Microalgae-based biodiesel: integrating AI, CRISPR and nanotechnology for sustainable biofuel development

Microalgae are a promising feedstock for biodiesel due to their rapid growth, high lipid content and ability to use non-arable land and wastewater. This review synthesises recent advances in artificial intelligence (AI)-driven strain optimisation, engineering, nanotechnology-assisted processing, and life cycle and technoeconomic insights to evaluate pathways for industrialisation. Over the past decade (2015-2024), genetic engineering and, more recently, AI-guided strain selection have improved lipid productivity by up to 40%. Cultivation advances, including hybrid photobioreactor-open pond systems and precision pH/CO2 control, have enhanced biomass yields while reducing costs. Innovation in lipid extraction, such as supercritical CO2 and microwave-assisted methods, now achieves >90% yields with lower toxicity, while magnetic nanoparticle-assisted harvesting and electroflocculation have reduced energy inputs by 20-30%. Life cycle analyses (net energy ratio ~2.5) and integration of high-value co-products (e.g. pigments and proteins) underscore the need to align biological innovations with techno-economic feasibility. This review uniquely integrates advances in AI, CRISPR and nanotechnology with life cycle and techno-economic perspectives, providing a comprehensive framework that links laboratory-scale innovation to industrial feasibility and positions microalgal biodiesel as a viable contributor to global decarbonisation strategies.

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Publication Details

Journal
Emerging Topics in Life Sciences
Published
2025-09-22
DOI
https://doi.org/10.1042/etls20240004
Citations
2
Primary Topic
Algal biology and biofuel production
Type
article
Field-Weighted Citation Impact
0.73
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article

Microalgae-based biodiesel: integrating AI, CRISPR and nanotechnology for sustainable biofuel development

Fariha Kanwal, Ambreen Aslam, Angel A. J. Torriero
2 citations
Emerging Topics in Life Sciences
Algal biology and biofuel production
0.73
article

Microalgae-based biodiesel: integrating AI, CRISPR and nanotechnology for sustainable biofuel development

Fariha Kanwal, Ambreen Aslam, Angel A. J. Torriero
article en
2 citations

Abstract

Microalgae are a promising feedstock for biodiesel due to their rapid growth, high lipid content and ability to use non-arable land and wastewater. This review synthesises recent advances in artificial intelligence (AI)-driven strain optimisation, engineering, nanotechnology-assisted processing, and life cycle and technoeconomic insights to evaluate pathways for industrialisation. Over the past decade (2015-2024), genetic engineering and, more recently, AI-guided strain selection have improved lipid productivity by up to 40%. Cultivation advances, including hybrid photobioreactor-open pond systems and precision pH/CO2 control, have enhanced biomass yields while reducing costs. Innovation in lipid extraction, such as supercritical CO2 and microwave-assisted methods, now achieves >90% yields with lower toxicity, while magnetic nanoparticle-assisted harvesting and electroflocculation have reduced energy inputs by 20-30%. Life cycle analyses (net energy ratio ~2.5) and integration of high-value co-products (e.g. pigments and proteins) underscore the need to align biological innovations with techno-economic feasibility. This review uniquely integrates advances in AI, CRISPR and nanotechnology with life cycle and techno-economic perspectives, providing a comprehensive framework that links laboratory-scale innovation to industrial feasibility and positions microalgal biodiesel as a viable contributor to global decarbonisation strategies.

Emerging Topics in Life SciencesVol. 8(3)
University of Lahore (PK), Deakin University (AU), Lahore College for Women University (PK)
Openalex Percentile: Top 28%
Algal biology and biofuel production
0.73
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Microalgae-based biodiesel: integrating AI, CRISPR and nanotechnology for sustainable biofuel development — Fariha Kanwal, Ambreen Aslam, et al. · Emerging Topics in Life Sciences (2025) | TGRS Research Map | TGRS