Sodium Nitrate/Formamide Deep Eutectic Solvent as Flame-Retardant and Anticorrosive Electrolyte Enabling 2.6 V Safe Supercapacitors with Long Cyclic Stability

Safe operation of electrochemical capacitors(supercapacitors)is hindered by the flammability of commercial organic electrolytes.Non-flammable Water-in-Salt(WIS)electrolytes are promising alternatives;however,they are plagued by the limited operation voltage window(typically≤2.3 V)and inherent corrosion of current collectors.Herein,a novel deep eutectic solvent(DES)-based electrolyte which uses formamide(FMD)as hydrogen-bond donor and sodium nitrate(NaNO_(3))as hydrogen-bond acceptor is demonstrated.The electrolyte exhibits the wide electrochemical stability window(3.14 V),high electrical conductivity(14.01 mScm^(-1)),good flame-retardance,anticorrosive property,and ultralow cost(7%of the commercial electrolyte and 2%of WIS).Raman spectroscopy and Density Functional Theory calculations reveal that the hydrogen bonds between the FMD molecules and NO_(3)^(-)ions are primarily responsible for the superior stability and conductivity.The developed NaNO_(3)/FMD-based coin cell supercapacitor is among the best-performing state-of-art DES and WIS devices,evidenced by the high voltage window(2.6 V),outstanding energy and power densities(22.77 Wh kg^(-1)at 630 W kg^(-1)and 17.37 kW kg^(-1)at 12.55 Wh kg^(-1)),ultralong cyclic stability(86%after 30000 cycles),and negligible current collector corrosion.The NaNO_(3)/FMD industry adoption potential is demonstrated by fabricating 100 F pouch cell supercapacitors using commercial aluminum current collectors.

Stoichiometry and Stability Constant Values for Copper (II) Chelates with Ethylene Diamine in Deep Eutectic Solvents (DES) (Ethaline) Solutions

In this study we used the deep eutectic solvents (ionic liquids) to investigate the reaction between copper (II) with ethylene diamine (en). Two of the existing methods for analyzing spectrophotometric measurements have been applied for establishing, the stoichiometry and whenever possible, the stability constants of the chelates formed. The method of continuous variations was necessary to determine first whether, the metal ion and the ligand ethylene diamine form one or more than one chelate, when more than one chelate formed, the results obtained depend on the wavelength and for meaningful conclusions the wavelengths were carefully selected. The empirical formulae of the chelates were further substantiated by the molar ratio method. The effect of time and temperature on the formation and stability of these chelates in solution is also studied. The stability constants, K1 and K2 for the copper (II) chelates were calculated, though reliable, and are comparable to literature values.

Heterogeneous information phase space reconstruction and stability prediction of filling body–surrounding rock combination

Traditional research believes that the filling body can effectively control stress concentration while ignoring the problems of unknown stability and the complex and changeable stress distribution of the filling body–surrounding rock combination under high-stress conditions.Current monitoring data processing methods cannot fully consider the complexity of monitoring objects,the diversity of monitoring methods,and the dynamics of monitoring data.To solve this problem,this paper proposes a phase space reconstruction and stability prediction method to process heterogeneous information of backfill–surrounding rock combinations.The three-dimensional monitoring system of a large-area filling body–surrounding rock combination in Longshou Mine was constructed by using drilling stress,multipoint displacement meter,and inclinometer.Varied information,such as the stress and displacement of the filling body–surrounding rock combination,was continuously obtained.Combined with the average mutual information method and the false nearest neighbor point method,the phase space of the heterogeneous information of the filling body–surrounding rock combination was then constructed.In this paper,the distance between the phase point and its nearest point was used as the index evaluation distance to evaluate the stability of the filling body–surrounding rock combination.The evaluated distances(ED)revealed a high sensitivity to the stability of the filling body–surrounding rock combination.The new method was then applied to calculate the time series of historically ED for 12 measuring points located at Longshou Mine.The moments of mutation in these time series were at least 3 months ahead of the roadway return dates.In the ED prediction experiments,the autoregressive integrated moving average model showed a higher prediction accuracy than the deep learning models(long short-term memory and Transformer).Furthermore,the root-mean-square error distribution of the prediction results peaked at 0.26,thus outperforming the no-prediction method in 70%of the cases.

Metaheuristic Optimization with Deep Learning Enabled Smart Grid Stability Prediction

Due to the drastic increase in global population as well as economy,electricity demand becomes considerably high.The recently developed smart grid(SG)technology has the ability to minimize power loss at the time of power distribution.Machine learning(ML)and deep learning(DL)models can be effectually developed for the design of SG stability techniques.This article introduces a new Social Spider Optimization with Deep Learning Enabled Statistical Analysis for Smart Grid Stability(SSODLSA-SGS)pre-diction model.Primarily,class imbalance data handling process is performed using Synthetic minority oversampling technique(SMOTE)technique.The SSODLSA-SGS model involves two stages of pre-processing namely data nor-malization and transformation.Besides,the SSODLSA-SGS model derives a deep belief-back propagation neural network(DBN-BN)model for the pre-diction of SG stability.Finally,social spider optimization(SSO)algorithm can be applied for determining the optimal hyperparameter values of the DBN-BN model.The design of SSO algorithm helps to appropriately modify the hyperparameter values of the DBN-BN model.A series of simulation analyses are carried out to highlight the enhanced outcomes of the SSODLSA-SGS model.The extensive comparative study reported the enhanced performance of the SSODLSA-SGS algorithm over the other recent techniques interms of several measures.

Slope stability prediction based on a long short-term memory neural network:comparisons with convolutional neural networks,support vector machines and random forest models

The numerical simulation and slope stability prediction are the focus of slope disaster research.Recently,machine learning models are commonly used in the slope stability prediction.However,these machine learning models have some problems,such as poor nonlinear performance,local optimum and incomplete factors feature extraction.These issues can affect the accuracy of slope stability prediction.Therefore,a deep learning algorithm called Long short-term memory(LSTM)has been innovatively proposed to predict slope stability.Taking the Ganzhou City in China as the study area,the landslide inventory and their characteristics of geotechnical parameters,slope height and slope angle are analyzed.Based on these characteristics,typical soil slopes are constructed using the Geo-Studio software.Five control factors affecting slope stability,including slope height,slope angle,internal friction angle,cohesion and volumetric weight,are selected to form different slope and construct model input variables.Then,the limit equilibrium method is used to calculate the stability coefficients of these typical soil slopes under different control factors.Each slope stability coefficient and its corresponding control factors is a slope sample.As a result,a total of 2160 training samples and 450 testing samples are constructed.These sample sets are imported into LSTM for modelling and compared with the support vector machine(SVM),random forest(RF)and convo-lutional neural network(CNN).The results show that the LSTM overcomes the problem that the commonly used machine learning models have difficulty extracting global features.Furthermore,LSTM has a better prediction performance for slope stability compared to SVM,RF and CNN models.

Stability control of gate groups in deep wells

In order to study stability control methods for a deep gate group under complex stresses,we conducted field investigations and analyses of reasons for damage in the Xuzhou mining district.Three reasons are proposed:deep high stress,improper roadway layout and support technology.The stability control countermeasures of the gate group consist of an intensive design technology and responding bolt-mesh-anchor truss support technology.Our research method has been applied at the -1000 m level gate group in Qishan Coal Mine.Suitable countermeasures have been tested by field monitoring.

Stability influence factors analysis and construction of a deep beam anchorage structure in roadway roof

Deep beam anchorage structures based on spatial distribution analysis of the cable prestressed field have been proposed for roadway roof support, Stability and other factors that influence deep beam structures are studied in this paper using mechanical calculations, numerical analysis and field measurements, A mechanical model of deep beam structure subjected to multiple loading is established, including analysis of roof support in the return airway of S1203 working face in the Yuwu coal mine, China, The expression of maximum shear stress in the deep beam structure is deduced according to the stress superposition criterion, It is found that the primary factors affecting deep beam structure stability are deep beam thickness, cable pre-tension and cable spacing, The variation of maximum shear stress distribution and prestressed field diffusion effects according to various factors are analyzed using Matlah and FLAC3DTM software, and practical support parameters of the S1203 return airway roof are determined, According to the observations of rock pressure, there is no evidence of roof separation, and the maximum values of roof subsidence and convergence of wall rock are 72 and 48 mm, respectively, The results show that the proposed roof support design with a deep beam structure is feasible and achieves effective control of the roadway roof,

Stability control of a deep shaft insert

The deterioration of a deep shaft insert at the Xing'an Coal Mine was analyzed by studying the physical and mechanical properties of the rock located at key positions relative to the shaft. Factors that influence shaft stability were obtained. The numerical simulation program FLAC3D was used to simulate the destruction of the deep shaft insert. Two different support methods were analyzed by simulation. The simulations demonstrate that a single stiffness support is inappropriate for this shaft insert. The appropriate support method is an integrated coupling method of rigid and flexible supports. The flexible support is applied first and then the rigid support is second. Engineering practice in the Xing'an Coal Mine shows that this technology can effectively control deep-shaft insert deterioration. This support approach provides an important direction for future project design and construction, as well.

Analysis of mechanical behavior of soft rocks and stability control in deep tunnels

Due to the weakness in mechanical properties of chlorite schist and the high in situ stress in Jinping II hydropower station, the rock mass surrounding the diversion tunnels located in chlorite schist was observed with extremely large deformations. This may significantly increase the risk of tunnel instability during excavation. In order to assess the stability of the diversion tunnels laboratory tests were carried out in association with the petrophysical properties, mechanical behaviors and waterlweakening properties of chlorite schist. The continuous deformation of surrounding rock mass, the destruction of the support structure and a large-scale collapse induced by the weak chlorite schist and high in situ stress were analyzed. The distributions of compressive deformation in the excavation zone with large deformations were also studied. In this regard, two reinforcement schemes for the excavation of diversion tunnel bottom section were proposed accordingly. This study could offer theoretical basis for deed tunnel construction in similar geological condition~

Non-Fullerene-Based Inverted Organic Photovoltaic Device with Long-Term Stability

In this work,we developed the PM6:Y6-based inverted structure organic photovoltaic(i-OPV)with improved power conversion efficiency(PCE)and long-term stability by resolving the origins of the performance deterioration.The deep defects between the metal oxide-based electron transport layer and bulk-heterojunction photoactive layer interface were responsible for suboptimal PCE and facilitated degradation of devices.While the density of deep traps is increased during the storage of i-OPV,the penetrative oxygen-containing defects additionally generated shallow traps below the band-edge of Y6,causing an additional loss in the open-circuit voltage.The suppression of interfacial defects by chemical modification effectively improved the PCE and long-term stability of i-OPV.The modified i-OPV(mi-OPV)achieved a PCE of 17.42%,which is the highest value among the reported PM6:Y6-based i-OPV devices.Moreover,long-term stability was significantly improved:~90%and~80%retention of its initial PCE after 1200 h of air storage and illumination,respectively.

Intelligent Smart Grid Stability Predictive Model for Cyber-Physical Energy Systems

A cyber physical energy system(CPES)involves a combination of pro-cessing,network,and physical processes.The smart grid plays a vital role in the CPES model where information technology(IT)can be related to the physical system.At the same time,the machine learning(ML)modelsfind useful for the smart grids integrated into the CPES for effective decision making.Also,the smart grids using ML and deep learning(DL)models are anticipated to lessen the requirement of placing many power plants for electricity utilization.In this aspect,this study designs optimal multi-head attention based bidirectional long short term memory(OMHA-MBLSTM)technique for smart grid stability predic-tion in CPES.The proposed OMHA-MBLSTM technique involves three subpro-cesses such as pre-processing,prediction,and hyperparameter optimization.The OMHA-MBLSTM technique employs min-max normalization as a pre-proces-sing step.Besides,the MBLSTM model is applied for the prediction of stability level of the smart grids in CPES.At the same time,the moth swarm algorithm(MHA)is utilized for optimally modifying the hyperparameters involved in the MBLSTM model.To ensure the enhanced outcomes of the OMHA-MBLSTM technique,a series of simulations were carried out and the results are inspected under several aspects.The experimental results pointed out the better outcomes of the OMHA-MBLSTM technique over the recent models.

Numerical analysis on stability of surrounding rock mass around deep roadway

Numerical simulations of the deep roadway were carried out through application of the strain-softening constitutive model. Differences between the deep and shallow roadway of the rock bearing structure were analyzed. Influences of the supporting resistance on the rock bearing structure at the deep roadway were discussed. The results show that there is alternation of strong and weak strength-softening region in the surrounding rock of deep roadway. However, the increase in the supporting resistance cuts down the size of strength-softening region of surrounding rock, decreases its strength-softening degree, and im- proves the stress distribution condition of the surrounding rock mass. It is concluded that the supporting resistance can raise the self-supporting ability of surrounding rock through controlling its strength-softening so as to make the rock bearing structure of deep roadway stable.

THEORETICAL ANALYSIS OF THE STABILITY OF A DEEP ROADWAY

In this paper the thickness of a broken zone, a state parameter of roadway surrounding rock, is used as the index to evaluate the stabi1ity of surrounding rock of a deep roadway. The paper gives a theoretic formula for calculating the thickness of the broken zone. The author points out that not only the ultimate strength of rockmass but its residual strength and strain-softening level all have a great influence on the stability of surrounding rock of a deep roadway. The paper’s results show that to reinforce surrounding rock, raise its residual strength and lower its strain-softening level should be taken as a basic requirement for supports of a deep roadway. In addition, the research also indicates that it is impossible for roadway supports to change surrounding rock states of a deep roadway, so it is certain for them to work in a broken state. For this reason, a sufficient yieldable quantity is necessary for roadway supports used in deep mining.

Frictional stability of Longmaxi shale gouges and its implication for deep seismic potential in the southeastern Sichuan Basin

Microearthquakes accompanying shale gas recovery highlight the importance of exploring the frictional and stability properties of shale gouges.Aiming to reveal the influencing factors on fault stability,this paper explores the impact of mineral compositions,effective stress and temperature on the frictional stability of Longmaxi shale gouges in deep reservoirs located in the Luzhou area,southeastern Sichuan Basin.Eleven shear experiments were conducted to define the frictional strength and stability of five shale gouges.The specific experimental conditions were as follows:temperatures:90–270°C;a confining stress:95 MPa;and pore fluid pressures:25–55 MPa.The results show that all five shale gouges generally display high frictional strength with friction coefficients ranging from 0.60 to 0.70 at the aforementioned experiment condition of pressures,and temperatures.Frictional stability is significantly affected by temperature and mineral compositions,but is insensitive to variation in pore fluid pressures.Fault instability is enhanced at higher temperatures(especially at>200°C)and with higher tectosilicate/carbonate contents.The results demonstrate that the combined effect of mineral composition and temperature is particularly important for induced seismicity during hydraulic fracturing in deep shale reservoirs.

Walking Stability Control Method for Biped Robot on Uneven Ground Based on Deep Q-Network

A gait control method for a biped robot based on the deep Q-network (DQN) algorithm is proposed to enhance the stability of walking on uneven ground. This control strategy is an intelligent learning method of posture adjustment. A robot is taken as an agent and trained to walk steadily on an uneven surface with obstacles, using a simple reward function based on forward progress. The reward-punishment (RP) mechanism of the DQN algorithm is established after obtaining the offline gait which was generated in advance foot trajectory planning. Instead of implementing a complex dynamic model, the proposed method enables the biped robot to learn to adjust its posture on the uneven ground and ensures walking stability. The performance and effectiveness of the proposed algorithm was validated in the V-REP simulation environment. The results demonstrate that the biped robot's lateral tile angle is less than 3° after implementing the proposed method and the walking stability is obviously improved.

Water Wave Optimization with Deep Learning Driven Smart Grid Stability Prediction

Smart Grid(SG)technologies enable the acquisition of huge volumes of high dimension and multi-class data related to electric power grid operations through the integration of advanced metering infrastructures,control systems,and communication technologies.In SGs,user demand data is gathered and examined over the present supply criteria whereas the expenses are then informed to the clients so that they can decide about electricity consumption.Since the entire procedure is valued on the basis of time,it is essential to perform adaptive estimation of the SG’s stability.Recent advancements inMachine Learning(ML)andDeep Learning(DL)models enable the designing of effective stability prediction models in SGs.In this background,the current study introduces a novel Water Wave Optimization with Optimal Deep Learning Driven Smart Grid Stability Prediction(WWOODL-SGSP)model.The aim of the presented WWOODL-SGSP model is to predict the stability level of SGs in a proficient manner.To attain this,the proposed WWOODL-SGSP model initially applies normalization process to scale the data to a uniform level.Then,WWO algorithm is applied to choose an optimal subset of features from the pre-processed data.Next,Deep Belief Network(DBN)model is followed to predict the stability level of SGs.Finally,Slime Mold Algorithm(SMA)is exploited to fine tune the hyperparameters involved in DBN model.In order to validate the enhanced performance of the proposedWWOODL-SGSP model,a wide range of experimental analyses was performed.The simulation results confirmthe enhanced predictive results of WWOODL-SGSP model over other recent approaches.

Study on the Submarine Slope Stability of the Deep Channel in the Caofeidian Harbor

Objective The greatest advantage of the Caofeidian Harbor is its deep channel facing the Bohai Bay. The deep channel is a natural port hub for shipping of the Caofeidian Habor. The construction of the Caofeidian Harbor has impacted the hydrodynamic environment and the sediments movement, which has attracted much attention about the geomorphic evolution, slope stability and the evolution trend after submarine slope destruction. Insight from this study might be significant for the future development of the Caofeidian Habor, including planning, operation and maintenance.

A Comparative Study of Stability of Extra Virgin Olive Oil, Virgin Coconut Oil and Grape Seed Oil against Domestic Deep Frying

The aim of this study was to evaluate the relative stability of extra virgin olive oil （EVOO）, virgin coconut oil （VCO） and grape seed oil （GSO） against domestic deep frying. Oil samples were subjected to deep frying at 190 ℃ for 30, 60, and 90 min and then compared with fresh oil samples in terms of fatty acid composition, peroxide value （PV）, p-anisidine value （p-AV）, total oxidation value （TOTOX）, iodine value （IV）, free fatty acid content （%FFA） and total phenolic content （TPC）. Experimental results showed that the changes in the fatty acid composition, p-AV and TOTOX were in the order, GSO 〉 EVOO 〉 VCO throughout the experiment, while PV was in the order, VCO 〉 EVO0 〉 GSO. Meanwhile, the reduction in the IV was in the order, GSO 〉 VCO 〉 EVOO throughout the experiment. On the other hand, the changes in the %FFA were in the order, VCO 〉 GSO 〉 EVO0 throughout the experiment. VCO had the greatest stability against domestic deep frying, followed by EVO0 and GSO had the least stability against domestics deep frying.

Experimental study on stability control technology of surrounding rock of deep roadways in coal mine

In order to solve effectively the problems of deep mining with safety and high efficiency, the multi- pie factors influencing the stability of deep rock roadway and technical problems are analyzed in the light of the severe situation of effective mining for deep coal resource, and the stability control methods for deep rock road- way are provided, which are based on the idea of combined support with separated steps and integral control of surrounding rock of deep rock roadway. The suggested methods were applied to a deep rock roadway with -648 m depth in Gubei coal mine of Huainan area. The field test was carried out and the in-situ monitoring was imple- mented, and the support scheme was optimized and adjusted to improve the stability of the surrounding rock of the roadway based on the feedback analysis. The results showed that the stability can be improved greatly by the provided control methods tbr deep roadway. The present methods lbr stability control of deep rock roadway can be used to other deep rock roadways with the similar conditions.

Rotor Angle Stability Prediction Using Temporal and Topological Embedding Deep Neural Network Based on Grid-informed Adjacency Matrix

Rotor angle stability(RAS)prediction is critically essential for maintaining normal operation of the interconnected synchronous machines in power systems.The wide deployment of phasor measurement units(PMUs)promotes the development of data-driven methods for RAS prediction.This paper proposes a temporal and topological embedding deep neural network(TTEDNN)model to accurately and efficiently predict RAS by extracting the temporal and topological features from the PMU data.The grid-informed adjacency matrix incorporates the structural and electrical parameter information of the power grid.Both the small-signal RAS with disturbance under initial operating conditions and the transient RAS with short circuits on transmission lines are considered.Case studies of the IEEE 39-bus and IEEE 300-bus power systems are used to test the performance,scalability,and robustness against measurement uncertainties of the TTEDNN model.Results show that the TTEDNN model performs best among existing deep learning models.Furthermore,the superior transfer learning ability from small-signal RAS conditions to transient RAS conditions has been proved.