Full title of the project: AI-guided biohybrid assembly platform for e-textiles (Aalto University Bioinnovation Center project)
In his doctoral research conducted at Aalto University’s Bioinnovation Center, Iannacchero uses machine learning to develop ecologically sustainable electronic yarns. This is an opportunity to come up with something completely new.
A podcast hosted by Alicja Lawrynowicz and Matteo Iannacchero, in which they discuss the innovative world of smart textiles, uniquely framed by Nordic perspectives. Different guests will show different points of view: from academic research to innovation on a larger scale.
Bioinnovation Center's research focuses on sustainable textiles and packaging. Currently we have fifteen research projects ongoing. Read more about the first research projects here.
Electronic textiles, e-textiles or smart textiles are terms that are interchangeably used for digitally enhanced fabrics. The electronic components in e-textiles augment their aesthetic appeal or enable new functionality, like wearable electronics. Common problems hindering the commercialization of current e-textiles are durability (e.g., brittleness), user discomfort due to integrated bulky electronics, and sustainability. In AI-yarn, we address these challenges by developing novel biomaterial-based yarns for e-textiles. This will be achieved by combining different biodegradable and/or biobased materials (i.e. cellulose, potato viruses, PLA) for a fully bio-based assembly line for electrically active yarns.
With machine learning, namely Bayesian optimization, we optimize filaments' and yarns' functional properties starting from the main variables that affect them. This approach allows to save time as the experiments suggested by the algorithm are specifically aimed to find the global maximum (or minimum), using a probabilistic function in the desired variables-space.
AI-yarn will produce novel biomaterial yarns and deliver the first use-case of artificial intelligence (AI) for bioinnovations.
In AI-yarn, two young PIs combine their complementary expertise in biomaterials and computer science for the first time in a funded project. In this interdisciplinary project, machine learning techniques facilitate and accelerate biomaterials synthesis. Machine learning unlocks unprecedented opportunities for materials and process optimization, unveils previously unseen correlations between materials characteristics and functional properties, and aids in the discovery of new compounds.
To the best of our knowledge, AI-yarn would deliver the first functional biohybrid material for e-textiles and provide sustainable options of smart textiles. Scientifically, AI-yarn establishes a new paradigm of AI-guided biomaterials research and accelerated materials development.
As a fundamental interdisciplinary project, AI-yarn enables completely bio-based functional conductive and triboelectric yarns and fabrics. We will also create an alternative for Ag-coated conductive yarns commonly applied in current e-textiles. We believe that our biohybrid platform will significantly improve the flexibility and durability of Ag nanowire-based conductive yarns. This opens new opportunities for the ever-growing application areas of e-textiles.
"I am currently working on the production of functionalised fibres suitable for the creation of e-textiles, but in general on the use of AI for the production of optimal materials.
The goal of this project is precisely the combination of laboratory work (data acquisition) and machine learning (data processing) that allows artificial intelligence to predict the optimal reaction conditions to obtain the best desired properties.
The complexity of the use of AI depends on the number of variables involved, but it has an enormous advantage in the study of any reaction or process since it can discover the position, in an n-dimensional space of variables, of the points of greatest interest, i.e. those that can guarantee an optimised functional product."
Leveraging machine learning and crafted textile design to maximize sustainable energy generation exploiting the triboelectric effect of PLA fabrics
When two surfaces come into contact and separate, we can observe an exchange of electrical charge, or 'triboelectric effect'. If we connect these two surfaces to a circuit, we can detect the charge and possibly harvest it (if a device for energy storage is available).
Using bio-degradable and bio-based PLA (polylactic acid) yarn, the goal is to develop garments that can exploit this phenomenon and harvest electricity through human motion. Machine learning will be used to evaluate and optimize the parameters that affect this phenomenon.
This research work is part of 'AI-yarn' project and is being implemented in collaboration with Prof. Lena Kramer Pedersen from VIA University College. A prototype of this work was exhibited at 'Designs for a Cooler Planet' festival in Marsio (Aalto University) in September-October 2024, as well as Dutch Design Week in October 2024.
Nordic Network on Smart Light-Conversion Textiles Beyond Electric Circuits, 2021-2025 (Nordic Programme for Interdisciplinary Research – NordForsk / ID: 103894)
Conference booklet | SCALES in Textiles - a three-day conference hosted by Aalto University on April 8–10, 2025, bringing together the ArcInTex network and the Beyond e-Textiles project.
Project team:
Doctoral Candidate Matteo Iannacchero (CHEM MMD) (matteo.iannacchero@aalto.fi)
Professor Jaana Vapaavuori (CHEM MMD) (jaana.vapaavuori@aalto.fi)
Professor Patrick Rinke (SCI CEST) (patrick.rinke@aalto.fi)
This project is a project of the Aalto University Bioinnovation Center which focuses on innovations in sustainable bio-based materials, especially on textiles and packaging. Its target is to accelerate the transition to a circular economy and bioeconomy, and to create opportunities for sustainable economic growth in Finland.
