Three-dimensional printed electrodes seem to overcome many structural and operational limitations compared to ones fabricated with conventional methods. Compared to other 3D printing techniques, direct ink writing (DIW), as a sub-category of extrusion-based 3D printing techniques, allows for easier fabrication, the utilization of various materials, and high flexibility in electrode architectures with low costs. Despite the conveniences in fabrication procedures that are facilitated by DIW, what qualifies an ink as 3D printable has become challenging to discern. Probing rheological ink properties such as viscoelastic moduli and yield stress appears to be a promising approach to determine 3D printability. Yet, issues arise regarding standardization protocols. It is essential for the ink filament to be extruded easily and continuously to maintain dimensional accuracy, even after post-processing methods related to electrode fabrication. Additives frequently present in the inks need to be removed, and this procedure affects the electrical and electrochemical properties of the 3D-printed electrodes. In this context, the aim of the current review was to analyze various energy devices, highlighting the type of inks synthesized and their measured rheological properties. This review fills a gap in the existing literature. Thus, according to the inks that have been formulated, we identified two categories of DIW electrode architectures that have been manufactured: supported and free-standing architectures.

The modelling of magnetic hyperthermia using nanoparticles of ellipsoid tumor shapes has not been studied adequately. To fill this gap, a computational study has been carried out to determine two key treatment parameters: the therapeutic temperature distribution and the extent of thermal damage. Prolate and oblate spheroidal tumors, of various aspect ratios, surrounded by a large healthy tissue region are assumed. Tissue temperatures are determined from the solution of Pennes’ bio-heat transfer equation. The mortality of the tissues is determined by the Arrhenius kinetic model. The computational model is successfully verified against a closed-form solution for a perfectly spherical tumor. The therapeutic temperature and the thermal damage in the tumor center decrease as the aspect ratio increases and it is insensitive to whether tumors of the same aspect ratio are oblate or prolate spheroids. The necrotic tumor area is affected by the tumor prolateness and oblateness. Good comparison is obtained of the present model with three sets of experimental measurements taken from the literature, for animal tumors exhibiting ellipsoid-like geometry. The computational model enables the determination of the therapeutic temperature and tissue thermal damage for magnetic hyperthermia of ellipsoidal tumors. It can be easily reproduced for various treatment scenarios and may be useful for an effective treatment planning of ellipsoidal tumor geometries.

Artificial intelligence (AI) methods have significantly impacted various areas of technology, particularly in fields where large datasets are available. Screw designs are proprietary, and there is very limited information available in the open literature. In this study, we generated a dataset of 232 designs using computer simulation software for screw extrusion, involving solids transport, melting, and melt pumping. The parameters (features) and the outputs (targets) were introduced into four powerful machine learning (ML) algorithms. The capabilities of the four algorithms were assessed by comparing the predictions of each of the algorithms to the corresponding results of the simulations. Three of the algorithms demonstrated satisfactory performance, with the best‐performing one being further tested on an “unseen” dataset, which involved a screw of 75 mm and another of 127 mm in diameter. A machine‐learning technique called Permutation Feature Importance (PFI) was used to identify the features (parameters) with the greatest impact on the predictions. It is suggested that the same ML methodologies could be applied to datasets of existing real screw designs.Highlights: Dataset obtained from simulation software. Four machine learning algorithms were employed. Assessment of algorithms based on training and testing data. Identification of parameters having greatest impact. Satisfactory predictions of mass flow rate, exit temperature, melting length, and more.

Hyperthermia, an alternative medical approach aiming at locally increasing the temperature of a tumor, can cause the “death” of cancer cells or the sensitization of them to chemotherapeutic drugs and radiation. In contrast with the conventional treatments, hyperthermia provokes no injury to normal tissues. In particular, magnetic hyperthermia can utilize iron oxide nanoparticles, which can be administered intravenously to heat tumors under an alternating magnetic field. Currently, there is no theoretical model in the relative literature for the effective thermal conductivity of blood and magnetic nanoparticles. The scope of the present study is twofold: (a) development of a theoretical relationship, based on experimental findings and blood structure and (b) study of the laminar natural convection in a simplified rectangular porous enclosure, by using the asymptotic expansions method for deriving ordinary differential equations of the mass, momentum and energy balances, as a first approach of investigating heat transfer and providing theoretical guidelines. In short, the thermal conductivity of the resulting bio-nanofluid tends to increase by both increasing the concentration of the nanoparticles and the temperature. Furthermore, the heat transfer is enhanced for more intense internal heating (large Rayleigh numbers) and more permeable media (large Darcy numbers), while larger nanoparticle concentrations tend to suppress the flow.

Recent advances in three-dimensional concrete printing necessitated the detailed understanding of the operation and performance of screw extruders. This paper shows that the volumetric output rate can be approximated using the rotating barrel and stationary screw assumption (drag flow equation), which is used routinely in polymer melt extrusion calculations. Verification is provided by comparisons to experimental results available in the literature and to computer flow simulations for fluids with yield stress. Significant insight is obtained using fully three-dimensional simulations. This includes particle pathlines, which form “a helix within a helix,” axial pressure profiles, and the effect of yield stress, which is relatively small on the output rate but large on torque and power. The computer simulation also predicts unyielded flow zones in the extruder channel at low screw rotation speeds.