Researchers at Mohammed VI Polytechnic University (UM6P) are advancing a novel approach to converting agricultural waste into high-value materials, positioning Morocco at the forefront of emerging biomass-to-graphene innovation.
Morocco generates tens of millions of tonnes of plant-based waste annually, much of it underutilised or burned, contributing to environmental degradation. Scientists at UM6P’s Materials Science and Nano-Engineering department are instead seeking to transform this waste into graphene-like materials—advanced carbon structures prized for their strength, conductivity and versatility in industrial applications.
At the centre of this research is lignin, a naturally occurring polymer found in plant biomass. According to lead researcher Mehdi Mennani, lignin offers a promising alternative to fossil-based inputs due to its abundance, renewability and high carbon content.
The process developed by the UM6P team relies on a technique known as laser-induced graphitisation. By applying a focused laser beam to lignin-based materials, researchers generate extremely high temperatures over short timeframes. This rapid heating reorganises carbon atoms into a porous, conductive network resembling graphene.
Unlike conventional methods, which typically require prolonged heating in furnaces and the use of chemical solvents, the laser-based approach operates in seconds and avoids the need for catalysts or complex processing steps. This not only reduces production time but also limits environmental impact, making the method attractive for scalable clean manufacturing.
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The team has experimented with a range of locally available feedstocks, including sugarcane bagasse, olive pomace and alfa fibres—materials commonly produced by Moroccan agricultural industries. In some cases, researchers extract lignin prior to processing to improve material quality and consistency.
The resulting graphene-like materials exhibit properties suitable for a variety of applications. Their high porosity and electrical conductivity make them ideal for use in energy storage technologies such as batteries and supercapacitors. They can also be adapted for electrochemical sensors, flexible electronics, and environmental monitoring systems, including water purification and pollutant detection.
Despite its potential, the technology faces challenges before it can be deployed at an industrial scale. One major issue is the variability of raw materials. Lignin derived from different plants or processing methods can differ in composition, affecting how it responds to laser treatment and, ultimately, the quality of the final product.
Scaling production is another hurdle. While laboratory experiments demonstrate feasibility, industrial applications will require continuous production systems capable of maintaining consistent output at higher volumes.
To address these constraints, the UM6P team is integrating experimental work with modelling and data-driven techniques. The use of artificial intelligence and machine learning allows researchers to optimise processing parameters—such as laser power and scanning speed—while reducing the number of physical experiments needed.
The initiative also involves collaboration with local industries, including Morocco’s sugar sector, to test real-world waste streams and ensure the technology aligns with available resources.
Looking ahead, researchers aim to standardise feedstocks, refine processing conditions and develop industrial-scale systems capable of delivering consistent performance. Their broader vision includes the creation of an AI-driven platform linking biomass resources with optimal processing techniques, potentially reducing costs and accelerating innovation.
As global demand for advanced materials continues to grow, the ability to convert low-value agricultural waste into high-performance carbon products could offer both economic and environmental benefits. For Morocco, the research highlights an opportunity to build new industrial value chains rooted in local resources while contributing to cleaner manufacturing practices.
