Fast Performance Research Articles

Separator with active coating for fast and stable Li-ion batteries

Li-ion batteries are widely used in various applications, but their performance is limited by the stability and capacity of the separator. Developing a separator that can prevent thermal runaway and provide additional capacity is a significant challenge in battery research. In this study, a novel method of applying a Lithium titanate (LTO) active coating on the separator of Li-ion batteries is proposed. The LTO active coating can participate in electrochemical reactions and provide additional capacity. The active coating also improves the separator stability by preventing thermal shrinkage and enhancing thermal stability. The active coating has little impact on the Li-ion diffusion, and enables fast and stable battery performance at high C rates. The separator with active coating exhibits the highest ionic conductivity of 1.45 mS/cm, the lowest thermal shrinkage of 1.1% at 160 °C, and the highest capacity of 80.2 mAh/g at 15 C among the tested separators. This method offers a simple and effective way to enhance the performance of Li-ion batteries.

Exploring Numba and CuPy for GPU-Accelerated Monte Carlo Radiation Transport

This paper examines the performance of two popular GPU programming platforms, Numba and CuPy, for Monte Carlo radiation transport calculations. We conducted tests involving random number generation and one-dimensional Monte Carlo radiation transport in plane-parallel geometry on three GPU cards: NVIDIA Tesla A100, Tesla V100, and GeForce RTX3080. We compared Numba and CuPy to each other and our CUDA C implementation. The results show that CUDA C, as expected, has the fastest performance and highest energy efficiency, while Numba offers comparable performance when data movement is minimal. While CuPy offers ease of implementation, it performs slower for compute-heavy tasks.

LiViT-Net: A U-Net-like, lightweight Transformer network for retinal vessel segmentation

The intricate task of precisely segmenting retinal vessels from images, which is critical for diagnosing various eye diseases, presents significant challenges for models due to factors such as scale variation, complex anatomical patterns, low contrast, and limitations in training data. Building on these challenges, we offer novel contributions spanning model architecture, loss function design, robustness, and real-time efficacy. To comprehensively address these challenges, a new U-Net-like, lightweight Transformer network for retinal vessel segmentation is presented. By integrating MobileViT+ and a novel local representation in the encoder, our design emphasizes lightweight processing while capturing intricate image structures, enhancing vessel edge precision. A novel joint loss is designed, leveraging the characteristics of weighted cross-entropy and Dice loss to effectively guide the model through the task's challenges, such as foreground-background imbalance and intricate vascular structures. Exhaustive experiments were performed on three prominent retinal image databases. The results underscore the robustness and generalizability of the proposed LiViT-Net, which outperforms other methods in complex scenarios, especially in intricate environments with fine vessels or vessel edges. Importantly, optimized for efficiency, LiViT-Net excels on devices with constrained computational power, as evidenced by its fast performance. To demonstrate the model proposed in this study, a freely accessible and interactive website was established (https://hz-t3.matpool.com:28765?token=aQjYR4hqMI), revealing real-time performance with no login requirements.

Interfacial carbon tailoring of porous SiOx/carbon nanotube hybrids towards high-rate lithium storage

Fast charging Lithium-ion batteries (LIBs) is highly required with the massive development of the electric vehicle market. Integrating silicon with carbon nanotubes (CNTs) has shown great promise for constructing high-rate anodes of LIBs. However, current reported silicon axially coated CNTs electrodes fail to provide a robust conductive connection within the interfacial layer, causing unsatisfactory rate performance. In this paper, a series of novel coaxial hollow nanocables of SiOx/C coated CNTs composite were presented based on a simple sol–gel method and subsequent calcination. Due to the uniform composition of carbon and SiOx at sub-nanometer scale in the coating layer, a strong 3D conductive network is formed between the internal carbon nanotubes and the neighboring electrode particles. When utilized as LIBs anodes, such novel hybrids manifest high reversible capacity (511 mA h g−1 remained after 500 cycles at 0.5 A g−1), high-rate capability (232 mA h g−1 at 5 A g−1) and ultra-long high-current cycling stability (396 mA h g−1 remained after 1000 cycles at 1.0 A g−1). The structural characterization and electrochemical dynamics analysis show that the synergistic effect of abundant mesoporous channels in the coating layer and strong carbon 3D conductive network makes this unique composite structure exhibit excellent electrochemical performance. This work sheds novel light on the wisely design of advanced Si-based anodes with enhanced fast charging performance.

Analysis of vitamin D and its metabolites in biological samples – Part I: Optimization and comparison of UHPSFC-MS/MS and UHPLC-MS/MS methods

Fat-soluble vitamin D is an essential bioactive compound important for human health. Insufficient vitamin D levels can result not only in bone disease but also in other disorders, such as cancer, metabolic disorders, and diseases related to poor immune function. The current methods commonly used for vitamin D analysis are often applied to determine the levels of the most abundant metabolite in plasma, i.e., 25-OH-D2/D3. These methods do not consider the presence of other hydroxylated and esterified metabolites, including isomers and epimers, which are typically found in low concentrations.In this study, we developed a fast and selective ultra-high performance supercritical fluid chromatography (UHPSFC) method using a 150 mm long 1-amino anthracene (1-AA) column and a mobile phase consisting of carbon dioxide and methanol/isopropanol (1/1, v/v) mixed with 8 % water. After thorough optimization of column temperature and back pressure, the separation of four vitamin D3 esters, vitamin D3 and D2, and eight mono- and di-hydroxylated metabolites, including three groups of isomers, was achieved in 10 min. Two ion sources, atmospheric pressure chemical ionization (APCI) and atmospheric pressure photoionization optimized within this study, were compared in tandem mass spectrometry (MS/MS) detection. No significant sensitivity differences were observed. Subsequently, the same 1-AA column chemistry was examined in ultra-high performance liquid chromatography (UHPLC) as the stationary phase that could hypothetically bring different selectivity in the separation of vitamin D and its metabolites. However, this hypothesis was rejected, and C18 was used as a stationary phase in the final optimized UHPLC-MS/MS method. Despite detailed optimization, the final 15 min UHPLC method was not able to separate di-hydroxylated isomers of vitamin D3, while it enabled better resolution of esterified forms compared to UHPSFC.Optimized methods provided similar repeatability of retention times and peak areas, with RSD < 2 % and 10 %, respectively. The lowest limits of quantification were in the range of 1.2 – 4.9 ng/mL for UHPSFC-APCI-MS/MS, while for UHPLC-APCI-MS/MS, they were typically in the range of 2.6 – 9.6 ng/mL. Based on the obtained results, the UHPSFC-APCI-MS/MS method was the most promising approach for fast, selective, and sensitive analysis that could be applied in the analysis of biological samples with emphasis on the separation of both hydroxylated and esterified metabolites, including isomeric forms.

Synthesis and Application of Polymer SXFA in the Detection of Organophosphine Agents with a SAW Sensor.

The effective detection of isopropyl methylfluorophosphonate (GB, sarin), a type of organophosphine poisoning agent, is an urgent issue to address to maintain public safety. In this research, a gas-sensitive film material, poly (4-hydroxy-4,4-bis trifluoromethyl)-butyl-1-enyl)-siloxane (SXFA), with a structure of hexafluoroisopropyl (HFIP) functional group was synthesized by using methyl vinylpropyl dichlorosilane and hexafluoroacetone trihydrate as initial materials. The synthesis process products were characterized using FTIR. SXFA was prepared on a 200 MHz shear surface wave delay line using the spin-coating method for GB detection. A detection limit of <0.1 mg/m3 was achieved through conditional experiments. Meanwhile, we also obtained a maximum response of 2.168 mV at a 0.1 mg/m3 concentration, indicating the much lower detection limit of the SAW-SXFA sensor. Additionally, a maximum response standard deviation of 0.11 mV with a coefficient of variation of 0.01 and a maximum recovery standard deviation of 0.22 mV with a coefficient of variation of 0.02 were also obtained through five repeated experiments. The results show that the SAW-SXFA sensor has strong selectivity and reproducibility, good selectivity, positive detection ability, high sensitivity, and fast alarm performance for sarin detection.

A novel method for reference parameters identification and electrical property estimation of PV modules under varying operating conditions

The parameters identification of the diode model under reference condition are crucial and have significant impact on output characteristics estimation of photovoltaic (PV) module under varying operating conditions. The traditional methods of extracting model parameters have always focused on solving the parameter optimization problem only under some certain condition, which neglects their effect of performance estimation under other operating condition. In this paper, a novel method is proposed to identify the reference physical parameters and enhance the accuracy of electrical property estimation for PV modules under varying operating conditions. New objective function with penalty function is constructed by considering the estimated performance not only under reference condition but also under multiple real operating conditions. The maximum permissible error (MPE) is proposed to constrain the accuracy for key electrical output indicators and added in penalty function to ensure the accuracy under reference condition. Taking into account the inequality constraints, the guaranteed convergence particle swarm optimization with penalty function (P-GCPSO) is introduced for parameter identification. A set of comparisons between the measured and calculated results of PV modules indicate that the proposed method substantially more accurate than other documented methods for both single and double diode model. For the six different materials tested, the root mean square error (RMSE) was compared. The RMSE of the single-diode model is reduced by at least 57.98% compared to the compared method, with a maximum reduction of 86.57%. Similarly, the RMSE of the double -diode model was reduced by at least 47.24% with a maximum reduction of 88.71% compared to the compared methods. Additionally, the tests with six different types of PV modules at varying environmental conditions for over one year prove that the proposed method is reliable and practical for real-world applications. The relative error of power generation reached a minimum of 2.20%. This proves the reliability and practicability of the proposed method in practical applications. The proposed method is envisaged to be valuable for both offline analysis and online monitoring applications where an accurate, fast and consistent performance estimation tool is required.

An Integrated Monitoring, Diagnostics, and Prognostics System for Aero-Engines under Long-Term Performance Deterioration

In the field of aircraft engine diagnostics, many advanced algorithms have been proposed over the last few years. However, there is still wide room for improvement, especially in the development of more integrated and complete engine health management systems to detect, identify, and forecast complex faults in a short time. Furthermore, it is necessary to ensure that these systems preserve their capabilities over time despite engine deterioration. This paper addresses these necessities by proposing an integrated system that considers the joint operation of feature extraction, anomaly detection, fault identification, and prognostic algorithms for engines with long operation times. To effectively reveal the actual engine condition, light adaptive degraded engine models are computed along with different health indicators that are used as inputs to train and test recognition and prediction models. The system is developed and evaluated using a specialized NASA platform which provides data from a turbofan engine fleet simultaneously experiencing long-term performance deterioration and faults. Contrary to other compared solutions, our results show that the proposed system is robust against the effects of engine deterioration, maintaining its level of detection, recognition, and prediction accuracy over a total engine service life. The low computational cost algorithms has generally fast performance in all stages, making the system suitable for online applications.

Validation of tidal turbine wake simulations using an open regional-scale 3D model against 1MW machine and site measurements

Full-scale tidal turbines deployed in tidal channels are subjected to complex flow due to the effects of local bathymetry and coastline shape which modify the flow directionality, shear, twist and speed of the underlying tidally forced flow. In response to these effects, the wake of an operational tidal turbine will vary spatially and temporally. In order to predict wake form, which is a key step for scaling up tidal energy as developments move from single devices to arrays, we have developed and demonstrated an open source turbine-embedded regional three-dimensional (3D) hydrodynamic model for power and wake prediction. Simulation outputs have been validated against in-situ measurements acquired via ADCPs positioned downstream of and adjacent to the rotor-plane of an operating tidal turbine. Model predictive performance is parameterised by the relative difference between modelled and measured power-weighted rotor averaged velocity using speed binning and temporal-averaging. In the absence of the turbine representation, the difference between model and measurement for ebb and flood was found to be 0.81% and 1.04% respectively at the flow speed of 2.5 m/s. With the turbine represented as an actuator disc in the model, the averaged error of the velocity profiles in the wake is 4.4% and 5.2% at ADCP locations that are 3.7D and 6.2D downstream of the rotor plane during ebb. The model has been designed for use on moderately-priced workstations and the promising results, in terms of capturing key 3D flow features and predictions of wake velocity deficits, is a starting point for further development and may provide baseline data for alternative (e.g., higher fidelity, slower performance, or lower fidelity including 2D models with much faster performance).

Internet Financial Information Security Risk and Prevention in the Age of Artificial Intelligence

With the progress and development of science and technology, artificial intelligence technology and its convenient, fast, and efficient performance characteristics have been widely applied in various fields, which has brought great changes to the financial industry and unprecedented breakthroughs and development. Although artificial intelligence brings huge development opportunities for the Internet financial industry, it also brings some potential risks and challenges, causing Internet financial information to face severe security risks. How to use artificial intelligence technology to effectively prevent and control financial information security risks is a major problem facing the financial industry. This paper analyzes the security risks of Internet financial information in the era of artificial intelligence and puts forward relevant preventive measures to effectively deal with various security risks, ensure the information security management of financial cloud platforms, and promote the healthy and stable development of the financial industry.

Effect of ball milling on the structure and electrochemical hydrogen storage properties of a RE-Mg-Ni alloy

RE-Mg-Ni alloy is considered a highly promising anode material for the next generation of MH-Ni batteries. However, the low cycle life, fast performance, and high manufacturing cost of this ideal MH-Ni battery anode material have become obstacles to its optimization. In this work, an as-cast La0.7Sm0.3MgNi3.6Co0.4 superlattice alloy was prepared by vacuum medium frequency induction melting. Then, La0.7Sm0.3MgNi3.6Co0.4 + 10 wt% Ni (0–2 h) composite hydrogen storage alloys with excellent properties were prepared by adding a catalyst (Ni) and conducting ball milling. XRD, SEM, and HRTEM were conducted to analyse the phase compositions, microstructures and macrostructures of the samples. The experimental results showed that with the increase in ball milling time, the microstructural characteristics of the alloy changed, such as the phase abundance, grain refinement, particle size reduction, and amorphous structure increase, directly leading to the change in electrochemical performance. The discharge capacity (Cmax) of the sample decayed from 264.0 mAh/g to 219.1 mAh/g, the cycle stability (Sn) gradually increased with the extension of the grinding time, and the rate discharge performance first increased and then decreased. When the grinding time was 1 h, the best performance was HRD900 = 36.53%. The critical factors affecting the dynamic performance, such as the charge transfer resistance (Rct), limiting current density (IL), and hydrogen diffusion coefficient (D), all exhibited the same trend and reached their respective optimal values at 1 h.

Achievement of dynamic tablet defect detection mechanism using biaxial slope symmetry algorithm

An inspection in tablet appearance integrity before bottling is regarded as a routine task in a pharmaceutical factory. Although some methods such as automated optical instrument, video or artificial intelligence (AI) are currently available in industry, it usually pays for a complex computational process as well as high cost. Based on the symmetry of tablet appearance in reality, this study develops a biaxial scanning slope symmetry algorithm to realize a dynamic real-time tablet defect detection with a simple arithmetic operation. First, the tablet is discretely scanned using image sensor in two axes, i.e. X and Y directions, simultaneously. Second, the analogy output signals generated from the sensor during the scanning process is discretely digitized and stored in an array. Third, the coordinate of center point in the series data array is identified from every line scanning. Fourth, every section slope between two nearby center points from the first to last lines is formulated and calculated sequentially. Finally, the square mean error (SME) is used to evaluate the shape defect situation according to all accumulated errors from every slope variation. The experimental results verify that the proposed algorithm can achieve both fast and accurate detection performance.

Variational Estimation for Multidimensional Generalized Partial Credit Model.

Multidimensional item response theory (MIRT) models have generated increasing interest in the psychometrics literature. Efficient approaches for estimating MIRT models with dichotomous responses have been developed, but constructing an equally efficient and robust algorithm for polytomous models has received limited attention. To address this gap, this paper presents a novel Gaussian variational estimation algorithm for the multidimensional generalized partial credit model. The proposed algorithm demonstrates both fast and accurate performance, as illustrated through a series of simulation studies and two real data analyses.

A rapid interactive chitosan-based medium with antioxidant and pro-vascularization properties for infected burn wound healing

Large-pore hydrogels are better suited to meet the management needs of nutrient transportation and gas exchange between infected burn wounds and normal tissues. However, better construction strategies are required to balance the pore size and mechanical strength of hydrogels to construct a faster substance/gas interaction medium between tissues. Herein, we developed spongy large pore size hydrogel (CS-TA@Lys) with good mechanical properties using a simple ice crystal-assisted method based on chitosan (CS), incorporating tannic acid (TA) and ε-polylysine (Lys). A large-pore and mechanically robust hydrogel medium was constructed based on hydrogen bonding between CS molecules. On this basis, a pro-restorative functional platform with antioxidation and pro-vascularization was constructed using TA and Lys. In vitro experiments displayed that the CS-TA@Lys hydrogel possessed favorable mechanical properties and fast interaction performances. In addition, the CS-TA@Lys hydrogel possessed the capacity to remove intra/extracellular reactive oxygen species (ROS) and possessed antimicrobial and pro-angiogenic properties. In vivo experiments displayed that the CS-TA@Lys hydrogel inhibited wound inflammation and promoted wound vascularization. In addition, the CS-TA@Lys hydrogel showed the potential for rapid hemostasis. This study provides a potential functional wound dressing with rapid interaction properties for skin wound repair.

Metal- and binder-free dual-ion battery based on green synthetic nano-embroidered spherical organic anode and pure ionic liquid electrolyte

Dual-ion batteries (DIBs) have attracted extensive attention and investigations due to their inherent wide operating voltage and environmental friendliness. Nevertheless, the vast majority of DIBs employ metal-based anode active materials or electrolytes, which are relatively costly and unsustainable. Moreover, the utilization of binders and current collectors in the preparation of cathodes and anodes reduces the energy density to a certain extent, which weakens the advantages of DIBs. Here, we synthesized three types of binder-free nano-embroidered spherical polyimide anode materials composed entirely of renewable elements, paired with pure ionic liquid electrolyte without metal elements and flexible self-supporting independent graphite paper cathode without current collector, to construct a class of totally metal and binder-free DIBs. It significantly improves specific discharge capacity, energy density, cyclic stability, and fast charging performance while remarkably reducing costs and self-discharge rate. Additionally, we overcame the drawbacks of conventional synthesis methods and innovatively prepared nanoscale polyimide materials by a green and facile hydrothermal method, which effectively minimizes synthesis costs and avoids risks. This novel battery system design strategy will promote the advancement of low-cost, high-performance DIBs and could be a promising candidate for large-scale energy storage applications.

An effective material for solar steam generation applications: Gradient graphene sponge

The efficient use of solar energy for clean water is a renewable and environmentally-friendly route to solve global water scarcity. For this, we report the preparation of light, mechanically durable, and effective photothermal material. A simple acid impregnation method was applied to graphene sponge material (GSM) possessing hydrophobic character to gain partial hydrophilicity to obtain a gradient structure. The morphological and structural properties of the gradient graphene sponge material (GGSM) were carried out using scanning electron microscopy, X-ray photoelectron spectroscopy, Raman spectroscopy, X-ray powder diffraction patterns, Fourier-transformed infrared spectroscopy, and water drop contact angle measurements. GGSM showed fast and effective water vapor conversion performance due to the gradient structure, as well as the high rate of sunlight absorption and photothermal conversion thanks to graphene. With the advantageous feature of gradient structure, GGSM achieved a high water vapor conversion capacity (1.79 kg/m2h) and solar thermal efficiency of 57% compared to GSM (1.58 kg/m2h; 45%). This new material provides a novel approach to solar energy applications.

Altered Lateral Prefrontal Cortex Functioning During Emotional Interference Resistance Is Associated With Affect Lability in Adults With Persisting Symptoms of Attention-Deficit/Hyperactivity Disorder From Childhood

BackgroundAttention-deficit/hyperactivity disorder (ADHD) is a common neurodevelopmental disorder characterized by inattention and/or impulsivity/hyperactivity. ADHD, especially when persisting into adulthood, often includes emotional dysregulation, such as affect lability; however, the neural correlates of emotionality in adults with heterogeneous ADHD symptom persistence remain unclear. MethodsThe present study sought to determine shared and distinct functional neuroanatomical profiles of neural circuitry during emotional interference resistance using the emotional face n-back task in adult participants with persisting (n = 47), desisting (n = 93), or no (n = 42) childhood ADHD symptoms while undergoing functional magnetic resonance imaging. ResultsParticipants without any lifetime ADHD diagnosis performed significantly better (faster and more accurately) than participants with ADHD diagnoses on trials with high cognitive loads (2-back) that included task-irrelevant emotional distractors, tapping into executive functioning and emotion regulatory processes. In participants with persisting ADHD symptoms, more severe emotional symptoms were related to worse task performance. Heightened dorsolateral and ventrolateral prefrontal cortex activation was associated with more accurate and faster performance on 2-back emotional faces trials, respectively. Reduced activation was associated with greater affect lability in adults with persisting ADHD, and dorsolateral prefrontal cortex activation mediated the relationship between affect lability and task accuracy. ConclusionsThese findings suggest that alterations in dorsolateral prefrontal cortex function associated with greater interference in cognitive processes from emotion could represent a marker of risk for problems with emotional dysregulation in individuals with persisting ADHD and thus represent a potential therapeutic target for those with greater emotional symptoms of ADHD.

Correction to: Exfoliated few-layered graphite anode with broadened delithiation voltage plateau and fast charging performance for lithium-ion batteries

Correction to: Exfoliated few-layered graphite anode with broadened delithiation voltage plateau and fast charging performance for lithium-ion batteries

Physics-informed ConvNet: Learning physical field from a shallow neural network

We introduce a novel methodology for solving nonlinear partial differential equation (PDE) on regular or irregular domains using physics-informed ConvNet, which we call the PICN. The network structure consists of three parts: 1) a convolutional neural network for physical field generation, 2) a pre-trained convolutional layer corresponding to the finite-difference filters to estimate differential fields of the generated physical field, and 3) an interpolation network for loss analysis in irregular geometry domains. From a CNN perspective, the physical field is generated by a deconvolution layer and a convolution layer. Unlike the standard Physics-informed Neural Network (PINN) approach, the convolutions corresponding to the finite-difference filters estimate the spatial gradients forming the physical operator and then construct the PDE residual in a PINN-like loss function. The total loss function involving boundary conditions and the physical constraints in irregular geometry domains can be calculated from an efficient linear interpolation network. The theoretical analysis of PICN convergence is performed on a simplified case for solving a one-dimensional physical field, and several examples of nonlinear PDE of solutions with multifrequency characteristics are executed. The theory and examples confirm the effective learning capability of PICN for the physical field solution with high-frequency components, compared to the standard PINN. A series of numerical cases are performed to validate the current PICN, including the solving (and estimation) of nonlinear physical operator equations and recovering physical information from noisy observations. First, the ability of PICN to solve nonlinear PDE has been verified by executing three nonlinear problems including ODE with sine nonlinearity, PDE involving nonlinear sine-square operators, and Schrödinger equation. The proposed PICN has been assessed by solving some nonlinear PDE on irregular domains such as star-shaped domain, bird-like domain, and starfish domain. Moreover, PICN is applied to identify the thermal diffusivity parameters in an anisotropic heat transfer problem from noisy data, and a denoising display of the temperature field from strong noisy data with standard deviations ranging from 0.1 to 0.4. The numerical results demonstrate the high accuracy approximation and fast convergence performance of PICN. The potential advantage in approximating complex physical field with multi-frequency components indicates that PICN may become an alternative efficient neural network solver in physics-informed machine learning. This paper is adapted from the work originally posted on arXiv.com by the same authors (arXiv:2201.10967, Jan 26, 2022). The data and code accompanying this paper are publicly available at https://github.com/zengzhi2015/PICN.

Exfoliated few-layered graphite anode with broadened delithiation voltage plateau and fast charging performance for lithium-ion batteries

Exfoliated few-layered graphite anode with broadened delithiation voltage plateau and fast charging performance for lithium-ion batteries