Kotiba Hamad

5.4k total citations · 3 hit papers
106 papers, 4.2k citations indexed

About

Kotiba Hamad is a scholar working on Mechanical Engineering, Materials Chemistry and Biomaterials. According to data from OpenAlex, Kotiba Hamad has authored 106 papers receiving a total of 4.2k indexed citations (citations by other indexed papers that have themselves been cited), including 53 papers in Mechanical Engineering, 53 papers in Materials Chemistry and 50 papers in Biomaterials. Recurrent topics in Kotiba Hamad's work include Aluminum Alloys Composites Properties (35 papers), Magnesium Alloys: Properties and Applications (28 papers) and biodegradable polymer synthesis and properties (22 papers). Kotiba Hamad is often cited by papers focused on Aluminum Alloys Composites Properties (35 papers), Magnesium Alloys: Properties and Applications (28 papers) and biodegradable polymer synthesis and properties (22 papers). Kotiba Hamad collaborates with scholars based in South Korea, Syria and Pakistan. Kotiba Hamad's co-authors include Mosab Kaseem, Fawaz Deri, Young Gun Ko, Umer Masood Chaudry, Muhammad Ayyoob, Haozhe Yang, Jinho Joo, Tea‐Sung Jun, Jung‐Gu Kim and Jung-Gu Kim and has published in prestigious journals such as Advanced Functional Materials, Progress in Polymer Science and Journal of Power Sources.

In The Last Decade

Kotiba Hamad

104 papers receiving 4.1k citations

Hit Papers

Properties and medical applications of polylactic acid: A... 2015 2026 2018 2022 2015 2018 2022 100 200 300 400 500
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Properties and medical applications of polylactic acid: A review
    2015 543 citations Kotiba Hamad, Mosab Kaseem et al. profile →
  2. Polylactic acid blends: The future of green, light and tough
    2018 500 citations Kotiba Hamad, Mosab Kaseem et al. profile →
  3. Poly(butylene succinate) (PBS): Materials, processing, and industrial applications
    2022 256 citations Kotiba Hamad et al. profile →

Peers

Kotiba Hamad
Mosab Kaseem South Korea
Andrea Sorrentino Italy
Roberto Pantani Italy
Yunxuan Weng China
José Ignácio Velasco Spain
Xiuqin Zhang China
Henri Vahabi France
Ismaeil Ghasemi Iran
Xiang Lu China
Gang Wu China
Mosab Kaseem South Korea
Kotiba Hamad
Citations per year, relative to Kotiba Hamad Kotiba Hamad (= 1×) peers Mosab Kaseem

Countries citing papers authored by Kotiba Hamad

Since Specialization
Citations

This map shows the geographic impact of Kotiba Hamad'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 Kotiba Hamad with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kotiba Hamad more than expected).

Fields of papers citing papers by Kotiba Hamad

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Kotiba Hamad. 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 Kotiba Hamad. The network helps show where Kotiba Hamad may publish in the future.

Co-authorship network of co-authors of Kotiba Hamad

This figure shows the co-authorship network connecting the top 25 collaborators of Kotiba Hamad. A scholar is included among the top collaborators of Kotiba Hamad 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 Kotiba Hamad. Kotiba Hamad 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.
2.
Kim, Jung-Gu, et al.. (2025). Artificial Intelligence and Li Ion Batteries: Basics and Breakthroughs in Electrolyte Materials Discovery. Crystals. 15(2). 114–114. 5 indexed citations
3.
Dutta, Subhajit, et al.. (2025). Mixed‐Dimensional Cu‐Based Perovskites for Stable and Energy‐Efficient Neuromorphic Memristors. Advanced Functional Materials. 36(17). 2 indexed citations
4.
Singh, Nirpendra, et al.. (2024). Introducing MagBERT: A language model for magnesium textual data mining and analysis. Journal of Magnesium and Alloys. 12(8). 3216–3228. 6 indexed citations
5.
Hamad, Kotiba, et al.. (2024). Introducing Materials Fingerprint (MatPrint): A novel method in graphical material representation and features compression. Computational Materials Science. 246. 113444–113444.
6.
Kim, Jung‐Gu, et al.. (2024). Utilizing machine learning and phonon density of states for innovative approaches to design and optimize high-performance solid-state Mg-ion electrolytes. Journal of Power Sources. 606. 234575–234575. 5 indexed citations
7.
Jeong, Seonghun, Jee-Hyun Kang, Junyoung Mun, et al.. (2024). Phonon DOS‐Based Machine Learning Model for Designing High‐Performance Solid Electrolytes in Li‐Ion Batteries. International Journal of Energy Research. 2024(1). 4 indexed citations
8.
Hamad, Kotiba, et al.. (2023). Discovery of solid-state electrolytes for Na-ion batteries using machine learning. Materials Letters. 349. 134848–134848. 14 indexed citations
9.
Dutta, Subhajit, et al.. (2023). Learning techniques for designing solid-state lithium-ion batteries with high thermomechanical stability. Materials Letters. 351. 135049–135049. 3 indexed citations
10.
Hamad, Kotiba, et al.. (2023). Solid electrolytes for Li-ion batteries via machine learning. Materials Letters. 337. 133926–133926. 21 indexed citations
11.
Chaudry, Umer Masood, et al.. (2023). Effect of CaO Content and Annealing Treatment on the Room-Temperature Mechanical Properties of AZ61 and AZ61-CaO Alloys. Metals. 13(12). 1962–1962. 16 indexed citations
12.
Dutta, Subhajit, et al.. (2023). Accelerated discovery of perovskite materials guided by machine learning techniques. Materials Letters. 353. 135311–135311. 6 indexed citations
13.
Zhai, Zhengjun, et al.. (2022). Deep Learning-Based Software Defect Prediction via Semantic Key Features of Source Code—Systematic Survey. Mathematics. 10(17). 3120–3120. 16 indexed citations
14.
Chaudry, Umer Masood, et al.. (2021). Age-hardening behavior guided by the multi-objective evolutionary algorithm and machine learning. Journal of Alloys and Compounds. 893. 162104–162104. 16 indexed citations
15.
Malik, Abdul, Umer Masood Chaudry, Kotiba Hamad, & Tea‐Sung Jun. (2021). Microstructure Features and Superplasticity of Extruded, Rolled and SPD-Processed Magnesium Alloys: A Short Review. Metals. 11(11). 1766–1766. 40 indexed citations
16.
Ko, Young Gun, Min Jun Kim, & Kotiba Hamad. (2019). Structural evolutions and mechanical properties of IF steel deformed by differential speed rolling at various per-pass-thickness reductions. Materials Letters. 250. 178–181. 5 indexed citations
17.
Hamad, Kotiba & Young Gun Ko. (2016). A cross-shear deformation for optimizing the strength and ductility of AZ31 magnesium alloys. Scientific Reports. 6(1). 29954–29954. 56 indexed citations
18.
Kaseem, Mosab, Kotiba Hamad, Fawaz Deri, & Young Gun Ko. (2016). A review on recent researches on polylactic acid/carbon nanotube composites. Polymer Bulletin. 74(7). 2921–2937. 45 indexed citations
19.
Hamad, Kotiba, Hae Woong Yang, & Young Gun Ko. (2016). Interpretation of annealing texture changes of severely deformed Al-Mg-Si alloy. Journal of Alloys and Compounds. 687. 300–305. 22 indexed citations
20.
Hamad, Kotiba, Mosab Kaseem, & Fawaz Deri. (2011). Melt Rheology of Poly(Lactic Acid)/Low Density Polyethylene Polymer Blends. Advances in Chemical Engineering and Science. 1(4). 208–214. 60 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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