Accurate estimation of the state of charge (SoC) of lithium-ion batteries is crucial for battery management systems, particularly in electric vehicle (EV) applications where real-time monitoring ensures safe and robust operation. This study introduces three advanced algorithms to estimate the SoC: deep neural network (DNN), gated recurrent unit (GRU), and long short-term memory (LSTM). The DNN, GRU, and LSTM models are trained and validated using laboratory data from a lithium-ion 18650 battery and simulation data from Matlab/Simulink for a LiCoO₂ battery cell. These models are designed to account for varying temperatures during charge/discharge cycles and the effects of battery aging due to cycling. This paper is the first to estimate the SoC by a deep neural network using a variable current profile that provides the SoC curve during both the charge and discharge phases. The DNN model is implemented in Matlab/Simulink, featuring customizable activation functions, multiple hidden layers, and a variable number of neurons per layer, thus providing flexibility and robustness in the SoC estimation. This approach uniquely integrates temperature and aging effects into the input features, setting it apart from existing methodologies that typically focus only on voltage, current, and temperature. The performance of the DNN model is benchmarked against the GRU and LSTM models, demonstrating superior accuracy with a maximum error of less than 2.5%. This study highlights the effectiveness of the DNN algorithm in providing a reliable SoC estimation under diverse operating conditions, showcasing its potential for enhancing battery management in EV applications.

While many studies on fused filament fabrication (FFF)-printed polymers focus on ultimate tensile strength or failure analysis, the elastic region of the stress–strain curve is frequently overlooked. However, in most engineering applications, components operate well within the elastic range. In mechanical joints, support frames, and other load-bearing structures, stiffness and elastic response are more critical than post-failure behavior, as these properties determine system performance during standard operating conditions before any damage occurs. This study examines the elastic properties of acrylonitrile butadiene styrene (ABS) components fabricated via FFF, with a focus on the impact of printing orientation and nozzle temperature. Tensile tests were performed according to ISO 527-2:1993, and the results were compared to those of milled ABS parts (referred to as FT). Two print orientations were studied: XT, where the layers are oriented perpendicular to the loading direction, and ZT, where the layers are aligned parallel to the loading direction (load-aligned). The study reveals that printing orientation has a significant impact on mechanical behavior. The specimens printed in the ZT orientation exhibited superior elastic modulus and tensile strength compared to the XT specimens and also outperformed the milled FT parts. At 245 °C, the ZT specimens achieved an average tensile strength of 41.0 MPa, substantially higher than the FT’s 31.1 MPa. Moreover, the ZT had approximately 12.6% higher elastic moduli than the FT (1.97 GPa ZT compared to 1.74 GPa FT). Although the FT parts showed higher strain at break, the ZT-printed parts demonstrated a stiffness and strength that suggest their viability as replacements for machined components in load-bearing applications.

The results of colossal magnetoresistance (CMR) properties of La_0.83Sr_0.17Mn_1.21O₃ (LSMO) films grown by pulsed injection MOCVD technique onto various substrates are presented. The films with thicknesses of 360 nm and 60 nm grown on AT-cut single crystal quartz, polycrystalline Al₂O₃, and amorphous Si/SiO₂ substrates were nanostructured with column-shaped crystallites spread perpendicular to the film plane. It was found that morphology, microstructure, and magnetoresistive properties of the films strongly depend on the substrate used. The low-field MR at low temperatures (25 K) showed twice higher values (−31% at 0.7 T) for LSMO/quartz in comparison to films grown on the other substrates (−15%). This value is high in comparison to results published in literature for manganite films prepared without additional insulating oxides. The high-field MR measured up to 20 T at 80 K was also the highest for LSMO/quartz films (−56%) and demonstrated the highest sensitivity S = 0.28 V/T at B = 0.25 T (voltage supply 2.5 V), which is promising for magnetic sensor applications. It was demonstrated that Mn excess Mn/(La + Sr) = 1.21 increases the metal-insulator transition temperature of the films up to 285 K, allowing the increase in the operation temperature of magnetic sensors up to 363 K. These results allow us to fabricate CMR sensors with predetermined parameters in a wide range of magnetic fields and temperatures.

Atomic force microscopy (AFM) is widely used to quantify mechanical properties, typically Young’s modulus, by fitting force–indentation data with various mathematical contact models. However, results across laboratories often diverge, and the causes remain unresolved. Here, we interrogate the methodology by which mechanical properties are defined in AFM indentation and identify key limitations of the Hertz model, the standard model for determining mechanical properties, notably that the contact radius is not directly determined, which limits the accuracy of the estimated Young’s modulus. We hypothesize that this inference systematically overestimates the true tip–sample contact, which inflates the contact area and thereby underestimates Young’s modulus. This bias is amplified under large indentation conditions, which are frequently used in soft-material studies. To isolate and clarify the issue, we focus on a well-characterized polymer, polyvinyl chloride (PVC), using it as a controlled testbed for contact radius overestimation. Our analysis is focused on the contact radius and Hertz-based extraction of Young’s modulus. We determined the contact radius and Young’s modulus using AFM with two different probes: a sphere with a 20 nm radius (SPHERE20) and a sphere with a 2 µm radius (SPHERE2000). The results were compared to macroscopic data obtained using a standard measurement (ISO 527-1:2019) of Young’s modulus. The contact was modeled using finite element analysis (FEA). The dependence of the contact radius on the indentation was compared to the Hertz model. The results from FEA fit corrected contact radius values, and it is smaller by 15.46% (SPHERE20) and 57.9% (SPHERE2000) than those calculated by the Hertz model.

This study introduces an innovative two-range, 12-stage Marx pulse generator employing thyristor switches designed specifically for the electroporation of biological cells. The generator consists of two module capacitors of different capacitances (1 μF and 0.25 μF), which enable the generation of electrical pulses with different durations and amplitudes of up to 25 kV. Safety aspects, including overcurrent and overvoltage protection mechanisms, are implemented in both the software and the hardware. In the experimental section, the tests of the Marx generator with resistive load are described in detail, and the results for the voltage fluctuations, pulse duration, and output characteristics of the generator are presented. The advantages of the design, including the high output voltage, the wide range of repetition rates, and the flexibility of the pulse parameters, are emphasized. Additionally, the research showcases the utilization of the devised generator for industrial purposes. Hence, an investigation into the efficiency of protein extraction from microalgae (Chlorella vulgaris) and the impacts of pulsed electric fields (PEFs) on the structural characteristics of casein micelles (CSMs) was chosen as an illustrative example. The obtained results provide valuable insights into the application of PEF in food processing and biotechnology and underline the potential of the developed generator for sustainable and environmentally friendly practices.