Sluggish Response Times Research Articles

A novel approach for state-of-charge estimation of lithium-ion batteries by quasi-static component generation of ultrasonic waves

Lithium-ion batteries content complex internal components, such as porous media and electrolytes, which result in strong scattering and high attenuation of ultrasonic waves in these batteries. The low attenuative feature of the quasi-static components (QSCs) of ultrasonic waves offers great potential for nondestructive assessment of highly attenuating and porous materials. This paper presents an innovative approach for estimating the state-of-charge (SOC) of lithium-ion batteries using QSC of ultrasonic waves. Experimental results demonstrate a clear and repeatable linear relationship between the amplitudes of the generated QSC and the SOC of lithium-ion batteries. In addition, the relationships between different SOCs of the battery and the conventional linear ultrasonic parameters, second harmonic generation (SHG), and the QSC were compared to verify the improved sensitivity of the proposed approach. Notably, compared to linear ultrasonic features and the SHG, the generated QSC shows much higher sensitivity to the variations of SOC. We employ the phase-reversal method to further enhance the signal-to-noise ratio of measured QSC signals. The experimental results demonstrate that the proposed method exhibits a heightened sensitivity to changes in the SOC of batteries, resulting in significantly enhanced detection accuracy and resolution. This method effectively addresses the deficiencies observed in the current detection methods such as limited accuracy and sluggish response times. This method provides a new solution to overcome this challenge. Meanwhile, it also confirms that nonlinear ultrasound promises an alternative method for SOC assessment, providing a foundation for efficient and safe battery management practices.

Developing a framework for integrating blockchain technology into earthquake risk mitigation and disaster management strategies of smart cities

PurposeEarthquakes pose a significant challenge to human safety and the durability of infrastructure, highlighting the urgent need for innovative disaster management strategies. This study addresses the gap in current earthquake disaster management approaches, which are often related to issues of transparency, centralization and sluggish response times. By exploring the integration of blockchain technology into seismic hazard management, the purpose of the research is to overcome these limitations by offering a novel framework for integrating blockchain technology into earthquake risk mitigation and disaster management strategies of smart cities.Design/methodology/approachThis study develops an innovative approach to address these issues by introducing a blockchain-based seismic monitoring and automated decision support system for earthquake disaster management in smart cities. This research aims to capitalize on the benefits of blockchain technology, specifically its real-time data accessibility, decentralization and automation capabilities, to enhance earthquake disaster management. The methodology employed integrates seismic monitoring data into a blockchain framework, ensuring accurate, reliable and comprehensive information. Additionally, smart contracts are utilized to handle decision-making and enable rapid responses during earthquake disasters, offering an effective alternative to traditional approaches.FindingsThe study results highlight the system’s potential to foster reliability, decentralization and efficiency in earthquake disaster management, promoting enhanced collaboration among stakeholders and facilitating swift actions to minimize human and capital loss. This research lays the foundation for further exploration of blockchain technology’s practical applications in other disaster management contexts and its potential to transform traditional practices.Originality/valueCurrent methodologies, while contributing to the reduction of earthquake-related impacts, are often hindered by limitations such as lack of transparency, centralization and slow response times. In contrast, the adoption of blockchain technology can address these challenges and offer benefits over various aspects, including decentralized control, improved security, real-time data accessibility and enhanced inter-organizational collaboration.

Demonstration of a Gel-Polymer Electrolyte-Based Electrochromic Device Outperforming Its Solution-Type Counterpart in All Merits: Architectural Benefits of CeO2 Quantum Dot and Nanorods.

For years, solution-type electrochromic devices (ECDs) have intrigued researchers' interest and eventually rendered themselves into commercialization. Regrettably, challenges such as electrolyte leakage, high flammability, and complicated edge-encapsulation processes limit their practical utilization, hence necessitating an efficient alternate. In this quest, although the concept of solid/gel-polymer electrolyte (SPE/GPE)-based ECDs settled some issues of solution-type ECDs, an array of problems like high operating voltage, sluggish response time, and poor cycling stability have paralyzed their commercial applicability. Herein, we demonstrate a choreographed-CeO2-nanofiller-doped GPE-based ECD outperforming its solution-type counterpart in all merits. The filler-incorporated polymer electrolyte assembly was meticulously weaved through the electrospinning method, and the resultant host was employed for immobilizing electrochromic viologen species. The filler engineering benefits conceived through the tuned shape of CeO2 nanorod and quantum dots, along with the excellent redox shuttling effect of Ce3+/Ce4+, synchronously yielded an outstanding class of GPE, which upon utilization in ECDs delivered impressive electrochromic properties. A combination of features possessed by a particular device (QD-NR/PVDF-HFP/IL/BzV-Fc ECD) such as exceptionally low driving voltage (0.9 V), high transmittance change (ΔT, ∼69%), fast response time (∼1.8 s), high coloration efficiency (∼339 cm2/C), and remarkable cycling stability (∼90% ΔT-retention after 25,000 cycles) showcased a striking potential in the yet-to-realize market of GPE-based ECDs. This study unveils the untapped potential of choreographed nanofillers that can promisingly drive GPE-based ECDs to the doorstep of commercialization.

Crosslinked WO3 nanonet for rapid detection of sulfur mustard gas simulant: Mechanism insights and sensing application

Effective detection of mustard gas is significant for human health and public security on account of its high toxicity and volatility. However, the sluggish response time of current sensors hinders their rapid identification of gases in actual hazardous environments. Herein, crosslinked WO3 nanonet was successfully synthesized on ceramic tubes by a facile in situ solvothermal method. The unique structure with abundant pores could not only facilitate charge transfer but also provide large amounts adsorption sites for gas sensing. The resultant WO3-4:1 sensor exhibited ultra-fast response time (1 s) and high response (58) toward 50 ppm 2-chloroethyl ethyl sulfide (2-CEES). Moreover, a new model of sensing mechanism for the 2-CEES sensing process was proposed through a series of characterization and testing technologies. This work offers guidance for developing other high-performance mustard gas sensors in practical applications.

Predictive Maintenance for IoT-Enabled Smart Cities using Recurrent Neural Networks

This work delves into the pressing need for more efficient Predictive Maintenance solutions in the context of IoT-enabled Smart Cities. Existing methodologies often fall short, lacking the precision and accuracy required to keep these rapidly evolving urban environments running smoothly for different use cases. The limitations of current approaches become apparent when considering their inability to cope with the intricacies of IoT data. They struggle to harness the wealth of information generated by countless interconnected devices and systems, resulting in suboptimal performance. This issue is further compounded by their relatively sluggish response times, hindering the timely detection of critical maintenance needs. In response to these challenges, this paper presents an innovative approach that leverages the fusion of Bidirectional Long Short-Term Memory (BiLSTM) with Autoencoders in conjunction with Recurrent Neural Networks (RNNs). This combination of advanced techniques brings forth a powerful predictive maintenance model process. The utilization of BiLSTM and Autoencoders adds a layer of sophistication, allowing for a deeper understanding of the underlying data patterns. BiLSTM's ability to capture contextual information from both past and future data points enriches the model's predictive capabilities. The inclusion of Autoencoders aids in feature extraction and reconstruction, enhancing the model's ability to discern relevant information sets. The advantages of this proposed model are profound. It exhibits 4.9% higher precision, ensuring that maintenance actions are precisely targeted, thus reducing unnecessary interventions. Moreover, the model achieves a remarkable 5.5% increase in accuracy, guaranteeing more reliable predictions. Its 4.5% boost in recall ensures that potential issues are identified promptly. The model's 3.9% increase in speed means that maintenance responses are faster and more effective, minimizing downtemporal instance sets. Lastly, an 8.5% improvement in the Area Under the Curve (AUC) underscores its superior performance compared to existing methodologies. The impacts of this work extend far beyond the realm of predictive maintenance levels. It contributes significantly to the realization of truly smart and efficient cities, where resources are optimized, disruptions are minimized, and the quality of life for citizens is enhanced for different use cases. This research marks a pivotal step towards the seamless integration of IoT technologies into urban environments, with the potential to revolutionize the way we manage and maintain our cities in the future scenarios.

A highly-selective chloride microelectrode based on a mercuracarborand anion carrier

The chloride gradient plays an important role in regulating cell volume, membrane potential, pH, secretion, and the reversal potential of inhibitory glycine and GABAA receptors. Measurement of intracellular chloride activity, {{boldsymbol{a}}}_{{boldsymbol{Cl}}}^{{boldsymbol{i}}}, using liquid membrane ion-selective microelectrodes (ISM), however, has been limited by the physiochemical properties of Cl− ionophores which have caused poor stability, drift, sluggish response times, and interference from other biologically relevant anions. Most importantly, intracellular {bf{HC}}{{bf{O}}}_{{bf{3}}}^{-} may be up to 4 times more abundant than Cl− (e.g. skeletal muscle) which places severe constraints on the required selectivity of a Cl− – sensing ISM. Previously, a sensitive and highly-selective Cl− sensor was developed in a polymeric membrane electrode using a trinuclear Hg(II) complex containing carborane-based ligands, [9]-mercuracarborand-3, or MC3 for short. Here, we have adapted the use of the MC3 anion carrier in a liquid membrane ion-selective microelectrode and show the MC3-ISM has a linear Nernstian response over a wide range of aCl (0.1 mM to 100 mM), is highly selective for Cl− over other biological anions or inhibitors of Cl− transport, and has a 10% to 90% settling time of 3 sec. Importantly, over the physiological range of aCl (1 mM to 100 mM) the potentiometric response of the MC3-ISM is insensitive to {bf{HC}}{{bf{O}}}_{{bf{3}}}^{-} or changes in pH. Finally, we demonstrate the biological application of an MC3-ISM by measuring intracellular aCl, and the response to an external Cl-free challenge, for an isolated skeletal muscle fiber.

Selecting Ultracapacitors for Smoothing Voltage Deviations in Local Grids Fed by Transformer with Tap-Changer and Distributed PV Facilities

Widespread use of photovoltaic (PV) small and middle-power plants close or inside existing townships and villages may cause significant deviations of the grid voltage. Owing to the oscillation of solar irradiation and corresponding power flows these voltage instabilities can damage equipment and must be prevented. Designated for the voltage regulation tap-changers in distribution transformers located in a significant distance of such settlements have a sluggish response time. As a possible answer for their delay is the smoothing energy of flows in PV power installation by intermittent capacitor low-pass filtering (LPF) located near those PV facilities. The application of ultracapacitors (UC) for LPF is remarkable due to their sustainability and relatively low costs of energy storage. The parameters selection of such appliances is a well-designed procedure for linear circuits. However, DC–AC inverters in PV facilities are represented by a power (instead of a voltage) source. As a result, the total circuit including such LPF becomes a non-linear and its transient process and consequently, its efficiency is difficult to assess requiring each time of development the UC storage an application complex numerical procedure. Engineers are usual to work with linear circuits that are describing fine by a time constant is designated as a multiplication of a capacitance times load equivalent resistance. In the case of PV DC–AC inverters, such an approach can be applied as well but a value of a time constant should be corrected. Considering a significant cost of UC storage, the non-optimal selection of a correcting coefficient may cause considerable loses. Submitted in the presented article is an original approximation procedure giving an efficiently approachable technique to select correcting coefficient for describing non-linear dynamic process by its linear analog. This way the development low-pass UC filtering in electrical systems with PV plants becomes more efficient and simpler task.

Tailoring Thin-Film Piezoelectrics for Crash Sensing.

Crash sensing and its assessment play a pivotal role in autonomous vehicles for preventing fatal casualties. Existing crash sensors are severely bottlenecked by sluggish response time, rigid mechanical components, and space constraints. Miniaturized sensors embedded with custom-tailored nanomaterials upholds potential to overcome these limitations. In this article, piezoelectric Zinc-Oxide thin film as a crash sensing layer is integrated onto a flexible metal-alloy cantilever. Material characterization studies are conducted to confirm piezoelectric property of sputtered ZnO film. The piezoelectric d 31 coefficient value of ZnO film was 7.2 pm V-1 . The ZnO sensing element is firmly mounted on a scaled car model and used in a crash sensing experimental set-up. A comprehensive theoretical analysis for two different real scenarios (nearly elastic and nearly inelastic collision) of crash events followed by experimental study is discussed. The crash sensor's output exhibits a linear relationship with magnitude of impact forces experienced at crash events. The response time of ZnO crash sensor is 18.2 ms, and it exhibits a sensitivity of 28.7 mV N-1 . The developed crash sensor has potential to replace bulk material sensors owing to its faster response time, high sensitivity, and compactness as the demand for crash sensors in next-generation automobile industries is progressively growing.

Ionic Liquid Microstrips Impregnated with Magnetic Nanostirrers for Sensitive Gas Sensors.

Ionic liquids (IL) have been regarded as promising electrolytes as substitutes for volatile aqueous or organic solvents for electrochemical gas sensors. However, ILs are viscous, and the slow diffusion of gas molecules leads to poor sensitivity and sluggish response times. Herein, we describe a strategy using an array of microstrips of IL containing magnetic nanoparticles as nanostirrers for enhanced mass transport and gas sensing. Magnetic CoFe2O4 nanoparticles are synthesized and dispersed in a hydrophobic IL [BMP][Ntf2]. First, the convection effect of the IL dispersion was studied using the reversible redox couple ferrocene/ferrocenium ion. In a rotating magnetic field, steady-state currents for oxidation of dissolved ferrocene are three to five times greater than that in an unstirred solution. Then, the IL dispersion is micropatterned onto a gold electrode using microcontact printing. A self-assembled monolayer was printed onto a gold surface creating 70 μm wide hydrophobic lines with a 30 μm gap between them. Upon applying the IL dispersion into the gap, a 30 μm wide array of microstrips was successfully fabricated. The system is demonstrated as an oxygen sensor in the range of volume fraction of O2 of 50-500 ppm giving a linear calibration with a sensitivity of 1.94 nA cm-2 ppm-1.

Clinical Facts Along With a Feedback Control Perspective Suggest That Increased Response Time Might Be the Cause of Parkinsonian Rest Tremor

Parkinson's disease (PD) is a neurodegenerative disorder characterized by increased response times leading to a variety of biomechanical symptoms, such as tremors, stooping, and gait instability. Although the deterioration in biomechanical control can intuitively be related to sluggish response times, how the delay leads to such biomechanical symptoms as tremor is not yet understood. Only recently has it been explained from the perspective of feedback control theory that delay beyond a threshold can be the cause of Parkinsonian tremor (Palanthandalam-Madapusi and Goyal, 2011, “Is Parkinsonian Tremor a Limit Cycle?” J. Mech. Med. Biol., 11(5), pp. 1017–1023). The present paper correlates several observations from this perspective to clinical facts and reinforces them with simple numerical and experimental examples. Thus, the present work provides a framework toward developing a deeper conceptual understanding of the mechanism behind PD symptoms. Furthermore, it lays a foundation for developing tools for diagnosis and progress tracking of the disease by identifying some key trends.

When cold becomes hot

When cold becomes hot