Speed Of Recovery Research Articles

Link between Cognitive Abilities and Physical Activity: Cognitive Training in Physical Rehabilitation

Patients in physical rehabilitation programs need to learn motor skills that they previously performed automatically. Therefore, understating learning processes involved in physical rehabilitation may be useful to implement rehabilitation programs in terms of achieved goals, recovery speed, and patient satisfaction.

Comprehensive data security and compliance framework for SMEs

Small and Medium-sized Enterprises (SMEs) are increasingly relying on cloud platforms to support critical business operations, making effective disaster recovery (DR) strategies essential for ensuring business continuity. This review proposes a robust disaster recovery framework tailored for SMEs, designed to minimize downtime and data loss in the event of a system failure, cyberattack, or natural disaster. The framework integrates advanced cloud technologies to create a cost-effective, scalable solution that aligns with the resource constraints of SMEs while providing enterprise-grade resilience. Key components of the disaster recovery framework include cloud-based data replication, automated backup solutions, and geo-redundant storage to ensure that data is continuously available and recoverable. This model employs real-time data synchronization and incremental backups to minimize Recovery Point Objectives (RPO), ensuring that critical data is not lost during an unexpected outage. Additionally, the framework leverages automated failover mechanisms to achieve low Recovery Time Objectives (RTO), allowing businesses to restore operations quickly after an interruption. Cloud orchestration tools such as AWS Elastic Disaster Recovery or Azure Site Recovery are utilized to automate disaster recovery processes, reducing manual intervention and improving the speed of recovery. The framework also incorporates regular testing of disaster recovery plans, using simulation tools to identify weaknesses and optimize response times. For SMEs, cost-effectiveness and ease of management are crucial. The framework emphasizes a pay-as-you-go model for cloud resources, allowing businesses to scale their disaster recovery solutions as they grow without incurring excessive upfront costs. By providing continuous monitoring and proactive threat detection, this disaster recovery framework ensures that SMEs can maintain uninterrupted business operations on cloud platforms, thereby enhancing resilience and mitigating the financial and operational risks associated with data loss and system downtime.

Common regulatory mutation increases single-cell survival to antibiotic exposures in Pseudomonas aeruginosa.

Typical antibiotic susceptibility testing (AST) of microbial samples is performed in homogeneous cultures in steady environments, which does not account for the highly heterogeneous and dynamic nature of antibiotic responses. The most common mutation found in P. aeruginosa lineages evolved in the human lung, a loss of function of repressor MexZ, increases basal levels of multidrug efflux MexXY, but does not increase resistance by traditional MIC measures. Here, we use single cell microfluidics to show that P. aeruginosa response to aminoglycosides is highly heterogeneous, with only a subpopulation of cells surviving exposure. mexZ mutations then bypass the lengthy process of MexXY activation, increasing survival to sudden drug exposures and conferring a fitness advantage in fluctuating environments. We propose a simple "Response Dynamics" assay to quantify the speed of population-level recovery to drug exposures. This assay can be used alongside MIC for resistance profiling to better predict clinical outcomes.

Adaptive control strategy for microgrid inverters based on Narendra model

In recent years, microgrid technology has been widely studied and applied. However, with times developing, the installed capacity of distributed power generation devices has been improved, and work is being carried out in increasingly complex situations, resulting in a decline in the control performance of microgrids. In view of this, to effectively improve inverter’s control performance, research is conducted on the fusion of Narendra model and adaptive control strategies for real-time voltage correction and compensation in complex situations. Compared to traditional inverters, inverters under research methods have faster voltage recovery speed when encountering load switching, and can recover in about one cycle, with good control performance. In the comparison between the improved inverter adaptive control system and the inverter adaptive system, the improved inverter voltage recovery speed is faster, can be restored within one cycle, and the control effect of the inverter is better. The harmonic rate of the port voltage has decreased from 10.43 to 1.92%. The applicability of the research method was verified. It indicats that the research method can improve inverter’s control effect and solve problems such as voltage deviation, three-phase asymmetry, harmonic pollution, etc. that are easily generated by the output terminal voltage. Simultaneously, research has provided theoretical basis and data support for the research of microgrids.

Effect of extracorporeal shock wave therapy on the microbial community in burn scars: retrospective case-control study.

The effectiveness of extracorporeal shock wave therapy (ESWT) has been demonstrated in various medical fields, including burn medicine. It promotes wound healing, improves blood flow, and modulates the inflammatory responses. The recovery speed and outcomes of skin diseases are influenced by the skin microbiome; however, studies examining the effects of specific treatments on the skin microbiome are lacking. This study investigated the impact of ESWT on the skin microbiome of burn patients, focusing on the microbial diversity and community structure within burn scars. In the retrospective case-control study, nineteen patients with burn scars were treated with ESWT, and changes in their skin microbiome were evaluated. ESWT was administered weekly for three months, and samples were collected from the ESWT-treated burn scars and untreated normal skin. Blood chemistry, and pain and itching scores were evaluated during sample collection. The collected samples were then subjected to 16S rRNA sequencing. Microbial community analysis was conducted using the QIIME2 and R packages. After ESWT, changes in alpha diversity indices were observed in burn scars. Faith phylogenetic diversity (P<0.05) and observed features (P<0.01) increased, whereas the evenness index decreased (P<0.01); no marked changes were noted in untreated skin. Beta diversity analysis showed stable microbial community structures in both the treated and untreated areas. A considerable increase in Micrococcus and Staphylococcus abundance was observed. Network analysis revealed a more open microbial network structure after ESWT, indicating adaptive changes in the microbial community. ESWT enhances microbial diversity and modifies microbial community structure in burn scars, promoting a more balanced and functionally supportive microbiome. ESWT aids in scar remodeling and positively influences skin microbiome dynamics, contributing to improved skin health and recovery.

Non destructive qualification of bovine meat tenderness

Tenderness is one of the most important criteria in bovine meat, as it determines whether the meat is used for grilling, long-time cooking or post-processing. It is therefore of great interest for the industry to measure it. Common systems require the extraction and destruction of samples, inducing time and material expenses. In this article, a new nondestructive characterization apparatus, based on monitored indentation, relaxation, and recovery, is proposed. Samples from two cuts known for their difference in tenderness were used from the same carcass. Their tenderness were assessed via compression tests and then compared to indicators from the force–displacement measurements. Results showed that the indentation and recovery speed, and the force relaxation enable the differentiation of the two cuts (p<0.05). These results were obtained on samples from a single carcass. Measurements on other carcasses should be performed to ensure that the results presented in this article can be generalized to bovine meat.

Machine learning model identifies patient gait speed throughout the episode of care, generating notifications for clinician evaluation

IntroductionThe advent of digital and mobile health innovations, especially use of wearables for passive data collection, allows remote monitoring and creates an abundance of data. For this information to be interpretable, machine learning (ML) processes are necessary. Research questionCan a machine learning model successfully identify patients expected to have low gait speed in the early recovery period following joint replacement surgery? MethodsA commercial database from a smartphone-based care management platform passively collecting mobility data pre- and post-lower limb arthroplasty was used. We sought to create a ML model to predict gait speed recovery curves and identify patients at risk of poor gait speed outcome, a measure associated with range of motion and patient-reported outcomes. Model performance including sensitivity, specificity, precision, and accuracy were determined. Receiver operator curve (ROC) analysis was used to compare true and false positive rates. To benchmark our model, we compared threshold-based notifications based on the patient’s current gait speed. ResultsThe performance of the predictive model was significantly improved compared to baseline of threshold-based exceptions using current gait speed. The ML model currently provides 53 % precision, 88 % accuracy, 36 % sensitivity, and 95 % specificity on the held-out test set. The ROC analysis suggests good clinical performance (AUC=0.81). SignificanceUtilization of ML to predict gait recovery following total joint replacement is feasible and provides results with excellent specificity. This model will allow inclusion of additional data for retraining as patient populations evolve. Clinician feedback regarding notifications, including resulting actions and outcomes, can be used to further inform the model and improve clinical utility.

The influence of rehabilitation on the quality of life after radical treatment of endometrial cancer and atypical endometrial hyperplasia

Background. Gynecological cancers in women are a widespread type of oncology. Among them, endometrial cancer (EC) is often highlighted as one of the most common diseases in this group. The analysis of publications showed insufficient effectiveness of rehabilitation in patients with EC and recurrent atypical endometrial hyperplasia (rAEH).Objective: to compare the state of patients with rAEH or EC after hysterectomy with bilateral salpingo-oophorectomy, depending on the recovery tactics.Material and methods. The study included 119 patients diagnosed with rAEH (n=58) or stage IA EC (n=61) after hysterectomy with bilateral salpingo-oophorectomy. The group of women with rAEH was divided into two subgroups: 1A (“active” rehabilitation, n=27) and 1B (“passive” rehabilitation, n=31); two subgroups were also formed from the cohort of EC patients: 2A (“active” rehabilitation, n=29) and 2B (“passive” rehabilitation, n=32). “Active’ rehabilitation included a comprehensive, personalized program, “passive” one was based on standard rehabilitation protocols. Apart from standard clinical examination of patients, their quality of life (QoL), physical condition as well as the levels of cytokines and other biochemical blood parameters were assessed.Results. An analysis of the overall picture showed a high incidence of rAEH or EC in women aged 40–49 years. Comorbid pathologies, low QoL level and a number of significant changes in laboratory parameters were often observed. Regardless of the approach to rehabilitation, the patients' health improved over the course of 12 months. “Active” rehabilitation significantly improved psychological well-being, sexual function, overall QoL and a number of other objective health indicators of patients with rAEH or EC compared to standard rehabilitation course.Conclusion. The overall picture of rAEH or EC patients was presented, the main factors that reduce QoL in women with these diseases were identified. A system of integral QoL assessment in patients with rAEH or stage IA EC was developed including subjective and objective parameters obtained during general clinical examination, laboratory tests and analysis of questionnairing results, which should be used to adequately monitor the state of patients after surgery and the effectiveness of rehabilitation. The advantage of comprehensive rehabilitation over standard protocols in terms of efficiency and speed of recovery was shown.

Fast Optimization of the Net Present Value of Unconventional Wells Using Rapid Rate-Transient Analysis

Summary Decline-curve analysis (DCA) is the industry standard to predict hydrocarbon production to then estimate the net present value (NPV) of unconventional wells. Conventional DCA assumes the flowing bottomhole pressure (BHP) is constant. This is an unrealistic assumption for many unconventional wells that can lead to incorrect estimates of ultimate recovery and thus erroneous NPV calculations. This work illustrates the application of a novel technique that combines variable BHP conditions with decline-curve models [Rapid rate-transient analysis (RTA)] to estimate the NPV and select the optimal cluster spacing and number of fractures for a given well. We compare the results of DCA and Rapid RTA to estimate and optimize the cluster spacing for a tight oil and shale gas well. The Rapid RTA results show marked differences in terms of the shape and the optimal value of the NPV function from the simpler DCA method. For the two cases illustrated, DCA results are a direct consequence of the rate-time analysis failing to correctly detect the onset of boundary-dominated flow (BDF). In contrast, Rapid RTA correctly detects the onset of BDF and presents a defined maximum value of the NPV vs. cluster spacing (number of fractures) function. This optimal value is a trade-off between the fracture treatment cost, the speed of recovery of hydrocarbons, and the value of money with time (discount rate). The major contribution of this work is the implementation of the Rapid RTA technique allowing fast computation of pressure-rate-time analysis and NPV calculations with computational times comparable with DCA for optimizing fracture cluster spacing and the total number of fractures of unconventional wells. We have developed a web-based application to give readers a hands-on experience of this new technique and to analyze and optimize the NPV of unconventional wells.

Single-Atom Catalysts in Conductive Metal-Organic Frameworks: Enabling Reversible Gas Sensing at Room Temperature.

Conductive metal-organic frameworks (cMOFs) offer high porosity and electrical conductivity simultaneously, making them ideal for application in chemiresistive sensors. Recently, incorporating foreign elements such as catalytic nanoparticles into cMOFs has become a typical strategy to enhance their sensing properties. However, this approach has led to critical challenges, such as pore blockage that impedes gas diffusion, as well as limited improvement in reversibility. Herein, single-atom catalyst (SAC)-functionalized cMOF is presented as a robust solution to the current limitations. Facile functionalization of SACs in a cMOF can be achieved through electrochemical deposition of metal precursors. As a proof of concept, a Pd SAC-functionalized cMOF is synthesized. The Pd SACs are stabilized at the interplanar sites of cMOF with Pd-N4 coordination while preserving the porosity of the MOF matrix. Notably, the microenvironment created by Pd SACs prevents irreversible structural distortion of cMOFs and facilitates a reversible charge transfer with NO2. Consequently, the cMOF exhibits a fully recoverable NO2 response, which was not previously attainable with the nanoparticle functionalization. Additionally, with the combination of preserved porosity for gas diffusion, it demonstrates the fastest level of response and recovery speed compared to other 2D-cMOFs of this class.

Highly sensitive flexible pressure sensor based on laser ablated Ag-MWCNT /MXene for human motion detection

The complex and dynamic human environment, higher requirements are put forward for the high-performance flexible pressure sensors. The materials selection and microstructure design are crucial to achieve high sensitivity and stability. In this work, we fabricated pressure sensitive layer with multiple dimensional materials. Using laser ablation in liquid method, Ag nanoparticles were uniformly distributed in surface of multi-walled carbon nanotubes (MWCNT). Combined with the delaminated MXene, highly pressure sensitive composite could be prepared due to the reduction of contact resistance under pressure. The corresponding piezoresistive sensors showed high sensitivity (12.7 kPa−1), excellent response speed (150 ms), recovery speed (100 ms), and distinguished cycle stability (3000 cycles). Based on the performance of the sensor, it can realize the monitoring of tiny pressure on the human body, and further realize the detection of human activities and health. In addition, the sensor array has spatial recognition capability, providing a way of thinking for the development needs of future intelligent wearable electronic devices. The sensors can measure changes in resistance caused by finger bending, slight breathing, pulse, vocal cord vibration, and other movements to realize human activity and health monitoring. At the same time, the 3×3 sensor array system can recognize the layout of pressure in space. This simple preparation and low cost of the sensor can Facilitate the progress towards sophisticated wearable electronic gadgets.

Optimization research on control strategies for photovoltaic energy storage systems considering multi-mode operation

In this paper, a selective input/output strategy is proposed for improving the life of photovoltaic energy storage (PV-storage) virtual synchronous generator (VSG) caused by random load interference, which can sharply reduce costs of storage device. The strategy consists of two operating modes and a power coordination control method for the VSGs. Firstly, a selective VSG input strategy is proposed based on the magnitude of disturbances, a method of offline solving model equation is used for determine the VSG input time. An online method is used for matching the disturbance frequency variations, which enables a selective VSG startup method, allowing the grid to prioritize the utilization of the generator's physical inertia. Secondly, a dynamic VSG exit strategy is developed based on dynamic frequency characteristics to prevent secondary oscillations in the frequency recovery phase of the PV-storage VSG following grid disturbances. This strategy is crucial as grid variations may affect energy storage lifespan and reduce frequency recovery speed. Finally, the proposed approach is validated for correctness and effectiveness through computer simulations and semi-physical experiments using the NI-PXI + LabVIEW platform. Through the above optimization and research, the selective start of VSG is realized, the energy storage life is improved, the capacity of charge and discharge cycles is reduced by 37.82 % compared with the strategy without investment and withdrawal, and the life loss in the secondary charge and discharge process of PV-storage VSG is avoided, which is conducive to frequency recovery.

Nanofluidic sensing inspired by the anomalous water dynamics in electrical angstrom-scale channels

Manipulation of confined water dynamics by voltage keeps great importance for diverse applications. However, limitations on the membrane functions, voltage-control range, and unclear dynamics need to be addressed. Herein, we report an anomalous electrically controlled gating phenomenon on cation-intercalated multi-layer Ti3C2 membranes and reveal the confined water dynamics. The water permeation rate was improved rapidly following the application and rise of voltage and finally reached a maximum rate at 0.9 V. The permeation rate starts to decrease from 0.9 V. Below 0.9 V, the electric field affects the charge and polarity of water molecules and then leads to ordered and denser rearrangement in the two-dimensional (2D) channel to accelerate the permeation rate. Above 0.9 V, with the assistance of metal cations, the surge in current induced aggregation of water molecules into clusters, thereby limiting the water mobility. Based on these findings, a high-performance humidity sensor was developed by simultaneously optimizing the response and recovery speeds through electric manipulation. This work provides flexible strategies in intelligent membrane design and nanofluidic sensing.

High recovery speed optical humidity sensing enabled with GQD for non-contact finger distance detection

Finger distance perception plays a crucial role in enabling humanoid intelligent robotic systems to achieve smooth interaction with humans. Here, a rapidly response optical humidity sensor enabled with graphene quantum dots (GQD) for non-contact finger distance detection was proposed. The GQD was prepared through the functionalization of graphene oxide (GO) with hydrophilic groups. This modification enhances the responsiveness of GO to atmospheric humidity and promotes the deposition of conformal films of functional materials on the surfaces of optical fiber. Utilizing a light-driven coating method, the GQD was deposited onto a tilted fiber Bragg grating (TFBG) to fabricate humidity sensor. When water molecules were absorbed by GQD, the dielectric constant of GQD film was changed, causing an alteration in the intensities of the TFBG’s cladding modes. The proposed sensor shows a sensitivity of 0.032 dB/%RH with the equilibrium response and recovery time of 3.66 s and 0.92 s, respectively. The equilibrium recovery time is the fastest as far as we know. Furthermore, the sensor is capable of precisely identifying the action of a finger, such as approaching, remaining, and departing with a detection range of 0–10 mm.

NiAl-LDHs-derived NiO/Al2O3 with high specific surface area for wide range hydrogen detection

The construction of heterojunctions is generally considered as one of the effective strategies to improve gas sensing performance. Herein, the NiO/Al2O3 (NAO) hybrid structure is constructed by calcining Ni–Al layered double hydroxides (NiAl-LDHs). The specific surface area and pore size of NAO are effectively regulated by changing the content of NaOH. The XPS and BET characterization results indicate that NAO-5 exhibits a high oxygen vacancy content, a large specific surface area (468.524 m2 g−1), and abundant pore size. The hydrogen (H2) sensitivity property of NAO was investigated, and NAO-5 exhibited outstanding gas sensing performance to 500 ppm H2 at 370 °C, including high response (10.524), fast response recovery speed (21 and 30 s), and wide detection range (0.1–5000 ppm). The improved gas property is mainly attributed to the increased oxygen vacancies and specific surface area. This study provides a feasible approach for NiAl-LDHs in H2 monitoring and early warning.

Exploring postictal recovery with acetaminophen or nimodipine: A randomized-controlled crossover trial.

The postictal state is underrecognized in epilepsy. Animal models show improvement of postictal symptoms and cerebral perfusion with acetaminophen or nimodipine. We studied the effects of acetaminophen or nimodipine on postictal electroencephalographic (EEG) recovery, clinical reorientation, and hypoperfusion in patients with ECT-induced seizures. In this prospective clinical trial with three-condition randomized crossover design, study interventions were administered orally 2 h before ECT sessions (1000 mg acetaminophen, 60 mg nimodipine, or a placebo condition). Primary outcome measure was the speed of postictal EEG recovery. Secondary outcomes were the extent of postictal EEG recovery, clinical reorientation time, and postictal cerebral blood flow as assessed by perfusion-weighted MRI. Bayesian generalized mixed-effects models were applied for analyses. We included 300 seizures, postictal EEGs, and reorientation time values, and 76 MRI perfusion measures from 33 patients (median age 53 years, 19 female). Pretreatment with acetaminophen or nimodipine was not associated with change in speed of EEG recovery compared to placebo (1.13 [95%CI 0.92, 1.40] and 1.07 [95%CI 0.87, 1.31], respectively), nor with the secondary outcomes. No patient reached full EEG recovery at 1 h post-seizure, despite clinical recovery in 89%. Longer seizures were associated with slower EEG recovery and lower postictal perfusion. Nimodipine altered regional perfusion in the posterior cortex. Pretreatment with acetaminophen or nimodipine did not alleviate symptoms and signs of the postictal state. Systematic study of the postictal state after ECT-induced seizures is feasible.

VPreparation of Temperature/pH Dual‐Sensitive Hydrogels and Study of their Properties

AbstractA temperature/pH dual‐sensitive hydrogel was prepared through simple solution radical polymerization. A novel temperature/pH dual‐sensitive terpolymerized semi‐interpenetrating network, P(NIPAAm‐co‐AA‐co‐HEAA)/SA hydrogel, was synthesized using interpenetrating network technology (IPN) with sodium alginate (SA) as the raw material. The temperature‐sensitive monomer N‐isopropylacrylamide (NIPAAm), the pH‐sensitive monomer acrylic acid (AA), and the hydrophilic monomer N‐hydroxyethylacrylamide (HEAA) were incorporated into the structure. The structure and morphology of the hydrogels were investigated using FTIR, TGA and SEM. Additionally, the swelling properties, as well as temperature and pH sensitivity, were examined. The results showed that the content of HEAA and SA influenced the swelling properties of the gels. The gels exhibited distinct swelling behaviors at different temperatures and pH levels, demonstrating remarkable temperature and pH sensitivity. Moreover, it shows rapid response speed and efficient recovery, making them suitable for use as anticancer drug carriers that meet specific requirements.

An integrated Bayesian-principal component approach to macroeconomic resilience: the case of the Central Europe and Baltic macro-region

PurposeIn the fashion of Martin (2012), we develop an innovative application to a standard, well-grounded methodology to investigate resilience in two critical dimensions: recovery and resistance. Our novel approach allows us to investigate the resilience performance to the 2008 financial crisis within countries of this macro-region according to their shock isolation and absorptive capacities.Design/methodology/approachBy individually estimating six open economy DSGE models within the Central Europe and Baltic macro-region, we identify the business-cycle-volatility drivers for each country. Then, we use the outcome of our six estimates to conduct a principal component analysis to determine structural common characteristics required to explain economic resilience in the CEB macro-region.FindingsIn terms of resilience, Central European economies exhibit quite similar paths in terms of recovery, meaning they have similar economic structures. By contrast, Baltic countries behave differently, being outliers in opposite extreme positions. The contrary occurs for resistance: Baltic countries share a similar ranking, whereas Central European economies exhibit substantial differences.Research limitations/implicationsIt is important to acknowledge that a limitation of the analysis is that we explicitly consider each country as a stand-alone open economy which are subject to stochastic disturbances. Precisely, we do not model trade or other interactions across countries within the CEB region and with the rest of the world. Consequently, spillover effects in the aftermath of the shock are not accounted for.Originality/valueWe estimate the relative vulnerability or sensitivity of economies within the macro-region to disturbances and disruptions (resistance) and the speed and extent of recovery from such a disruption or recession (recovery). First, we built two different kinds of measures of resilience by aggregating the estimated parameters through non-centered and centered principal component analysis. Then, we use our model to investigate the relation between financial shock and the economic resilience across the region. The approach can be applied to several case studies, parsimoniously.

Land use history and landscape forest cover determine tropical forest recovery

Abstract To conserve biodiversity and combat climate change it is vital to restore forest ecosystems. Natural forest regrowth is a nature‐based solution to restore forests, but it has rarely been evaluated how this is affected by the combination of previous land use intensity and surrounding forest cover, and how this varies between the two main tropical forest types; dry and wet forests. Thirty‐three plots were established on abandoned agricultural fields in a dry (13 plots) and wet (20 plots) tropical forest in Mexico and monitored 3 years for the following tree community attributes: structure, diversity, regeneration mode, potential symbioses with N fixing bacteria and mycorrhizal fungi. Previous land use intensity was described using interviews, and landscape forest cover and fragmentation within a 1000 m radius surrounding the plots were quantified using satellite images. Variable importance analyses indicated that land use intensity was more important than forest cover and fragmentation for the state of the tree community attributes after 3 years. This suggests that previous land use impacts the start of succession and leaves important legacies on the vegetation. Land use intensity, forest cover and fragmentation were equally important in determining the change in tree community attributes over time, indicating that both management practices and dispersal shape subsequently community assembly. A higher land use intensity decreases tree richness recovery, while size of the largest forest patch decreases tree density and connectivity of these patches increases tree density. The dry forest had a faster increase in tree density recovery compared to the wet forest through a high initial resprouting capacity and abiotically dispersed trees. Synthesis and applications: Both a higher land use intensity and fragmented forest landscape decreases the speed of forest recovery, indicating that human actions and landscape transformation shape the course of succession. Restoration in fragmented forest landscapes through natural regeneration is more suitable in areas with low previous land use intensity, higher forest connectivity and abiotically dispersed species. Effective restoration should therefore consider both land use history and landscape forest cover and be tailored to local socio‐ecological conditions.

Versatile Lamellar Wrap-Structured PVDF/PZT/CNTs Piezoelectric Sensor for Road Traffic Information Sensing, Monitoring, and Energy Harvesting

Active monitoring and accurate sensing of road traffic information provide crucial data for road managers and traffic law enforcement. However, current sensors and monitoring systems struggle to capture comprehensive vehicle information simultaneously. In the study, a novel piezoelectric sensor using PVDF/PZT/CNTs composites was developed for actively monitoring road traffic information and explored for energy harvesting suitability. Piezoelectric composites with a lamellar wrapped structure and excellent localized conductive networks were prepared through solution blending, non-solvent-induced phase separation, and layered hot pressing. The results revealed that this structural design improves the proportions, crystallinity, and content of the β-phase crystals in the composites, while significantly enhancing their piezoelectric properties, dielectric properties, and electronegativity. When the content of CNTs was 3.0 wt%, the proportion and content of β-phase crystals in the composites reached the highest values (91.36 % and 62.34 %), respectively. Additionally, the open-circuit voltage (Voc) and the short-circuit current (Isc) were 3.36 and 6.16 times higher, respectively, than those of the undoped CNTs composites. The developed piezoelectric sensor shows a wide pressure monitoring range (0.1 MPa–2.1 MPa), excellent sensitivity (6.94 MPa−1 at (<0.6 MPa) and 12.35 MPa−1 at (0.6–2.1 MPa)). As a vehicle non-stop weighing sensor, it presents extremely fast response (15 ms) and recovery (16 ms) speeds with long-term stability of 500,000 cycles. The most important feature of the developed piezoelectric sensor is its dual function as both a sensor and an energy harvester. Furthermore, the developed piezoelectric sensor can actively monitor vehicle axle weight, speed, and wheelbase while capturing energy from vehicle movement. This work opens up innovative solutions for road traffic monitoring and vehicle movement energy harvesting, with promising applications in intelligent transportation.