Tex4Clear. Innovación y sostenibilidad para el sector textil
We are very excited to announce that we have just kicked off a pioneering and transformative project: TEX4CLEAR.
In this article, we tell you briefly what this initiative is, what its main goals are, and which entities are accompanying us on this exciting journey towards industrial sustainability.
What is TEX4CLEAR?
TEX4CLEAR (Textile Innovation for the Sustainable Elimination of Emerging Contaminants) is a collaborative R&D project focused on accelerating the ecological transition of the textile sector through the integration of clean technologies, biotechnology, and artificial intelligence.
Its core purpose is to research and develop innovative solutions to reduce, monitor, and eliminate two of the pollutants with the greatest environmental and health impact in the industry’s wastewater:
- Micro and nanoplastics (MNPs): particles that are shed through the wear and washing of synthetic fibers.
- PFAS (perfluoroalkyl and polyfluoroalkyl substances): highly persistent chemical compounds commonly used in repellent and technical finishes.
General project objectives
Under the Safe and Sustainable by Design methodological framework, TEX4CLEAR is structured around four main lines of action:
- Source reduction: Develop bio-based alternatives and new textile finishes free of fluorinated substances, in addition to optimizing processes to curb the release of microfibers.
- Biofiltration and decontamination: Implement advanced solutions based on adsorbent materials (functionalized diatomites) and microbial consortia capable of biodegrading and retaining PFAS and MNPs.
- Biodetection and rapid monitoring: Design advanced sensor systems combining microfluidic chips, optical spectroscopy, and high-affinity biosensors for in situ and real-time measurement.
- Artificial Intelligence and predictive modeling: Research AI models capable of predicting the dispersion and accumulation of contaminants, anticipating emission levels in production plants, and automatically tracking new scientific knowledge.
Collaboration and Funding
Tackling such a complex challenge requires the sum of complementary capabilities and a high-level collaborative ecosystem.
Participating companies
- LAURENTIA Technologies: Project coordinator and experts in nanomaterials, microencapsulation, and surface functionalization.
- LABAQUA: A benchmark in environmental analysis, sampling, and analytical technologies for the comprehensive water cycle.
- DARWIN Bioprospecting Excellence: Specialists in bioprospecting and isolation of microorganisms for enzymatic biodegradation.
- GEISA Fabrics: Leading manufacturer of knitted technical fabrics, in charge of testing and validating sustainable finishes in an industrial environment.
- Luce IT: Technology partner in charge of data architecture, hydraulic twins, and predictive and generative Artificial Intelligence models.
Research and technology support centers
- ITENE (Technological Institute of Packaging, Transport and Logistics).
- AITEX (Textile Technological Institute).
- ITCL (Technological Institute of Castile and León).
Funding and public support entities
The development of TEX4CLEAR has the backing and institutional funding of:
- European Union – NextGenerationEU
- Recovery, Transformation and Resilience Plan
- Government of Spain – Ministry of Science and Innovation
- CDTI (Center for Technological Development and Innovation)
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Frequently Asked Questions about Tex4Clear
What is the main mission of the TEX4CLEAR project?
To research, develop, and validate sustainable solutions to prevent the emission, continuously monitor, and eliminate microplastics and PFAS in textile industry effluents.
Which entities make up the project’s business consortium?
The consortium is led by LAURENTIA Technologies and includes the participation of LABAQUA, DARWIN Bioprospecting Excellence, GEISA Fabrics, and Luce IT, with the technical support of centers such as ITENE, AITEX, and ITCL.
How does Artificial Intelligence contribute to water treatment in this project?
It allows processing large volumes of sensory data, modeling the dispersion dynamics of contaminants using digital twins, and predicting emission levels before discharges occur.
