Locally Enhanced Flow and Electric Fields Through a Tip Effect for Efficient Flow‑Electrode Capacitive Deionization

Low-electrode capacitive deionization(FCDI)is an emerging desalination technology with great potential for removal and/or recycling ions from a range of waters.However,it still suffers from inefficient charge transfer and ion transport kinetics due to weak turbulence and low electric intensity in flow electrodes,both restricted by the current collectors.Herein,a new tip-array current collector(designated as T-CC)was developed to replace the conventional planar current collectors,which intensifies both the charge transfer and ion transport significantly.The effects of tip arrays on flow and electric fields were studied by both computational simulations and electrochemical impedance spectroscopy,which revealed the reduction of ion transport barrier,charge transport barrier and internal resistance.With the voltage increased from 1.0 to 1.5 and 2.0 V,the T-CC-based FCDI system(T-FCDI)exhibited average salt removal rates(ASRR)of 0.18,0.50,and 0.89μmol cm^(-2) min^(-1),respectively,which are 1.82,2.65,and 2.48 folds higher than that of the conventional serpentine current collectors,and 1.48,1.67,and 1.49 folds higher than that of the planar current collectors.Meanwhile,with the solid content in flow electrodes increased from 1 to 5 wt%,the ASRR for T-FCDI increased from 0.29 to 0.50μmol cm^(-2) min^(-1),which are 1.70 and 1.67 folds higher than that of the planar current collectors.Additionally,a salt removal efficiency of 99.89%was achieved with T-FCDI and the charge efficiency remained above 95%after 24 h of operation,thus showing its superior long-term stability.

Recent Advances in Fibrous Materials for Hydroelectricity Generation

Depleting fossil energy sources and conventional polluting power generation pose a threat to sustainable development.Hydroelectricity generation from ubiquitous and spontaneous phase transitions between liquid and gaseous water has been considered a promising strategy for mitigating the energy crisis.Fibrous materials with unique flexibility,processability,multifunctionality,and practicability have been widely applied for fibrous materials-based hydroelectricity generation(FHG).In this review,the power generation mechanisms,design principles,and electricity enhancement factors of FHG are first introduced.Then,the fabrication strategies and characteristics of varied constructions including 1D fiber,1D yarn,2D fabric,2D membrane,3D fibrous framework,and 3D fibrous gel are demonstrated.Afterward,the advanced functions of FHG during water harvesting,proton dissociation,ion separation,and charge accumulation processes are analyzed in detail.Moreover,the potential applications including power supply,energy storage,electrical sensor,and information expression are also discussed.Finally,some existing challenges are considered and prospects for future development are sincerely proposed.

Engendered nanoparticles for treatment of brain tumors

Brain metastasis and primary glioblastoma multiforme represent the most common and lethal malignant brain tumors.Its median survival time is typically less than a year after diagnosis.One of the major challenges in treating these cancers is the efficiency of the transport of drugs to the central nervous system.The blood-brain barrier is cooperating with advanced stages of malignancy.The blood-brain barrier poses a significant challenge to delivering systemic medications to brain tumors.Nanodrug delivery systems have emerged as promising tools for effectively crossing this barrier.Additionally,the development of smart nanoparticles brings new hope for cancer diagnosis and treatment.These nanoparticles improve drug delivery efficiency,allowing for the creation of targeted and stimuli-responsive delivery methods.This review highlights recent advancements in nanoparticle and smart nanoparticle technologies for brain cancer treatment,exploring the range of nanoparticles under development,their applications,targeting strategies,and the latest progress in enhancing transport across the blood-brain barrier.It also addresses the ongoing challenges and potential benefits of these innovative approaches.

Single-atom Pt on carbon nanotubes for selective electrocatalysis

Utilizing supported single atoms as catalysts presents an opportunity to reduce the usage of critical raw materials such as platinum,which are essential for electrochemical reactions such as hydrogen oxidation reaction(HOR).Herein,we describe the synthesis of a Pt single electrocatalyst inside single-walled carbon nanotubes(SWCNTs)via a redox reaction.Characterizations via electron microscopy,X-ray photoelectron microscopy,and X-ray absorption spectroscopy show the single-atom nature of the Pt.The electrochemical behavior of the sample to hydrogen and oxygen was investigated using the advanced floating electrode technique,which minimizes mass transport limitations and gives a thorough insight into the activity of the electrocatalyst.The single-atom samples showed higher HOR activity than state-of-the-art 30%Pt/C while almost no oxygen reduction reaction activity in the proton exchange membrane fuel cell operating range.The selective activity toward HOR arose as the main fingerprint of the catalyst confinement in the SWCNTs.

Reactor field reconstruction from sparse and movable sensors using Voronoi tessellation-assisted convolutional neural networks

The aging of operational reactors leads to increased mechanical vibrations in the reactor interior.The vibration of the incore sensors near their nominal locations is a new problem for neutronic field reconstruction.Current field-reconstruction methods fail to handle spatially moving sensors.In this study,we propose a Voronoi tessellation technique in combination with convolutional neural networks to handle this challenge.Observations from movable in-core sensors were projected onto the same global field structure using Voronoi tessellation,holding the magnitude and location information of the sensors.General convolutional neural networks were used to learn maps from observations to the global field.The proposed method reconstructed multi-physics fields(including fast flux,thermal flux,and power rate)using observations from a single field(such as thermal flux).Numerical tests based on the IAEA benchmark demonstrated the potential of the proposed method in practical engineering applications,particularly within an amplitude of 5 cm around the nominal locations,which led to average relative errors below 5% and 10% in the L_(2) and L_(∞)norms,respectively.

Autolysosomal acidification impairment as a mediator for TNFR1 induced neuronal necroptosis in Alzheimer’s disease

Neuronal necroptosis-an emerging form of regulated cell death associated with neuroinflammatory signaling:Alzheimer’s disease(AD)is characterized by the presence of extracellular amyloid-β(Aβ)plaques and intracellular tau neurofibrillary tangles as well as progressive neuronal loss.Recent evidence has suggested that prolonged neuroinflammation with increased levels of cytokines,arising from neuronal injury,innate immune responses from glial cells,and peripheral inflammation,leads to neuronal death and AD progression.

Boric Acid-Assisted Pyrolysis for High-Loading Single-Atom Catalysts to Boost Oxygen Reduction Reaction in Zn-Air Batteries

The emerging of single-atom catalysts(SACs)offers a great opportunity for the development of advanced energy storage and conversion devices due to their excellent activity and durability,but the actual mass production of high-loading SACs is still challenging.Herein,a facile and green boron acid(H_(3)BO_(3))-assisted pyrolysis strategy is put forward to synthesize SACs by only using chitosan,cobalt salt and H_(3)BO_(3)as precursor,and the effect of H_(3)BO_(3)is deeply investigated.The results show that molten boron oxide derived from H_(3)BO_(3)as ideal high-temperature carbonization media and blocking media play important role in the synthesis process.As a result,the acquired Co/N/B tri-doped porous carbon framework(Co-N-B-C)not only presents hierarchical porous structure,large specific surface area and abundant carbon edges but also possesses high-loading single Co atom(4.2 wt.%),thus giving rise to outstanding oxygen catalytic performance.When employed as a catalyst for air cathode in Zn-air batteries,the resultant Co-N-B-C catalyst shows remarkable power density and long-term stability.Clearly,our work gains deep insight into the role of H_(3)BO_(3)and provides a new avenue to synthesis of high-performance SACs.

Deep learning for joint channel estimation and feedback in massive MIMO systems

The great potentials of massive Multiple-Input Multiple-Output(MIMO)in Frequency Division Duplex(FDD)mode can be fully exploited when the downlink Channel State Information(CSI)is available at base stations.However,the accurate CsI is difficult to obtain due to the large amount of feedback overhead caused by massive antennas.In this paper,we propose a deep learning based joint channel estimation and feedback framework,which comprehensively realizes the estimation,compression,and reconstruction of downlink channels in FDD massive MIMO systems.Two networks are constructed to perform estimation and feedback explicitly and implicitly.The explicit network adopts a multi-Signal-to-Noise-Ratios(SNRs)technique to obtain a single trained channel estimation subnet that works well with different SNRs and employs a deep residual network to reconstruct the channels,while the implicit network directly compresses pilots and sends them back to reduce network parameters.Quantization module is also designed to generate data-bearing bitstreams.Simulation results show that the two proposed networks exhibit excellent performance of reconstruction and are robust to different environments and quantization errors.

Biochemical consequences of glucocerebrosidase 1 mutations in Parkinson’s disease

Parkinson’s disease(PD,OMIM#168600)is a common neurodegenerative disorder with a global prevalence of approximately 8.5 million.PD is characterized by four cardinal motor symptoms:bradykinesia,rigidity,resting tremor,and subsequently by postural instability.It usually involves non-motor symptoms such as rapid eye movement sleep disorder,dementia,anosmia,and autonomic dysfunction.The gene glucocerebrosidase 1(GBA1),which encodes the lysosomal enzyme glucocerebrosidase(GCase)(IUBMB:EC 3.2.1.45),shows strong linkage with PD;variants of GBA1 are the commonest genetic association with PD(Sidransky et al.,2009).Several mechanisms may underlie the relationship between GBA1 mutations/variants and the molecular pathology of PD(Figure 1A and B).

基于耦合发酵策略的乳酰-N-三糖Ⅱ模块化合成研究

母乳寡糖在调节婴儿肠道菌群和保障婴儿健康方面都有积极的作用。乳酰-N-三糖Ⅱ(lacto-N-trioseⅡ,LNTⅡ)是母乳寡糖的核心结构单元。目前合成LNTⅡ的方法存在价格昂贵、操作繁琐等缺点。该研究以乳糖和N-乙酰氨基葡萄糖为底物用三菌株耦合发酵策略合成LNTⅡ。首先构建用于合成N-乙酰氨基葡萄糖-1-磷酸(N-acetylglucosamine-1-phosphate,GlcNAc-1-P)模块的Escherichia coli JM109(DE3)/pET28a-nahK工程菌,优化发酵条件后,GlcNAc-1-P最高产量为16.88 g/L,转化率为70.05%。然后构建分别包含agx1、agx2、pmglmU基因的3株工程菌,并筛选出E.coli JM109(DE3)/pET28a-agx1,其合成尿苷二磷酸乙酰氨基葡萄糖(uridine diphosphate acetylglucosamine,UDP-GlcNAc)的能力最优,在优化的生产体系中UDP-GlcNAc的最大产量为24.78 g/L。最后构建工程菌E.coli JM109(DE3)/pET28a-lgtA并与E.coli JM109(DE3)/pET28a-nahK、E.coli JM109(DE3)/pET28a-agx1耦合发酵合成LNTⅡ,通过优化三菌株耦合发酵体系,LNTⅡ产量达到3.03 g/L,比优化前提高近4倍。该文基于三菌株耦合发酵策略实现了LNTⅡ的合成,这种策略具有通用性强、简便快捷和成本低廉的优点,将为母乳寡糖的规模化生产提供一种新方法。

Two-photon polymerization lithography for imaging optics

Optical imaging systems have greatly extended human visual capabilities,enabling the observation and understanding of diverse phenomena.Imaging technologies span a broad spectrum of wavelengths from x-ray to radio frequencies and impact research activities and our daily lives.Traditional glass lenses are fabricated through a series of complex processes,while polymers offer versatility and ease of production.However,modern applications often require complex lens assemblies,driving the need for miniaturization and advanced designs with micro-and nanoscale features to surpass the capabilities of traditional fabrication methods.Three-dimensional(3D)printing,or additive manufacturing,presents a solution to these challenges with benefits of rapid prototyping,customized geometries,and efficient production,particularly suited for miniaturized optical imaging devices.Various 3D printing methods have demonstrated advantages over traditional counterparts,yet challenges remain in achieving nanoscale resolutions.Two-photon polymerization lithography(TPL),a nanoscale 3D printing technique,enables the fabrication of intricate structures beyond the optical diffraction limit via the nonlinear process of two-photon absorption within liquid resin.It offers unprecedented abilities,e.g.alignment-free fabrication,micro-and nanoscale capabilities,and rapid prototyping of almost arbitrary complex 3D nanostructures.In this review,we emphasize the importance of the criteria for optical performance evaluation of imaging devices,discuss material properties relevant to TPL,fabrication techniques,and highlight the application of TPL in optical imaging.As the first panoramic review on this topic,it will equip researchers with foundational knowledge and recent advancements of TPL for imaging optics,promoting a deeper understanding of the field.By leveraging on its high-resolution capability,extensive material range,and true 3D processing,alongside advances in materials,fabrication,and design,we envisage disruptive solutions to current challenges and a promising incorporation of TPL in future optical imaging applications.

Development of an experimental method for well-controlled blast induced traumatic limb fracture in rats

Heterotopic ossification(HO)is a consequence of traumatic bone and tissue damage,which occurs in 65%of military casualties with blast-associated amputations.However,the mechanisms behind blast-induced HO remain unclear.Animal models are used to study blast-induced HO,but developing such models is challenging,particularly in how to use a pure blast wave(primary blast)to induce limb fracture that then requires an amputation.Several studies,including our recent study,have developed platforms to induce limb fractures in rats with blast loading or a mixture of blast and impact loading.However,these models are limited by the survivability of the animal and repeatability of the model.In this study,we developed an improved platform,aiming to improve the animal's survivability and injury repeatability as well as focusing on primary blast only.The platform exposed only one limb of the rat to a blast wave while providing proper protection to the rest of the rat's body.We obtained very consistent fracture outcome in the tibia(location and pattern)in cadaveric rats with a large range of size and weight.Importantly,the rats did not obviously move during the test,where movement is a potential cause of uncontrolled injury.We further conducted parametric studies by varying the features of the design of the platform.These factors,such as how the limb is fixed and how the cavity through which the limb is placed is sealed,significantly affect the resulting injury.This platform and test setups enable well-controlled limb fracture induced directly by pure blast wave,which is the fundamental step towards a complete in vivo animal model for blast-induced HO induced by primary blast alone,excluding secondary and tertiary blast injury.In addition,the platform design and the findings presented here,particularly regarding the proper protection of the animal,have implications for future studies investigating localized blast injuries,such as blast induced brain and lung injuries.

Advances in Active Suspension Systems for Road Vehicles

Active suspension systems(ASSs)have been proposed and developed for a few decades,and have now once again become a thriving topic in both academia and industry,due to the high demand for driving comfort and safety and the compatibility of ASSs with vehicle electrification and autonomy.Existing review papers on ASSs mainly cover dynamics modeling and robust control;however,the gap between academic research outcomes and industrial application requirements has not yet been bridged,hindering most ASS research knowledge from being transferred to vehicle companies.This paper comprehensively reviews advances in ASSs for road vehicles,with a focus on hardware structures and control strategies.In particular,state-of-the-art ASSs that have been recently adopted in production cars are discussed in detail,including the representative solutions of Mercedes active body control(ABC)and Audi predictive active suspension;novel concepts that could become alternative candidates are also introduced,including series active variable geometry suspension,and the active wheel-alignment system.ASSs with compact structure,small mass increment,low power consumption,high-frequency response,acceptable economic costs,and high reliability are more likely to be adopted by car manufacturers.In terms of control strategies,the development of future ASSs aims not only to stabilize the chassis attitude and attenuate the chassis vibration,but also to enable ASSs to cooperate with other modules(e.g.,steering and braking)and sensors(e.g.,cameras)within a car,and even with high-level decision-making(e.g.,reference driving speed)in the overall transportation system-strategies that will be compatible with the rapidly developing electric and autonomous vehicles.

The W transform and its improved methods for time-frequency analysis of seismic data

The conventional linear time-frequency analysis method cannot achieve high resolution and energy focusing in the time and frequency dimensions at the same time,especially in the low frequency region.In order to improve the resolution of the linear time-frequency analysis method in the low-frequency region,we have proposed a W transform method,in which the instantaneous frequency is introduced as a parameter into the linear transformation,and the analysis time window is constructed which matches the instantaneous frequency of the seismic data.In this paper,the W transform method is compared with the Wigner-Ville distribution(WVD),a typical nonlinear time-frequency analysis method.The WVD method that shows the energy distribution in the time-frequency domain clearly indicates the gravitational center of time and the gravitational center of frequency of a wavelet,while the time-frequency spectrum of the W transform also has a clear gravitational center of energy focusing,because the instantaneous frequency corresponding to any time position is introduced as the transformation parameter.Therefore,the W transform can be benchmarked directly by the WVD method.We summarize the development of the W transform and three improved methods in recent years,and elaborate on the evolution of the standard W transform,the chirp-modulated W transform,the fractional-order W transform,and the linear canonical W transform.Through three application examples of W transform in fluvial sand body identification and reservoir prediction,it is verified that W transform can improve the resolution and energy focusing of time-frequency spectra.

Application of deep learning for informatics aided design of electrode materials in metal-ion batteries

To develop emerging electrode materials and improve the performances of batteries,the machine learning techniques can provide insights to discover,design and develop battery new materials in high-throughput way.In this paper,two deep learning models are developed and trained with two feature groups extracted from the Materials Project datasets to predict the battery electrochemical performances including average voltage,specific capacity and specific energy.The deep learning models are trained with the multilayer perceptron as the core.The Bayesian optimization and Monte Carlo methods are applied to improve the prediction accuracy of models.Based on 10 types of ion batteries,the correlation coefficients are maintained above 0.9 compared to DFT calculation results and the mean absolute error of the prediction results for voltages of two models can reach 0.41 V and 0.20 V,respectively.The electrochemical performance prediction times for the two trained models on thousands of batteries are only 72.9 ms and 75.7 ms.Besides,the two deep learning models are applied to approach the screening of emerging electrode materials for sodium-ion and potassium-ion batteries.This work can contribute to a high-throughput computational method to accelerate the rational and fast materials discovery and design.

Stability behavior of the Lanxi ancient flood control levee after reinforcement with upside-down hanging wells and grouting curtain

The stability of the ancient flood control levees is mainly influenced by water level fluctuations, groundwater concentration and rainfalls. This paper takes the Lanxi ancient levee as a research object to study the evolution laws of its seepage, displacement and stability before and after reinforcement with the upside-down hanging wells and grouting curtain through numerical simulation methods combined with experiments and observations. The study results indicate that the filled soil is less affected by water level fluctuations and groundwater concentration after reinforcement. A high groundwater level is detrimental to the levee's long-term stability, and the drainage issues need to be fully considered. The deformation of the reinforced levee is effectively controlled since the fill deformation is mainly borne by the upside-down hanging wells. The safety factors of the levee before reinforcement vary significantly with the water level. The minimum value of the safety factors is 0.886 during the water level decreasing period, indicating a very high risk of the instability. While it reached 1.478 after reinforcement, the stability of the ancient levee is improved by a large margin.

Predicting dynamic compressive strength of frozen-thawed rocks by characteristic impedance and data-driven methods

In cold regions,the dynamic compressive strength(DCS)of rock damaged by freeze-thaw weathering significantly influences the stability of rock engineering.Nevertheless,testing the dynamic strength under freeze-thaw weathering conditions is often both time-consuming and expensive.Therefore,this study considers the effect of characteristic impedance on DCS and aims to quickly determine the DCS of frozen-thawed rocks through the application of machine-learning techniques.Initially,a database of DCS for frozen-thawed rocks,comprising 216 rock specimens,was compiled.Three external load parameters(freeze-thaw cycle number,confining pressure,and impact pressure)and two rock parameters(characteristic impedance and porosity)were selected as input variables,with DCS as the predicted target.This research optimized the kernel scale,penalty factor,and insensitive loss coefficient of the support vector regression(SVR)model using five swarm intelligent optimization algorithms,leading to the development of five hybrid models.In addition,a statistical DCS prediction equation using multiple linear regression techniques was developed.The performance of the prediction models was comprehensively evaluated using two error indexes and two trend indexes.A sensitivity analysis based on the cosine amplitude method has also been conducted.The results demonstrate that the proposed hybrid SVR-based models consistently provided accurate DCS predictions.Among these models,the SVR model optimized with the chameleon swarm algorithm exhibited the best performance,with metrics indicating its effectiveness,including root mean square error(RMSE)﹦3.9675,mean absolute error(MAE)﹦2.9673,coefficient of determination(R^(2))﹦0.98631,and variance accounted for(VAF)﹦98.634.This suggests that the chameleon swarm algorithm yielded the most optimal results for enhancing SVR models.Notably,impact pressure and characteristic impedance emerged as the two most influential parameters in DCS prediction.This research is anticipated to serve as a reliable reference for estimating the DCS of rocks subjected to freeze-thaw weathering.

Effect of preload forces on multidimensional signal dynamic behaviours for battery early safety warning

Providing early safety warning for batteries in real-world applications is challenging.In this study,comprehensive thermal abuse experiments are conducted to clarify the multidimensional signal evolution of battery failure under various preload forces.The time-sequence relationship among expansion force,voltage,and temperature during thermal abuse under five categorised stages is revealed.Three characteristic peaks are identified for the expansion force,which correspond to venting,internal short-circuiting,and thermal runaway.In particular,an abnormal expansion force signal can be detected at temperatures as low as 42.4°C,followed by battery thermal runaway in approximately 6.5 min.Moreover,reducing the preload force can improve the effectiveness of the early-warning method via the expansion force.Specifically,reducing the preload force from 6000 to 1000 N prolongs the warning time(i.e.,227 to 398 s)before thermal runaway is triggered.Based on the results,a notable expansion force early-warning method is proposed that can successfully enable early safety warning approximately 375 s ahead of battery thermal runaway and effectively prevent failure propagation with module validation.This study provides a practical reference for the development of timely and accurate early-warning strategies as well as guidance for the design of safer battery systems.

Stable Organic Solar Cells Enabled by Simultaneous Hole and Electron Interlayer Engineering

The development of high-performance organic solar cells(OSCs)with high operational stability is essential to accelerate their commercialization.Unfortunately,our understanding of the origin of instabilities in state-of-the-art OSCs based on bulk heterojunction(BHJ)featuring non-fullerene acceptors(NFAs)remains limited.Herein,we developed NFA-based OSCs using different charge extraction interlayer materials and studied their storage,thermal,and operational stabilities.Despite the high power conversion efficiency(PCE)of the OSCs(17.54%),we found that cells featuring self-assembled monolayers(SAMs)as hole-extraction interlayers exhibited poor stability.The time required for these OSCs to reach 80%of their initial performance(T_(80))was only 6h under continuous thermal stress at 85℃in a nitrogen atmosphere and 1 h under maximum power point tracking(MPPT)in a vacuum.Inserting MoO_(x)between ITO and SAM enhanced the T_(80)to 50 and~15 h after the thermal and operational stability tests,respectively,while maintaining a PCE of 16.9%.Replacing the organic PDINN electron transport layer with ZnO NPs further enhances the cells'thermal and operational stability,boosting the T_(80)to 1000 and 170 h,respectively.Our work reveals the synergistic roles of charge-selective interlayers and device architecture in developing efficient and stable OSCs.

Power equipment vibration visualization using intelligent sensing method based on event-sensing principle

Vibration measurements can be used to evaluate the operation status of power equipment and are widely applied in equipment quality inspection and fault identification.Event-sensing technology can sense the change in surface light intensity caused by object vibration and provide a visual description of vibration behavior.Based on the analysis of the principle underlying the transformation of vibration behavior into event flow data by an event sensor,this paper proposes an algorithm to reconstruct event flow data into a relationship correlating vibration displacement and time to extract the amplitude-frequency characteristics of the vibration signal.A vibration measurement test platform is constructed,and feasibility and effectiveness tests are performed for the vibration motor and other power equipment.The results show that event-sensing technology can effectively perceive the surface vibration behavior of power and provide a wide dynamic range.Furthermore,the vibration measurement and visualization algorithm for power equipment constructed using this technology offers high measurement accuracy and efficiency.The results of this study provide a new noncontact and visual method for locating vibrations and performing amplitude-frequency analysis on power equipment.