Power Performance Research Articles

Hydrodynamic Interactions and Enhanced Energy Harnessing amongst Many WEC Units in Large-Size Wave Parks

Interactions between wave energy converters (WECs) can significantly affect the overall energy-harnessing performance of a wave park. Although large-size wave parks with many WEC units are commonly considered in practical applications, it is challenging to simulate such parks due to huge computational costs. This paper presents a numerical model that uses the boundary element method (BEM) to simulate wave parks. Each wave energy converter (WEC) was modelled as a comprehensive system, including WEC buoys, power take-off, and mooring systems, with hydrodynamic interactions included. Two classical layouts for arranging 16 units were simulated using this numerical model. The energy-harnessing performance of these array layouts was analyzed for both regular waves and a selection of irregular sea state conditions with different wave directions, wave heights, wave periods and water depths. For each layout, three WEC separation distances were studied. An increase of up to 16% in the power performance of the WEC under regular waves was observed, which highlights the importance of interaction effects.

The potential of optical and SAR time-series data for the improvement of aboveground biomass carbon estimation in Southwestern China’s evergreen coniferous forests

ABSTRACT Accurate assessments of forest biomass carbon are invaluable for managing forest resources, evaluating effects on ecological protection, and achieving goals related to climate change and sustainable development. Currently, the integration of optical and synthetic aperture radar (SAR) data has been extensively utilized in estimating forest aboveground biomass carbon (AGC), while it is limited by using single-phase remote sensing images. Time-series data, which capture the interannual dynamic growth and seasonal variations of photosynthetic phenology in forests, can sufficiently describe forest growth characteristics. However, there remains a gap in research focusing on utilizing satellite-based time-series data for AGC estimation, especially for SAR sensors. This study investigated the potential of satellite-based optical and SAR time-series data for estimating AGC. Here, we undertook nine quantitative experiments of AGC estimation from Landsat 8 and Sentinel-1 and tested several regression algorithms (including multiple linear regression (MLR), random forests (RF), artificial neural network (ANN), and extreme gradient boosting (XGBoost)) to explore the contributions of spatiotemporal features to AGC estimation. The results suggested that the XGBoost algorithm was suitable for AGC estimation with explanatory solid power and stable performance. The temporal features representing forest growth trends and periodic change characteristics (such as coefficients of continuous wavelet transform) were more valuable for AGC estimation than spatial features for both sensor types, accounting for around 40% ~50% of the variance compared to 17% ~25%. The combination of optical and SAR time-series data produced the best performance (R2 = 0.814, RMSE = 18.789 Mg C/ha, rRMSE = 26.235%), compared with when utilizing optical or SAR time-series data alone (optical: R2 of 0.657 and rRMSE of 35.317%; SAR: R2 of 0.672 and rRMSE of 34.701%). Feature importance analysis also verified that temporal features of optical vegetation indices, SWIR 1/2 bands, and SAR backscatter from VV polarization were the most critical variables for AGC estimation. Furthermore, incorporating temporal features into the modeling is illustrated to be effective in reducing saturation effects within high-biomass forests. This study demonstrated the superiority of time-series data for forest carbon estimation. While the applicability of this methodology has only been investigated in evergreen coniferous forests, it may provide a viable approach needed to make full use of increasingly better and free satellite time-series data to estimate forest AGC with high accuracy, supporting policy making of forest management and sustainable development.

Exploring the role of domesticated resistors in batch-mode microbial desalination cell

Microbial Desalination Cells (MDCs) are an electrochemical process that harnesses microbial reactions to simultaneously treat wastewater, generate power, and desalinate water. By utilizing microbial decomposition of organic pollutants in wastewater, MDCs offer a sustainable and energy-efficient alternative to conventional desalination technologies. The technical framework of MDCs emphasizes the integration of water-electricity principles, making them promising for future applications in seawater desalination, wastewater treatment, resource recovery, and water softening. This study investigates the impact of acclimation resistance, represented by four different domesticated resistors values of 1 kΩ, 100Ω, 51Ω, and 10Ω, on the performance of MDCs. Larger acclimation resistors exhibit higher power performance, with the case of 100Ω achieving a power density of 0.33 mA/m2 and the case of 1 kΩ achieving the highest current density of 1.90 mA/m2. Furthermore, the case with an acclimation resistance of 1 kΩ exhibits superior performance in terms of chemical oxygen demand (COD) removal, achieving a removal rate of 76.3% on day 1. Conversely, the case with an acclimation resistance of 10Ω demonstrates the best desalination performance, achieving a desalination rate of 9.0%. It should be noted that the optimal performance in terms of COD removal and desalination capacity varies due to the various operational mechanisms involved. . The findings of this study provide valuable insights for enhancing the performance of MDCs in future applications, enabling further improvements in their efficiency and effectiveness.

A polyimide separator inlaid by methylsilyl-modified silica aerogels for high thermal safety and low polarization lithium-ion battery

The separator is a crucial component of lithium-ion batteries, playing a vital role in ion transfer and preventing polarization of the battery. To solve the problem of poor thermal safety of separators in lithium-ion batteries, in this work, three types of hydrophobic silica aerogels are compared. A suitable separator is fabricated by adding hydrophobic silica aerogels with monomethylsilyl as a modifier to pure polyimide. The aerogel composite separator displays a highly cross-linked three-dimensional nanoporous network, a low thermal shrinkage rate (<4 % at 200 °C), and high thermal stability (peak temperature of 670 °C) and oxygen index (>45 %). In addition, owing to the introduced hydrophobic functional groups of Si–CH3, the prepared separator decreases the polarization of the lithium-ion batteries and leads to improved power performance and cycle stability (its discharge voltage difference is 0.42 V higher than that of Celgard 2325 at 5 C). This work provides a new method of improving the safety and simultaneously reducing the polarization of lithium-ion batteries, which has promising application potential in power batteries.

Magnetic Nanoparticle-Based Microfluidic Platform for Automated Enrichment of High-Purity Extracellular Vesicles.

Extracellular vesicles (EVs) are available in various biological fluids and have highly heterogeneous sizes, origins, contents, and functions. Rapid enrichment of high-purity EVs remains crucial for enhancing research on EVs in tumors. In this work, we present a magnetic nanoparticle-based microfluidic platform (ExoCPR) for on-chip isolation, purification, and mild recovery of EVs from cell culture supernatant and plasma within 29 min. The ExoCPR chip integrates bubble-driven micromixers and immiscible filtration assisted by surface tension (IFAST) technology. The bubble-driven micromixer enhances the mixing between immunomagnetic beads and EVs, eliminating the need for manual pipetting or off-chip oscillatory incubation. The high-purity EVs were obtained after passing through the immiscible phase interface where hydrophilic or hydrophobic impurities nonspecifically bound to SIMI were removed. The ExoCPR chip had a capture efficiency of 75.8% and a release efficiency of 62.7% for model EVs. We also demonstrated the powerful performance of the ExoCPR in isolating EVs from biological samples (>90% purity). This chip was further employed in clinical plasma samples and showed that the number of GPC3-positive EVs isolated from hepatocellular carcinoma patients was significantly higher than that of healthy individuals. This ExoCPR chip may provide a promising tool for EV-based liquid biopsy and other fundamental research.

Improved power and temperature performance of half-disk diode microlasers.

The power and temperature characteristics of Ø200 µm half-disk microlasers with a half-ring metal contact and high-density InGaAs/GaAs quantum dots are studied. In a continuous wave (CW) mode, the maximal optical power at 20°C was 134 mW, and the maximal CW lasing temperature reached 113°C. In a pulsed regime the maximal optical power of 1.6 W, limited by catastrophic degradation, was achieved. By comparing the CW and pulsed current-voltage characteristics, the dependence of a microlaser temperature on CW pumping current was determined. At CW currents corresponding to the maximal wall-plug efficiency, the maximal optical power, and complete lasing quenching, the laser temperatures were 60, 99, and 149°C, respectively.

Strategic Way of Synthesizing Heteroatom-Doped Carbon Nano-onions Using Waste Chicken Fat Oil for Energy Storage Devices.

An easy way of synthesizing low-cost carbon nanomaterials without the need for high-temperature processing approach is critical for energy storage applications because the demand has increased for affordable, long-term, and environmentally friendly synthesized carbon-based materials. Herein, we synthesized multilayered graphitic carbon nano-onions (CNOs) using an oil-wick flame pyrolysis approach, employing biowaste (chicken fat) oil as a cost-effective precursor. The prepared CNOs can provide enhanced ion movement and less resistance for electron transport by interconnecting CNO particles with one another. Furthermore, heteroatom (S,N)-doped CNOs (h-CNOs) were synthesized to optimize the hydrophilic and conductive properties of carbon materials, which eventually exalted the capacitive charge transfer kinetics. The h-CNOs demonstrated superior, highest specific capacitance of 261 F/g, while the undoped CNOs showed a capacitance of 180.6 F/g at a current density of 1 A/g. In addition to capacitance, the h-CNOs also demonstrated a rate capability of 69% and a good cycling stability of 97.5% under high current densities. An asymmetric supercapacitor was fabricated using the h-CNOs as the negative and MnCo2S4 (MCS) as the positive electrode. The device showed high energy and power performance of 32.8 Wh/kg and 7350 W/kg, respectively, with a capacitance retention of 97% over 5000 cycles. Considering the facile strategic way to produce novel carbonaceous materials derived from biowaste oil (chicken fat oil), this could be considered a potential advantage for commercial energy storage devices and may open the door to producing inexpensive, industrially revolutionizing energy storage devices.

Development and validation of a risk prediction model for aspiration in patients with acute ischemic stroke

BackgroundAspiration is a frequently observed complication in individuals diagnosed with acute ischemic stroke, leading to potentially severe consequences. However, the availability of predictive tools for assessing aspiration probabilities remains limited. Hence, our study aimed to develop and validate a nomogram for accurately predicting aspiration probability in patients with acute ischemic stroke. MethodsWe analyzed 30 potential risk factors associated with aspiration in 359 adult patients diagnosed with acute ischemic stroke. Advanced statistical techniques, such as Least absolute shrinkage and selection operator (LASSO) and Multivariate Logistic regression, were employed to identify independent predictors. Subsequently, we developed a nomogram prediction model based on these predictors, which underwent internal validation through 1000 bootstrap resampling. Two additional cohorts (Cohort A n = 64; Cohort B, n = 105) were included for external validation. The discriminatory power and calibration performance of the nomogram were assessed using rigorous methods, including the Hosmer-Lemeshow test, area under the receiver operating characteristic curve (AUC), calibration curve analyses, and decision curve analyses (DCA). ResultsThe nomogram was established based on four variables: sputum suction, brain stem infarction, temporal lobe infarction, and Barthel Index score. The predictive model exhibited satisfactory discriminative ability, with an area under the receiver operating characteristic curve of 0.853 (95 % confidence interval, 0.795–0.910), which remained consistent at 0.852 (95 % confidence interval, 0.794–0.912) during the internal validation. The Hosmer-Lemeshow test (P = 0.394) and calibration curve demonstrated favorable consistency between the predicted and observed outcomes in the development cohort. The AUC was 0.872 (95 % confidence interval, 0.783–0.962) in validation cohort A and 0.877 (95 % confidence interval, 0.764–0.989) in validation cohort B, demonstrating sustained accuracy. DCA showed a good net clinical benefit of the nomogram. ConclusionsA nomogram for predicting the probability of aspiration in patients with acute ischemia has been successfully developed and validated.

Effects of Adding WB-EMS to the Warm-Up on Football Players Power Performance Tests: a Crossover Study

Background: Football is an intermittent effort sport that demands high intensity power performance. To enhance this performance before training and competition, a short and intense warm-up is convenient. However, there is a gap between the performance reached in the warm-up and the performance needed during competition. Thus, the aim of this study was to analyze if adding WB-EMS to the warm-up increases or not football players performance. Methods: Twelve semiprofessional football players randomly performed 2 FIFA11+ protocol warm-up sessions, one wearing a WB-EMS suit and one without it. Player´s body temperature, blood lactate, and jumping and sprint abilities were measured. Results: Thermography showed that skin temperature (Tsk) significantly decreased after both protocols, and only left popliteal Tsk was significantly lower after WB-EMS warm-up protocol compared to NO WB-EMS warm-up (p &lt; 0.05). Lactate was greater after No WB-EMS but not after WB-EMS warm-up. Sprint performance in 20 meters was significantly faster after WB-EMS compared to NO WB-EMS. No significant differences were found between trials in the rest of sprint and jumping abilities tests. Conclusions: Adding WB-EMS to the warm-up does not seem to increase body temperature, only left popliteal Tsk, and to improve acute sprint performance in semi-professional football players.

Machine learning developed a fibroblast-related signature for predicting clinical outcome and drug sensitivity in ovarian cancer.

Ovarian cancer (OC) is the leading cause of gynecological cancer death. Cancer-associated fibroblasts (CAF) is involved in wound healing and inflammatory processes, tumor occurrence and progression, and chemotherapy resistance in OC. GSE184880 dataset was used to identify CAF-related genes in OC. CAF-related signature (CRS) was constructed using integrative 10 machine learning methods with the datasets from the Cancer Genome Atlas, GSE14764, GSE26193, GSE26712, GSE63885, and GSE140082. The performance of CRS in predicting immunotherapy benefits was verified using 3 immunotherapy datasets (GSE91061, GSE78220, and IMvigor210) and several immune calculating scores. The Lasso + StepCox[forward] method-based predicting model having a highest average C index of 0.69 was referred as the optimal CRS and it had a stable and powerful performance in predicting clinical outcome of OC patients, with the 1-, 3-, and 5-year area under curves were 0.699, 0.708, and 0.767 in the Cancer Genome Atlas cohort. The C index of CRS was higher than that of tumor grade, clinical stage, and many developed signatures. Low CRS score demonstrated lower tumor immune dysfunction and exclusion score, lower immune escape score, higher PD1&CTLA4 immunophenoscore, higher tumor mutation burden score, higher response rate and better prognosis in OC, suggesting a better immunotherapy response. OC patients with low CRS score had a lower half maximal inhibitory concentration value of some drugs (Gemcitabine, Tamoxifen, and Nilotinib, etc) and lower score of some cancer-related hallmarks (Notch signaling, hypoxia, and glycolysis, etc). The current study developed an optimal CRS in OC, which acted as an indicator for the prognosis, stratifying risk and guiding treatment for OC patients.

Optimized FOPID controller for nuclear research reactor using enhanced planet optimization algorithm

Nuclear reactor control is pivotal for the safe and efficient operation of nuclear power plants, facilitating the regulation of reactor reactivity. This study introduces an optimized fractional-order proportional-integral-derivative (FOPID) controller tailored for maintaining reactivity levels in nuclear power plants, particularly during load-following operations. The controller adjusts the position of control rod to regulate power output effectively. We enhance FOPID controller's performance using a modification of Planet Optimization Algorithm (POA-M), leveraging the strengths of the Arithmetic Optimization Algorithm (AOA) to improve its exploitation capabilities. We evaluate the efficacy of POA-M-FOPID controller against traditional techniques, including POA, AOA, and Particle Swarm Optimization (PSO). We assess its performance using the Egyptian Testing Research Reactor (ETRR-2) as a case study. Our results demonstrate that the POA-M-FOPID controller outperforms alternative algorithms across various control metrics, exhibiting superior resilience and efficiency. Notably, the utilization of the POA-M-FOPID controller yields remarkable improvements in reactor power performance, achieving significantly reduced settling time (25.27 sec) and maximum overshoot (0.67 %) compared to conventional FOPID controllers incorporating POA, AOA, and PSO methods. These findings underscore the effectiveness of POA-M-FOPID in enhancing nuclear reactor control systems, offering potential benefits for broader nuclear power industry in terms of safety, stability, and operational efficiency.

The hierarchical structure of galactic haloes: differentiating clusters from stochastic clumping with astrolink

ABSTRACT We present astrolink, an efficient and versatile clustering algorithm designed to hierarchically classify astrophysically relevant structures from both synthetic and observational data sets. We build upon clustar-nd, a hierarchical galaxy/(sub)halo finder, so that astrolink now generates a 2D representation of the implicit clustering structure as well as ensuring that clusters are statistically distinct from the noisy density fluctuations implicit within the n-dimensional input data. This redesign replaces the three cluster extraction parameters from clustar-nd with a single parameter, S – the lower statistical significance threshold of clusters, which can be automatically and reliably estimated via a dynamical model-fitting process. We demonstrate the robustness of this approach compared to astrolink’s predecessors by applying each algorithm to a suite of simulated galaxies defined over various feature spaces. We find that astrolink delivers a more powerful clustering performance while being $\sim 27~{{\ \rm per \, cent}}$ faster and using less memory than clustar-nd. With these improvements, astrolink is ideally suited to extracting a meaningful set of hierarchical and arbitrarily shaped astrophysical clusters from both synthetic and observational data sets – lending itself as a great tool for morphological decomposition within the context of hierarchical structure formation.

Strategic turnaround: unraveling the impact of CEO power on firm performance during retrenchment

Purpose This paper aims to investigate the impact of retrenchment strategy on firm performance in the context of Pakistani firms while considering the moderating role of chief executive officer (CEO) power. By examining the influence of CEO duality and CEO share ownership on the relationship, this study contributes to strategic management and corporate governance knowledge within the Pakistani business environment. Design/methodology/approach A quantitative approach was used to analyze the relationship using data from annual financial statements. The sample consisted of 76 companies from the KSE-100 index from the year 2015 to 2020. Random effects regression models were used, along with hierarchical regression to explore the moderating effect of CEO power. Findings The findings demonstrate that the implementation of a retrenchment strategy positively impacts firm performance in Pakistani firms. The study also reveals that CEO power plays a crucial role in strengthening the relationship between retrenchment strategy and firm performance. Moreover, the study highlights the importance of considering the temporal sequence, size and age of firms when examining the impact of CEO power and retrenchment strategy on firm performance. Research limitations/implications The study enhances the understanding of the contingent nature of retrenchment strategies and the influence of CEO power in the Pakistani business context. Practically, the research contributes to strategic management and corporate governance dynamics, facilitating the development of strategies that enhance firm performance and sustainability in Pakistan. Originality/value This research provides original insights by specifically focusing on the Pakistani context and analyzing the interplay between retrenchment strategy, CEO power and firm performance. The study adds to the limited literature on the relationship between retrenchment and performance in the Pakistani business environment. Additionally, it highlights the significance of CEO power as a critical factor in determining the success of retrenchment.

On the improved RF performance of step field plate LDMOS transistor

This paper presents an analysis of the Step Field Plate Laterally Diffused Metal Oxide Semiconductor (SFP LDMOS) structure for improved high power and RF performance. The proposed structure is cost-effective, CMOS integrable, and has a high power figure of merit (FoM) due to its high off-state breakdown voltage (BVDS,off) and low specific on-resistance (Rsp). Significant improvement in frequency behavior is observed, as demonstrated by the flatter trans-conductance (gm), 12.5x lower gate-to-drain capacitance (Cgd), 2.5x lower gate-to-source capacitance (Cgs), and lower output conductance (gds). Single tone and double tone analyses are performed on an RF PA circuit implemented in TCAD. Results show ∼5 dBm higher output power (Pout), ∼6 dB higher power gain, and 46% higher drain efficiency (η), which are attributed to ∼1.9x lower drain-to-source capacitance (Cds) and ∼15 dBm lower magnitude of third-order intermodulation distortion due to lower third order derivative of drain current (ID). The paper also explains the device physics behind these observations.

Potential of a deep eutectic solvent in silver nanoparticle fabrication for antibiotic residue detection.

Deep eutectic solvents (DESs) have recently emerged as an alternative solvent for nanoparticle synthesis. There have been numerous advancements in the fabrication of silver nanoparticles (Ag NPs), but the potential of DESs in Ag NP synthesis was neither considered nor studied carefully. In this study, we present a novel strategy to fabricate Ag NPs in a DES (Ag NPs-DES). The DES composed of ᴅ-glucose, urea, and glycerol does not contain any anions to precipitate with Ag+ cations. Our Ag NPs-DES sample is used in a surface-enhanced Raman scattering (SERS) sensor. The two analytes for SERS quantitation are nitrofurantoin (NFT) and sulfadiazine (SDZ) whose residues can be traced down to 10-8 M. The highest enhancement factors (EFs) are competitive at 6.29 × 107 and 1.69 × 107 for NFT and SDZ, respectively. Besides, the linearity coefficients are extremely close to 1 in the range of 10-8 to 10-3 M of concentration, and the SERS substrate shows remarkable uniformity along with great selectivity. This powerful SERS performance indicates that DESs have tremendous potential in the synthesis of nanomaterials for biosensor substrate construction.

Power Generation Enhancement through Latching Control for a Sliding Magnet-Based Wave Energy Converter

A Surface-Riding Wave Energy Converter (SR-WEC) featuring a sliding magnet inside a pitching cylindrical hull is investigated as an easily deployable small power device to support small-scale marine operations. This study extends the earlier development of the system by authors to enhance power performance through the application of end spring and latching control. The inclusion of springs at the tube’s end enhances the magnet release and travel speeds as well as the average power output compared to systems without them. Further improvement of power output can also be achieved by employing optimal latching control. We introduced constant-angle and variable-angle unlatching strategies to determine optimal parameters in combination with passive and reactive power take-off (PTO) controls to assess their effectiveness. The optimized latching control and end spring can increase 60–80% more power output compared with the case without them under certain PTO damping. Additionally, we discussed the effects of limiting peak powers and associated energy leaks with latching.

Effect of Variable-Nozzle-Turbocharger-Coupled Exhaust Gas Recirculation on Natural Gas Engine Emissions and Collaborative Optimization

Equivalent combustion natural gas engines typically utilize exhaust gas recirculation (EGR) systems to tackle their high thermal burden and NOx emissions. Variable nozzle turbochargers (VNT) can increase the engine intake and EGR rate simultaneously, resulting in NOx reduction while ensuring robust power performance. Using a VNT along with engine bench testing, the impact of VNT- and EGR-coordinated control on the performance and emissions of equivalent combustion natural gas engines was investigated under different operating conditions. Subsequently, multi-objective optimization was performed using a support vector machine. The results demonstrated that the use of VNTs in equivalent combustion natural gas engines could bolster the capacity to introduce EGR under several operative conditions and extend the scope of EGR regulation, thereby decreasing the engine’s thermal burden, improving fuel efficiency, and curbing emissions. Owing to the implementation of a multi-objective optimization method based on a support vector regression model and NSGA-II genetic algorithm, VNT and EGR control parameters could be optimized to slightly improve the economy and significantly reduce NOx emissions while maintaining the original engine power performance. At 20 operating points optimized for validation, brake-specific fuel consumption (BSFC) and NOx decreased by 0.94% and 47.0%, respectively, and CH4 increased by 3.7%, on average.

Powerful and accurate detection of temporal gene expression patterns from multi-sample multi-stage single-cell transcriptomics data with TDEseq

We present a non-parametric statistical method called TDEseq that takes full advantage of smoothing splines basis functions to account for the dependence of multiple time points in scRNA-seq studies, and uses hierarchical structure linear additive mixed models to model the correlated cells within an individual. As a result, TDEseq demonstrates powerful performance in identifying four potential temporal expression patterns within a specific cell type. Extensive simulation studies and the analysis of four published scRNA-seq datasets show that TDEseq can produce well-calibrated p-values and up to 20% power gain over the existing methods for detecting temporal gene expression patterns.

A degradome-related signature for predicting the prognosis and immunotherapy benefit in stomach adenocarcinoma based on machine learning procedure.

Stomach adenocarcinoma (STAD) is one of the subtype of gastric cancer with high invasiveness, extreme heterogeneity, high morbidity, and high mortality. The degradome is the most abundant class of cellular enzymes that play an essential role in regulating cellular activity and carcinogenesis. An integrative machine learning procedure including 10 methods was performed to develop a prognostic degradome-based prognostic signature (DPS) in TCGA, GSE15459, GSE26253, and GSE62254 datasets. Investigations of the DPS concerning immune infiltration, immunotherapy benefits, and drug priority were orchestrated. The DPS developed by Enet [alpha = 0.3] method was regarded as the optimal prognostic model. The DPS had a stable and powerful performance in predicting the clinical outcome of STAD and served as an independent risk factor in training and testing cohorts. The C-index of DPS was higher than that of age, sex, and clinical stage. STAD patients with low DPS scores had a higher abundance of B cells, CD8+ T cells, higher cytolytic scores, and T cell co-stimulation scores. Moreover, low DPS score indicated a lower tumor immune dysfunction and exclusion score, lower T cell dysfunction and exclusion score, higher PD1&CTLA4 immunophenoscore, and higher tumor mutation burden score in STAD, demonstrating a better immunotherapy response. STAD patients with a high DPS score had a lower IC50 value of common chemotherapy and targeted therapy regimens (Cisplatin, Docetaxel, Gefitinib, etc). Our study developed an optimal DPS for STAD. The DPS could predict the prognosis, risk stratification and guide treatment for STAD patients.

A unique choline nitrate-based organo-aqueous electrolyte enables carbon/carbon supercapacitor operation in a wide temperature window (-40°C to 60°C).

Some drawbacks of aqueous electrolytes, such as freezing at low temperatures and extensive evaporation at high temperatures, restrict their industrial viability. This article introduces a stabilized neutral aqueous choline nitrate electrolyte with a 10 vol.% methanol additive that improves the temperature stability of the electrolyte via enhanced hydrogen bonding with the choline cation and water and maintains the good state of health of the supercapacitor cells under extreme operating conditions. The symmetric carbon/carbon supercapacitor in 5mol/kg choline nitrate + 10 vol.% methanol (σ = 76ms/cm at 25°C) exhibits 103F/g at room temperature during galvanostatic charge/discharge up to 1.5V, which decreases to 78F/g at -40°C due to the suppressed Faradaic reactions occurring at the carbon electrode. However, under similar charge/discharge conditions, the capacitance increases to 112F/g when the supercapacitor operates at 60°C. This capacitance increase at high temperatures is due to the Faradaic reactions related to enhanced hydrogen adsorption and desorption. The most remarkable aspect of the proposed supercapacitor is its ability to maintain capacitance and power performance during high voltage floating at 1.5V at three tested temperatures (-40°C, 24°C, and 60°C).