# Additively Manufactured Polymers for Electronic Components
## 5 Current Limitations and Overcoming Strategies
To strengthen the design of durable printable electronics, it is essential to integrate accelerated ageing protocols in formulation workflows. Addressing these gaps is essential not only to ensure device functionality in real-world applications but also to guide formulation strategies that enhance material robustness beyond laboratory conditions. Regarding extrinsically conductive nanocomposites, the literature provides plenty of research papers [182]. Knowledge regarding the formulation of electrically conductive polymer-based nanocomposites is well established. What is missing is a full understanding of the most appropriate processing conditions for the different 3D printing technologies. For example, considering some works on SL printing of CNT- and graphene-based polymer nanocomposites, the loading of conductive fillers is relatively limited, being 5 wt% at most. This is due to two main factors. First, the addition of fillers drastically increases the viscosity of the nanocomposite by several orders of magnitude, making processing very challenging. This is true not only for SL technology but for many others, e.g., IJP, AJP, and DIW. Second, graphene and CNTs interact with the UV-curing process by absorbing a large amount of UV radiation that should be used for cross-linking. Consequently, too high loading of fillers hinders cross-linking, leading to poorly cured or uncured nanocomposites that, therefore, have detrimental mechanical properties. There are different approaches to improve the situation. Adding some rheological modifiers could help to reduce the viscosity while increasing the filler concentration in the nanocomposite. Among all of the rheological modifiers, reactive diluents are a very interesting category for their two-fold effect. They help to reduce the viscosity while also helping with the cross-linking reaction at the same time. However, reactive diluents have some drawbacks as well. Cured nanocomposites exhibit a lower glass transition temperature as a result of the chemical nature of the reactive diluents. These diluents promote mobility in polymer chains, thereby increasing the free volume and lowering the \(\mathrm{T_{g}}\). It would be challenging when the \(\mathrm{T_{g}}\) of the nanocomposites is lowered to a level close to the service temperature because it would yield a material without proper thermal and mechanical characteristics. Another issue is that the overall mechanical properties of nanocomposites decrease. This occurs in the opposite direction of the effect of the addition of filler, which provides an increase in both electrical and mechanical properties. Therefore, the concentration of the type of reactive diluent should be carefully selected. Recent advances in artificial intelligence and machine learning are now facilitating the optimisation of data-driven formulation. These tools have been used not only to identify correlations between formulation parameters and rheological properties but also to predict the electrical performance and mechanical integrity of printed components under operational stress [183]. Generative models and neural networks have also shown potential in optimising and controlling AM process parameters, thereby reducing the number of development cycles [184]. Integrating machine learning into the design of conductive inks and composites could likewise speed up the discovery of printable, durable inks tailored for electronic applications [185]. Machine learning methods and AI-driven formulation design have already been applied for this purpose to optimise the viscosity and mechanical properties of additively manufactured ECPs, as demonstrated in the creation of 3D lattice structures [186] and smart responsive materials [187]. Another challenge is the interaction between polymers and fillers, which is critical but scarcely understood to date. Poor polymer-filler interaction leads to inadequate electrical and mechanical performance.