ReviewFrontiers in microbiology2026
Biology-aware scaling of microalgal biofuels: bioprocess constraints and data-centric digital twins.
Review in Frontiers in microbiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Stress-Induced Metabolic Reprogramming in the Green MicroalgaMicroorganisms · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Microalgal biofuels remain a promising route for renewable fuel production, carbon utilization, wastewater valorization, and the development of a circular bioeconomy. However, their industrial deployment is still limited by the difficulty of translating laboratory performance into robust, economically viable, and environmentally sustainable large-scale systems. This review presents a biology-aware and data-centric perspective that connects biological, engineering, operational, techno-economic, and computational dimensions to address industrial microalgal biofuel scale-up. It focuses on process observability, soft sensors, and the data requirements needed to transform microalgal cultivation into a measurable, modellable, and controllable bioprocess. It further evaluates artificial intelligence, hybrid modeling, uncertainty-aware digital twins, and decision-support systems for improving monitoring, prediction, optimization, and scale-up. Rather than presenting digital twins as autonomous solutions, it demonstrates that their value lies in integrating biological knowledge, heterogeneous data, mechanistic understanding, and uncertainty estimation within human-in-the-loop decision-support frameworks. The available evidence indicates that biological constraints, limited process observability, and insufficient integration of validated data remain the principal barriers to industrial implementation. In contrast, biology-aware, hybrid, and uncertainty-aware digital twins represent the most realistic direction for near-term deployment. Finally, techno-economic and life-cycle implications are discussed to highlight that industrial viability will depend not on a single technological breakthrough, but on the convergence of robust strains, resource-efficient cultivation, circular biorefineries, validated data infrastructures, and biology-informed AI tools. A roadmap for 2026-2036 is proposed to guide the transition of microalgal biofuels from laboratory promise toward industrial relevance. The integrated perspective presented here can guide future research and support the sustainable industrial deployment of microalgal biofuel systems.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.