Nanocomposites based on carbon nanotubes (CNTs) are suitable for sensors, due to matrix ability to incorporate nanotube properties. Thus, we developed a low-cost, nanostructured poly(acrylonitrile-butadiene-styrene) (ABS) polymer piezoresistive sensor produced by additive manufacturing. For this, solution layers of acetone, dimethylformamide and CNTs functionalized with carboxylic acid were pulverized on an ABS substrate using an aerograph. Electrical characterization revealed an anisotropic piezoresistive response of the material, induced by the printing lines direction. Field Emission Gun-Scanning Electron Microscopy showed the nanostructured film spreading after five layers of CNTs as well as the random entanglement of nanotubes on parallel and perpendicular 3D-printed ABS substrates. Raman spectroscopy indicated compression and p-type doping of CNTs in interaction with the polymer, as seen mainly by the blueshift of the G and 2D subbands. The results show that the material is promising for pressure sensors, with potential applications in robotic haptic feedback systems.

With the advancement of Additive Manufacturing and its applications in various industrial segments, it becomes increasingly important to investigate the processability parameters associated with nano-reinforcement. This study examines the influence of infill parameters, such as patterns (hexagonal, triangular and concentric), shape (solid and honeycomb) and concentrations by mass of Carbon Nanotubes (CNTs) at 1.0 wt% and 2.0 wt% on the mechanical compression properties of the 3D printed material. In this sense, nanocomposites based on Polylactic Acid (PLA) matrix nano-reinforced by CNTs were characterized using the Scanning Electron Microscopy -SEM, X-ray Diffraction, Raman Spectroscopy techniques and mechanical analysis. The mechanical properties of both the matrix and the nanocomposites were determined through mechanical compression tests, in accordance with [1]. SEM revealed compacted regions, voids, and detachment in the structures. XRD characterization indicated PLA predominantly amorphous nature, while CNTs exhibited characteristic diffractions of carbon nanotubes. Raman characterization showcased bands and characteristic vibrations of CNTs and PLA, nanocomposites' vibrational modes combine those of CNTs and PLA. Mechanical compression analysis indicates a direct influence of infill pattern, shape, and nano-reinforcement. The triangle pattern outperformed other patterns, exhibiting a 15.03% increase compared to concentric and 8.8% compared to hexagon infill. In nanocomposites, all showed higher compressive strength than the matrix. For PLA/1.0%CNTs, strength was 16.8%, and for PLA/2.0%CNTs, it reached 39.2%. In honeycomb shapes with infill variations, the triangular pattern excelled with a 0.97% increase over concentric and 6.12% over hexagon patterns. Honeycomb results indicate a 59.6% increase for (PLA/1.0%CNTs) and a 0.48% increase for (PLA/2.0%CNTs) compared to the matrix. This study highlights 3D printing parameters, emphasizing the effectiveness of the triangular pattern and the enhanced strength with the addition of CNTs, providing insights to enhance products manufactured through Additive Manufacturing.

The increasing demand for materials with enhanced properties and high-performance devices has driven substantial research into nanomanufacturing, particularly using carbon nanotubes (CNTs), because of their exceptional properties and high sensitivity to chemical doping. In this way, this work summarizes nanomanufacturing methods for CNT-based devices developed in Brazil, covering the complete cycle from nanocomposite production to functional device assembly across cellulosic, polymeric, and metallic matrix systems. For cellulosic matrices, vacuum filtration enables the production of buckypaper, which is subsequently assembled into chemiresistive, thermoresistive, and thermoelectric devices. For polymeric matrices, 3D printing combined with surface functionalization techniques (spray coating, inverted immersion, and direct immersion) produces piezoresistive robotic sensors, metal-free thermal sensors, and biomedical scaffolds for tissue engineering. For metallic matrices, electrodeposition can produce Cu-CNT-coated aluminum comparable to traditional copper power transmission cables, while arc welding produces stainless steel composites with properties comparable to commercial high-grade steels. These devices have commercial and industrial applications, with low-cost and scalable production methods in comparison with conventional materials. Characterization results demonstrate that CNT integration into diverse matrices successfully bridges nanoscale properties to macroscopic functional devices. Current challenges include uniform CNT dispersion and structural defect control, laboratory to industry scale transition, and long-term device stability under environmental conditions. Future perspectives encompass lab-on-chip systems, wearable devices, 3D-printed smart structures, Internet of Things integration, and machine learning-enhanced analytics.

This paper describes the development of a novel instrumented wheel with onboard sensors to estimate tire deformation, together with a recurrent neural network capable of classifying terrain types. We present a functional prototype manufactured using 3D printing and a nanostructuring process that integrates piezoresistive sensors directly into the structure of an airless wheel. This process enables precise acquisition of wheel–terrain interaction signals that are informative for terrain recognition. Using these signals, along with the robot’s linear velocity, we train a bidirectional GRU to distinguish concrete, gravel, and grass terrains. Experiments on a service ground robot traversing a 180 m circuit yield a test accuracy of 95.7%, with the confusion matrix indicating consistent performance across classes. The results suggest that embedded wheel sensing combined with sequence modeling can provide accurate terrain classification while reducing payload and integration complexity. The proposed sensing wheel and inference pipeline offer a compact basis for terrain-aware mobility and potential extensions such as slip detection and payload monitoring.