A novel integrated microfluidic chip for on-demand electrostatic droplet charging and sorting

On-demand droplet sorting is extensively applied for the efficient manipulation and genome-wide analysis of individual cells.However,state-of-the-art microfluidic chips for droplet sorting still suffer from low sorting speeds,sample loss,and labor-intensive preparation procedures.Here,we demonstrate the development of a novel microfluidic chip that integrates droplet generation,on-demand electrostatic droplet charging,and high-throughput sorting.The charging electrode is a copper wire buried above the nozzle of the microchannel,and the deflecting electrode is the phosphate buffered saline in the microchannel,which greatly simplifies the structure and fabrication process of the chip.Moreover,this chip is capable of high-frequency droplet generation and sorting,with a frequency of 11.757 kHz in the drop state.The chip completes the selective charging process via electrostatic induction during droplet generation.On-demand charged microdroplets can arbitrarilymove to specific exit channels in a three-dimensional(3D)-deflected electric field,which can be controlled according to user requirements,and the flux of droplet deflection is thereby significantly enhanced.Furthermore,a lossless modification strategy is presented to improve the accuracy of droplet deflection or harvest rate from 97.49% to 99.38% by monitoring the frequency of droplet generation in real time and feeding it back to the charging signal.This chip has great potential for quantitative processing and analysis of single cells for elucidating cell-to-cell variations.

ZnO Additive Boosts Charging Speed and Cycling Stability of Electrolytic Zn–Mn Batteries

Electrolytic aqueous zinc-manganese(Zn–Mn) batteries have the advantage of high discharge voltage and high capacity due to two-electron reactions. However, the pitfall of electrolytic Zn–Mn batteries is the sluggish deposition reaction kinetics of manganese oxide during the charge process and short cycle life. We show that, incorporating ZnO electrolyte additive can form a neutral and highly viscous gel-like electrolyte and render a new form of electrolytic Zn–Mn batteries with significantly improved charging capabilities. Specifically, the ZnO gel-like electrolyte activates the zinc sulfate hydroxide hydrate assisted Mn^(2+) deposition reaction and induces phase and structure change of the deposited manganese oxide(Zn_(2)Mn_(3)O_8·H_(2)O nanorods array), resulting in a significant enhancement of the charge capability and discharge efficiency. The charge capacity increases to 2.5 mAh cm^(-2) after 1 h constant-voltage charging at 2.0 V vs. Zn/Zn^(2+), and the capacity can retain for up to 2000 cycles with negligible attenuation. This research lays the foundation for the advancement of electrolytic Zn–Mn batteries with enhanced charging capability.

Flexible bidirectional pulse charging regulation achieving long-life lithium-ion batteries

Typical application scenarios,such as vehicle to grid(V2G)and frequency regulation,have imposed significant long-life demands on lithium-ion batteries.Herein,we propose an advanced battery life-extension method employing bidirectional pulse charging(BPC)strategy.Unlike traditional constant current charging methods,BPC strategy not only achieves comparable charging speeds but also facilitates V2G frequency regulation simultaneously.It significantly enhances battery cycle ampere-hour throughput and demonstrates remarkable life extension capabilities.For this interesting conclusion,adopting model identification and postmortem characterization to reveal the life regulation mechanism of BPC:it mitigates battery capacity loss attributed to loss of lithium-ion inventory(LLI)in graphite anodes by intermittently regulating the overall battery voltage and anode potential using a negative charging current.Then,from the perspective of internal side reaction,the life extension mechanism is further revealed as inhibition of solid electrolyte interphase(SEI)and lithium dendrite growth by regulating voltage with a bidirectional pulse current,and a semi-empirical life degradation model combining SEI and lithium dendrite growth is developed for BPC scenarios health management,the model parameters are identified by genetic algorithm with the life simulation exhibiting an accuracy exceeding 99%.This finding indicates that under typical rate conditions,adaptable BPC strategies can extend the service life of LFP battery by approximately 123%.Consequently,the developed advanced BPC strategy offers innovative perspectives and insights for the development of long-life battery applications in the future.

A comparative study of data-driven battery capacity estimation based on partial charging curves

With its generality and practicality, the combination of partial charging curves and machine learning(ML) for battery capacity estimation has attracted widespread attention. However, a clear classification,fair comparison, and performance rationalization of these methods are lacking, due to the scattered existing studies. To address these issues, we develop 20 capacity estimation methods from three perspectives:charging sequence construction, input forms, and ML models. 22,582 charging curves are generated from 44 cells with different battery chemistry and operating conditions to validate the performance. Through comprehensive and unbiased comparison, the long short-term memory(LSTM) based neural network exhibits the best accuracy and robustness. Across all 6503 tested samples, the mean absolute percentage error(MAPE) for capacity estimation using LSTM is 0.61%, with a maximum error of only 3.94%. Even with the addition of 3 m V voltage noise or the extension of sampling intervals to 60 s, the average MAPE remains below 2%. Furthermore, the charging sequences are provided with physical explanations related to battery degradation to enhance confidence in their application. Recommendations for using other competitive methods are also presented. This work provides valuable insights and guidance for estimating battery capacity based on partial charging curves.

Ultraconformable Integrated Wireless Charging Micro-Supercapacitor Skin

Conformable and wire-less charging energy storage devices play important roles in enabling the fast development of wearable,non-contact soft electronics.However,current wire-less charging power sources are still restricted by limited flexural angles and fragile connection of components,resulting in the failure expression of performance and constraining their fur-ther applications in health monitoring wearables and moveable artificial limbs.Herein,we present an ultracompatible skin-like integrated wireless charging micro-supercapacitor,which building blocks(including electrolyte,electrode and substrate)are all evaporated by liquid precursor.Owing to the infiltration and permeation of the liquid,each part of the integrated device attached firmly with each other,forming a compact and all-in-one configuration.In addition,benefitting from the controllable volume of electrode solution precursor,the electrode thickness is easily regulated varying from 11.7 to 112.5μm.This prepared thin IWC-MSC skin can fit well with curving human body,and could be wireless charged to store electricity into high capacitive micro-supercapacitors(11.39 F cm-3)of the integrated device.We believe this work will shed light on the construction of skin-attachable electronics and irregular sensing microrobots.

Unraveling the Fundamental Mechanism of Interface Conductive Network Influence on the Fast‑Charging Performance of SiO‑Based Anode for Lithium‑Ion Batteries

Progress in the fast charging of high-capacity silicon monoxide(SiO)-based anode is currently hindered by insufficient conductivity and notable volume expansion.The construction of an interface conductive network effectively addresses the aforementioned problems;however,the impact of its quality on lithium-ion transfer and structure durability is yet to be explored.Herein,the influence of an interface conductive network on ionic transport and mechanical stability under fast charging is explored for the first time.2D modeling simulation and Cryo-transmission electron microscopy precisely reveal the mitigation of interface polarization owing to a higher fraction of conductive inorganic species formation in bilayer solid electrolyte interphase is mainly responsible for a linear decrease in ionic diffusion energy barrier.Furthermore,atomic force microscopy and Raman shift exhibit substantial stress dissipation generated by a complete conductive network,which is critical to the linear reduction of electrode residual stress.This study provides insights into the rational design of optimized interface SiO-based anodes with reinforced fast-charging performance.

Electrochemical Hydrogen Charging on Corrosion Behavior of Ti-6Al-4V Alloy in Artificial Seawater

This study employs advanced electrochemical and surface characterization techniques to investigate the impact of electrochemical hydrogen charging on the corrosion behavior and surface film of the Ti-6Al-4V alloy.The findings revealed the formation ofγ-TiH andδ-TiH_(2) hydrides in the alloy after hydrogen charging.Prolonging hydrogen charging resulted in more significant degradation of the alloy microstructure,leading to deteriorated protectiveness of the surface film.This trend was further confirmed by the electrochemical measurements,which showed that the corrosion resistance of the alloy progressively worsened as the hydrogen charging time was increased.Consequently,this work provides valuable insights into the mechanisms underlying the corrosion of Ti-6Al-4V alloy under hydrogen charging conditions.

Multilevel carbon architecture of subnanoscopic silicon for fast‐charging high‐energy‐density lithium‐ion batteries

Silicon(Si)is widely used as a lithium‐ion‐battery anode owing to its high capacity and abundant crustal reserves.However,large volume change upon cycling and poor conductivity of Si cause rapid capacity decay and poor fast‐charging capability limiting its commercial applications.Here,we propose a multilevel carbon architecture with vertical graphene sheets(VGSs)grown on surfaces of subnanoscopically and homogeneously dispersed Si–C composite nanospheres,which are subsequently embedded into a carbon matrix(C/VGSs@Si–C).Subnanoscopic C in the Si–C nanospheres,VGSs,and carbon matrix form a three‐dimensional conductive and robust network,which significantly improves the conductivity and suppresses the volume expansion of Si,thereby boosting charge transport and improving electrode stability.The VGSs with vast exposed edges considerably increase the contact area with the carbon matrix and supply directional transport channels through the entire material,which boosts charge transport.The carbon matrix encapsulates VGSs@Si–C to decrease the specific surface area and increase tap density,thus yielding high first Coulombic efficiency and electrode compaction density.Consequently,C/VGSs@Si–C delivers excellent Li‐ion storage performances under industrial electrode conditions.In particular,the full cells show high energy densities of 603.5 Wh kg^(−1)and 1685.5 Wh L^(−1)at 0.1 C and maintain 80.7%of the energy density at 3 C.

Collaborative Charging Scheduling in Wireless Charging Sensor Networks

Wireless sensor networks (WSNs) have the trouble of limited battery power, and wireless charging provides apromising solution to this problem, which is not easily affected by the external environment. In this paper, we studythe recharging of sensors in wireless rechargeable sensor networks (WRSNs) by scheduling two mobile chargers(MCs) to collaboratively charge sensors. We first formulate a novel sensor charging scheduling problem with theobjective of maximizing the number of surviving sensors, and further propose a collaborative charging schedulingalgorithm(CCSA) for WRSNs. In the scheme, the sensors are divided into important sensors and ordinary sensors.TwoMCs can adaptively collaboratively charge the sensors based on the energy limit ofMCs and the energy demandof sensors. Finally, we conducted comparative simulations. The simulation results show that the proposed algorithmcan effectively reduce the death rate of the sensor. The proposed algorithm provides a solution to the uncertaintyof node charging tasks and the collaborative challenges posed by multiple MCs in practical scenarios.

Research on Demand Response Potential of Adjustable Loads in Demand Response Scenarios

To address the issues of limited demand response data,low generalization of demand response potential evaluation,and poor demand response effect,the article proposes a demand response potential feature extraction and prediction model based on data mining and a demand response potential assessment model for adjustable loads in demand response scenarios based on subjective and objective weight analysis.Firstly,based on the demand response process and demand response behavior,obtain demand response characteristics that characterize the process and behavior.Secondly,establish a feature extraction and prediction model based on data mining,including similar day clustering,time series decomposition,redundancy processing,and data prediction.The predicted values of each demand response feature on the response day are obtained.Thirdly,the predicted data of various characteristics on the response day are used as demand response potential evaluation indicators to represent different demand response scenarios and adjustable loads,and a demand response potential evaluation model based on subjective and objective weight allocation is established to calculate the demand response potential of different adjustable loads in different demand response scenarios.Finally,the effectiveness of the method proposed in the article is verified through examples,providing a reference for load aggregators to formulate demand response schemes.

A Combination Prediction Model for Short Term Travel Demand of Urban Taxi

This study proposes a prediction model considering external weather and holiday factors to address the issue of accurately predicting urban taxi travel demand caused by complex data and numerous influencing factors.The model integrates the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise(CEEMDAN)and Convolutional Long Short Term Memory Neural Network(ConvLSTM)to predict short-term taxi travel demand.The CEEMDAN decomposition method effectively decomposes time series data into a set of modal components,capturing sequence characteristics at different time scales and frequencies.Based on the sample entropy value of components,secondary processing of more complex sequence components after decomposition is employed to reduce the cumulative prediction error of component sequences and improve prediction efficiency.On this basis,considering the correlation between the spatiotemporal trends of short-term taxi traffic,a ConvLSTM neural network model with Long Short Term Memory(LSTM)time series processing ability and Convolutional Neural Networks(CNN)spatial feature processing ability is constructed to predict the travel demand for urban taxis.The combined prediction model is tested on a taxi travel demand dataset in a certain area of Beijing.The results show that the CEEMDAN-ConvLSTM prediction model outperforms the LSTM,Autoregressive Integrated Moving Average model(ARIMA),CNN,and ConvLSTM benchmark models in terms of Symmetric Mean Absolute Percentage Error(SMAPE),Root Mean Square Error(RMSE),Mean Absolute Error(MAE),and R2 metrics.Notably,the SMAPE metric exhibits a remarkable decline of 21.03%with the utilization of our proposed model.These results confirm that our study provides a highly accurate and valid model for taxi travel demand forecasting.

EV Charging Station Load Prediction in Coupled Urban Transportation and Distribution Networks

The increasingly large number of electric vehicles(EVs)has resulted in a growing concern for EV charging station load prediction for the purpose of comprehensively evaluating the influence of the charging load on distribution networks.To address this issue,an EV charging station load predictionmethod is proposed in coupled urban transportation and distribution networks.Firstly,a finer dynamic urban transportation network model is formulated considering both nodal and path resistance.Then,a finer EV power consumption model is proposed by considering the influence of traffic congestion and ambient temperature.Thirdly,the Monte Carlo method is applied to predict the distribution of EVcharging station load based on the proposed dynamic urban transportation network model and finer EV power consumption model.Moreover,a dynamic charging pricing scheme for EVs is devised based on the EV charging station load requirements and the maximum thresholds to ensure the security operation of distribution networks.Finally,the validity of the proposed dynamic urban transportation model was verified by accurately estimating five sets of test data on travel time by contrast with the BPR model.The five groups of travel time prediction results showed that the average absolute percentage errors could be improved from 32.87%to 37.21%compared to the BPR model.Additionally,the effectiveness of the proposed EV charging station load prediction method was demonstrated by four case studies in which the prediction of EV charging load was improved from27.2 to 31.49MWh by considering the influence of ambient temperature and speed on power energy consumption.

Stochastic programming based coordinated expansion planning of generation,transmission,demand side resources,and energy storage considering the DC transmission system

With the increasing penetration of wind and solar energies,the accompanying uncertainty that propagates in the system places higher requirements on the expansion planning of power systems.A source-grid-load-storage coordinated expansion planning model based on stochastic programming was proposed to suppress the impact of wind and solar energy fluctuations.Multiple types of system components,including demand response service entities,converter stations,DC transmission systems,cascade hydropower stations,and other traditional components,have been extensively modeled.Moreover,energy storage systems are considered to improve the accommodation level of renewable energy and alleviate the influence of intermittence.Demand-response service entities from the load side are used to reduce and move the demand during peak load periods.The uncertainties in wind,solar energy,and loads were simulated using stochastic programming.Finally,the effectiveness of the proposed model is verified through numerical simulations.

Optimal dispatching strategy for residential demand response considering load participation

To facilitate the coordinated and large-scale participation of residential flexible loads in demand response(DR),a load aggregator(LA)can integrate these loads for scheduling.In this study,a residential DR optimization scheduling strategy was formulated considering the participation of flexible loads in DR.First,based on the operational characteristics of flexible loads such as electric vehicles,air conditioners,and dishwashers,their DR participation,the base to calculate the compensation price to users,was determined by considering these loads as virtual energy storage.It was quantified based on the state of virtual energy storage during each time slot.Second,flexible loads were clustered using the K-means algorithm,considering the typical operational and behavioral characteristics as the cluster centroid.Finally,the LA scheduling strategy was implemented by introducing a DR mechanism based on the directrix load.The simulation results demonstrate that the proposed DR approach can effectively reduce peak loads and fill valleys,thereby improving the load management performance.

The principle and amelioration of lithium plating in fast-charging lithium-ion batteries

Fast charging is restricted primarily by the risk of lithium(Li)plating,a side reaction that can lead to the rapid capacity decay and dendrite-induced thermal runaway of lithium-ion batteries(LIBs).Investigation on the intrinsic mechanism and the position of Li plating is crucial to improving the fast rechargeability and safety of LIBs.Herein,we investigate the Li plating behavior in porous electrodes under the restricted transport of Li^(+).Based on the theoretical model,it can be concluded that the Li plating on the anodeseparator interface(ASI)is thermodynamically feasible and kinetically advantageous.Meanwhile,the prior deposition of metal Li on the ASI rather than the anode-current collector interface(ACI)is verified experimentally.In order to facilitate the transfer of Li^(+)among the electrode and improve the utilization of active materials without Li plating,a bilayer asymmetric anode composed of graphite and hard carbon(GH)is proposed.Experimental and simulation results suggest that the GH hybrid electrode homogenizes the lithiated-rate throughout the electrode and outperforms the pure graphite electrode in terms of the rate performance and inhibition of Li plating.This work provides new insights into the behavior of Li plating and the rational design of electrode structure.

Enhanced energy density and fast-charging ability via directional particle configuration

The limited energy density of lithium-ion capacitors poses a significant obstacle to their widespread application,primarily stemming from the inability of the electrodes to simultaneously fulfill both high energy density and rapid charging requirements.Experimental data demonstrate that a directional particle configuration can enhance charging speed while maintaining high-capacity density,but it is rarely discussed.Here,we have developed a particle-level electrochemical model capable of reconstructing an electrode with a directional particle configuration.By employing this method,an investigation was conducted to explore how the spatial morphology characteristics of particle configuration impact the energy storage characteristics of electrodes.Results demonstrate that rational particle configuration can effectively enhance the transport of lithium ions and create additional space for lithium-ion storage.With the same particle size distribution,the best electrode can increase the discharge capacity by up to132.4% and increase the charging SOC by 11.3% compared to the ordinary electrode under the condition of 6 C.These findings provide a further understanding of the energy storage mechanism inside the anisotropic particle distribution electrode,which is important for developing high-performance lithium-ion capacitors.

Electrothermal Model Based Remaining Charging Time Prediction of Lithium-Ion Batteries against Wide Temperature Range

Battery remaining charging time(RCT)prediction can facilitate charging management and alleviate mileage anxiety for electric vehicles(EVs).Also,it is of great significance to improve EV users’experience.However,the RCT for a lithiumion battery pack in EVs changes with temperature and other battery parameters.This study proposes an electrothermal model-based method to accurately predict battery RCT.Firstly,a characteristic battery cell is adopted to represent the battery pack,thus an equivalent circuit model(ECM)of the characteristic battery cell is established to describe the electrical behaviors of a battery pack.Secondly,an equivalent thermal model(ETM)of the battery pack is developed by considering the influence of ambient temperature,thermal management,and battery connectors in the battery pack to calculate the temperature which is then fed back to the ECM to realize electrothermal coupling.Finally,the RCT prediction method is proposed based on the electrothermal model and validated in the wide temperature range from-20℃to 45℃.The experimental results show that the prediction error of the RCT in the whole temperature range is less than 1.5%.

Optimal Bidding Strategies of Microgrid with Demand Side Management for Economic Emission Dispatch Incorporating Uncertainty and Outage of Renewable Energy Sources

In the restructured electricity market,microgrid(MG),with the incorporation of smart grid technologies,distributed energy resources(DERs),a pumped-storage-hydraulic(PSH)unit,and a demand response program(DRP),is a smarter and more reliable electricity provider.DER consists of gas turbines and renewable energy sources such as photovoltaic systems and wind turbines.Better bidding strategies,prepared by MG operators,decrease the electricity cost and emissions from upstream grid and conventional and renewable energy sources(RES).But it is inefficient due to the very high sporadic characteristics of RES and the very high outage rate.To solve these issues,this study suggests non-dominated sorting genetic algorithm Ⅱ(NSGA-Ⅱ)for an optimal bidding strategy considering pumped hydroelectric energy storage and DRP based on outage conditions and uncertainties of renewable energy sources.The uncertainty related to solar and wind units is modeled using lognormal and Weibull probability distributions.TOU-based DRP is used,especially considering the time of outages along with the time of peak loads and prices,to enhance the reliability of MG and reduce costs and emissions.

A Novel Defender-Attacker-Defender Model for Resilient Distributed Generator Planning with Network Reconfiguration and Demand Response

To improve the resilience of a distribution system against extreme weather,a fuel-based distributed generator(DG)allocation model is proposed in this study.In this model,the DGs are placed at the planning stage.When an extreme event occurs,the controllable generators form temporary microgrids(MGs)to restore the load maximally.Simultaneously,a demand response program(DRP)mitigates the imbalance between the power supply and demand during extreme events.To cope with the fault uncertainty,a robust optimization(RO)method is applied to reduce the long-term investment and short-term operation costs.The optimization is formulated as a tri-level defenderattacker-defender(DAD)framework.At the first level,decision-makers work out the DG allocation scheme;at the second level,the attacker finds the optimal attack strategy with maximum damage;and at the third level,restoration measures,namely distribution network reconfiguration(DNR)and demand response are performed.The problem is solved by the nested column and constraint generation(NC&CG)method and the model is validated using an IEEE 33-node system.Case studies validate the effectiveness and superiority of the proposed model according to the enhanced resilience and reduced cost.

Strategic Placement of Charging Stations for Enhanced Electric Vehicle Adoption in San Diego, California

California mandated that 100% of vehicles sold must be electric by 2035. As electric vehicles (EVs) reach a higher penetration of the car sector, cities will need to provide publicly accessible charging stations to meet the charging demand of people who do not have access to a private charging spot like a personal garage. We have chosen to limit our scope to San Diego County due to its non-trivial size, well-defined shape, and dependence on personal vehicles;this project models 100% of current vehicles as electric, roughly 2.5 million. By planning for the future, our model becomes more useful as well as more equitable. We anticipate that our model will find locations that can service multiple population centers, while also maximizing distance to other stations. Sensitivity analysis and testing of our algorithms are conducted for Coronado Island, an island with 24,697 residents. Our formulation is then scaled to set the parameters for the whole county.