Evidence map›Paper›PMID 42460039›Full record

ReviewFrontiers in bioengineering and biotechnology2026

Smart nanobiocatalysts for waste-to-biofuel conversion: integrating Nano-Bio interfaces and AI-driven design.

Hotaf Hassan Makki

Abstract readReview
In one paragraph

Review in Frontiers in bioengineering and biotechnology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

Hotaf Hassan MakkiBiology Department, Faculty of Science, University of Tabuk, Umluj, Saudi Arabia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Converting diverse waste streams, including agricultural residues, food waste, and industrial by-products, into biofuels is a major challenge for a circular bioeconomy. Smart green nanobiocatalysts are emerging as next-generation tools for sustainable waste-to-biofuel conversion. They combine enzyme specificity with the tunable properties of nanomaterials within circular biorefinery systems. Significant progress has been made in nano-bio interface engineering, AI-assisted catalyst discovery, and circular biorefinery frameworks. However, a comprehensive overview integrating these fields for rational biocatalyst design is still lacking. This review addresses this gap by providing a forward-looking synthesis of nano-bio interface engineering, stimuli-responsive catalytic systems, hybrid nanozyme-enzyme architectures, and AI-assisted catalyst design strategies to improve biochemical conversion and waste valorization. Emphasis is placed on structure-function relationships controlling enzyme immobilization, interfacial electron transfer, and multi-enzyme cascade organization. These features enhance catalytic efficiency, stability, and recyclability under industrial conditions. Applications include waste-derived lignocellulosic biomass, biodiesel feedstocks, food waste, wastewater streams, and anaerobic digestion. Additionally, techno-economic feasibility, life-cycle sustainability, carbon mitigation, and environmental safety are also evaluated. Overall, smart nanobiocatalysts provide a promising pathway toward efficient, climate-friendly, and digitally optimized biofuel production. They support sustainable waste management, resource recovery, and global net-zero energy goals, aligning biofuel innovation with circular bioeconomy objectives.

Indexed as

circular biorefinerynano-bio interfacesmart nanobiocatalystssustainable bioenergywaste to biofuel conversionwaste valorization

Identifiers

PMID42460039
PMCPMC13369270

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.