Moe Elbadawi

2.7k total citations · 1 hit paper
39 papers, 1.8k citations indexed

About

Moe Elbadawi is a scholar working on Biomedical Engineering, Automotive Engineering and Molecular Biology. According to data from OpenAlex, Moe Elbadawi has authored 39 papers receiving a total of 1.8k indexed citations (citations by other indexed papers that have themselves been cited), including 21 papers in Biomedical Engineering, 10 papers in Automotive Engineering and 9 papers in Molecular Biology. Recurrent topics in Moe Elbadawi's work include 3D Printing in Biomedical Research (15 papers), Innovative Microfluidic and Catalytic Techniques Innovation (12 papers) and Additive Manufacturing and 3D Printing Technologies (10 papers). Moe Elbadawi is often cited by papers focused on 3D Printing in Biomedical Research (15 papers), Innovative Microfluidic and Catalytic Techniques Innovation (12 papers) and Additive Manufacturing and 3D Printing Technologies (10 papers). Moe Elbadawi collaborates with scholars based in United Kingdom, Spain and Saudi Arabia. Moe Elbadawi's co-authors include Abdul W. Basit, Simon Gaisford, Jun Jie Ong, Álvaro Goyanes, Laura E. McCoubrey, Thomas D. Pollard, Mine Orlu, Francesca K. H. Gavins, Atheer Awad and Sarah J. Trenfield and has published in prestigious journals such as Advanced Functional Materials, Advanced Drug Delivery Reviews and Journal of Controlled Release.

In The Last Decade

Moe Elbadawi

38 papers receiving 1.7k citations

Hit Papers

align trajectories

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.

Connected healthcare: Improving patient care using digital health technologies

2021 217 citations Atheer Awad, Sarah J. Trenfield et al. Advanced Drug Delivery Reviews profile →

Peers

Moe Elbadawi

CTM

Jun Jie Ong United Kingdom

Rapti D. Madurawe United States

Dolores R. Serrano Spain

Christine M. Madla United Kingdom

Renata Jachowicz Poland

F. Mohammadi Iran

Eko Supriyanto Malaysia

Ziyaur Rahman United States

Xujie Wang China

Natalja Genina Denmark

Jun Jie Ong United Kingdom

Moe Elbadawi

922 ×1.1

607 ×1.2

153 ×0.5

146 ×0.9

61 ×0.4

CTM

Citations per year, relative to Moe Elbadawi Moe Elbadawi (= 1×) peers Jun Jie Ong

Countries citing papers authored by Moe Elbadawi

Since Specialization

Citations

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

Fields of papers citing papers by Moe Elbadawi

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Moe Elbadawi

This figure shows the co-authorship network connecting the top 25 collaborators of Moe Elbadawi. A scholar is included among the top collaborators of Moe Elbadawi 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 Moe Elbadawi. Moe Elbadawi is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Siamidi, Angeliki, et al.. (2025). Machine Learning Predicts Drug Release Profiles and Kinetic Parameters Based on Tablets’ Formulations. The AAPS Journal. 27(5). 124–124. 4 indexed citations

Chapman, Christopher A. R., et al.. (2024). 3D printed electro-responsive system with programmable drug release. Materials Today Advances. 23. 100509–100509. 4 indexed citations

Elbadawi, Moe, et al.. (2024). HgbNet: Predicting Hemoglobin Level/Anemia Degree From Irregular EHR. IEEE Access. 12. 144837–144854. 1 indexed citations

Abdalla, Youssef, Martin Ferianc, Atheer Awad, et al.. (2024). Smart laser Sintering: Deep Learning-Powered powder bed fusion 3D printing in precision medicine. International Journal of Pharmaceutics. 661. 124440–124440. 14 indexed citations

Türkgeldi, Engin, et al.. (2023). Do probiotic interventions improve female unexplained infertility? A critical commentary. Reproductive BioMedicine Online. 48(4). 103734–103734. 4 indexed citations

Abdalla, Youssef, Moe Elbadawi, Atheer Awad, et al.. (2023). Machine learning using multi-modal data predicts the production of selective laser sintered 3D printed drug products. International Journal of Pharmaceutics. 633. 122628–122628. 42 indexed citations

Elbadawi, Moe, Christopher A. R. Chapman, Rylie A. Green, et al.. (2023). Electroactive Polymers for On‐Demand Drug Release. Advanced Healthcare Materials. 13(3). e2301759–e2301759. 30 indexed citations

Basit, Abdul W., et al.. (2023). Optimizing environmental sustainability in pharmaceutical 3D printing through machine learning. International Journal of Pharmaceutics. 648. 123561–123561. 13 indexed citations

McCoubrey, Laura E., Vipul Yadav, Moe Elbadawi, et al.. (2023). Advancing oral delivery of biologics: Machine learning predicts peptide stability in the gastrointestinal tract. International Journal of Pharmaceutics. 634. 122643–122643. 23 indexed citations

10.

Trenfield, Sarah J., Atheer Awad, Laura E. McCoubrey, et al.. (2022). Advancing pharmacy and healthcare with virtual digital technologies. Advanced Drug Delivery Reviews. 182. 114098–114098. 72 indexed citations

11.

Elbadawi, Moe, et al.. (2022). Multimodal Diagnosis for Pulmonary Embolism from EHR Data and CT Images. 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). 2022. 2053–2057. 6 indexed citations

12.

Ong, Jun Jie, Thomas D. Pollard, Álvaro Goyanes, et al.. (2021). Optical biosensors - Illuminating the path to personalized drug dosing. Biosensors and Bioelectronics. 188. 113331–113331. 49 indexed citations

13.

Elbadawi, Moe, Jun Jie Ong, Thomas D. Pollard, et al.. (2021). Machine learning predicts 3D printing performance of over 900 drug delivery systems. Journal of Controlled Release. 337. 530–545. 128 indexed citations

14.

Elbadawi, Moe, Laura E. McCoubrey, Francesca K. H. Gavins, et al.. (2021). Disrupting 3D printing of medicines with machine learning. Trends in Pharmacological Sciences. 42(9). 745–757. 82 indexed citations

15.

McCoubrey, Laura E., Moe Elbadawi, Mine Orlu, Simon Gaisford, & Abdul W. Basit. (2021). Machine Learning Uncovers Adverse Drug Effects on Intestinal Bacteria. Pharmaceutics. 13(7). 1026–1026. 32 indexed citations

16.

Elbadawi, Moe, Simon Gaisford, & Abdul W. Basit. (2020). Advanced machine-learning techniques in drug discovery. Drug Discovery Today. 26(3). 769–777. 116 indexed citations

17.

Pollard, Thomas D., Jun Jie Ong, Álvaro Goyanes, et al.. (2020). Electrochemical biosensors: a nexus for precision medicine. Drug Discovery Today. 26(1). 69–79. 59 indexed citations

18.

Elbadawi, Moe, et al.. (2020). 3D printing tablets: Predicting printability and drug dissolution from rheological data. International Journal of Pharmaceutics. 590. 119868–119868. 92 indexed citations

19.

Elbadawi, Moe, et al.. (2017). Tape casting and lost carbonate sintering processes for production of heat sinks for portable electronics. Advanced Materials Letters. 8(7). 807–812. 1 indexed citations

20.

Elbadawi, Moe, et al.. (2015). Mechanical and physical properties of particleboards made from Ailanthus wood and UF resin fortified by Acacias tannins blend. Universiti Putra Malaysia Institutional Repository (Universiti Putra Malaysia). 19 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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