Machine learning screening of biomass precursors to prepare biomass carbon for organic wastewater purification: A review

Wang, Bao-Ying; Li, Bo; Xu, Huan-Yan

doi:10.1016/j.chemosphere.2024.142597

Chemosphere

2024

DOI: 10.1016/j.chemosphere.2024.142597

|View full text |Cite

Machine learning screening of biomass precursors to prepare biomass carbon for organic wastewater purification: A review

Bao-Ying Wang,

Bo Li,

Huan-Yan Xu

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article2

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

References 143 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Data-driven neural networks for biological wastewater resource recovery: Development and challenges

Xu,

Cao,

Luo

et al. 2024

Journal of Cleaner Production

View full text Add to dashboard Cite

Data-driven neural networks for biological wastewater resource recovery: Development and challenges

Xu,

Cao,

Luo

et al. 2024

Journal of Cleaner Production

View full text Add to dashboard Cite

Nanomaterial Texture-Based Machine Learning of Ciprofloxacin Adsorption on Nanoporous Carbon

Käärik,

Krjukova,

Maran

et al. 2024

IJMS

View full text Add to dashboard Cite

Drug substances in water bodies and groundwater have become a significant threat to the surrounding environment. This study focuses on the ability of the nanoporous carbon materials to remove ciprofloxacin from aqueous solutions under specific experimental conditions and on the development of the mathematical model that would allow describing the molecular interactions of the adsorption process and calculating the adsorption capacity of the material. Thus, based on the adsorption measurements of the 87 carbon materials, it was found that, depending on the porosity and pore size distribution, adsorption capacity values varied between 55 and 495 mg g−1. For a more detailed analysis of the effects of different carbon textures and pores characteristics, a Quantitative nano-Structure–Property Relationship (QnSPR) was developed to describe and predict the ability of a nanoporous carbon material to remove ciprofloxacin from aqueous solutions. The adsorption capacity of potential nanoporous carbon-based adsorbents for the removal of ciprofloxacin was shown to be sufficiently accurately described by a three-parameter multi-linear QnSPR equation (R2 = 0.70). This description was achieved only with parameters describing the texture of the carbon material such as specific surface area (Sdft) and pore size fractions of 1.1–1.2 nm (VN21.1–1.2) and 3.3–3.4 nm (VN23.3−3.4) for pores.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Machine learning screening of biomass precursors to prepare biomass carbon for organic wastewater purification: A review

Cited by 2 publications

References 143 publications

Data-driven neural networks for biological wastewater resource recovery: Development and challenges

Data-driven neural networks for biological wastewater resource recovery: Development and challenges

Nanomaterial Texture-Based Machine Learning of Ciprofloxacin Adsorption on Nanoporous Carbon

Contact Info

Product

Resources

About