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Machine learning and computational chemistry to improve biochar fertilizers : a review

Traditional fertilizers are highly inefficient, with a major loss of nutrients and associated pollution. Alternatively, biochar loaded with phosphorous is a sustainable fertilizer that improves soil structure, stores carbon in soils, and provides plant nutrients in the long run, yet most biochars ar...

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Main Authors:	Ahmed I., Osman, Yubing, Zhang, Lai, Zhi Ying, Ahmed K., Rashwan, Mohamed, Farghali, Ashour A., Ahmed, Liu, Yunfei, Fang, Bingbing, Chen, Zhonghao, Ahmed, Al-Fatesh, David W., Rooney, Yiin, Chung Loong, Yap, Pow Seng
Format:	Article
Language:	English
Published:	Springer Nature 2023
Subjects:	T Technology (General) TA Engineering (General). Civil engineering (General) TP Chemical technology
Online Access:	http://ir.unimas.my/id/eprint/42613/1/Environmental_Chemistry_Letters.pdf http://ir.unimas.my/id/eprint/42613/ https://link.springer.com/article/10.1007/s10311-023-01631-0 https://doi.org/10.1007/s10311-023-01631-0
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spelling	my.unimas.ir.426132023-08-17T02:44:28Z http://ir.unimas.my/id/eprint/42613/ Machine learning and computational chemistry to improve biochar fertilizers : a review Ahmed I., Osman Yubing, Zhang Lai, Zhi Ying Ahmed K., Rashwan Mohamed, Farghali Ashour A., Ahmed Liu, Yunfei Fang, Bingbing Chen, Zhonghao Ahmed, Al-Fatesh David W., Rooney Yiin, Chung Loong Yap, Pow Seng T Technology (General) TA Engineering (General). Civil engineering (General) TP Chemical technology Traditional fertilizers are highly inefficient, with a major loss of nutrients and associated pollution. Alternatively, biochar loaded with phosphorous is a sustainable fertilizer that improves soil structure, stores carbon in soils, and provides plant nutrients in the long run, yet most biochars are not optimal because mechanisms ruling biochar properties are poorly known. This issue can be solved by recent developments in machine learning and computational chemistry. Here we review phosphorus-loaded biochar with emphasis on computational chemistry, machine learning, organic acids, drawbacks of classical fertilizers, biochar production, phosphorus loading, and mechanisms of phosphorous release. Modeling techniques allow for deciphering the influence of individual variables on biochar, employing various supervised learning models tailored to different biochar types. Computational chemistry provides knowledge on factors that control phosphorus binding, e.g., the type of phosphorus compound, soil constituents, mineral surfaces, binding motifs, water, solution pH, and redox potential. Phosphorus release from biochar is controlled by coexisting anions, pH, adsorbent dosage, initial phosphorus concentration, and temperature. Pyrolysis temperatures below 600 °C enhance functional group retention, while temperatures below 450 °C increase plant-available phosphorus. Lower pH values promote phosphorus release, while higher pH values hinder it. Physical modifications, such as increasing surface area and pore volume, can maximize the adsorption capacity of phosphorus-loaded biochar. Furthermore, the type of organic acid affects phosphorus release, with low molecular weight organic acids being advantageous for soil utilization. Lastly, biochar-based fertilizers release nutrients 2–4 times slower than conventional fertilizers. Springer Nature 2023-08-17 Article PeerReviewed text en http://ir.unimas.my/id/eprint/42613/1/Environmental_Chemistry_Letters.pdf Ahmed I., Osman and Yubing, Zhang and Lai, Zhi Ying and Ahmed K., Rashwan and Mohamed, Farghali and Ashour A., Ahmed and Liu, Yunfei and Fang, Bingbing and Chen, Zhonghao and Ahmed, Al-Fatesh and David W., Rooney and Yiin, Chung Loong and Yap, Pow Seng (2023) Machine learning and computational chemistry to improve biochar fertilizers : a review. Environmental Chemistry Letters. pp. 1-86. ISSN 1610-3653 https://link.springer.com/article/10.1007/s10311-023-01631-0 https://doi.org/10.1007/s10311-023-01631-0
institution	Universiti Malaysia Sarawak
building	Centre for Academic Information Services (CAIS)
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Malaysia Sarawak
content_source	UNIMAS Institutional Repository
url_provider	http://ir.unimas.my/
language	English
topic	T Technology (General) TA Engineering (General). Civil engineering (General) TP Chemical technology
spellingShingle	T Technology (General) TA Engineering (General). Civil engineering (General) TP Chemical technology Ahmed I., Osman Yubing, Zhang Lai, Zhi Ying Ahmed K., Rashwan Mohamed, Farghali Ashour A., Ahmed Liu, Yunfei Fang, Bingbing Chen, Zhonghao Ahmed, Al-Fatesh David W., Rooney Yiin, Chung Loong Yap, Pow Seng Machine learning and computational chemistry to improve biochar fertilizers : a review
description	Traditional fertilizers are highly inefficient, with a major loss of nutrients and associated pollution. Alternatively, biochar loaded with phosphorous is a sustainable fertilizer that improves soil structure, stores carbon in soils, and provides plant nutrients in the long run, yet most biochars are not optimal because mechanisms ruling biochar properties are poorly known. This issue can be solved by recent developments in machine learning and computational chemistry. Here we review phosphorus-loaded biochar with emphasis on computational chemistry, machine learning, organic acids, drawbacks of classical fertilizers, biochar production, phosphorus loading, and mechanisms of phosphorous release. Modeling techniques allow for deciphering the influence of individual variables on biochar, employing various supervised learning models tailored to different biochar types. Computational chemistry provides knowledge on factors that control phosphorus binding, e.g., the type of phosphorus compound, soil constituents, mineral surfaces, binding motifs, water, solution pH, and redox potential. Phosphorus release from biochar is controlled by coexisting anions, pH, adsorbent dosage, initial phosphorus concentration, and temperature. Pyrolysis temperatures below 600 °C enhance functional group retention, while temperatures below 450 °C increase plant-available phosphorus. Lower pH values promote phosphorus release, while higher pH values hinder it. Physical modifications, such as increasing surface area and pore volume, can maximize the adsorption capacity of phosphorus-loaded biochar. Furthermore, the type of organic acid affects phosphorus release, with low molecular weight organic acids being advantageous for soil utilization. Lastly, biochar-based fertilizers release nutrients 2–4 times slower than conventional fertilizers.
format	Article
author	Ahmed I., Osman Yubing, Zhang Lai, Zhi Ying Ahmed K., Rashwan Mohamed, Farghali Ashour A., Ahmed Liu, Yunfei Fang, Bingbing Chen, Zhonghao Ahmed, Al-Fatesh David W., Rooney Yiin, Chung Loong Yap, Pow Seng
author_facet	Ahmed I., Osman Yubing, Zhang Lai, Zhi Ying Ahmed K., Rashwan Mohamed, Farghali Ashour A., Ahmed Liu, Yunfei Fang, Bingbing Chen, Zhonghao Ahmed, Al-Fatesh David W., Rooney Yiin, Chung Loong Yap, Pow Seng
author_sort	Ahmed I., Osman
title	Machine learning and computational chemistry to improve biochar fertilizers : a review
title_short	Machine learning and computational chemistry to improve biochar fertilizers : a review
title_full	Machine learning and computational chemistry to improve biochar fertilizers : a review
title_fullStr	Machine learning and computational chemistry to improve biochar fertilizers : a review
title_full_unstemmed	Machine learning and computational chemistry to improve biochar fertilizers : a review
title_sort	machine learning and computational chemistry to improve biochar fertilizers : a review
publisher	Springer Nature
publishDate	2023
url	http://ir.unimas.my/id/eprint/42613/1/Environmental_Chemistry_Letters.pdf http://ir.unimas.my/id/eprint/42613/ https://link.springer.com/article/10.1007/s10311-023-01631-0 https://doi.org/10.1007/s10311-023-01631-0
_version_	1775627330365947904
score	13.211869

Machine learning and computational chemistry to improve biochar fertilizers : a review

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