Industrial Use Case 5

UC5: Functional Coatings

Computational discovery, AI-driven screening, and optimisation of analyte-sensitive functional materials and printable ink formulations.

Use case 5

UC5

Discover & optimise sensing materials

Analyte-sensitive coatings for digital olfaction

Use Case 5 focuses on the computational discovery, AI-driven screening, and optimisation of analyte-sensitive functional materials and printable ink formulations.

Traditional sensing technologies rely on one-to-one analyte calibration, which often fails in complex, dynamic industrial environments. UC5 shifts this paradigm towards holistic chemical state monitoring.

By leveraging the SimuLingua Scientific Foundation Model (SciFM), researchers can predict and tailor the electrical and physical responses of functional coatings when exposed to multi-component chemical changes.

Led by Dycotec Materials Ltd (UK), UC5 curates extensive formulation datasets (>200 ink recipes) to accelerate the inverse design of functional coatings. These novel materials enable real-time tracking of solvent degradation, scaling tendencies, contamination levels, and fluid health in demanding applications such as carbon capture systems and geothermal energy infrastructure.

Paired with UC6 — full digital olfaction system (array deployment, industrial targets & AI learning hierarchy).

Lead partner
Dycotec Materials
Consortium partners
Dycotec Materials, University of Sheffield, Oliveris Tech Incubator

Paired use case

UC6: 64×128 digital olfactory array

Deploy materials & generate chemical intelligence

digital olfactionformulationVOCinkjetcarbon capturegeothermalDYC

Target KPIs

  • ≥500 formulations computationally evaluated
  • ≥80% reduction in wet-lab trials vs manual DoE
  • 30–50 candidates advanced to printability/stability simulation
  • 10–15 synthesized/characterized; 3–5 integrated into prototype IDC arrays
  • LoD ≤1–5 ppm on ≥2 coatings (per VOC class)
  • Selectivity ≥10:1 vs named interferents; t90 ≤5 s; drift ≤2%/24 h
  • ≥90% coating yield on ≥8×8 coupon subarrays; <5% pixel variance
  • Carbon capture & geothermal chemical-state fingerprints (kick-off domains)

Validation: WP13 (definition) → WP14 (in-silico screening) → WP15 (experimental validation). Reports published as D14.x and D15.x deliverables become public.

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Get in touch with the SimuLingua project.

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Project enquiries

HORIZON-RIA · GenAI4EU

1 June 2026May 2030

HORIZON-CL4-INDUSTRY-2025-01-DIGITAL-61

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