Electrically Functional Photopolymer Resins for Vat Photopolymerisation: A Compact Framework for System-Dependent Performance

Abstract

Background. Vat photopolymerisation (VPP) is increasingly used to fabricate electrically functional structures in which electrical behaviour must coexist with photochemical cure, geometric fidelity, rheological processability and service stability. The literature spans electrostatic-control materials, carbon-based percolation composites, conductingpolymer systems, metallic-particle composites, high-permittivity dielectric composites and multi-material workflows. A recurrent interpretive problem is that electrical values are often reported without sufficient description of the curedsystem configuration that produced them. Scope and approach. This compact narrative review synthesises peer-reviewed literature, current standards and selected application studies relevant to electrically functional VPP. It is a framework-driven review, not a PRISMA systematic review, and therefore does not claim exhaustive database coverage or numerical screening completeness. The review separates established literature observations from review-specific engineering classifications and from industrial interpretation. Framework. A System-Dependent Conductivity Framework (SDCF) is used to organise seven process stages chemistry, dispersion/loading, printer optics, exposure history, geometry/orientation, post-cure, and storage/service that condition the electrical response of the cured part. A four-level reporting hierarchy (intrinsic, effective, realised and operational performance) is used to distinguish constituent properties, coupon values, part-specific measurements and in-service trajectories. Implications. The principal recommendation is methodological: electrically functional VPP materials should not be characterised by an isolated conductivity or resistance value. Reports should state the electrical figure of merit, measurement method and units, specimen geometry, print platform, exposure and post-cure history, ambient condition and time after manufacture. The framework is descriptive, not a closed-form predictive model.