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Abstract The introduction of porosity into ferroelectric ceramics can decrease the effective permittivity, thereby enhancing the open circuit voltage and electrical energy generated by the direct piezoelectric effect. However, the decrease in the longitudinal piezoelectric coefficient (d33) with increasing porosity levels currently limiting the range of pore fractions that can be employed. By introducing aligned lamellar pores into (Ba0.85Ca0.15)(Zr0.1Ti0.9)O3, this paper demonstrates an unusual 22–41% enhancement in the d33 compared to its dense counterpart. This unique combination of high d33 and a low permittivity leads to a significantly improved voltage coefficient (g33), energy harvesting figure of merit (FoM33) and electromechanical coupling coefficient (k332). The underlying mechanism for the improved properties is demonstrated to be a synergy between the low defect concentration and high internal polarizing field within the porous lamellar structure. This work provides insights into the design of porous ferroelectrics for applications related to sensors, energy harvesters, and actuators.

Abstract Hydraulic fluid power systems are essential for a range of engineering applications such as transportation, heavy industry, and robotics. The scale of the industry is such that hydraulic pumps are estimated to account for 15% of all the energy consumption in the European Union and yet the average efficiency of fluid power systems is only 22%. The digitalization of hydraulic systems offers significant advantages in terms of energy efficiency, performance, reduced maintenance, and automation. However, this requires advances in the integration of smart sensing technologies to provide real‐time feedback on the operation and health of hydraulic components. This review details developing trends in hydraulic fluid power research and provides an overview of progress related to the digitalization of these systems and their integration within an Industry 4.0 framework. The fundamentals of relevant sensor technologies and innovative approaches for integrating sensors into hydraulics systems are discussed. Methods to deliver power to the sensors and associated electronics through harvested pressure ripples are also reviewed. An outlook with respect to future directions in this field is given, including an assessment of the potential for exploiting advanced manufacturing technologies, in particular additive manufacturing, to facilitate successful sensor integration into hydraulic fluid power systems.

As a promising energy‐harvesting technique, an increasing number of researchers seek to exploit the piezoelectric effect to power electronic devices by harvesting the energy associated with water flow. In this emerging field, a variety of research themes attract interest for investigation; these include selection of the excitation mechanism, oscillation structure, piezoelectric material, power management interface circuit, and application. Since there has been no comprehensive review to date with respect to the harvesting of water flow using piezoelectric materials, herein relevant work in the last 25 years is reviewed. To ensure that key aspects of the water‐flow energy harvester are overviewed, they are discussed in the context of energy‐flow theory, which includes the three stages of energy extraction, energy conversion, and energy transfer. The development of each energy‐flow process is reviewed in detail and combined with meta‐analysis of the published literature. Correlations between the harvesting processes and their contribution to the overall energy‐harvesting performance are illustrated, and directions for future research are also proposed. In this review, a comprehensive understanding of water‐flow piezoelectric energy harvesting is provided and it is aimed to guide future research and the development of piezoelectric harvesters for water‐flow‐powered devices is promoted.

Abstract Soft robotic bodies are susceptible to mechanical fatigue, punctures, electrical breakdown, and aging, which can result in the degradation of performance or unexpected failure. To overcome these challenges, a soft self‐healing robot is created using a thermoplastic methyl thioglycolate–modified styrene–butadiene–styrene (MG‐SBS) elastomer tube fabricated by melt‐extrusion, to allow the robot to self‐heal autonomously at room temperature. After repeated damage and being separated into several parts, the robot is able to heal its stiffness and elongation to break to enable almost complete recovery of robot performance after being allowed to heal at room temperature for 24 h. The self‐healing capability of the robot is examined across the material scale to robot scale by detailed investigations of the healing process, healing efficiency, mechanical characterization of the robot, and assessment of dynamic performance before and after healing. The self‐healing robot is driven by a new micro two‐way shape‐memory alloy (TWSMA) spring actuator which achieved a crawling speed of 21.6 cm/min, equivalent to 1.57 body length per minute. An analytical model of the robot is created to understand the robot dynamics and to act as an efficient tool for self‐healing robot design and optimization. This work therefore provides a new methodology to create efficient, robust, and damage‐tolerant soft robots.

Abstract Finite element studies were conducted on 0.5Ba(Zr0.2 Ti0.8) O3–0.5(Ba0.7 Ca0.3)TiO3 (BCZT) piezoelectric particles embedded in polyethylene matrix to create a piezocomposite having a positive and negative Poisson's ratio of −0.32 and 0.2. Polyethylene with a positive Poisson's ratio is referred to as non-auxetic while those with negative Poisson's ratio are referred to as auxetic or inherently auxetic. The effective elastic and piezoelectric properties were calculated at volume fractions of (4%, 8% to 24%) to study their sensing and harvesting performance. This study compared lead-free auxetic 0–3 piezocomposite for sensing and energy harvesting with non-auxetic one. Inherently auxetic piezocomposites have been studied for their elastic and piezoelectric properties and improved mechanical coupling, but their sensing and energy harvesting capabilities and behavior patterns have not been explored in previous literatures. The effect of Poisson's ratio ranging between −0.9 to 0.4 on the sensing and energy harvesting performance of an inherently auxetic lead free piezocomposite composite with BCZT inclusions has also not been studied before, motivating the author to conduct the present study. Auxetic piezocomposite demonstrated an overall improvement in performance in terms of higher sensing voltage and harvested power. The study was repeated at a constant volume fraction of 24% for a range of Poisson's ratio varied between −0.9 to 0.4. Enhanced performance was observed at the extreme negative end of the Poisson's ratio spectrum. This paper demonstrates the potential improvements by exploiting auxetic matrices in future piezocomposite sensors and energy harvesters.

Due to the unique electromechanical energy conversion capability of ferroelectric materials, they have been at the forefront of materials science for a variety of applications such as sensors, actuators and energy harvesting. Researchers have focused on exploring approaches to achieve improved ferroelectric performance, and to ensure that the available material systems are more environmentally friendly. This comprehensive review summarizes recent research progress on porous ceramics and highlights the variety of factors that are often ignored, namely the influence of porosity on the Curie temperature, and applications of porous ferroelectric materials with adjustable Curie temperature. Finally, the development trends and challenges of porous ferroelectric materials are discussed, aiming to provide new insights for the design and construction of ferroelectric materials.

Objective Neurodegenerative diseases affect millions of families around the world, while various wearable sensors and corresponding data analysis can be of great support for clinical diagnosis and health assessment. This systematic review aims to provide a comprehensive overview of the existing research that uses wearable sensors and features for the diagnosis of neurodegenerative diseases. Methods A systematic review was conducted of studies published between 2015 and 2022 in major scientific databases such as Web of Science, Google Scholar, PubMed, and Scopes. The obtained studies were analyzed and organized into the process of diagnosis: wearable sensors, feature extraction, and feature selection. Results The search led to 171 eligible studies included in this overview. Wearable sensors such as force sensors, inertial sensors, electromyography, electroencephalography, acoustic sensors, optical fiber sensors, and global positioning systems were employed to monitor and diagnose neurodegenerative diseases. Various features including physical features, statistical features, nonlinear features, and features from the network can be extracted from these wearable sensors, and the alteration of features toward neurodegenerative diseases was illustrated. Moreover, different kinds of feature selection methods such as filter, wrapper, and embedded methods help to find the distinctive indicator of the diseases and benefit to a better diagnosis performance. Conclusions This systematic review enables a comprehensive understanding of wearable sensors and features for the diagnosis of neurodegenerative diseases.

This work describes the correlations between the ultrasonic pulse velocity (UPV) and physical properties of fibrous-particulate hybrid composites made of glass/carbon fabrics and three different micro-inclusions such as silica particles, recycled carbon fibre powder and Portland cement. A full factorial design (214151) is established to identify the effects of volume fraction, stacking sequence and particle type on the physical properties obtained by Archimedes’ principle. An electroacoustic transducer (50 ​kHz) is used to determine the ultrasonic pulse velocity data. Statistical models reveal correlations between UPV and the physical properties of hybrid composites. The combination of UPV and statistical techniques can identify all constitutive phases, being useful to characterise and estimate physical properties in a hybrid system composed of fibres and microparticles.

Abstract This paper presents a piezoelectric wind energy harvester that operates by a galloping mechanism with different shaped attachments attached to a bluff body. A comparison is made between harvesters that consist of different shaped attachments on a bluff body; these include triangular, circular, square, Y‐shaped, and curve‐shaped attachments. Simulation of the pressure field and the velocity field variation around the different shaped bluff bodies is performed and it is found that a high pressure difference creates a high lift force on the bluff body with curve‐shaped attachments. A theoretical model based on a galloping mechanism is presented, which is verified by experiments. It is observed that the proposed harvester with curve‐shaped attachments provides the best performance, where the harvester with a curve‐shaped attachments provides the highest voltage and power output compared to the other shaped harvesters examined in this study. This paper provides a new concept for improving the power performance of the piezoelectric wind energy harvesters with modifications made on the bluff body.

Top-down processes such as expectations play a key role in pain perception. In specific contexts, inferred threat of impending pain can affect perceived pain more than the noxious intensity. This biasing effect of top-down threats can affect some individuals more strongly than others due to differences in fear of pain. The specific characteristics of intrinsic brain characteristics that mediate the effects of top-down threat bias are mainly unknown. In this study, we examined whether threat bias is associated with structural and functional brain connectivity. The variability in the top-down bias was mapped to the microstructure of white matter in diffusion weighted images (DWI) using MRTrix3. Mean functional connectivity of five canonical resting state networks was tested for association with bias scores and with the identified DWI metrics. We found that the fiber density of the splenium of the corpus callosum was significantly low in individuals with high top-down threat bias (FWE corrected with 5000 permutations, p

Magnéli, a fascinating sub-stoichiometric titanium oxide phase that contains oxygen vacancies of TiO2, is proposed as a potential photo-electrode for photocatalysis due to its high conductivity and photoelectric conversion efficiency. In this work, partially reduced porous TiO2 ceramics with a graded reduction profile and an aligned pore structure were prepared by carbo-thermal reduction of freeze-cast TiO2 with a range of porosities from 10 to 30 ​vol.%. AC conductivity evaluation revealed that the oxygen vacancies that exist in Magnéli lead to a relatively high AC conductivity of ∼10 ​S/m at 1 ​kHz, as compared to that of the stoichiometric porous TiO2. A maximum photocurrent density and incident photon-to-current efficiency (IPCE) of 9.14 ​mA/cm2 and 40.52% respectively were achieved from the gradient porous Magnéli with a porosity of 30 ​vol.%.

The hydraulically amplified self‐healing electrostatic actuator is an emerging driving component for soft robotics, which is composed of a flexible dielectric polymer shell that is partially covered by conductive electrodes and filled with a liquid dielectric. However, the low permittivity and dielectric strength of the polymer shell remain a challenge that limits the actuator performance. Herein, a Peano‐hydraulically amplified self‐healing electrostatic actuator is constructed by innovatively integrating a bilayer polymer shell for combined favorable properties of high dielectric strength, dielectric permittivity, and elastic modulus. Compared with a traditional single‐layer shell actuator, the new bilayer actuator architecture generates an increased strain (164%) at 5 kV and improves load‐bearing capability (620 mN) at 6 kV, thereby providing a significantly enhanced actuation performance. The new actuator is further applied in driving a ratchet system, which converts the reciprocating motion of the actuator into a rotating motion and a flexible output torque, in order to protect the rotating components from impact. The high strain and load characteristics of the bilayer configuration and the easy‐to‐deform characteristics of the new actuator design make it an attractive approach to fabricate complex geometries and achieve a variety of motion modes in soft systems.

Soft actuators and robotic devices have been increasingly applied to the field of rehabilitation and assistance, where safe human and machine interaction is of particular importance. Compared with their widely used rigid counterparts, soft actuators and robotic devices can provide a range of significant advantages; these include safe interaction, a range of complex motions, ease of fabrication, and resilience to a variety of environments. In recent decades, significant effort has been invested in the development of soft rehabilitation and assistive devices for improving a range of medical treatments and quality of life. This review provides an overview of the current state‐of‐the‐art in soft actuators and robotic devices for rehabilitation and assistance, in particular systems that achieve actuation by pneumatic and hydraulic fluid‐power, electrical motors, chemical reactions, and soft active materials such as dielectric elastomers, shape memory alloys, magnetoactive elastomers, liquid crystal elastomers, and piezoelectric materials. Current research on soft rehabilitation and assistive devices is in its infancy, and new device designs and control strategies for improved performance and safe human–machine interaction are identified as particularly untapped areas of research. Finally, insights into future research directions are outlined.

Powder-based piezoelectric catalysts have been widely examined due to their high catalytic activity for applications such as water treatment and dye degradation. However, challenges remain which are associated with secondary pollution as a result of employing a powder-based catalyst. While the use of bulk catalysts can overcome this challenge, their catalytic activity has been shown to decrease significantly compared to fine-scale catalytic powders. In this study, a simple, efficient, cost-effective and in situ approach is developed that is able to successfully retrieve a powder-based catalyst by coating catalytic particles with dopamine and exploiting the interaction between dopamine and a porous polyvinylidene fluoride (PVDF) substrate to collect the catalytic particles. Detailed characterisation and molecular dynamics modeling are used to determine the mechanisms of the chemical interactions and piezocatalysis. The universality of this new approach is demonstrated by conducting a range of experiments with a range of ceramic particulates, catalyst morphologies and potential dyes. Using this new strategy, we demonstrate the ability of ferroelectric particles to achieve a high piezocatalytic activity while being anchored onto a porous PVDF layer, thereby limiting secondary pollution. This work therefore provides a novel approach for the retrieval of powder-based catalysts, with potential to expand their application potential to other forms of powder-based catalysts. [ABSTRACT FROM AUTHOR]

In the modern era, patients often resort to the internet for answers to their health-related concerns, and clinics face challenges to providing timely response to patient concerns. This has led to a need to investigate the capabilities of AI chatbots for ophthalmic diagnosis and triage. In this in silico study, 80 simulated patient complaints in ophthalmology with varying urgency levels and clinical descriptors were entered into both ChatGPT and Bard in a systematic 3-step submission process asking chatbots to triage, diagnose, and evaluate urgency. Three ophthalmologists graded chatbot responses. Chatbots were significantly better at ophthalmic triage than diagnosis (90.0% appropriate triage vs. 48.8% correct leading diagnosis; p < 0.001), and GPT-4 performed better than Bard for appropriate triage recommendations (96.3% vs. 83.8%; p = 0.008), grader satisfaction for patient use (81.3% vs. 55.0%; p < 0.001), and lower potential harm rates (6.3% vs. 20.0%; p = 0.010). More descriptors improved the accuracy of diagnosis for both GPT-4 and Bard. These results indicate that chatbots may not need to recognize the correct diagnosis to provide appropriate ophthalmic triage, and there is a potential utility of these tools in aiding patients or triage staff; however, they are not a replacement for professional ophthalmic evaluation or advice.