Discharge Profiles Research Articles

Box-Behnken Design Optimization of Captopril-Loaded Transferosomes: Formulation and Characterization.

This study aimed to overcome the limitations of oral delivery and enhance the permeation of Captopril (CPL) by developing transferosomes. Transferosomes encapsulating CPL were prepared using a thin film hydration method. Seventeen formulations were prepared, varying Soya lecithin, Tween-20, and sonication speed. The formulations were evaluated for vesicle size (VS), polydispersity index (PDI), and entrapment efficiency (EE%). The optimized formulation’s discharge profile was compared with that of the CPL suspension. Transferosomes exhibited VS ranging from 244 to 376 nm and EE% from 65 to 91%, with zeta potentials ranging from -51 to -26 MV. In-vitro and ex-vivo permeation studies demonstrated superior drug permeation for transferosomal formulations compared to plain CPL suspension. CPL transferosomes represent a promising delivery system for prolonged and controlled drug discharge, offering potential improvements in hypertension management.

Preparation Evaluation of Microspheres of Nateglinide for the Treatment of Diabetes Mellitus

Oral controlled drug conveyance frameworks, being the most broadly used type of supported drug conveyance frameworks because of the intrinsic benefits of oral organization, offer both consistent and variable arrival of medications. These frameworks display a trademark example of medication discharge wherein the medication fixation stays inside the helpful window for a lengthy timeframe, guaranteeing supported restorative activity. Besides, they are normally utilized as a solitary dose structure. The current review means to create and assess supported discharge microspheres of Nateglinide utilizing Ascophyllum, a characteristic polymer, alongside HypromelloseK15 and Deacetylated chitin. The ionotropic gelation procedure has been utilized for the arrangement of these medication stacked microspheres. The improvement of such conveyance frameworks assumes an essential part in upgrading drug viability and patient consistence, as they offer a few benefits like better bioavailability, decreased dosing recurrence, and limited secondary effects. These frameworks likewise give a way to defeat difficulties related with regular medication conveyance strategies, like unfortunate medication strength, low solvency, and enzymatic debasement. By figuring out supported discharge microspheres, a controlled arrival of the medication can be accomplished, prompting delayed restorative impacts and worked on quiet results. Moreover, the decision of regular polymers in this study is worthwhile as they are biocompatible, biodegradable, and show great medication discharge qualities. The utilization of Ascophyllum, HypromelloseK15, and Deacetylated chitin together in the plan considers enhancement of medication discharge profiles and further developed drug exemplification productivity. Through fastidious readiness and exhaustive examination, this exploration attempt plans to add to the improvement of effective oral conveyance frameworks that can meet the restorative requirements of patients, giving maintained and designated drug discharge for constant sicknesses.

Flexible magnesium-ion-conducting solid poly-blend electrolyte films for magnesium-ion batteries

Solid biodegradable polymer electrolyte systems are considered the optimal choice for energy storage devices because they are both cost-effective and energy-efficient. A solid blend polymer electrolyte (SBPE) membrane capable of transporting magnesium ions was prepared using a mixture of 70 wt% methylcellulose, 30 wt% chitosan, and varying wt% magnesium perchlorate salt. X-ray diffraction analysis revealed an increase in the amorphous nature caused by the inclusion of Mg(ClO4)2 salt in the polymer blend matrix. A Fourier transform infrared spectroscopy study of samples containing varying salt concentrations revealed secondary interactions between polymer segments and salt, which provides the basis for energy density. Moreover, through impedance analysis, it was determined that the bulk resistance decreased with increasing salt concentration. The SBPE containing 30 wt% magnesium perchlorate exhibited the highest ionic conductivity, with a value of 2.49 × 10–6 S cm−1. A comprehensive evaluation of the ion transport parameters, including mobility, carrier density, and diffusion, was conducted for the prepared electrolyte samples. Notably, an ionic transference number (tion) of approximately 0.83 was observed for the SBPE sample with 30 wt% magnesium salt, indicating ions’ prevalence as the system’s primary charge carriers. Electrochemical analyses demonstrated that the SBPE with the highest ion conductivity possessed an electrochemical stability window (ESW) of 1.92 V. Additionally, the thermal characteristics of the samples were evaluated using thermogravimetric analysis (TGA) to assess the thermal stability of the electrolyte. Finally, the highest conducting polymer electrolyte was employed to construct a primary magnesium battery, and its discharge profile with different cathode materials was studied. Based on these findings, the current study suggests an environmentally friendly, biodegradable, and economically viable electrolyte option suitable for separator cum electrolytes in magnesium-ion batteries.

Degradation analysis of lithium-ion batteries under ultrahigh-rate discharge profile

Lithium-ion batteries (LIBs) demonstrate significant potential in military applications. While, in application scenarios such as electromagnetic emission, directional energy, LIBs need to be discharged under ultrahigh rates for a long time, which can lead to accelerated performance degradation. To conduct an in-depth study on the degradation behaviour of LIBs under these conditions, this study proposes a peak decomposition method for incremental capacity analysis. This method permits a nuanced quantification of distinct degradation modes within LIBs. The effects of ultrahigh-rate constant-current discharge, ultrahigh-rate pulse discharge, and standard-rate discharge on battery degradation mode are compared. Furthermore, we delve into the fundamental processes potentially underpinning the disparities observed among these degradation modes. The results show that the degradation mode of the batteries aged under ultrahigh-rate discharge profile is dominated by the loss of active material compared with that of the batteries aged under standard-rate discharge profile. Upon reaching 80% state of health, the batteries aged under ultrahigh-rate constant-current discharge and ultrahigh-rate pulse discharge retain 87.3% and 88.51% of their initial active material content, respectively. In addition, lithium plating may have occurred in the charging process of the battery aged under ultrahigh-rate pulse discharge.

Accelerating Battery Research through Atomistic and Multi-Scale Computational Chemistry

Computational screening and advanced insights from simulations will help you develop new and improved battery materials quicker and with less resources. We will discuss our efforts to help researchers to do that with the Amsterdam Modeling Suite (AMS). The AMS framework separates the atomistic engines (DFT, DFTB, ReaxFF, ML potentials) from the driver, which enables the exploration of potential energy surfaces (PESs), mechanical, and electronic properties at several levels of theory. The central AMS driver supports advanced PES explorations, molecular dynamics (MD) and Grand Canonical Monte Carlo (GCMC), based on energies and forces from the engines.With this driver-engine set up, you can calculate and predict relevant processes and properties for battery materials. We will discuss recent developments and examples using: DFT and GW with COSMO-RS for accurate electrode and redox potentials(e)ReaxFF for discharge processes including diffusion, discharge profiles, electrolyte degradation, and solid-electrolyte interface formationpolarizable force fields for accurate viscosities and charge mobilitiespre-parametrized and on-the-fly machine learning potentials for diffusion barriers, intercalation potentials, and reactive processeskinetic Monte Carlo to describe mesoscale processes such as dendrite formation We will also discuss where we envision future ML and multi-scale developments to accelerate materials discovery, particularly focusing on battery applications. Figure 1

Efficient state of charge estimation of lithium-ion batteries in electric vehicles using evolutionary intelligence-assisted GLA–CNN–Bi-LSTM deep learning model

The battery's performance heavily influences the safety, dependability, and operational efficiency of electric vehicles (EVs). This paper introduces an innovative hybrid deep learning architecture that dramatically enhances the estimation of the state of charge (SoC) of lithium-ion (Li-ion) batteries, crucial for efficient EV operation. Our model uniquely integrates a convolutional neural network (CNN) with bidirectional long short-term memory (Bi-LSTM), optimized through evolutionary intelligence, enabling an advanced level of precision in SoC estimation. A novel aspect of this work is the application of the Group Learning Algorithm (GLA) to tune the hyperparameters of the CNN–Bi-LSTM network meticulously. This approach not only refines the model's accuracy but also significantly enhances its efficiency by optimizing each parameter to best capture and integrate both spatial and temporal information from the battery data. This is in stark contrast to conventional models that typically focus on either spatial or temporal data, but not both effectively. The model's robustness is further demonstrated through its training across six diverse datasets that represent a range of EV discharge profiles, including the Highway Fuel Economy Test (HWFET), the US06 test, the Beijing Dynamic Stress Test (BJDST), the dynamic stress test (DST), the federal urban driving schedule (FUDS), and the urban development driving schedule (UDDS). These tests are crucial for ensuring that the model can perform under various real-world conditions. Experimentally, our hybrid model not only surpasses the performance of existing LSTM and CNN frameworks in tracking SoC estimation but also achieves an impressively quick convergence to true SoC values, maintaining an average root mean square error (RMSE) of less than 1 %. Furthermore, the experimental outcomes suggest that this new deep learning methodology outstrips conventional approaches in both convergence speed and estimation accuracy, thus promising to significantly enhance battery life and overall EV efficiency.

Electrochemical Circuit Model Based State of Health Prognostics for Evaluation of Reusability of Lithium-Ion Batteries from Electric Vehicle.

For the purpose of predicting the state of health of already used lithium-ion batteries from 85 kWh electric vehicles, a simplified equivalent circuit model is utilized to estimate the electrochemical time constant from constant current discharge profiles. The grading process among as-obtained LIB cells is classified into three level types according to the remaining capacity and direct current resistance. Theoretically, the logarithmic equation describing cycling behavior is derived and utilized in the prediction of the state of health of the used cells. After the selection of the electrochemical time constant obtained from the best-fitting results in constant current discharge data, the suitable cycle number until the 20th cycle was selected for the prediction of the state of health after the 250th cycling data, which revealed that a narrow error range below 5% was for high and medium battery grades. Also, this error range became abruptly wider in lowest grade batteries, indicating that our proposed model for cycling behavior was highly useful in the prediction of the future state of health of the used batteries.

Correlative Survey of Blended LiCoO2 and LiMn2O4 Cathode Materials for Lithium-ion Batteries

We employed the lithium-ion battery model in Multiphysics to simulate the electrochemical characteristics of lithium-ion batteries. These batteries consist of a cathode mixture comprising LiCoO2 and LiMn2O4, as well as quasi-graphite anode. Our simulations successfully replicated the discharge profiles of both unblended and blended cathodes across different current rates, aligning with results obtained from experiments. The energy and power densities of the blended cathode framework were regulated by adjusting the mix ratio in the simulation model. Additionally, the blended electrodes of LiCoO2 and LiMn2O4 demonstrated an above-average electrochemical performance, combining the characteristics of the two active materials.Keywords: Lithium-ion Battery; Blended Cathode Active Materials; Simulation; Experiment

Battery Electrolyte Design for Electric Vertical Takeoff and Landing (eVTOL) Platforms

AbstractThe development of robust and high‐performance battery systems is crucial for the advancement of Electric Vertical Takeoff and Landing (eVTOL) vehicles for urban air mobility. This study evaluates the performance of different lithium‐ion battery chemistries under Electric Vertical Takeoff and Landing (eVTOL) load profiles. The actual flight data coupled with physical models is used to create discharge profiles for testing on developed lithium‐ion cells for eVTOLs. The performance of a standard liquid electrolyte (1.2 M LiPF6 in EC:EMC), labeled Gen‐2, is benchmarked and compared with a fast‐charging electrolyte (1.2 M LiFSI in EC:EMC), labeled XFC. Cell analysis involves the use of various techniques, such as impedance spectroscopy, polarization curves, and capacity retention measurements. Capacity retention is stable for both systems over 500 cycles, but unique discharge capacity trends are observed for different mission segments. During the initial takeoff hover stages, Gen‐2 electrolytes experience substantial voltage fade, while XFC electrolytes maintain consistent behavior. In general, the Gen‐2 electrolyte demonstrated lower discharge overpotentials and higher decay during cycling compared to the XFC electrolyte. This work highlights the complexity of eVTOL battery behavior and provides insights into battery system design, contributing to the advancement of battery energy storage solutions for urban air mobility.

Hydrokinetic resource assessment in Colombia’s Non-Interconnected Zones: Case study

Energy accessibility in Non-Interconnected Zones (NIZ), together with the energy transition context, converges on exploring non-conventional renewable energy sources and the technology to harness them. Hydrokinetics is an interesting and abundant resource in Colombia, present in many cases near NIZ communities but with unavailable information about (flow velocity) its actual power generation potential. This work presents a method to assess the hydrokinetic resource in NIZ based on the estimation of river flow velocity from river discharge, river level, and cross-sectional profile data available from the Colombian Institute of Hydrology, Meteorology and Environmental Studies (IDEAM). It is applied in a case study where a year of data was studied to characterise the hydrokinetic resource and match a local household load demand with the potential power generation of a hydrokinetic turbine. An average river flow velocity of 1.14 m/s was obtained, showing a consistent behaviour that means reliability in potential energy production. This approach could be helpful for researchers or power suppliers who aim to harness hydrokinetic resources near NIZ users with hydrokinetic technology in the project planning stages.

Plasma current tomography for HL-2A based on Bayesian inference

An accurate plasma current profile has irreplaceable value for the steady-state operation of the plasma. In this study, plasma current tomography based on Bayesian inference is applied to an HL-2A device and used to reconstruct the plasma current profile. Two different Bayesian probability priors are tried, namely the Conditional AutoRegressive (CAR) prior and the Advanced Squared Exponential (ASE) kernel prior. Compared to the CAR prior, the ASE kernel prior adopts non-stationary hyperparameters and introduces the current profile of the reference discharge into the hyperparameters, which can make the shape of the current profile more flexible in space. The results indicate that the ASE prior couples more information, reduces the probability of unreasonable solutions, and achieves higher reconstruction accuracy.

Thiourea modulated supercapacitive behavior of reduced graphene oxide

In this report, we investigated the role of the concentration of nitrogen and sulfur containing thiourea in modulating the electrochemical properties of reduced Graphene Oxide (rGO) by controlling the percentage of heteroatoms on the rGO surface. The synthesis route involved the preparation of Graphene Oxide (GO) using an improved method of synthesis in an economical way by reducing the volume of concentrated acids used. Furthermore, GO is reduced by using different mass proportions of thiourea via a simple reflux method. Characterization of the rGO samples by Fourier Transform Infrared (FTIR) spectroscopy, Raman spectroscopy, X-ray Diffraction (XRD), Scanning Electron Microscopy with Energy Dispersive X-ray Spectroscopy (SEM-EDAX) and elemental analysis through CHNS analyzer exhibited successful doping and impact of nitrogen and sulfur atoms on the graphene framework. The comparative electrochemical performance from Cyclic Voltammograms (CV), Galvanostatic Charge Discharge (GCD) profiles and Electrochemical Impedance Spectroscopy (EIS) measurements on the various rGO samples revealed the superiority of 1:8rGO having 15.190 wt% of nitrogen and 26.849 wt% of sulfur and S/N ratio of 1.768, in delivering highest specific capacitance of 465.21 F·g−1 at 1 mV·s−1 scan rate with a remarkable cyclic stability exhibiting a capacitive retention of 119 % and offering lowest charge transfer resistance and diffusion resistance. The study also demonstrated detrimental effect of excess thiourea on electrochemical properties of rGO. This work suggests an effective and simple approach for optimizing the nitrogen and sulfur content in rGO to enhance its electronic properties using thiourea.

Novel LiFePO4/very-few-layer-graphene (LFP/VFLG) composites to improve structural and electrochemical properties of lithium-ion battery cathode

LiFePO4/Very-few-layer-graphene (LFP/VFLG) composites have been prepared using the sol-gel method for lithium-ion battery cathode. VFLG dominated by 1∼3 layers was obtained from an economical and environmentally benign process. Structural properties of LFP/VFLG composites were studied using Fourier-transform infrared spectra (FTIR) spectroscopy, X-ray diffractometry (XRD), high-resolution transmission electron microscopy (HRTEM) and Raman spectroscopy, while the electrochemical performance was measured using galvanostatic discharge, cyclic voltammetry (CV), and electrochemical impedance spectroscopy (EIS) tests. The FTIR analysis confirmed that incorporating VFLG neither affected the chemical structure of LiFePO4 nor initiated any side reactions during the LiFePO4 formation reaction. The XRD results showed that LFP/VFLG composites had better lattice parameters, crystallinity, and phase purity than LiFePO4 without VFLG addition. The Raman spectroscopy analysis indicated that LFP/VFLG composites had a lower disorder in the carbon arrangement. HRTEM results indicated that a VFLG was successfully wrapped around the LFP particle offers a versatile way to enhanced electrochemical performance of LFP/VFLG composites. The galvanostatic discharge profiles showed an enhancing specific discharge capacity of up to 58.3% after incorporating VFLG. The cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS) tests revealed that LFP/VFLG delivered small polarization, low inner resistance, high electrochemical reaction reversibility, and high lithium-ion diffusion coefficient. VFLG is a promising additive to improve the structural and electrochemical properties of the LiFePO4 cathode of lithium-ion batteries.

State of charge estimator based on tractable extended state observers for supercapacitor packs

A state of charge estimator (SOCE) is proposed for a single and a supercapacitor pack aimed at easy implementation and low computational cost extended state observers. This SOCE reduces modeling errors by implementing extended states using the classic supercapacitor equation. The proposed method involves a simple characterization of supercapacitors, which are tested using different discharge currents up to 40 [A] and subjected to added measurement noise. The proposed estimator is compared against different performance indexes, including the Unscented Kalman filter (UKF), unscented Kalman filter with parameter adaptation (PA), Extended Kalman Filter (EKF), Kalman filter with open-circuit voltage (OCV), Particle Filter (PF), and H-infinity estimator. This comparison was conducted using discharge profiles under different experimental conditions, where the SOCE herein proposed leads to the lowest error among all documented indexes and a performance improvement between 5.53 [%] and 99.90 [%]. Similarly, SOCE facilitates the characterization of the model inputs, reducing the preprocessing time while improving the execution time by 37 [%], and its estimation error by up to 86.77 [%]. These features enable the robust online implementation of embedded systems, improving SOC estimation.

Investigation of Hydraulic Performance and Arrangement Optimization of Non-Prismatic Water Conveyance Tunnel—A Case Study of Project X

The water conveyance tunnel is an important hydraulic structure in water conservancy projects. Typically, it manifests as a non-prismatic tunnel due to variations in the cross-sectional geometry, which give rise to an unpredictable flow pattern that significantly impacts the secure and stable operation of the tunnel. This study takes water conservancy project X as a case and investigates the hydraulic performances of a non-prismatic water conveyance tunnel using a scale model test and numerical simulation; the hydraulic performance, such as flow pattern, discharge, flow velocity, and water depth profile are analyzed for both free flow and submerged flow conditions. The results show that the simulation data fit well with the experimental value observed in the scale model test for free flow; however, a substantial discrepancy emerges under submerged flow conditions. This discrepancy is attributable to the presence of a vacuum in the distortional plan section. Notably, the effect of water entrainment phenomena on flow rate and velocity is diminished in the scale model test, emphasizing the advantage of numerical simulation in predicting hydraulic parameters for the prototype. On this basis, the optimization of the gate shaft arrangement is executed through numerical simulation; the results indicate that the vacuum issues in the contraction section were partially resolved through structure optimization. The simulation values are close to the experimental results, and the modified design of the tunnel can be used in a water conveyance project.

Lifetime prediction and maintenance assessment of Lithium-ion batteries based on combined information of discharge voltage curves and capacity fade

Current two-stage Lithium-ion battery degradation models commonly treat the change point (CP) in two ways. First, it is a random variable, which increases model complexity and the computational cost for predicting the remaining useful life (RUL). Second, it is a deterministic value, which will simplify the degradation models. The value of CP needs to be well identified for an accurate description of the degradation process. However, the capacity data is generally non-monotonic due to energy regeneration, which makes it hard to use in determining the CP. In addition, our laboratory data on the discharge voltage profile shows a potential for CP detection. Thus, we developed a hybrid method that combines dynamic time warping and nonnegative matrix factorization to detect the CPs of battery cells by using voltage discharge profiles. Then, we proposed a two-stage Wiener process incorporating CP detection method to describe the battery capacity degradation pattern. The proposed model is applied to our lab data and NASA data to predict RUL. Finally, maintenance strategies are analyzed to enhance the management of Lithium-ion batteries by minimizing the long-term cost rate under different replacement policies.

High Li-ion diffusion coefficient in LiMn1.5Ni0.5O4 thin films for all-solid Li-ion battery applications

Among various choices of high-performance cathode materials, Ni substituted LiMn2O4 spinel LiMn1.5Ni0.5O4 (LMNO) has proved to be one of the solutions to yield a high-performance and high-energy density material, which can operate at a higher potential window. This study explores the high diffusion coefficient and electrochemical performance at wide current rates of LMNO thin films with less than 50 nm thickness, fabricated on SrTiO3 and SS substrates by the pulsed laser deposition method. The LMNO film with less thickness and the performance without binder and conducting carbon widen the Li-ion path and decrease diffusion length, resulting a higher diffusion coefficient on the order of 10−5 to 10−6 cm2/s, which is several orders higher than the previously obtained reports for bulk counterpart. The cycling stability and rate capability at different current densities of LMNO thin film electrodes are further investigated. Charge–discharge profile at varying current rates of 5, 10, 15, 20, 25, 50, 75, 100, and 125 mA/cm2 is obtained for 500 cycles. The as fabricated film shows a stable profile for 500 cycles with 1.3 and 2.3 μA h/cm2 areal discharge capacity at 25 and 5 mA current rates, respectively. Electrode interfacial properties of LMNO film (before and after cycles) are analyzed using impedance technique. This study indicates that the LMNO thin films can be employed as a cathode layer for fabricating the all-solid thin film batteries for energy applications.

Long-term outcomes of mechanical thrombectomy in acute ischaemic stroke patients with concomitant malignancy.

Patients with cancer (CP) need a different approach to acute ischaemic stroke (AIS) treatment as intravenous thrombolysis (IVT) may be contraindicated. Mechanical thrombectomy (MT) is a treatment of choice for otherwise eligible patients, although the literature on its long-term outcomes in CP is limited. Assessing outcomes of MT-treated AIS patients with concomitant malignancy in a year-long follow-up. The study included 593 MT-treated AIS patients admitted in 2019-2021. The group was divided into CP (defined as a diagnosis of malignancy and undergoing/qualified for cancer treatment within previous 5 years) and a control group. The profile of cardiovascular risk factors, stroke severity and discharge, 90-day and 365-day outcomes were compared between the groups. CP and controls had a similar profile of cardiovascular risk factors and comparable stroke severity. CP were less frequently treated with IVT (25.7% vs. 59.1%, p < 0.001). There were no differences between the groups in the successful reperfusion rate and occurrence of haemorrhagic complications. Discharge and 90-day outcomes were similar. CP had higher 365-day mortality (48.6% vs. 29.9%, p = 0.024) but the percentage of patients achieving good functional outcome in a year-long observation was comparable. Treatment with MT seems beneficial for AIS patients with concomitant malignancy both in short- and long-term observation.

Numerical study on effects of voltage amplitude in CO2 pulsed discharges under Martian conditions by deep neural network

In recent years, non-thermal plasma (NTP) has received an increasing attention for in situ resource utilization of CO2 in the Martian atmosphere. As an important approach to exploring the underpinning physics of NTP, fluid models with tens of species and hundreds of reactions are very time-consuming in simulating CO2 plasmas under Martian conditions, especially driven by the nanosecond pulsed voltage. In this paper, a deep neural network (DNN) with multiple hidden layers is proposed as an example to replace the fluid model to accurately describe the essential discharge features of CO2 pulsed discharge under Martian conditions. After trained by the data from the experimental measurements or numerical simulation and continuously optimized to minimize the loss function, the constructed DNN can achieve a satisfied prediction performance. Compared to the fluid model, the DNN takes only a few seconds to predict the discharge characteristics and profiles of the electric field and particle density, especially to show the spatial–temporal distribution of the given products in CO2 plasmas, such as CO2+, CO3−, CO2v1. This study indicates that a DNN can efficiently yield the essential characteristics in CO2 pulsed discharge even with plenty of species involved in seconds, strongly showing the potential ability to be a highly efficient numerical tool in NTPs with multiple temporal–spatial scales.

Semi-supervised deep learning for lithium-ion battery state-of-health estimation using dynamic discharge profiles

Semi-supervised deep learning for lithium-ion battery state-of-health estimation using dynamic discharge profiles