Multi-modal, configurable optical lab-on-chip platform for low-cost multipurpose diagnostics & monitoring


Objective 1

Development of optical (bio)sensing modules

Focus is on developing three sensing modules on a single Photonic Integrated Circuit (PIC) for simultaneous detection of various proteins, miRNA, pathogens, inorganic species, and biomarkers, as well as extending sensing capabilities to the mid-IR region for multi-component analysis.

Objective 2

Integrate the optical sensing platform

The goal is to create cost-efficient SiN platforms with interchangeable sensing modules, combined with customized microfluidic structures, to accelerate measurements and enhance the limit of detection.

Objective 3

Machine Learning for signal classification

ML will be exploited to analyze patterns from a novel sensor module, providing accurate measurements with data integration into a user-friendly platform for accurate decision-making.

Objective 4

Integration of sensing instruments and validation through case studies

Aiming on design & develop a cost-effective readout system, compatible with all sensing modules on the PIC.

Objective 5

Facilitate the uptake of the technology

DIHs and PPPs will be exploited to share the results widely. The sensing platform will be licensed to different users ensuring easy integration with an open readout system.

Use Cases

Diagnosis of fever without an apparent source and differentiation between viral & bacterial infections

Bacterial and viral infections often appear with similar symptoms. Fever without a source is often difficult to diagnose and requires multiple analysis, both for understanding the cause and for establishing a prognosis. However, early diagnosis and rapid initiation of treatment are crucial to control infectious diseases. MultiLab will propose a new precise medicine method for rapid and accurate diagnosis. The fundamental advantage is the possibility to simultaneously quantify multiple targets of different nature: bacteria, viruses, enzymes, proteins, RNA.

Early warning systems for HAB

Eutrophication of surface waters can lead to the formation of harmful algal blooms (HABs) that directly affect water quality. Old techniques cannot be applied as an early warning tool, causing a big gap in monitoring tools for continuous analysis of water quality. MultiLab will provide an IoT-enabled, affordable, and reliable monitoring tool, facilitating early warning for HABs. The system will monitor the possibly developing bloom and will automatically analyze water samples. This will allow exploiting predictive models for confident early warning in freshwaters.

Work Packages


WP2 – Case Studies

WP2 focuses on identification of key stakeholders and collection of related requirements.


WP3 – Optical bio-sensing modules development

WP3 aims for the design, development and optimization of the MultiLab sensing modules (AWG, PTS, ECL).


WP4 – PIC platform and sensors integration

WP4 will focus on PIC platform design & manufacturing. The sensing modules and the microfluidic module will be optimized and integrated on PICs.


WP5 – Instrument development and data platform

WP5 will look further into the design & the development of the optical readout module, integrating the two versions of the MultiLab instrument prototypes, and the ML-enabled data platform.


WP6 – Validation, assessments & recommendations

WP6 works as the quality control of the new system by firstly validating the performance assessment of the technologies and then interacting with decision makers & standardization bodies, all within the framework of policy and governance recommendations.

MultiLab Consortium


Start date: 01/01/2024


Partners: 11


Duration: 48 months


Countries: 9


Funding: € 4,856,697.50


News & Results

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Latest News

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A successful F2F MultiLab project meeting was held on 25th April in Nicosia, Cyprus and was hosted by the project coordinator CyRIC | Cyprus Research & Innovation Center Ltd. This[…]

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    EN Co-funded by the EU_POS

    Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or of the granting authority. Neither the European Union nor the granting authority can be held responsible for them.
    This work has received funding from the Swiss State Secretariat for Education,Research and Innovation (SERI)