Mohammad Rahimi

1.1k total citations
30 papers, 791 citations indexed

About

Mohammad Rahimi is a scholar working on Mechanical Engineering, Mechanics of Materials and Ocean Engineering. According to data from OpenAlex, Mohammad Rahimi has authored 30 papers receiving a total of 791 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Mechanical Engineering, 5 papers in Mechanics of Materials and 5 papers in Ocean Engineering. Recurrent topics in Mohammad Rahimi's work include Electrochemical sensors and biosensors (4 papers), Conducting polymers and applications (4 papers) and Supercapacitor Materials and Fabrication (4 papers). Mohammad Rahimi is often cited by papers focused on Electrochemical sensors and biosensors (4 papers), Conducting polymers and applications (4 papers) and Supercapacitor Materials and Fabrication (4 papers). Mohammad Rahimi collaborates with scholars based in Iran, Canada and Vietnam. Mohammad Rahimi's co-authors include Abbas Rohani, Mohammad Hossein Abbaspour‐Fard, Hung Vo Thanh, Zhenxue Dai, Hemeng Zhang, Umar Ashraf, Hossein Mashhadimoslem, Saeed Bagherifam, Amir Lakzian and Mobin Safarzadeh Khosrowshahi and has published in prestigious journals such as SHILAP Revista de lepidopterología, The Science of The Total Environment and Journal of Power Sources.

In The Last Decade

Mohammad Rahimi

28 papers receiving 750 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Mohammad Rahimi Iran 17 276 185 129 123 122 30 791
Hirokazu Okawa Japan 15 182 0.7× 201 1.1× 61 0.5× 80 0.7× 136 1.1× 82 784
Congcong Chen China 15 278 1.0× 252 1.4× 71 0.6× 50 0.4× 121 1.0× 44 905
Majid Mohammadi Iran 21 278 1.0× 166 0.9× 47 0.4× 256 2.1× 218 1.8× 56 996
Lanyun Wang China 19 434 1.6× 203 1.1× 89 0.7× 422 3.4× 320 2.6× 47 1.2k
Fahimeh Hadavimoghaddam China 20 345 1.3× 156 0.8× 71 0.6× 297 2.4× 263 2.2× 65 1.0k
Ayat A.‐E. Sakr Egypt 7 242 0.9× 253 1.4× 98 0.8× 31 0.3× 97 0.8× 10 666
Saeed Mozaffari United States 13 140 0.5× 232 1.3× 24 0.2× 215 1.7× 146 1.2× 27 816
M. R. Islam Canada 15 186 0.7× 136 0.7× 78 0.6× 348 2.8× 78 0.6× 54 772
Kai Liang China 18 190 0.7× 219 1.2× 31 0.2× 116 0.9× 171 1.4× 56 1.1k
S. Faramawy Egypt 10 244 0.9× 238 1.3× 115 0.9× 38 0.3× 119 1.0× 23 728

Countries citing papers authored by Mohammad Rahimi

Since Specialization
Citations

This map shows the geographic impact of Mohammad Rahimi's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Mohammad Rahimi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mohammad Rahimi more than expected).

Fields of papers citing papers by Mohammad Rahimi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mohammad Rahimi. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Mohammad Rahimi. The network helps show where Mohammad Rahimi may publish in the future.

Co-authorship network of co-authors of Mohammad Rahimi

This figure shows the co-authorship network connecting the top 25 collaborators of Mohammad Rahimi. A scholar is included among the top collaborators of Mohammad Rahimi based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Mohammad Rahimi. Mohammad Rahimi is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Thanh, Hung Vo, Zhenxue Dai, & Mohammad Rahimi. (2025). Data-driven explainable machine learning approaches for predicting hydrogen adsorption in porous crystalline materials. Journal of Alloys and Compounds. 1028. 180709–180709. 6 indexed citations
2.
Wang, Yongjun, Hung Vo Thanh, Hemeng Zhang, et al.. (2025). Low-carbon advancement through cleaner production: A machine learning approach for enhanced hydrogen storage predictions in coal seams. Renewable Energy. 241. 122342–122342. 4 indexed citations
3.
Rahimi, Mohammad & Shakirudeen A. Salaudeen. (2025). Synthesis-feature-coupled machine learning approaches to predict the capacitance of biomass-derived carbon electrodes in supercapacitors. Materials Chemistry and Physics. 348. 131525–131525.
4.
Khosrowshahi, Mobin Safarzadeh, et al.. (2024). Green synthesis of a ZnO/ZnS-decorated magnetic porous carbon hybrid for enhanced rhodamine B adsorption and photodegradation: A combined DFT and experimental study. Sustainable materials and technologies. 43. e01231–e01231. 3 indexed citations
5.
Thanh, Hung Vo, et al.. (2024). Modeling the thermal transport properties of hydrogen and its mixtures with greenhouse gas impurities: A data-driven machine learning approach. International Journal of Hydrogen Energy. 83. 1–12. 13 indexed citations
6.
Rahimi, Mohammad & Shakirudeen A. Salaudeen. (2024). Optimizing hydrogen-rich gas production by steam gasification with integrated CaO-based adsorbent materials for CO2 capture: Machine learning approach. International Journal of Hydrogen Energy. 95. 695–709. 5 indexed citations
7.
Thanh, Hung Vo, Hemeng Zhang, Mohammad Rahimi, et al.. (2024). Enhancing carbon sequestration: Innovative models for wettability dynamics in CO2-brine-mineral systems. Journal of environmental chemical engineering. 12(5). 113435–113435. 16 indexed citations
8.
Khosrowshahi, Mobin Safarzadeh, et al.. (2024). Recent progress on advanced solid adsorbents for CO2 capture: From mechanism to machine learning. Materials Today Sustainability. 27. 100900–100900. 34 indexed citations
9.
10.
Rahimi, Mohammad, et al.. (2024). Improving stance detection accuracy in low-resource languages: a deep learning framework with ParsBERT. International Journal of Data Science and Analytics. 19(3). 517–535. 1 indexed citations
11.
Rahimi, Mohammad, Hossein Mashhadimoslem, Hung Vo Thanh, et al.. (2023). Yield prediction and optimization of biomass-based products by multi-machine learning schemes: Neural, regression and function-based techniques. Energy. 283. 128546–128546. 31 indexed citations
12.
Rahimi, Mohammad, et al.. (2023). A multi-criteria decision-making (MCDM) approach to determine the synthesizing routes of biomass-based carbon electrode material in supercapacitors. Journal of Cleaner Production. 397. 136606–136606. 24 indexed citations
13.
Thanh, Hung Vo, Mohammad Rahimi, Zhenxue Dai, Hemeng Zhang, & Tao Zhang. (2023). Predicting the wettability rocks/minerals-brine-hydrogen system for hydrogen storage: Re-evaluation approach by multi-machine learning scheme. Fuel. 345. 128183–128183. 54 indexed citations
14.
Khosrowshahi, Mobin Safarzadeh, Hossein Mashhadimoslem, Hadi Shayesteh, et al.. (2023). Natural Products Derived Porous Carbons for CO2 Capture. Advanced Science. 10(36). e2304289–e2304289. 57 indexed citations
15.
Zhang, Hemeng, Hung Vo Thanh, Mohammad Rahimi, et al.. (2023). Improving predictions of shale wettability using advanced machine learning techniques and nature-inspired methods: Implications for carbon capture utilization and storage. The Science of The Total Environment. 877. 162944–162944. 61 indexed citations
16.
Thanh, Hung Vo, et al.. (2023). Hydrogen Storage on Porous Carbon Adsorbents: Rediscovery by Nature-Derived Algorithms in Random Forest Machine Learning Model. Energies. 16(5). 2348–2348. 35 indexed citations
17.
Safaei-Farouji, Majid, Hung Vo Thanh, Zhenxue Dai, et al.. (2022). Exploring the power of machine learning to predict carbon dioxide trapping efficiency in saline aquifers for carbon geological storage project. Journal of Cleaner Production. 372. 133778–133778. 70 indexed citations
18.
Rahimi, Mohammad, et al.. (2022). Modeling and classifying the in-operando effects of wear and metal contaminations of lubricating oil on diesel engine: A machine learning approach. Expert Systems with Applications. 203. 117494–117494. 25 indexed citations
19.
Rahimi, Mohammad, et al.. (2022). Towards the Modeling and Prediction of the Yield of Oilseed Crops: A Multi-Machine Learning Approach. Agriculture. 12(10). 1739–1739. 6 indexed citations
20.
Moghimi, Ali, et al.. (2009). Analysing the Radioprotective Effect of Cotoneaster Nummularia in Mouse Bone Marrow Cells Using Micronucleus Assay. SHILAP Revista de lepidopterología. 1 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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