Effects of hydrogen permeation on the mechanical characteristics of electroless nickel-plated free-cutting steel for application to the hydrogen valves of hydrogen fuel cell electric vehicles.

Electroless nickel plating is a suitable technology for the hydrogen industry because electroless nickel can be mass-produced at a low cost. Investigating in a complex environment where hydrogen permeation and friction/wear work simultaneously is necessary to apply it to hydrogen valves for hydrogen fuel cell vehicles. In this research, the effects of hydrogen permeation on the mechanical characteristics of electroless nickel-plated free-cutting steel (SUM 24L) were investigated. Due to the inherent characteristics of electroless nickel plating, the damage (cracks and delamination of grain) and micro-particles by hydrogen permeation were clearly observed at the grain boundaries and triple junctions. In particular, the cracks grew from grain boundary toward the intergranualr. This is because the grain boundaries and triple junctions are hydrogen permeation pathways and increasing area of the hydrogen partial pressure. As a result, its surface roughness increased by a maximum of two times, and its hardness and adhesion strength decreased by hydrogen permeation. In particular, hydrogen permeation increased the friction coefficient of the electroless nickel-plated layer, and the damage caused by adhesive wear was significantly greater, increasing the wear depth by up to 5.7 times. This is believed to be due to the decreasing in wear resistance of the electroless nickel plating layer damaged by hydrogen permeation. Nevertheless, the Vickers hardness and the friction coefficient of the electroless nickel plating layer were improved by about 3 and 5.6 times, respectively, compared with those of the free-cutting steel. In particular, the electroless nickel-plated specimens with hydrogen embrittlement exhibited significantly better mechanical characteristics and wear resistance than the free-cutting steel.

Harmonics management and hosting capacity enhancement: Optimal double-resistor damped double-tuned power filter with artificial hummingbird optimization.

This paper introduces a novel and improved double-resistor damped double-tuned passive power filter (DR-DDTF), designed using multi-objective optimization algorithms to mitigate harmonics and increase the hosting capacity of distribution systems with distributed energy resources. Although four different topologies of single-resistor damped double-tuned filters (DDTFs) have been studied before in the literature, the effectiveness of two different DR-DDTF configurations has not been examined. This work redresses this gap by demonstrating that via comprehensive simulations on two power systems, DR-DDTF provides better harmonic suppression and resonance mitigation than single-resistor alternatives. When it comes to optimizing the DR-DDTF for maximum hosting capacity and minimum system active power losses, the multi-objective artificial hummingbird outperformed six other algorithms in the benchmark. To allow for higher penetration of distributed generation without requiring grid upgrades, this newly developed harmonic mitigation filter provides a good alternative.

Hybrid optimal-FOPID based UPQC for reducing harmonics and compensate load power in renewable energy sources grid connected system.

Integration of renewable energy sources (RES) to the grid in today's electrical system is being encouraged to meet the increase in demand of electrical power and also overcome the environmental related problems by reducing the usage of fossil fuels. Power Quality (PQ) is a critical problem that could have an effect on utilities and consumers. PQ issues in the modern electric power system were turned on by a linkage of RES, smart grid technologies and widespread usage of power electronics equipment. Unified Power Quality Conditioner (UPQC) is widely employed for solving issues with the distribution grid caused by anomalous voltage, current, or frequency. To enhance UPQC performance, Fractional Order Proportional Integral Derivative (FOPID) is developed; nevertheless, a number of tuning parameters restricts its performance. The best solution for the FOPID controller problem is found by using a Coati Optimization Algorithm (COA) and Osprey Optimization Algorithm (OOA) are combined to make a hybrid optimization CO-OA algorithm approach to mitigate these problems. This paper proposes an improved FOPID controller to reduce PQ problems while taking load power into account. In the suggested model, a RES is connected to the grid system to supply the necessary load demand during the PQ problems period. Through the use of an enhanced FOPID controller, both current and voltage PQ concerns are separately modified. The pulse signal of UPQC was done using the optimal controller, which analyzes the error value of reference value and actual value to generate pulses. The integrated design mitigates PQ issues in a system at non-linear load and linear load conditions. The proposed model provides THD of 12.15% and 0.82% at the sag period, 10.18% and 0.48% at the swell period, and 10.07% and 1.01% at the interruption period of non-linear load condition. A comparison between the FOPID controller and the traditional PI controller was additionally taken. The results showed that the recommended improved FOPID controller for UPQC has been successful in reducing the PQ challenges in the grid-connected RESs system.

Direct synthesis of a lithium carboxymethyl cellulose binder using wood dissolving pulp for high-performance LiFePO4 cathodes in lithium-ion batteries.

Lithium carboxymethyl cellulose (CMC-Li) is a promising novel water-based binder for lithium-ion batteries. The direct synthesis of CMC-Li was innovatively developed using abundant wood dissolving pulp materials from hardwood (HW) and softwood (SW). The resulting CMC-Li-HW and CMC-Li-SW binders possessed a suitable degree of substitutions and excellent molecular weight distributions with an appropriate quantity of long- and short-chain celluloses, which facilitated the construction of a reinforced concrete-like bonding system. When used as cathode binders in LiFePO4 batteries, they uniformly coated and dispersed the electrode materials, formed a compact and stable conductive network with high mechanical strength and showed sufficient lithium replenishment. The prepared LiFePO4 batteries exhibited good mechanical stability, low charge transfer impedance, high initial discharge capacity (∼180 mAh/g), high initial Coulombic efficiency (99 %), excellent cycling performance (<3% loss over 200 cycles) and good rate capability, thereby outperforming CMC-Na and the widely used cathode binder polyvinylidene fluoride.

Monolithically Defined Wireless Fully Implantable Nervous System Interfaces.

ConspectusEvolution of implantable neural interfaces is critical in addressing the challenges in understanding the fundamental working principles and therapeutic applications for central and peripheral nervous systems. Traditional approaches utilizing hermetically sealed, rigid electronics and detached electrodes face challenges in power supply, encapsulation, channel count, dispersed application location, and modality. Employing thin-film, wirelessly powered devices is promising to expand capabilities. Devices that forego bulky power supplies, favoring a configuration where electronics are integrated directly onto thin films, reduce displacement volumes for seamless, fully implantable interfaces with high energy availability and soft mechanics to conform to the neuronal target. We discuss 3 device architectures: (1) Highly miniaturized devices that merge electronics and neural interfaces into a single, injectable format; (2) Interfaces that consolidate power, computation, and neural connectivity on a thin sheet applied directly to the target area; (3) A spatially dislocated approach where power and computation are situated subdermally, connected via a thin interconnect to the neural interface.Each has advantages and constraints in terms of implantation invasiveness, power capturing efficiency, and directional sensitivity of power delivery. In powering these devices, near-field power delivery emerges as the most implemented technique. Key parameters are size and volume of primary and secondary antennas, which determine coupling efficiency and power delivery. Based on application requirements, ranging from small to large animal models, subjects require system level designs. Material strategies play a crucial role; monolithic designs, with materials like polyimide substrates, enable scalability with high performance. This contrasts with established hermetic encapsulation approaches that use a stainless steel or titanium box with passthroughs that result in large tissue displacements and prohibit intimate integration with target organ systems. Encapsulation, particularly with parylene, enables longevity and effectiveness; more research is needed to enable human lifetime operation. Implant-to-ambient device communication, focusing on strategies compatible with well-established standards and off-the-shelf electronics, is discussed with the goal of enabling seamless system integration, reliability, and scalability. The interface with the central nervous system is explored through various wireless, battery-free devices capable of both stimulation (electrical and optogenetic) and recording (photometric and electrochemical). These devices show advanced capabilities for chronic studies and insights into neural dynamics. In the peripheral nervous system, stimulation devices for applications, such as spinal and muscle stimulation, are discussed. The challenges lie in the mechanical and electrochemical durability. Examples that successfully navigate these challenges offer solutions for chronic studies in this domain. The potential of wireless, fully implantable nervous system interfaces using near field resonant power transfer is characterized by monolithically defined device architecture, providing a significant leap toward seamless access to the central and peripheral nervous systems. New avenues for research and therapeutic applications supporting a multimodal and multisite approach to neuromodulation with a high degree of connectivity and a holistic approach toward deciphering and supplementing the nervous system may enable recovery and treatment of injury and chronic disease.

Is renewable energy development endangering power supply reliability?

The development of renewable energy is indispensable to promoting the low-carbon transition of power systems. Nevertheless, it also brings uncertainty to the reliability of power systems. Herein, the panel model and panel threshold model are established based on the provincial data in China from 2012 to 2020. The results reveal that the negative effect of renewable energy development (RED) on power supply reliability (PSR) is gradually lessening. If the development of renewable energy is a rational way, power supply reliability can be improved. Additionally, energy-exporting regions bear more risks of RED than energy-importing regions. If the coal prices are stable and natural disasters are manageable, the RED can enhance the PSR. However, if they are not stable or controllable, a high proportion of renewable energy in the power system could cause even more severe problems with PSR. Based on these critical results, some suggestions are made to promote the formation of a new power system.

Robustly and Intrinsically Stretchable Ionic Gel-Based Moisture-Enabled Power Generator with High Human Body Conformality.

Direct harvesting of energy from moist air will be a promising route to supply electricity for booming wearable and distributed electronics, with the recent rapid development of the moisture-enabled electricity generator (MEG). However, the easy spatial distortion of rigid MEG materials under severe deformation extremely inconveniences the human body with intense physical activity, seriously hindering the desirable applications. Here, an intrinsically stretchable moisture-enabled electricity generator (s-MEG) is developed based on a well-fabricated stretchable functional ionic gel (SIG) with a flexible double-network structure and reversible cross-linking interactions, demonstrating stable electricity output performance even when stretched up to 150% strain and high human body conformality. This SIG exhibits ultrahigh tensile strain (∼600%), and a 1 cm × 1 cm SIG film-based s-MEG can generate a voltage of ∼0.4 V and a current of ∼5.7 µA when absorbing water from humidity air. Based on the strong adhesion and the excellent interface combination of SIG and rough fabric electrodes induced by the fabrication process, s-MEG is able to realize bending or twisting deformation and shows outstanding electricity output stability with ∼90% performance retention after 5000 cycles of bending tests. By connecting s-MEG units in series or parallel, an integrated device of "moisture-powered wristband" is developed to wear on the wrist of humans and drive a flexible sensor for tracking finger motions. Additionally, a comfortable "moisture-powered sheath" based on s-MEGs is created, which can be worn like clothing on human arms to generate energy while walking and flexing the elbow, which is promising in the field of wearable electronics.

Miniature battery-free epidural cortical stimulators.

Miniaturized neuromodulation systems could improve the safety and reduce the invasiveness of bioelectronic neuromodulation. However, as implantable bioelectronic devices are made smaller, it becomes difficult to store enough power for long-term operation in batteries. Here, we present a battery-free epidural cortical stimulator that is only 9 millimeters in width yet can safely receive enough wireless power using magnetoelectric antennas to deliver 14.5-volt stimulation bursts, which enables it to stimulate cortical activity on-demand through the dura. The device has digitally programmable stimulation output and centimeter-scale alignment tolerances when powered by an external transmitter. We demonstrate that this device has enough power and reliability for real-world operation by showing acute motor cortex activation in human patients and reliable chronic motor cortex activation for 30 days in a porcine model. This platform opens the possibility of simple surgical procedures for precise neuromodulation.

A Self-Powered Lactate Sensor Based on the Piezoelectric Effect for Assessing Tumor Development.

The build-up of lactate in solid tumors stands as a crucial and early occurrence in malignancy development, and the concentration of lactate in the tumor microenvironment may be a more sensitive indicator for analyzing primary tumors. In this study, we designed a self-powered lactate sensor for the rapid analysis of tumor samples, utilizing the coupling between the piezoelectric effect and enzymatic reaction. This lactate sensor is fabricated using a ZnO nanowire array modified with lactate oxidase (LOx). The sensing process does not require an external power source or batteries. The device can directly output electric signals containing lactate concentration information when subjected to external forces. The lactate concentration detection upper limit of the sensor is at least 27 mM, with a limit of detection (LOD) of approximately 1.3 mM and a response time of around 10 s. This study innovatively applied self-powered technology to the in situ detection of the tumor microenvironment and used the results to estimate the growth period of the primary tumor. The availability of this application has been confirmed through biological experiments. Furthermore, the sensor data generated by the device offer valuable insights for evaluating the likelihood of remote tumor metastasis. This study may expand the research scope of self-powered technology in the field of medical diagnosis and offer a novel perspective on cancer diagnosis.

Triple Generative Self-Supervised Learning Method for Molecular Property Prediction.

Molecular property prediction is an important task in drug discovery, and with help of self-supervised learning methods, the performance of molecular property prediction could be improved by utilizing large-scale unlabeled dataset. In this paper, we propose a triple generative self-supervised learning method for molecular property prediction, called TGSS. Three encoders including a bi-directional long short-term memory recurrent neural network (BiLSTM), a Transformer, and a graph attention network (GAT) are used in pre-training the model using molecular sequence and graph structure data to extract molecular features. The variational auto encoder (VAE) is used for reconstructing features from the three models. In the downstream task, in order to balance the information between different molecular features, a feature fusion module is added to assign different weights to each feature. In addition, to improve the interpretability of the model, atomic similarity heat maps were introduced to demonstrate the effectiveness and rationality of molecular feature extraction. We demonstrate the accuracy of the proposed method on chemical and biological benchmark datasets by comparative experiments.

GSB: GNGS and SAG-BiGRU network for malware dynamic detection.

With the rapid development of the Internet, the continuous increase of malware and its variants have brought greatly challenges for cyber security. Due to the imbalance of the data distribution, the research on malware detection focuses on the accuracy of the whole data sample, while ignoring the detection rate of the minority categories' malware. In the dataset sample, the normal data samples account for the majority, while the attacks' malware accounts for the minority. However, the minority categories' attacks will bring great losses to countries, enterprises, or individuals. For solving the problem, this study proposed the GNGS algorithm to construct a new balance dataset for the model algorithm to pay more attention to the feature learning of the minority attacks' malware to improve the detection rate of attacks' malware. The traditional malware detection method is highly dependent on professional knowledge and static analysis, so we used the Self-Attention with Gate mechanism (SAG) based on the Transformer to carry out feature extraction between the local and global features and filter irrelevant noise information, then extracted the long-distance dependency temporal sequence features by the BiGRU network, and obtained the classification results through the SoftMax classifier. In the study, we used the Alibaba Cloud dataset for malware multi-classification. Compared the GSB deep learning network model with other current studies, the experimental results showed that the Gaussian noise generation strategy (GNGS) could solve the unbalanced distribution of minority categories' malware and the SAG-BiGRU algorithm obtained the accuracy rate of 88.7% on the eight-classification, which has better performance than other existing algorithms, and the GSB model also has a good effect on the NSL-KDD dataset, which showed the GSB model is effective for other network intrusion detection.

Application of the surface engineered recombinant Escherichia coli to the industrial battery waste solution for lithium recovery.

Escherichia coli were engineered to selectively adsorb and recover lithium from the environment by employing a bacterial cell surface display strategy. Lithium binding peptide (LBP1) was integrated into the Escherichia coli membrane protein OmpC. The effect of environmental conditions on the adsorption of lithium by a recombinant strain was evaluated, and lithium particles on the cellular surface were analyzed by FE-SEM and XRD. To elevate the lithium adsorption, dimeric, trimeric, and tetrameric repeats of the LBP1 peptide were constructed and displayed on the surface of E. coli. The constructed recombinant E. coli displaying the LBP1 trimer was applied to real industrial lithium battery wastewater to recover lithium.

Designing process and analysis of a new SOI-MESFET structure with enhanced DC and RF characteristics for high-frequency and high-power applications.

This research introduces a new designing process and analysis of an innovative Silicon-on-Insulator Metal-Semiconductor Field-Effect (SOI MESFET) structure that demonstrates improved DC and RF characteristics. The design incorporates several modifications to control and reduce the electric field concentration within the channel. These modifications include relocating the transistor channel to sub-regions near the source and drain, adjusting the position of the gate electrode closer to the source, introducing an aluminum layer beneath the channel, and integrating an oxide layer adjacent to the gate. The results show that the AlOx-MESFET configuration exhibits a remarkable increase of 128% in breakdown voltage and 156% in peak power. Furthermore, due to enhanced conductivity and a significant reduction in gate-drain capacitance, there is a notable improvement of 53% in the cut-off frequency and a 28% increase in the maximum oscillation frequency. Additionally, the current gain experiences a boost of 15%. The improved breakdown voltage and peak power make it suitable for applications requiring robust performance under high voltage and power conditions. The increased maximum oscillation frequency and cut-off frequency make it ideal for high-frequency applications where fast signal processing is crucial. Moreover, the enhanced current gain ensures efficient amplification of signals. The introduced SOI MESFET structure with its modifications offers significant improvements in various performance metrics. It provides high oscillation frequency, better breakdown voltage and good cut-off frequency, and current gain compared to the traditional designs. These enhancements make it a highly desirable choice for applications that demand high-frequency and high-power capabilities.

Effects of duty cycle on passive acoustic monitoring metrics: The case of blue whale songs.

Long-term fixed passive acoustic monitoring of cetacean populations is a logistical and technological challenge, often limited by the battery capacity of the autonomous recorders. Depending on the research scope and target species, temporal subsampling of the data may become necessary to extend the deployment period. This study explores the effects of different duty cycles on metrics that describe patterns of seasonal presence, call type richness richness, and daily call rate of three blue whale acoustics populations in the Southern Indian Ocean. Detections of blue whale calls from continuous acoustic data were subsampled with three different duty cycles of 50%, 33%, and 25% within listening periods ranging from 1 min to 6 h. Results show that reducing the percentage of recording time reduces the accuracy of the observed seasonal patterns as well as the estimation of daily call rate and call call type richness. For a specific duty cycle, short listening periods (5-30 min) are preferred to longer listening periods (1-6 h). The effects of subsampling are greater the lower the species' vocal activity or the shorter their periods of presence. These results emphasize the importance of selecting a subsampling scheme adapted to the target species.

Study on green closed-loop regeneration of waste lithium iron phosphate based on oxalic acid system.

The recovery of valuable metals from used lithium batteries is essential from an environmental and resource management standpoint. However, the most widely used acid leaching method causes significant ecological harm. Here, we proposed a method of recovering Li and Fe selectively from used lithium iron phosphate batteries by using low-concentration organic acid and completing the closed-loop regeneration. Low-concentration oxalic acid is used to carry out PO43-, which is significantly less soluble in aqueous solution than Li, two-stage selective leaching Li, where the leaching rate of Li reaches 99 %, and the leaching rate of Fe is only 2.4 %. The leach solution is then decontaminated. The solubility of Li3PO4 in aqueous solution is much smaller than that of Li2C2O4, which was required to recover Li to change the pH and Li can be recovered as Li3PO4; Fe can be retrieved as FeC2O4·2H2O, and re-prepared into lithium iron phosphate.

Lithium and cobalt extraction from LiCoO2 assisted by p(VBPDA-co-FDA) copolymers in supercritical CO2.

Supercritical CO2 (scCO2) extraction assisted by complexing copolymers is a promising process to recover valuable metals from lithium-ion batteries (LIBs). CO2, in addition to being non-toxic, abundant and non-flammable, allows an easy separation of metal-complexes from the extraction medium by depressurization, limiting the wastewater production. In this study, CO2-philic gradient copolymers bearing phosphonic diacid complexing groups (poly(vinylbenzylphosphonic diacid-co-1,1,2,2-tetrahydroperfluorodecylacrylate), p(VBPDA-co-FDA)) were synthesized for the extraction of lithium and cobalt from LiCoO2 cathode material. Notably, the copolymer was able to play the triple role of leaching agent, complexing agent and surfactant. The proof of concept for leaching, complexation and extraction was achieved, using two different extraction systems. A first extraction system used aqueous hydrogen peroxide as reducing agent while it was replaced by ethanol in the second extraction system. The scCO2 extraction conditions such as extraction time, temperature, functional copolymer concentration, and the presence of additives were optimized to improve the metals extraction from LiCoO2 cathode material, leading to an extraction efficiency of Li and Co up to ca. 75 % at 60 °C and 250 bar.

Transformer with difference convolutional network for lightweight universal boundary detection.

Although deep-learning methods can achieve human-level performance in boundary detection, their improvements mostly rely on larger models and specific datasets, leading to significant computational power consumption. As a fundamental low-level vision task, a single model with fewer parameters to achieve cross-dataset boundary detection merits further investigation. In this study, a lightweight universal boundary detection method was developed based on convolution and a transformer. The network is called a "transformer with difference convolutional network" (TDCN), which implies the introduction of a difference convolutional network rather than a pure transformer. The TDCN structure consists of three parts: convolution, transformer, and head function. First, a convolution network fused with edge operators is used to extract multiscale difference features. These pixel difference features are then fed to the hierarchical transformer as tokens. Considering the intrinsic characteristics of the boundary detection task, a new boundary-aware self-attention structure was designed in the transformer to provide inductive bias. By incorporating the proposed attention loss function, it introduces the direction of the boundary as strongly supervised information to improve the detection ability of the model. Finally, several head functions with multiscale feature inputs were trained using a bidirectional additive strategy. In the experiments, the proposed method achieved competitive performance on multiple public datasets with fewer model parameters. A single model was obtained to realize universal prediction even for different datasets without retraining, demonstrating the effectiveness of the method. The code is available at https://github.com/neulmc/TDCN.

Resilience analysis and improvement strategy of microgrid system considering new energy connection.

With the increasing demand for electricity, microgrid systems are facing issues such as insufficient backup capacity, frequent load switching, and frequent malfunctions, making research on microgrid resilience crucial, especially to improve system power supply reliability. This paper proposes a method for analyzing the resilience metric of new energy grid-connected microgrid system, and proposes optimization strategies to improve resilience. Firstly, a measurement method for the resilience of the microgrid system is established based on the operating characteristics of the system components. Secondly, the sensitivity relationship between system resilience and parameters is established, and an optimization model for resilience improvement strategies of microgrid systems based on parameter sensitivity is constructed. Finally, simulation verification is conducted based on the IEEE 37-node microgrid system. The results show that the proposed measurement method can scientifically and reasonably measure the resilience of the microgrid system, and the resilience improvement strategy significantly improves the operational resilience, verifying the effectiveness and robustness of the proposed analysis method.

Prediction of remaining surgery duration in laparoscopic videos based on visual saliency and the transformer network.

BACKGROUND: Real-time prediction of the remaining surgery duration (RSD) is important for optimal scheduling of resources in the operating room. METHODS: We focus on the intraoperative prediction of RSD from laparoscopic video. An extensive evaluation of seven common deep learning models, a proposed one based on the Transformer architecture (TransLocal) and four baseline approaches, is presented. The proposed pipeline includes a CNN-LSTM for feature extraction from salient regions within short video segments and a Transformer with local attention mechanisms. RESULTS: Using the Cholec80 dataset, TransLocal yielded the best performance (mean absolute error (MAE) = 7.1 min). For long and short surgeries, the MAE was 10.6 and 4.4 min, respectively. Thirty minutes before the end of surgery MAE = 6.2 min, 7.2 and 5.5 min for all long and short surgeries, respectively. CONCLUSIONS: The proposed technique achieves state-of-the-art results. In the future, we aim to incorporate intraoperative indicators and pre-operative data.