Mohammad Talebi

1.4k total citations
34 papers, 1.2k citations indexed

About

Mohammad Talebi is a scholar working on Spectroscopy, Analytical Chemistry and Computational Theory and Mathematics. According to data from OpenAlex, Mohammad Talebi has authored 34 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 23 papers in Spectroscopy, 12 papers in Analytical Chemistry and 11 papers in Computational Theory and Mathematics. Recurrent topics in Mohammad Talebi's work include Analytical Chemistry and Chromatography (23 papers), Computational Drug Discovery Methods (11 papers) and Chromatography in Natural Products (10 papers). Mohammad Talebi is often cited by papers focused on Analytical Chemistry and Chromatography (23 papers), Computational Drug Discovery Methods (11 papers) and Chromatography in Natural Products (10 papers). Mohammad Talebi collaborates with scholars based in Australia, United States and United Kingdom. Mohammad Talebi's co-authors include Paul R. Haddad, Roman Szücs, Christopher A. Pohl, John W. Dolan, Ruth Amos, Emily F. Hilder, Maryam Taraji, R. Dario Arrua, Tim Causon and Ali Rassouli and has published in prestigious journals such as Analytical Chemistry, ACS Catalysis and Journal of Chromatography A.

In The Last Decade

Mohammad Talebi

33 papers receiving 1.2k 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 Talebi Australia 22 669 443 363 356 237 34 1.2k
Krzysztof Goryński Poland 20 566 0.8× 340 0.8× 381 1.0× 669 1.9× 53 0.2× 43 1.4k
Bob W.J. Pirok Netherlands 20 1.1k 1.7× 647 1.5× 632 1.7× 675 1.9× 62 0.3× 86 1.6k
Jian‐Liang Zhou China 22 188 0.3× 84 0.2× 790 2.2× 229 0.6× 41 0.2× 78 1.5k
Eslam Pourbasheer Iran 24 251 0.4× 205 0.5× 337 0.9× 370 1.0× 589 2.5× 87 1.6k
Caiying Wu China 23 708 1.1× 446 1.0× 260 0.7× 754 2.1× 17 0.1× 59 1.7k
Maria Anna Maggi Italy 21 313 0.5× 157 0.4× 253 0.7× 370 1.0× 104 0.4× 52 957
Xingchu Gong China 21 183 0.3× 274 0.6× 356 1.0× 482 1.4× 36 0.2× 126 1.5k
Min He China 20 460 0.7× 119 0.3× 439 1.2× 182 0.5× 83 0.4× 80 1.3k
Aliuska Morales Helguera Cuba 26 137 0.2× 88 0.2× 505 1.4× 58 0.2× 888 3.7× 64 1.6k
Yolanda Martı́n-Biosca Spain 19 668 1.0× 422 1.0× 232 0.6× 254 0.7× 109 0.5× 65 1.0k

Countries citing papers authored by Mohammad Talebi

Since Specialization
Citations

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

Fields of papers citing papers by Mohammad Talebi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Mohammad Talebi

This figure shows the co-authorship network connecting the top 25 collaborators of Mohammad Talebi. A scholar is included among the top collaborators of Mohammad Talebi 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 Talebi. Mohammad Talebi 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
2.
Amos, Ruth, Mohammad Talebi, Roman Szücs, et al.. (2018). Retention Index Prediction Using Quantitative Structure–Retention Relationships for Improving Structure Identification in Nontargeted Metabolomics. Analytical Chemistry. 90(15). 9434–9440. 39 indexed citations
3.
Talebi, Mohammad, Ruth Amos, Roman Szücs, et al.. (2018). Retention prediction in reversed phase high performance liquid chromatography using quantitative structure-retention relationships applied to the Hydrophobic Subtraction Model. Journal of Chromatography A. 1541. 1–11. 54 indexed citations
4.
5.
Taraji, Maryam, Paul R. Haddad, Ruth Amos, et al.. (2017). Error measures in quantitative structure-retention relationships studies. Journal of Chromatography A. 1524. 298–302. 40 indexed citations
6.
Taraji, Maryam, Paul R. Haddad, Ruth Amos, et al.. (2017). Chemometric-assisted method development in hydrophilic interaction liquid chromatography: A review. Analytica Chimica Acta. 1000. 20–40. 81 indexed citations
8.
Gupta, Vipul, Mohammad Talebi, Sara Sandron, et al.. (2016). 3D printed titanium micro-bore columns containing polymer monoliths for reversed-phase liquid chromatography. Analytica Chimica Acta. 910. 84–94. 67 indexed citations
9.
Park, Soo Hyun, Paul R. Haddad, Mohammad Talebi, et al.. (2016). Retention prediction of low molecular weight anions in ion chromatography based on quantitative structure-retention relationships applied to the linear solvent strength model. Journal of Chromatography A. 1486. 68–75. 29 indexed citations
10.
Talebi, Mohammad, Soo Hyun Park, Maryam Taraji, et al.. (2016). Retention Time Prediction Based on Molecular Structure in Pharmaceutical Method Development: A Perspective. LCGC North America. 34(8). 550–558. 9 indexed citations
11.
Tyteca, Eva, Mohammad Talebi, Ruth Amos, et al.. (2016). Towards a chromatographic similarity index to establish localized quantitative structure-retention models for retention prediction: Use of retention factor ratio. Journal of Chromatography A. 1486. 50–58. 33 indexed citations
12.
Taraji, Maryam, Paul R. Haddad, Ruth Amos, et al.. (2016). Prediction of retention in hydrophilic interaction liquid chromatography using solute molecular descriptors based on chemical structures. Journal of Chromatography A. 1486. 59–67. 47 indexed citations
13.
Park, Soo Hyun, Robert A. Shellie, Greg W. Dicinoski, et al.. (2016). Enhanced methodology for porting ion chromatography retention data. Journal of Chromatography A. 1436. 59–63. 9 indexed citations
15.
Talebi, Mohammad, Anna Nordborg, Andras Gaspar, et al.. (2013). Charge heterogeneity profiling of monoclonal antibodies using low ionic strength ion-exchange chromatography and well-controlled pH gradients on monolithic columns. Journal of Chromatography A. 1317. 148–154. 56 indexed citations
16.
Talebi, Mohammad, R. Dario Arrua, Andras Gaspar, et al.. (2012). Epoxy-based monoliths for capillary liquid chromatography of small and large molecules. Analytical and Bioanalytical Chemistry. 405(7). 2233–2244. 18 indexed citations
18.
Ebrahimi, Samad Nejad, et al.. (2007). Multivariate optimisation of microwave‐assisted extraction of capsaicin from Capsicum frutescens L. and quantitative analysis by 1H‐NMR. Phytochemical Analysis. 18(4). 333–340. 36 indexed citations
19.
Dabiri, Minoo, et al.. (2005). Optimization of microwave‐assisted extraction for alizarin and purpurin in Rubiaceae plants and its comparison with conventional extraction methods. Journal of Separation Science. 28(4). 387–396. 40 indexed citations
20.
Talebi, Mohammad, et al.. (2004). Optimization of the extraction of paclitaxel from Taxus baccata L. by the use of microwave energy. Journal of Separation Science. 27(13). 1130–1136. 53 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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