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

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

References

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

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

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

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

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

[6] 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.

[7] 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.

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

[9] 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.

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

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

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

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

[14] 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.

[15] 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.

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

[17] 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.

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

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

[20] 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.

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

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

[23] 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.

[24] 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.

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

Funding

National Key Research and Development Program of China(2023YFD1700800)

RIGHTS & PERMISSIONS

The Author(s)