xiao, F., Wu, M., Wang, Y., Yang, Z., He, J., & Sun, F. (2026). A multimodal convolutional neural network trained by in-situ OCT characterization for dynamic structural prediction of nanofiltration fouling. Desalination, 639, Article 120676.
    https://www.sciencedirect.com/science/article/pii/S0011916426008325
  3. Lu, G., Ma, R., Zhao, Y., Wang, D., Shang, W., Chen, H., Khan, S. A., Li, M., & Saiz, E. (2025). Solution-sheared supramolecular oligomers with enhanced thermal resistance in interfacial adhesion and bulk cohesion. Nature Communications, 16, 7754.
    https://www.nature.com/articles/s41467-025-63123-9
  4. Elsevier. (n.d.). Scopus author details: Wentao Shang, Author ID 57604364900. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=57604364900
  5. ORCID. (n.d.). Wentao Shang, ORCID 0000-0002-5168-7696. ORCID.
    https://orcid.org/0000-0002-5168-7696

Chengyu Liang | Renewable Energy | Innovative Research Award

Published on by Technology Scientists

Innovative Research Award

Chengyu Liang
Lanzhou University of Technology, China
               Chengyu Liang
Affiliation Lanzhou University of Technology
Country China
Scopus ID 57469530600
Documents 3
Citations 22
h-index 2
Subject Area Renewable Energy
Event Technology Scientists Awards

Chengyu Liang is a researcher affiliated with Lanzhou University of Technology whose scholarly work focuses on renewable energy technologies and intelligent condition assessment of engineering systems. The available Scopus profile indicates a growing publication record with measurable citation impact, reflecting sustained academic contributions to reliability analysis, predictive maintenance, and energy-related engineering research.[1]

Abstract

Chengyu Liang has contributed to engineering research involving renewable energy applications, machinery health monitoring, degradation assessment, and intelligent predictive models. Current scholarly records demonstrate emerging influence through peer-reviewed publications indexed in Scopus and measurable citation performance. Research activities emphasize adaptive state-space modelling, prediction error correction, and reliability evaluation for mechanical systems supporting sustainable engineering development. These studies combine mathematical modelling with practical engineering applications to improve equipment performance, operational efficiency, maintenance planning, and long-term system reliability. The available publication record reflects continuing academic development and meaningful contributions within renewable energy and engineering research communities.[1][2]

Keywords

Renewable Energy, Mechanical Systems, Performance Degradation, State-Space Model, Predictive Maintenance, Reliability Engineering, Adaptive Modeling, Engineering Diagnostics.

Introduction

Chengyu Liang conducts engineering research centered on renewable energy and intelligent mechanical system analysis. Published studies investigate advanced degradation assessment methodologies using adaptive mathematical models that improve equipment reliability, operational efficiency, and predictive maintenance while supporting sustainable engineering practices and modern industrial applications.[2]

Research Profile

According to publicly available Scopus records, Chengyu Liang has authored three indexed publications that have received twenty-two citations with an h-index of two. The research portfolio reflects specialization in engineering diagnostics, renewable energy technologies, reliability assessment, and predictive analytical modelling.[1]

Research Contributions

Research contributions include developing adaptive state-space approaches for evaluating mechanical performance degradation using prediction error correction techniques. These methods enhance condition monitoring accuracy, facilitate maintenance decision-making, and improve reliability evaluation across engineering systems supporting renewable energy and industrial sustainability objectives.[2]

Publications

The publication record includes peer-reviewed research focused on degradation assessment methodologies for mechanical systems. A representative article presents a dual adaptive drift coefficient state-space model integrated with autocorrelation prediction error correction, demonstrating practical applications in engineering reliability and intelligent equipment monitoring.[2]

Research Impact

Citation metrics and indexed publications indicate growing academic recognition within engineering research. The integration of predictive modelling with mechanical system assessment contributes valuable knowledge supporting efficient maintenance strategies, equipment longevity, and sustainable industrial operations in renewable energy and manufacturing environments.[1]

Award Suitability

The Innovative Research Award recognizes researchers demonstrating originality, measurable scholarly contributions, and practical engineering significance. Chengyu Liang’s research on adaptive degradation assessment and predictive maintenance aligns with these principles by advancing analytical methodologies applicable to renewable energy and engineering reliability studies.[2]

Conclusion

Chengyu Liang has established an emerging research profile through focused engineering investigations addressing renewable energy, reliability assessment, and intelligent predictive modelling. Available scholarly evidence indicates continuing academic development, making this body of work an appropriate example of innovative engineering research recognized through academic award evaluation.[1]

External Links

References

  1. Elsevier. (n.d.). Scopus author details: Chengyu Liang, Author ID 57469530600. Scopus.
    https://www.scopus.com/pages/authors/57469530600
  2. Liang, C., et al. (2025). Performance degradation assessment of mechanical system based on dual adaptive drift coefficient state-space model with autocorrelation prediction error correction. Mechanical Systems and Signal Processing. Elsevier.
    https://www.sciencedirect.com/science/article/abs/pii/S0888327025015055
  3. Technology Scientists Awards. (n.d.). Technology Scientists Awards official website.
    https://technologyscientists.com/

Marina Gravit | Sustainable Tech | Women Researcher Award

Published on by Technology Scientists

Women Researcher Award

Marina Gravit
Peter the Great St.Petersburg Polytechnic University

                             Marina Gravit
Affiliation Peter the Great St.Petersburg Polytechnic University
Country Russia
Scopus ID 56826013600
Documents 104
Citations 780
h-index 15
Subject Area Sustainable Tech
Event Technology Scientists Awards
ORCID 0000-0003-1071-427X

Marina Gravit is a researcher affiliated with Peter the Great St.Petersburg Polytechnic University whose scholarly work focuses on fire safety engineering, sustainable construction materials, structural fire resistance, and passive fire protection technologies. Her research contributes to the advancement of resilient infrastructure and evidence-based approaches for improving building safety under severe fire conditions.[1]

Abstract

This article presents an overview of Marina Gravit’s academic profile, emphasizing her contributions to fire resistance engineering, passive fire protection systems, and sustainable construction technologies. Her publications address critical challenges in structural safety, predictive fire resistance assessment, and the application of advanced protective materials for industrial and civil infrastructure.[1][2]

Keywords

Fire Resistance, Structural Engineering, Passive Fire Protection, Sustainable Construction, Fire Safety Materials, Hydrocarbon Fire Conditions, Steel Structures, Building Safety, Fire Protection Engineering, Sustainable Technology.

Introduction

Marina Gravit’s research addresses contemporary challenges in fire safety engineering through studies of fire-resistant materials, structural performance, and protective technologies. Her work integrates sustainability and engineering reliability, supporting safer infrastructure development while advancing scientific understanding of fire behavior and protection strategies in modern construction environments.[1]

Research Profile

As a scholar in fire safety and construction engineering, Marina Gravit has developed a substantial publication record focused on building resilience, fire protection materials, and structural safety assessment. Her interdisciplinary approach combines engineering analysis, material science, and sustainability principles to address practical and scientific challenges.[1][3]

Research Contributions

Her contributions include investigations of passive fire protection systems, bibliometric analyses of fire-resistant construction technologies, and predictive methodologies for assessing steel structures under hydrocarbon fire exposure. These studies support evidence-based engineering decisions and contribute to enhanced safety standards in industrial and commercial infrastructure.[2][3]

Publications

Notable publications examine fire resistance in building structures, passive protection materials for steel systems exposed to jet fires, and forecasting models for structural performance during hydrocarbon fire scenarios. These works provide valuable insights into fire engineering design, safety optimization, and protective material evaluation.[1][2][3]

Research Impact

The impact of Marina Gravit’s research is reflected in scholarly citations, practical relevance to fire safety engineering, and contributions to safer structural design practices. Her studies support researchers, engineers, and policymakers seeking improved methodologies for fire resistance assessment and infrastructure protection.[1][3]

Award Suitability

Marina Gravit demonstrates strong suitability for the Women Researcher Award through her sustained scholarly productivity, international research visibility, and contributions to sustainable technology and fire safety engineering. Her work addresses critical societal challenges while advancing knowledge relevant to resilient and sustainable built environments.[1][2]

Conclusion

Marina Gravit’s academic achievements illustrate a commitment to advancing fire safety science, sustainable construction technologies, and structural resilience. Through influential research and practical engineering applications, she has contributed valuable knowledge supporting safer infrastructure and ongoing innovation within the field of sustainable technology.[1][3]

References

  1. Gravit, M., et al. (2025). Fire Resistance of Building Structures and Fire Protection Materials: Bibliometric Analysis. Fire, 8(1), 10.
    https://www.mdpi.com/2571-6255/8/1/10
  2. Gravit, M., et al. (2024). Impact of Jet Fires on Steel Structures: Application of Passive Fire Protection Materials. Fire, 7(8), 281.
    https://www.mdpi.com/2571-6255/7/8/281/review_report
  3. Gravit, M., et al. (2024). Oil and Gas Structures: Forecasting the Fire Resistance of Steel Structures with Fire Protection under Hydrocarbon Fire Conditions. Fire, 7(6), 173.
    https://www.mdpi.com/2571-6255/7/6/173
  4. Elsevier. (n.d.). Scopus author details: Marina Gravit, Author ID 56826013600. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=56826013600
  5. ORCID. (n.d.). Marina Gravit ORCID Record.
    https://orcid.org/0000-0003-1071-427X

Yucen Yuan | New energy | Research Excellence Award

Published on by Technology Scientists

Mr. Yucen Yuan | New energy | Research Excellence Award

Lanzhou Jiaotong University | China

Mr. Yucen Yuan is an early-career researcher affiliated with Lanzhou Jiaotong University, China, with a focused research profile in intelligent fault diagnosis and data-driven condition monitoring of renewable energy systems. His work lies at the intersection of machine learning, optimization algorithms, and mechanical fault detection, with particular emphasis on wind turbine bearing health assessment. Yuan has authored 2 peer-reviewed publications, accumulating 1 citation to date and 1 h-index, reflecting emerging scholarly visibility. His 2025 article in Engineering Research Express introduces an improved dung beetle optimizer–enhanced LSTM framework, demonstrating methodological innovation in time-series fault diagnosis. This contribution highlights his expertise in deep learning optimization, signal analysis, and industrial predictive maintenance. Yuan has engaged in collaborative research, contributing as part of a small co-author network, and his work supports the reliability and sustainability of wind energy infrastructure. The societal impact of his research aligns with global clean energy goals by advancing intelligent monitoring technologies that reduce equipment failure, maintenance costs, and operational risks in renewable power systems.

Citation Metrics (Scopus)

2
1.5
1
0.5
0

Citations

1

Documents

2

h-index

1

Citations

Documents

h-index


View Scopus Profile View ORCID Profile

Featured Publications

Da-Zhen Xu | Green Technology | Best Researcher Award

Published on by Technology Scientists

Dr. Da-Zhen Xu | Green Technology | Best Researcher Award

Senior Chemical Engineer | Nankai University | China

Dr. Dazhen Xu is a distinguished researcher at Nankai University, Tianjin, China, specializing in organic synthesis, catalysis, and radical-mediated transformations. With 47 peer-reviewed publications and over 1,231 citations, Dr. Xu has established a notable presence in the field of synthetic and organometallic chemistry. His work primarily focuses on developing innovative, sustainable, and atom-economical methodologies for the construction of complex organic molecules, particularly through metal-catalyzed and metal-free multicomponent reactions. Recent studies highlight his group’s advancements in iron- and copper-mediated transformations, including Markovnikov-selective radical hydrothiolation of alkenes, oxidative arylation and hydroxylation of indolin-2-ones, and bromocyclization of olefinic amides, which contribute significantly to green chemistry and pharmaceutical synthesis. Dr. Xu’s research integrates mechanistic insight with practical synthetic utility, leading to scalable, cost-effective protocols that minimize environmental impact. His collaborations with over 70 co-authors reflect a strong interdisciplinary approach, bridging academic research and industrial application across catalysis, materials, and medicinal chemistry. With an h-index of 23, Dr. Xu’s publications have gained international recognition for their methodological innovation and relevance to sustainable chemical processes. His contributions not only advance the frontiers of organic chemistry but also align with global goals for environmentally benign synthesis, influencing future directions in both academic research and industrial innovation.

Profiles: Scopus | ORCID

Featured Publications 

1. Wang, Y.-N., Jia, H., Yao, L., Chen, Y., Liu, H.-L., Liang, F., … Xu, D.-Z. (2025). Bifunctional iron-mediated multicomponent Markovnikov-selective radical hydrothiolation of alkenes. Organic Chemistry Frontiers, 12, 4462-4468.

2. Li, T.-Y., Xu, L.-L., Wu, D.-Q., Liu, J.-J., Yang, Y., Miao, Z., … Xu, D.-Z. (2025). Copper-Catalyzed Oxidative Arylation and Hydroxylation of Indolin-2-ones for Direct Construction of Tetrasubstituted Carbon Centers. Journal of Organic Chemistry, 90(2), 960-970.
Cited by: 1

3. Xu, L.-L., Wang, S., Sun, J., Zhang, R., Tong, J., … Xu, D.-Z. (2024). Facile access to S-aryl/alkyl dithiocarbamates via a three-component reaction under metal-free conditions. Organic & Biomolecular Chemistry, 22, 7702.
Cited by: 1

4. Zhao, T.-T., Bian, Q., Zhao, Y.-W., Xu, L.-L., Xu, D.-Z., & Zhao, W.-G. (2024). Iron-Mediated Bromocyclization of Olefinic Amides for the Synthesis of Bromobenzoxazines. Synthesis, 56, 2993-3000.
Cited by: 3

Dr. Dazhen Xu’s pioneering research in sustainable catalysis and radical chemistry is transforming the way complex molecules are synthesized, promoting greener and more efficient chemical manufacturing. His vision is to integrate eco-conscious innovation with high-impact synthetic strategies, advancing both scientific knowledge and the global transition toward sustainable chemical industries.

Ali Yahia Cherif | Electrical Engineering | Best Researcher Award

Published on by Technology Scientists

Mr. Ali Yahia Cherif | Electrical Engineering | Best Researcher Award

Phd Student | Oum Elbouaghi University | Algeria

Ali Yahia-Cherif is a seasoned Algerian electrical and automation engineer born in 1991, with extensive expertise in predictive control systems, renewable energy, and electronic system diagnostics. He holds a Master’s in Industrial Automation and a Bachelor’s in Automatic Control from the University of Mentouri Constantine, and has been pursuing a Ph.D. in Electrical and Automatic Engineering since 2014 at the University of Larbi Ben M’hidi, focusing on the development of experimental platforms for shunt active filter systems. His professional experience spans over a decade, including roles as a principal engineer and project manager in high and low-current installations—covering solar energy systems, surveillance, access control, and fire safety networks. He also worked as an engineer in security systems and has teaching experience in programming and electrical engineering at the university level. His leadership in the F.E.A.T scientific electronics club demonstrates his early commitment to innovation. Ali has authored over 12 scientific publications in reputable journals such as IET Renewable Power Generation, International Journal of Circuit Theory and Applications, and European Journal of Electrical Engineering, addressing advanced topics in predictive control, hybrid energy storage, and photovoltaic systems. He has presented his work at key international conferences including GPECOM, IREC, and ICEEAC.

Profile: Google Scholar 

Featured Publications

1. Meddour, S., Rahem, D., Yahia Cherif, A., Hachelfi, W., & Hichem, L. (2019). A novel approach for PV system based on metaheuristic algorithm connected to the grid using FS-MPC controller. Energy Procedia, 162, 57–66. 
Cited by: 32

2. Remache, S. E. I., Yahia Cherif, A., & Barra, K. (2019). Optimal cascaded predictive control for photovoltaic systems: Application based on predictive emulator. IET Renewable Power Generation, 13(15), 2740–2751. 
Cited by: 29

3. Yahia Cherif, A., Remache, S. E. I., Barra, K., & Wira, P. (2019). Adaptive model predictive control for three phase voltage source inverter using ADALINE estimator. In 2019 1st Global Power, Energy and Communication Conference (GPECOM) (pp. 164–169). IEEE. 
Cited by: 7

4. Yahia Cherif, A., Hicham, L., & Kamel, B. (2018). Implementation of finite set model predictive current control for shunt active filter. In 2018 9th International Renewable Energy Congress (IREC) (pp. 1–6). IEEE. 
Cited by: 6

5. Meddour, S., Rahem, D., Wira, P., Laib, H., Yahia Cherif, A., & Chtouki, I. (2022). Design and implementation of an improved metaheuristic algorithm for maximum power point tracking algorithm based on a PV emulator and a double-stage grid-connected system. European Journal of Electrical Engineering, 24(3), 423–430.
Cited by: 5