Skip to main content
SpringerLink
Log in

Analysis and significance of prediction models for higher heating value of coal: an updated review

  • Published:
Journal of Thermal Analysis and Calorimetry Aims and scope Submit manuscript

Abstract

A nation's ability to use its energy resources efficiently and the expansion of its industrial sector are key factors in its prosperity. Most of the industrially developed nations in the world use coal as their main energy source. These resources have been utilized mainly for industrial growth, such as thermal power plants, steel industries, and other associated industries for the industrial revolution. For the exploitation and utilization of these resources, heating value is a significant concern. This can be determined directly or using models framed using readily available variables. Various mathematical models have been developed for estimating coal heating value quickly and precisely. The current review compiles the most recent and well-known models for estimating coal's heating value. Different models have relied upon elemental analysis, proximate analysis, proximate and ultimate analysis, spectroscopy, and other analyses to determine target variables. This compilation shall help the user to identify and apply best suited method, based on concerned organization’s economic condition and accessibility to methodology.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Data availability

The raw data used in this work can be obtained from the corresponding author upon reasonable request.

References

  1. BP. Statistical Review of world energy globally consistent data on world energy markets. [Internet]. 2020. Available from: https://www.bp.com/content/dam/bp/business-sites/en/global/corporate/pdfs/energy-economics/statistical-review/bp-stats-review-2020-full-report.pdf

  2. Vargas-Moreno JM, Callejón-Ferre AJ, Pérez-Alonso J, Velázquez-Martí B. A review of the mathematical models for predicting the heating value of biomass materials. Renew Sustain Energy Rev [Internet]. 2012;16:3065–83. https://doi.org/10.1016/j.rser.2012.02.054.

    Article  CAS  Google Scholar 

  3. Yin C-Y. Prediction of higher heating values of biomass from proximate and ultimate analyses. Fuel [Internet]. 2011;90:1128–32. Available from: https://www.sciencedirect.com/science/article/abs/pii/S0016236110006460

  4. Patel SU, Jeevan Kumar B, Badhe YP, Sharma BK, Saha S, Biswas S, et al. Estimation of gross calorific value of coals using artificial neural networks. Fuel [Internet]. Elsevier Ltd; 2007 [cited 2021 Mar 8];86:334–44. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0016236106002961

  5. Demirbas A. Theoretical heating values and impacts of pure compounds and fuels. Energy Sources, Part A Recover Util Environ Eff [Internet]. 2006;28:459–67. Available from: http://www.tandfonline.com/doi/abs/https://doi.org/10.1080/009083190927129

  6. Tan P, Zhang C, Xia J, Fang Q-Y, Chen G. Estimation of higher heating value of coal based on proximate analysis using support vector regression. Fuel Process Technol [Internet]. 2015;138:298–304. https://doi.org/10.1016/j.fuproc.2015.06.013.

    Article  CAS  Google Scholar 

  7. ASTM D3172–89. D3172–73 (Reapproved 1984), Standard method of proximate analysis of coal and coke. Gaseous fuels; coal coke Sect. 2002;5:5.

  8. ASTM D3176–84 [Internet]. 1990. Available from: https://www.osti.gov/biblio/569575

  9. ASTM D5865–10. Standard test method for gross calorific value of coal and coke. ASTM Int. West Conshohocken, PA; 2010.

  10. Lowry HH. Chemistry of coal utilization: supplementary volume. New York: John Wiley and Sons Inc; 1963.

    Google Scholar 

  11. WA Selvig IG. Chemistry of coal utilisation. Vol.1. H H Lowery, editor. New york: Wiley Online Library; 1945.

  12. Strache H LR. Kohlenchemie.Leipzig: akademische Verlagssesellschaft. 1924;476.

  13. Steuer W. General formulas for calculating the heating value of fossil fuels from their elementary analyses. Brennst Chem. 1926;7:344–7.

    CAS  Google Scholar 

  14. R Vondracek. Brennstoff-Chem. 1927;22–3.

  15. Schuster F. Calculation of the heating value of solid fuels. Glueckauf. 1931;67:232–5.

    CAS  Google Scholar 

  16. Grummel ES, Davis I. A new method of calculating the calorific value of a fuel from its ultimate analysis. Fuel. 1933;12:199.

    CAS  Google Scholar 

  17. Seyler CA. Petrology and the classification of coal. South Wales Institute of Engineers; 1937.

  18. Sumegi L. A formula for calculation of the calorific value of coal from chemical analysis. Magy Mernok-es Epit Kozlonye. 1939;73:345–6.

    CAS  Google Scholar 

  19. Mott RA, Spooner CE. The calorific value of carbon in coal: the Dulong relationship. Fuel. 1940;19(10–19):242–51.

    Google Scholar 

  20. Boie W. Fuel technology calculations. Energietechnik. 1953;3:309–16.

    Google Scholar 

  21. Graboski M, Bain R. Properties of biomass relevant to gasification in Biomass gasification--principles and technology, ed. by Reed TB. Noyes Data Corp New Jersey. 1981;41–69.

  22. Alwalkar AT. Coal conversion systems technical data book. Department of Energy, Office of Energy Technology, Division of Coal Conversion; 1978.

  23. Himus GW. The elements of fuel technology. L. Hill; 1958.

  24. Krevelen DW, Schuyer J. Coal science: aspects of coal constitution. Elsevier publishing company; 1957.

  25. Mason DM, Gandhi KN. Formulas for calculating the calorific value of coal and coal chars: Development, tests, and uses. Fuel Process Technol [Internet]. 1983;7:11–22. Available from: https://linkinghub.elsevier.com/retrieve/pii/037838208390022X

  26. Singh KP, Kakati MC. New models for prediction of specific energy of coal. Fuel [Internet]. 1994;73:301–3. Available from: https://linkinghub.elsevier.com/retrieve/pii/0016236194901295

  27. D’Huart K. The development of formulas for determining the heating value of solid fuels. Die Warme. 1930;53:313–7.

    Google Scholar 

  28. Goutal M. Industrial chemistry—on the calorific power of oil. Cr Hebd Acad Sci. 1902;135:477–9.

    Google Scholar 

  29. Schuster VF. Uber die Berechnung des Heizwertes von Kohlen der Immediatzusammensetzung. Brennst Chem. 1951;32:19–20.

    CAS  Google Scholar 

  30. Küçükbayrak S, Dürüs B, Meríçboyu AE, Kadiog̈lu E. Estimation of calorific values of Turkish lignites. Fuel [Internet]. 1991;70:979–81. Available from: https://linkinghub.elsevier.com/retrieve/pii/001623619190054E

  31. Cordero T, Marquez F, Rodriguez-Mirasol J, Rodriguez J. Predicting heating values of lignocellulosics and carbonaceous materials from proximate analysis. Fuel [Internet]. 2001 [cited 2021 Mar 8];80:1567–71. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0016236101000345

  32. Jiménez L, González F. Study of the physical and chemical properties of lignocellulosic residues with a view to the production of fuels. Fuel [Internet]. 1991;70:947–50. Available from: https://linkinghub.elsevier.com/retrieve/pii/001623619190049G

  33. Parikh J, Channiwala SA, Ghosal GK. A correlation for calculating HHV from proximate analysis of solid fuels. Fuel [Internet]. 2005;84:487–94. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0016236104003072

  34. Majumder AK, Jain R, Banerjee P, Barnwal JP. Development of a new proximate analysis based correlation to predict calorific value of coal. Fuel [Internet]. 2008;87:3077–81. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0016236108001488

  35. Erik NY, Yilmaz I. On the use of conventional and soft computing models for prediction of gross calorific value (GCV) of coal. Int J Coal Prep Util [Internet]. 2011;31:32–59. Available from: http://www.tandfonline.com/doi/abs/https://doi.org/10.1080/19392699.2010.534683

  36. Akkaya AV. Predicting coal heating values using proximate analysis via a neural network approach. Energy Sources, Part A Recover Util Environ Eff [Internet]. 2013;35:253–60. Available from: http://www.tandfonline.com/doi/abs/https://doi.org/10.1080/15567036.2010.509090

  37. Ghosh S, Chatterjee R, Shanker P. Prediction of coal proximate parameters and useful heat value of coal from well logs of the Bishrampur coalfield, India, using regression and artificial neural network modeling. Energy Fuels. 2016;30:7055–64.

    Article  CAS  Google Scholar 

  38. Feng Q, Zhang J, Zhang X, Wen S. Proximate analysis based prediction of gross calorific value of coals: A comparison of support vector machine, alternating conditional expectation and artificial neural network. Fuel Process Technol [Internet]. 2015;129:120–9. https://doi.org/10.1016/j.fuproc.2014.09.001.

    Article  CAS  Google Scholar 

  39. Qi M, Luo H, Wei P, Fu Z. Estimation of low calorific value of blended coals based on support vector regression and sensitivity analysis in coal-fired power plants. Fuel [Internet]. 2019;236:1400–7. https://doi.org/10.1016/j.fuel.2018.09.117.

    Article  CAS  Google Scholar 

  40. Acikkar M, Sivrikaya O. Prediction of gross calorific value of coal based on proximate analysis using multiple linear regression and artificial neural networks. TURKISH J Electr Eng Comput Sci [Internet]. 2018;26:2541–52. Available from: http://online.journals.tubitak.gov.tr/openDoiPdf.htm?mKodu=elk-1802-50

  41. Akhtar J, Sheikh N, Munir S. Linear regression-based correlations for estimation of high heating values of Pakistani lignite coals. Energy Sources, Part A Recover Util Environ Eff [Internet]. Taylor & Francis; 2017;39:1063–70. Available from: https://www.tandfonline.com/doi/full/https://doi.org/10.1080/15567036.2017.1289283

  42. Mesroghli S, Jorjani E, Chehreh Chelgani S. Estimation of gross calorific value based on coal analysis using regression and artificial neural networks. Int J Coal Geol [Internet]. 2009;79:49–54. https://doi.org/10.1016/j.coal.2009.04.002.

    Article  CAS  Google Scholar 

  43. Kavšek D, Bednárová A, Biro M, Kranvogl R, Vončina D, Beinrohr E. Characterization of Slovenian coal and estimation of coal heating value based on proximate analysis using regression and artificial neural networks. Open Chem [Internet]. 2013;11:1481–91. Available from: https://www.degruyter.com/view/journals/chem/11/9/article-p1481.xml

  44. Akkaya AV. Coal higher heating value prediction using constituents of proximate analysis: Gaussian process regression model. Int J Coal Prep Util [Internet]. Taylor & Francis; 2020;00:1–16. https://doi.org/10.1080/19392699.2020.1786374

  45. Spooner CE. Swelling power of coal. Fuel. 1951;30:193–202.

    CAS  Google Scholar 

  46. Mazumdar BK. Coal systematics: deductions from proximate analysis of coal part I. J Sci Ind Res B. 1954;13:857–63.

    Google Scholar 

  47. Ghugare SB, Tambe SS. Genetic programming based high performing correlations for prediction of higher heating value of coals of different ranks and from diverse geographies. J Energy Inst [Internet]. 2017;90:476–84. https://doi.org/10.1016/j.joei.2016.03.002.

    Article  CAS  Google Scholar 

  48. Go AW, Conag AT. A unified semi-empirical model for estimating the higher heating value of coals based on proximate analysis. Combust Sci Technol [Internet]. 2018;190:2203–23.

    Article  CAS  Google Scholar 

  49. Kumari P, Singh AK, Wood DA, Hazra B. Predictions of gross calorific value of Indian coals from their moisture and ash content. J Geol Soc India [Internet]. 2019;93:437–42. Available from: http://link.springer.com/https://doi.org/10.1007/s12594-019-1198-5

  50. Neavel RC, Smith SE, Hippo EJ, Miller RN. Interrelationships between coal compositional parameters. Fuel [Internet]. 1986;65:312–20. Available from: https://linkinghub.elsevier.com/retrieve/pii/0016236186902899

  51. Mazumdar B. Theoretical oxygen requirement for coal combustion: relationship with its calorific value. Fuel [Internet]. Elsevier Science Ltd; 2000 [cited 2021 Jan 10];79:1413–9. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0016236199002720

  52. Channiwala SA, Parikh PP. A unified correlation for estimating HHV of solid, liquid and gaseous fuels. Fuel [Internet]. Elsevier; 2002 [cited 2021 Jan 10];81:1051–63. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0016236101001314

  53. Mathews JP, Krishnamoorthy V, Louw E, Tchapda AHN, Castro-Marcano F, Karri V, et al. A review of the correlations of coal properties with elemental composition. Fuel Process Technol [Internet]. 2014;121:104–13. https://doi.org/10.1016/j.fuproc.2014.01.015.

    Article  CAS  Google Scholar 

  54. Wen X, Jian S, Wang J. Prediction models of calorific value of coal based on wavelet neural networks. Fuel [Internet]. 2017;199:512–22. https://doi.org/10.1016/j.fuel.2017.03.012.

    Article  CAS  Google Scholar 

  55. Patel SU, Jeevan Kumar B, Badhe YP, Sharma BK, Saha S, Biswas S, et al. Estimation of gross calorific value of coals using artificial neural networks. Fuel [Internet]. 2007;86:334–44. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0016236106002961

  56. Chelgani SC, Mesroghli S, Hower JC. Simultaneous prediction of coal rank parameters based on ultimate analysis using regression and artificial neural network. Int J Coal Geol [Internet]. 2010;83:31–4. https://doi.org/10.1016/j.coal.2010.03.004.

    Article  CAS  Google Scholar 

  57. Matin SS, Chelgani SC. Estimation of coal gross calorific value based on various analyses by random forest method. Fuel [Internet]. 2016;177:274–8. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0016236116300680

  58. Cloutis EA. Quantitative characterization of coal properties using bidirectional diffuse reflectance spectroscopy. Fuel [Internet]. 2003;82:2239–54. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0016236103002096

  59. Andrés JM, Bona MT. Analysis of coal by diffuse reflectance near-infrared spectroscopy. Anal Chim Acta [Internet]. 2005;535:123–32. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0003267004016411

  60. Andrés JM, Bona MT. ASTM clustering for improving coal analysis by near-infrared spectroscopy. Talanta [Internet]. 2006;70:711–9. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0039914006003651

  61. Bona MT, Andrés JM. Coal analysis by diffuse reflectance near-infrared spectroscopy: Hierarchical cluster and linear discriminant analysis. Talanta [Internet]. 2007;72:1423–31. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0039914007000938

  62. Bona MT, Andrés JM. Reflection and transmission mid-infrared spectroscopy for rapid determination of coal properties by multivariate analysis. Talanta [Internet]. Elsevier; 2008 [cited 2021 Mar 12];74:998–1007. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0039914007005644

  63. Alciaturi CE, Escobar ME, Vallejo R. Prediction of coal properties by derivative DRIFT spectroscopy. Fuel [Internet]. 1996;75:491–9. Available from: https://linkinghub.elsevier.com/retrieve/pii/0016236195002464

  64. Kaihara M, Takahashi T, Akazawa T, Sato T, Takahashi S. APPLICATION OF NEAR INFRARED SPECTROSCOPY TO RAPID ANALYSIS OF COALS. Spectrosc Lett [Internet]. 2002;35:369–76. Available from: http://www.tandfonline.com/doi/abs/https://doi.org/10.1081/SL-120005673

  65. Kim DW, Lee JM, Kim JS. Application of near infrared diffuse reflectance spectroscopy for on-line measurement of coal properties. Korean J Chem Eng [Internet]. 2009;26:489–95. Available from: http://link.springer.com/https://doi.org/10.1007/s11814-009-0083-0

  66. Zhai YY, Chen JB, Wu BM. Rapid detection method based on the near-infrared diffuse reflection spectrum of ash content. J Yunnan Univ. 2013;35:214–8.

    Google Scholar 

  67. Wang Y, Yang M, Wei G, Hu R, Luo Z, Li G. Improved PLS regression based on SVM classification for rapid analysis of coal properties by near-infrared reflectance spectroscopy. Sensors Actuators B Chem [Internet]. 2014;193:723–9. https://doi.org/10.1016/j.snb.2013.12.028.

    Article  CAS  Google Scholar 

  68. Mao Y, Ma B, Liu S, Wu L, Zhang X, Yu M. Study and validation of a remote sensing model for coal extraction based on reflectance spectrum features. Can J Remote Sens [Internet]. 2014;40:327–35. Available from: https://www.tandfonline.com/doi/full/https://doi.org/10.1080/07038992.2014.979486

  69. Xiao D, Le BT, Mao Y, Jiang J, Song L, Liu S. Research on coal exploration technology based on satellite remote sensing. J Sensors [Internet]. 2016;2016:1–9. Available from: http://www.hindawi.com/journals/js/2016/8214801/

  70. Song Z, Kuenzer C. Spectral reflectance (400–2500 nm) properties of coals, adjacent sediments, metamorphic and pyrometamorphic rocks in coal-fire areas: A case study of Wuda coalfield and its surrounding areas, northern China. Int J Coal Geol [Internet]. Elsevier B.V.; 2017 [cited 2021 Jan 19];171:142–52. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0166516216305742

  71. Le BT, Xiao D, Okello D, He D, Xu J, Doan TT. Coal exploration technology based on visible-infrared spectra and remote sensing data. Spectrosc Lett [Internet]. 2017;50:440–50. Available from: https://www.tandfonline.com/doi/full/https://doi.org/10.1080/00387010.2017.1354889

  72. Le BT, Xiao D, Mao Y, He D. Coal analysis based on visible-infrared spectroscopy and a deep neural network. Infrared Phys Technol [Internet]. Elsevier B.V.; 2018 [cited 2021 Mar 12];93:34–40. Available from: https://linkinghub.elsevier.com/retrieve/pii/S1350449518303542

  73. Yang E, Ge S, Wang S. Characterization and identification of coal and carbonaceous shale using visible and near-infrared reflectance spectroscopy. J Spectrosc [Internet]. 2018;2018:1–13. Available from: https://www.hindawi.com/journals/jspec/2018/2754908/

  74. Liu JJ, Zhao X, Zhang JB, An QB, Luan DF, Men H. Application of infrared spectroscopy in the detection of coal quality of power plant. Proc 2015 Int Conf Electr Autom Mech Eng [Internet]. 2015. p. 469–71. Available from: https://www.atlantis-press.com/article/22343

  75. Richardson MJ. The specific heats of coals, cokes and their ashes. Fuel [Internet]. 1993;72:1047–53. Available from: https://linkinghub.elsevier.com/retrieve/pii/001623619390307N

  76. Merrick D. Mathematical models of the thermal decomposition of coal2. Specific heats and heats of reaction. Fuel [Internet]. 1983;62:540–6. Available from: https://linkinghub.elsevier.com/retrieve/pii/0016236183902235

  77. Kirov NY. Specific heats and total heat contents of coals and related materials at elevated temperatures. BCURA Mon Bull British Coal Utilisation Res Assoc. 1965;29:33–9.

    CAS  Google Scholar 

  78. Postrzednik S. Thermodynamic analysis of solid fuel devolatilization, Zesz. Nauk; 1981.

  79. Fyans RL. Thermal analysis application study, No. 21. Perkin-Elmer Corp, Norwalk, CT, USA. 1977.

  80. Banerjee D, Hirani M, Sanyal S. Coal-quality deterioration in a coal stack of a power station. Appl Energy [Internet]. 2000;66:267–75. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0306261999001270

  81. Kumar J, Pratt B. Determination of calorific values of some renewable biofuels. Thermochim Acta [Internet]. 1996;279:111–20. Available from: http://linkinghub.elsevier.com/retrieve/pii/0040603195028528

  82. Ma S, Hill JO, Heng S. DSC determination of coal specific energy. Thermochim Acta [Internet]. 1991;190:291–7. Available from: https://linkinghub.elsevier.com/retrieve/pii/004060319185256H

  83. Hassel RL. Heat content of coal by pressure DSC. 2004;

  84. Earnest CM. Characterization of coal liquefaction residues by thermal methods of analysis. Anal Calorim [Internet]. Boston, MA: Springer US; 1984. p. 343–59. Available from: http://link.springer.com/https://doi.org/10.1007/978-1-4613-2699-1_28

  85. Bolortuya D, Zuzaan P, Gustova M V., Maslov OD. Study of the correlation between the coal calorific value and coal ash content using X-ray fluorescence analysis. Phys Part Nucl Lett [Internet]. 2013;10:723–6. Available from: http://link.springer.com/https://doi.org/10.1134/S1547477114010099

  86. Zuzaan P. Some results of a study to determine coal quality and elements contents. Proc. of the 3rd Intern. School on Contemporary Physics (ISCP-3), Ulaanbaatar. 2005:100ƒ103.

  87. Kök MV, Keskin C. Calorific value determination of coals by DTA and ASTM methods. Comparative study J Therm Anal Calorim. 2001;64:1265–70.

    Article  Google Scholar 

  88. Elder JP, Harris M Ben. Thermogravimetry and differential scanning calorimetry of Kentucky bituminous coals. Fuel [Internet]. 1984;63:262–7. Available from: https://linkinghub.elsevier.com/retrieve/pii/0016236184900486

  89. Muñoz-Guillena MJ, Linares-Solano A, de Lecea CS-M. Determination of calorific values of coals by differential thermal analysis. Fuel [Internet]. 1992;71:579–83. Available from: https://linkinghub.elsevier.com/retrieve/pii/001623619290157J

Download references

Funding

The authors did not receive support from any organization for the submitted work.

Author information

Authors and Affiliations

  1. Department of Mining Engineering, Birsa Institute of Technology Sindri, Dhanbad, Jharkhand, 828123, India

    Chinmay Mondal & Sumit Raj

  2. Department of Mining Engineering, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal, 721302, India

    Samir Kumar Pal & Biswajit Samanta

  3. Regional Remote Sensing Centre-East, NRSC (Indian Space Research Organization), Kolkata, West Bengal, 700163, India

    Dibyendu Dutta

Authors
  1. Chinmay Mondal
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Samir Kumar Pal
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Biswajit Samanta
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Dibyendu Dutta
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Sumit Raj
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

CM Investigation, Methodology, Formal Analysis, Software, Writing—original draft, Writing—Review and editing. SKP Conceptualization, Supervision, Writing—Review and Editing. BS Supervision, Writing—Review and Editing. DD Supervision, Writing—Review and Editing. SR Formal Analysis and Editing.

Corresponding author

Correspondence to Chinmay Mondal.

Ethics declarations

Conflict of interest

The authors declare no competing or financial interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mondal, C., Pal, S.K., Samanta, B. et al. Analysis and significance of prediction models for higher heating value of coal: an updated review. J Therm Anal Calorim 148, 7521–7538 (2023). https://doi.org/10.1007/s10973-023-12272-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10973-023-12272-4

Keywords

Search

Navigation