Dmitriy M. Makarov

1.2k total citations
72 papers, 960 citations indexed

About

Dmitriy M. Makarov is a scholar working on Fluid Flow and Transfer Processes, Biomedical Engineering and Organic Chemistry. According to data from OpenAlex, Dmitriy M. Makarov has authored 72 papers receiving a total of 960 indexed citations (citations by other indexed papers that have themselves been cited), including 51 papers in Fluid Flow and Transfer Processes, 47 papers in Biomedical Engineering and 31 papers in Organic Chemistry. Recurrent topics in Dmitriy M. Makarov's work include Thermodynamic properties of mixtures (51 papers), Phase Equilibria and Thermodynamics (44 papers) and Chemical Thermodynamics and Molecular Structure (27 papers). Dmitriy M. Makarov is often cited by papers focused on Thermodynamic properties of mixtures (51 papers), Phase Equilibria and Thermodynamics (44 papers) and Chemical Thermodynamics and Molecular Structure (27 papers). Dmitriy M. Makarov collaborates with scholars based in Russia, Iran and India. Dmitriy M. Makarov's co-authors include Gennadiy I. Egorov, A. M. Kolker, Yuliya A. Fadeeva, L. E. Shmukler, Igor V. Tetko, L. P. Safonova, Yury A. Budkov, Evgenia A. Safonova, M. N. Rodnikova and Alexander A. Ksenofontov and has published in prestigious journals such as SHILAP Revista de lepidopterología, Physical Chemistry Chemical Physics and Industrial & Engineering Chemistry Research.

In The Last Decade

Dmitriy M. Makarov

69 papers receiving 944 citations

Peers

Dmitriy M. Makarov
Dmitriy M. Makarov
Citations per year, relative to Dmitriy M. Makarov Dmitriy M. Makarov (= 1×) peers Gennadiy I. Egorov

Countries citing papers authored by Dmitriy M. Makarov

Since Specialization
Citations

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

Fields of papers citing papers by Dmitriy M. Makarov

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Dmitriy M. Makarov. 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 Dmitriy M. Makarov. The network helps show where Dmitriy M. Makarov may publish in the future.

Co-authorship network of co-authors of Dmitriy M. Makarov

This figure shows the co-authorship network connecting the top 25 collaborators of Dmitriy M. Makarov. A scholar is included among the top collaborators of Dmitriy M. Makarov 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 Dmitriy M. Makarov. Dmitriy M. Makarov 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.
Makarov, Dmitriy M., Yury A. Budkov, Pavel Gurikov, et al.. (2025). Improved Solubility Predictions in scCO 2 Using Thermodynamics-Informed Machine Learning Models. Journal of Chemical Information and Modeling. 65(8). 4043–4056. 5 indexed citations
2.
Jouyban‐Gharamaleki, Vahid, et al.. (2025). Solubility, thermodynamic properties and predictive modeling of glimepiride in various solvents. Journal of Molecular Liquids. 431. 127701–127701.
3.
Fadeeva, Yuliya A., Dmitriy M. Makarov, A. M. Kolker, & L. P. Safonova. (2025). Hybrid ionic liquid/alkanolamine solvents for enhanced CO2 absorption. Journal of Molecular Liquids. 439. 128872–128872.
4.
Makarov, Dmitriy M., Alexander A. Ksenofontov, & Yury A. Budkov. (2025). Consensus Modeling for Predicting Chemical Binding to Transthyretin as the Winning Solution of the Tox24 Challenge. Chemical Research in Toxicology. 38(3). 392–399. 2 indexed citations
5.
Ksenofontov, Alexander A., et al.. (2025). SpecML: web tool for predicting the spectral properties of BODIPYs. Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy. 348(Pt 2). 127091–127091.
6.
Makarov, Dmitriy M., et al.. (2025). Machine learning prediction of NMR shifts for rare and transition metal complexes (45Sc, 49Ti, 89Y, 91Zr, 139La). Journal of Molecular Liquids. 437. 128417–128417. 1 indexed citations
7.
Makarov, Dmitriy M. & A. M. Kolker. (2024). Viscosity of deep eutectic solvents: Predictive modeling with experimental validation. Fluid Phase Equilibria. 587. 114217–114217. 14 indexed citations
8.
Makarov, Dmitriy M., et al.. (2024). CO2 capture using choline chloride-based eutectic solvents. An experimental and theoretical investigation. Journal of Molecular Liquids. 413. 125910–125910. 7 indexed citations
9.
Egorov, Gennadiy I., Dmitriy M. Makarov, & A. M. Kolker. (2023). Thermodynamic properties of {glycerol (1) + tert-butanol (2)} mixtures at temperatures from 278 K to 323 K and pressures up to 100 MPa. The Journal of Chemical Thermodynamics. 186. 107124–107124. 2 indexed citations
10.
Makarov, Dmitriy M., et al.. (2023). Machine learning approach for predicting the yield of pyrroles and dipyrromethanes condensation reactions with aldehydes. Journal of Computational Science. 74. 102173–102173. 7 indexed citations
11.
Ksenofontov, Alexander A., et al.. (2023). Accurate prediction of 11B NMR chemical shift of BODIPYs via machine learning. Physical Chemistry Chemical Physics. 25(13). 9472–9481. 9 indexed citations
12.
Makarov, Dmitriy M., et al.. (2023). Designing deep eutectic solvents for efficient CO2 capture: A data-driven screening approach. Separation and Purification Technology. 325. 124614–124614. 43 indexed citations
13.
Makarov, Dmitriy M., Yuliya A. Fadeeva, Evgenia A. Safonova, & L. E. Shmukler. (2022). Predictive modeling of antibacterial activity of ionic liquids by machine learning methods. Computational Biology and Chemistry. 101. 107775–107775. 10 indexed citations
14.
Egorov, Gennadiy I. & Dmitriy M. Makarov. (2021). Liquid phase PVTx properties of {water (1) + 1,3-dimethylurea (2)} mixtures at temperatures from 278.15 to 323.15 K and pressures to 100 MPa. Journal of Molecular Liquids. 339. 116707–116707. 1 indexed citations
15.
Egorov, Gennadiy I. & Dmitriy M. Makarov. (2020). Densities and thermal expansions of (water + tetrahydrofuran) mixtures within the temperature range from (274.15 to 333.15) K at atmospheric pressure. Journal of Molecular Liquids. 310. 113105–113105. 16 indexed citations
16.
Makarov, Dmitriy M. & Gennadiy I. Egorov. (2018). Density and volumetric properties of the aqueous solutions of urea at temperatures from T = (278 to 333) K and pressures up to 100 MPa. The Journal of Chemical Thermodynamics. 120. 164–173. 30 indexed citations
17.
Egorov, Gennadiy I. & Dmitriy M. Makarov. (2017). Densities and Volumetric Properties of Aqueous Solutions of {Water (1) + N-Methylurea (2)} Mixtures at Temperatures of 274.15–333.15 K and at Pressures up to 100 MPa. Journal of Chemical & Engineering Data. 62(12). 4383–4394. 13 indexed citations
18.
Makarov, Dmitriy M., Gennadiy I. Egorov, Shiraz A. Markarian, & A. M. Kolker. (2016). Excess Gibbs Energy and Local Compositions in the Mixtures C2, C3 Alkane Diols and Triols with Water at Various Pressures. Journal of Solution Chemistry. 45(12). 1679–1688. 2 indexed citations
19.
Egorov, Gennadiy I. & Dmitriy M. Makarov. (2014). Volumetric properties of binary liquid-phase mixture of (water + glycerol) at temperatures of (278.15 to 323.15) K and pressures of (0.1 to 100) MPa. The Journal of Chemical Thermodynamics. 79. 135–158. 27 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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