Melt electrowriting (MEW) is an emerging additive process for high‐fidelity, microscale fibrous scaffold fabrication. However, achieving precise multilayered MEW‐enabled scaffolds is limited by the entrapped residual charges owing to charge‐based mechanisms. Specifically, the semi‐conductive nature of processed materials causes retainment of net positive charges and jet–fiber repulsion, while exposure to the electric field yields charge polarization with resultant jet–fiber attraction. These competing effects work in tandem to determine the distinctive features of jet–fiber interaction. To deconstruct various charge‐related phenomena, the collector temperature is manipulated as a key process variable to investigate its effect on printing outcomes in two printing modes. Moreover, energy analysis is introduced to explain how collector temperature affects the polarization extent, along with the jet–fiber interaction and printing outcomes. In single fiber printing mode, sets of two parallel fibers with variable set interfiber distances (sSf) are printed at different collector temperatures. At a low sSf threshold, significant fiber attraction is observed, but no significant difference is observed among the cases at different collector temperatures. In scaffold printing mode, 200‐layer scaffolds are printed at different collector temperatures, and the wall morphologies are found to vary with location, layer number, and collector temperature.
The printing accuracy of the melt electrowriting (MEW) process is adversely affected by residual charge entrapped within the printed fibers. To mitigate this effect, the residual charge amount (Q r ) must first be accurately determined. In this study, Q r is measured by a commercial electrometer at a nanocoulomb scale for MEW-enabled scaffolds. Based on this enabling measurement, the effects of various design parameters (including substrate surface conductivity 𝝈, printing time t, layer number N), and process parameters (including voltage U, translational stage speed v, and material temperature T m ), on Q r are investigated. An increase of 𝝈 or decrease of N helps to decrease Q r . The effects of different process parameters on the residual charge can be either dependent or independent of fiber morphologies. Moreover, the fiber-morphology dependent and independent effect can be either synergistic (U and T m ) or antagonistic (e.g., v) for different process parameters. Under same conditions, Q r in the interweaving scaffold design is generally smaller than that in the non-interweaving scaffold design. These results help to furnish necessary insights into the charge dissipation process for a melt-based electrohydrodynamic printing process while providing a systematic methodology to mitigate the residual charge accumulation.
This paper is mostly concerned with an experimental and numerical study to clarify the behaviour and failure in the mono‐adhesive joints and mixed‐adhesive joints under different environmental conditions (dry [E0], 75.3% relative humidity [E1], 84.2% relative humidity [E2], and submerged in tap water [E3] at 25°C) and different strain rates (1 and 100 mm/min). Experimental investigations are compared with the numerical analysis, which is carried out by bilinear cohesive zone model (CZM). Through this work, degradations of cohesive parameters are calculated by using open‐faced double cantilever beam (DCB) and end notch flexure (ENF) specimens. The experimental data show that the cohesive parameters of Araldite 2015 have negative correlation to moisture content. Although Araldite AV138 parameters experience a decrease in mode I, in mode II, its cohesive parameter increases. First, it is found that the decrease in experimental failure load of mixed‐adhesive joints with regard to the dry condition is recorded as 32.6%, 54%, and 59.1% for E1, E2, and E3 conditions, respectively. Following this further, single lap joint (SLJ) that has non‐uniform moisture content distribution is modelled by engaging specific CZM parameters related to the specific moisture content. The results show a good agreement between experimental and numerical data.
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