The sustainable reuse of batteries after their first life in electric vehicles requires accurate state-of-health (SoH) estimation to ensure safe and efficient repurposing. This study applies the systematic ProKnow-C methodology to analyze the state of the art in SoH estimation using machine learning (ML). A bibliographic portfolio of 534 papers (from 2018 onward) was constructed, revealing key research trends. Public datasets are increasingly favored, appearing in 60% of the studies and reaching 76% in 2023. Among 12 identified sources covering 20 datasets from different lithium battery technologies, NASA’s Prognostics Center of Excellence contributes 51% of them. Deep learning (DL) dominates the field, comprising 57.5% of the implementations, with LSTM networks used in 22% of the cases. This study also explores hybrid models and the emerging role of transfer learning (TL) in improving SoH prediction accuracy. This study also highlights the potential applications of SoH predictions in energy informatics and smart systems, such as smart grids and Internet-of-Things (IoT) devices. By integrating accurate SoH estimates into real-time monitoring systems and wireless sensor networks, it is possible to enhance energy efficiency, optimize battery management, and promote sustainable energy practices. These applications reinforce the relevance of machine-learning-based SoH predictions in improving the resilience and sustainability of energy systems. Finally, an assessment of implemented algorithms and their performances provides a structured overview of the field, identifying opportunities for future advancements.

The non-invasive intracranial pressure (NIICP) method based on a skull deformation has been proven to be a significant tool for an assessment of the intracranial pressure (ICP) and compliance. Herein, we present the development and characterization of a novel wireless sensor that uses this method as its working principle and was designed to be easy to use, to have a high resolution, and to achieve a good accessibility. Initially, a brief review of the physiology fundamentals of the ICP and the historic evolution of the NIICP method are mentioned. The sensor architecture and the rationale for the chosen components are then presented, aiming to ensure nanometer displacement measurements, the conversion of analog resolution to digital at a high speed, the fewest amount of distortion, wireless communication, and signal calibration. The NIICP signal has a typical amplitude of $5~\mu \text{m}$ , and thus a resolution of at least 1% of this amplitude is required for an NIICP waveform analysis. We also demonstrate a 40-nm resolution of the sensor using a nanometric displacement test system that can also respond dynamically for NIICP signals from 50 to 180 bpm without any significant distortion (maximum deviation of P2/P1 ratio of 2.6%). The future applications for this device are broad and can enhance a clinical assessment of the intracranial dynamics.

Electrical impedance spectroscopy has been used extensively for sensing and biosensing due to the multiple electrical properties that can be interrogated through varying the frequency of the electrical excitation. In this paper, we review the basic concepts and key issues for applying impedance spectroscopy in sensing and biosensing, with emphasis on the development of precise, low-cost and portable instruments. An impedance spectroscopy system can be divided into three parts: the signal processing unit, the sensing unit and the data analysis unit. Herein, we focus on the signal processing unit, responsible for generating the excitation signal and performing the impedance readout. Special attention is given to small and low-cost signal processing circuits, which are essential for portability and point-of-care diagnosis. We also elaborate upon the various methods to fabricate the sensing units, including the choice of nanomaterials and biomolecules in controlled molecular architectures. From an instrumentation perspective, we discuss possible sources of interference in the measurement protocols. When impedance spectroscopy measurements are performed with arrays of sensing units, as with electronic tongues, large amounts of data are generated. This has motivated an increasing use of statistical and computational methods for data analysis. We present some of these methods, with examples of information visualization and machine learning techniques, which have been employed in analyzing impedance spectroscopy data in recent years.

In its main definition, piezoelectrets are polymeric structures with electric charged internal cavities. The bipolar-trapped charges form macro-dipoles that can be mechanically deformed, thus presenting a high piezoelectric coefficient in the order of hundreds of pC/N. Such results outperform most of the traditional piezoelectric polymers. As piezoelectrets can be manufactured by different methods, some of the techniques allow the inner cavities to be geometrically controlled, therefore providing some degree of freedom in the piezoelectric behavior. This report provides a description of the most relevant methods, starting with a brief introduction of piezoelectret origin and reaching recent achievements in piezoelectret production, such as additive manufacturing (3D printing). Materials that are frequently employed in piezoelectret fabrication and methods for improving their characteristics are also presented.

This paper presents an overview of silicon-based optical sensors for the measurement of light in the visible spectrum range. The review is focused on sensors based on CMOS (complementary metal-oxide semiconductor) technology due to the high availability, low cost, ease of prototyping, and well-established fabrication processes. CMOS technology allows integration with the CMOS readout and control electronics in the same microdevice, featuring high-volume fabrication with high-reproducibility and low-cost. This review starts with an explanation of the phenomena behind opto-electronic transduction. It also presents and describes the most common components responsible for optical transduction, readout electronics, and their main characteristics. This review finishes with the presentation of selected applications to grasp where and how these sensors can be used.

This study demonstrates that the electron beam irradiation of materials, typically used in characterization measurements, could be employed for advanced fabrication, modification, and functionalization of composites. We developed irradiation equipment using an electron beam irradiation source to be applied in materials modification. Using this equipment, the formation of a thick Ag film on the Ag₃PO₄ semiconductor is carried out by electron beam irradiation for the first time. This is confirmed by various experimental techniques (X-ray diffraction, field-emission scanning electron microscopy, Raman spectroscopy, and X-ray photoelectron spectroscopy) and ab initio molecular dynamics simulations. Our calculations demonstrate that, at the earlier stages, metallic Ag growth is initiated preferentially at the (110) surface, with the reduction of surface Ag cations forming metallic Ag clusters. As the (100) and (111) surfaces have smaller numbers of exposed Ag cations, the reductions on these surfaces are slower and are accompanied by the formation of O₂ molecules.