Optimizing biochar for carbon sequestration: a synergistic approach using machine learning and natural language processing

Jiayi Li; Yixuan Chen; Chaojie Wang; Hanbo Chen; Yurong Gao; Jun Meng; Zhongyuan Han; Lukas Van Zwieten; Yi He; Caibin Li; Gerard Cornelissen; Hailong Wang

doi:10.1007/s42773-024-00424-0

Biochar ›› 2025, Vol. 7 ›› Issue (1) : 20. DOI: 10.1007/s42773-024-00424-0

Perspective

Optimizing biochar for carbon sequestration: a synergistic approach using machine learning and natural language processing

Jiayi Li¹^,² ,
Yixuan Chen¹^,³ ,
Chaojie Wang² ,
Hanbo Chen⁴ ,
Yurong Gao⁵ ,
Jun Meng¹ ,
Zhongyuan Han⁶ ,
Lukas Van Zwieten⁷ ,
Yi He⁸^,^a ,
Caibin Li⁸ ,
Gerard Cornelissen⁹^,10 ,
Hailong Wang¹^,²^,11^,^b

Author information +

History +

Abstract

Biochar is a promising technology for carbon storage and greenhouse gas (GHG) reduction, but optimizing it is challenging due to the complexity of natural systems. Machine learning (ML) and natural language processing (NLP) offer solutions through enhanced data analysis and pattern recognition, ushering in a new era of biochar research.

Graphical Abstract

Cite this article

EndNote

Ris (Procite)

Bibtex

Download citation ▾

Jiayi Li, Yixuan Chen, Chaojie Wang, Hanbo Chen, Yurong Gao, Jun Meng, Zhongyuan Han, Lukas Van Zwieten, Yi He, Caibin Li, Gerard Cornelissen, Hailong Wang. Optimizing biochar for carbon sequestration: a synergistic approach using machine learning and natural language processing. Biochar, 2025, 7(1): 20 https://doi.org/10.1007/s42773-024-00424-0

References

Publishing order | Descend order by publishing year | Descend order by cited within

[]	Bolan N, et al.. Multifunctional applications of biochar beyond carbon storage Int Mater Rev, 2022, 67: 150-200. CrossRef Google scholar

[]	Cao L, et al.. Straw and wood based biochar for CO₂ capture: adsorption performance and governing mechanisms Sep Purif Technol, 2022, 287: 120592. CrossRef Google scholar

[]	Chen H, et al.. Engineered biochar for environmental decontamination in aquatic and soil systems: a review Carbon Res, 2022, 1: 1-25. CrossRef Google scholar

[]	Chen Y, et al.. Biochar as a green solution to drive the soil carbon pump Carbon Res, 2024, 3: 44. CrossRef Google scholar

[]	Deng X, et al.. Exploring negative emission potential of biochar to achieve carbon neutrality goal in China Nat Commun, 2024, 15: 1085. CrossRef Google scholar

[]	Ding F, et al.. A meta-analysis and critical evaluation of influencing factors on soil carbon priming following biochar amendment J Soils Sediments, 2018, 18: 1507-1517. CrossRef Google scholar

[]	Dissanayake PD, et al.. Sustainable gasification biochar as a high efficiency adsorbent for CO₂ capture: a facile method to designer biochar fabrication Renew Sustain Energy Rev, 2020, 124: 109785. CrossRef Google scholar

[]	Domazetoski, V. 2024. Enhancing Ecological Knowledge Discovery Using Large Language Models. Master’s Thesis, Georg-August-Universität Göttingen.

[]	Fahad S, et al.. A combined application of biochar and phosphorus alleviates heat-induced adversities on physiological, agronomical and quality attributes of rice Plant Physiol Biochem, 2016, 103: 191-198. CrossRef Google scholar

[]	He M, et al.. Waste-derived biochar for water pollution control and sustainable development Nat Rev Earth Environ, 2022, 3: 444-460. CrossRef Google scholar

[]	Lehmann J, et al.. Biochar in climate change mitigation Nat Geosci, 2021, 14: 883-892. CrossRef Google scholar

[]	Lin Z, et al.. GeoGalactica: a scientific large language model in geoscience arXiv Preprint, 2024. CrossRef Google scholar

[]	Liu Z, et al.. Challenges and opportunities for carbon neutrality in China Nat Rev Earth Environ, 2022, 3: 141-155. CrossRef Google scholar

[]	Maik Jablonka K, et al.. 14 examples of how LLMs can transform materials science and chemistry: a reflection on a large language model hackathon Digit Discov, 2023, 2: 1233-1250. CrossRef Google scholar

[]	Paula AJ, et al.. Machine learning and natural language processing enable a data-oriented experimental design approach for producing biochar and hydrochar from biomass Chem Mater, 2022, 34: 979-990. CrossRef Google scholar

[]	Qiao L, et al.. Microbial carbon capture - evolving trends, interconnections, and recent spotlights of the past three decades Chem Eng J, 2024, 482: 148970. CrossRef Google scholar

[]	Tee JX, et al.. Prediction of carbon sequestration of biochar produced from biomass pyrolysis by artificial neural network J Environ Chem Eng, 2022, 10: 107640. CrossRef Google scholar

[]	Wang L, et al.. Role of biochar toward carbon neutrality Carbon Res, 2023, 2: 2. CrossRef Google scholar

[]	Wang C, et al.. Interpretable machine learning for predicting heavy metal removal and optimizing biochar characteristics J Water Process Eng, 2024, 68: 106484. CrossRef Google scholar

[]	Wei YM, et al.. A proposed global layout of carbon capture and storage in line with a 2°C climate target Nat Clim Change, 2021, 11: 112-118. CrossRef Google scholar

[]	Xia L, et al.. Integrated biochar solutions can achieve carbon-neutral staple crop production Nature Food, 2023, 4(3): 236-246. CrossRef Google scholar

[]	Yang H, et al.. An intelligent approach: integrating ChatGPT for experiment planning in biochar immobilization of soil cadmium Sep Purif Technol, 2025, 352: 128170. CrossRef Google scholar

[]	Yao P, et al.. Application of machine learning in carbon capture and storage: an in-depth insight from the perspective of geoscience Fuel, 2023, 333: 126296. CrossRef Google scholar

[]	Yuan X, et al.. Applied machine learning for prediction of CO₂ adsorption on biomass waste-derived porous carbons Environ Sci Technol, 2021, 55: 11925-11936. CrossRef Google scholar

[]	Yuan X, et al.. Active learning-based guided synthesis of engineered biochar for CO₂ capture Environ Sci Technol, 2024, 58: 6628-6636. CrossRef Google scholar