Surface repair of wide-bandgap perovskites for high-performance all-perovskite tandem solar cells

Wide-bandgap(WBG)perovskite solar cells(PSCs)play a fundamental role in perovskite-based tandem solar cells.However,the efficiency of WBG PSCs is limited by significant open-circuit voltage losses,which are primarily caused by surface defects.In this study,we present a novel method for modifying surfaces using the multifunctional S-ethylisothiourea hydrobromide(SEBr),which can passivate both Pb^(-1)and FA^(-1)terminated surfaces,Moreover,the SEBr upshifted the Fermi level at the perovskite interface,thereby promoting carrier collection.This proposed method was effective for both 1.67 and 1.77 eV WBG PSCs,achieving power conversion efficiencies(PCEs)of 22.47%and 19.90%,respectively,with V_(OC)values of 1.28 and 1.33 V,along with improved film and device stability.With this advancement,we were able to fabricate monolithic all-perovskite tandem solar cells with a champion PCE of 27.10%,This research offers valuable insights for passivating the surface trap states of WBG perovskite through rational multifunctional molecular engineering.

Accurate and efficient remaining useful life prediction of batteries enabled by physics-informed machine learning

The safe and reliable operation of lithium-ion batteries necessitates the accurate prediction of remaining useful life(RUL).However,this task is challenging due to the diverse ageing mechanisms,various operating conditions,and limited measured signals.Although data-driven methods are perceived as a promising solution,they ignore intrinsic battery physics,leading to compromised accuracy,low efficiency,and low interpretability.In response,this study integrates domain knowledge into deep learning to enhance the RUL prediction performance.We demonstrate accurate RUL prediction using only a single charging curve.First,a generalisable physics-based model is developed to extract ageing-correlated parameters that can describe and explain battery degradation from battery charging data.The parameters inform a deep neural network(DNN)to predict RUL with high accuracy and efficiency.The trained model is validated under 3 types of batteries working under 7 conditions,considering fully charged and partially charged cases.Using data from one cycle only,the proposed method achieves a root mean squared error(RMSE)of 11.42 cycles and a mean absolute relative error(MARE)of 3.19%on average,which are over45%and 44%lower compared to the two state-of-the-art data-driven methods,respectively.Besides its accuracy,the proposed method also outperforms existing methods in terms of efficiency,input burden,and robustness.The inherent relationship between the model parameters and the battery degradation mechanism is further revealed,substantiating the intrinsic superiority of the proposed method.

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 new electric field mill array with each of the mill’s rotor controlled precisely by a GPS module:Equipment and initial results

We have newly designed an electrostatic sensor,called an electric field mill(EFM),to simplify the estimation of the charge position and charge amount transferred by lightning discharges.It is necessary for this remote estimation of the transferred charge to measure electric field changes caused by charge loss at the time of a lightning strike at multiple locations.For multiple-station measurement of electric field changes,not only speed but also phase for exposure and shielding of the sensing plates inside each EFM of the array should be synchronized to maintain the sensitivities of the deployed instruments.Currently,there is no such EFM with specified speed and phase control performance of the rotary part.Thus,we developed a new EFM in which the rotary mechanism was controlled consistently to within 3%error by a GPS module.Five EFMs had been distributed in the Hokuriku area of Japan during the winter season of 2022-2023 for a test observation.Here we describe the design and a simple calibration method for our new EFM array.Data analysis method based on the assumption of a simple monopole charge structure is also summarized.For validation,locations of assumed point charges were compared with three-dimensional lightning mapping data estimated by radio observations in the MF-HF bands.Initial results indicated the validity to estimate transferred charge amounts and positions of winter cloud-to-ground lightning discharges with our new EFM array.

Numerical Study and Optimization of CZTS-Based Thin-Film Solar Cell Structure with Different Novel Buffer-Layer Materials Using SCAPS-1D Software

This study explored the performances of CZTS-based thin-film solar cell with three novel buffer layer materials ZnS, CdS, and CdZnS, as well as with variation in thickness of buffer and absorber-layer, doping concentrations of absorber-layer material and operating temperature. Our aims focused to identify the most optimal thin-film solar cell structure that offers high efficiency and lower toxicity which are desirable for sustainable and eco-friendly energy sources globally. SCAPS-1D, widely used software for modeling and simulating solar cells, has been used and solar cell fundamental performance parameters such as open-circuited voltage (), short-circuited current density (), fill-factor() and efficiency() have been optimized in this study. Based on our simulation results, it was found that CZTS solar cell with Cd0.4Zn0.6S as buffer-layer offers the most optimal combination of high efficiency and lower toxicity in comparison to other structure investigated in our study. Although the efficiency of Cd0.4Zn0.6S, ZnS and CdS are comparable, Cd0.4Zn0.6S is preferable to use as buffer-layer for its non-toxic property. In addition, evaluation of performance as a function of buffer-layer thickness for Cd0.4Zn0.6S, ZnS and CdS showed that optimum buffer-layer thickness for Cd0.4Zn0.6S was in the range from 50 to 150nm while ZnS offered only 50 – 75 nm. Furthermore, the temperature dependence performance parameters evaluation revealed that it is better to operate solar cell at temperature 290K for stable operation with optimum performances. This study would provide valuable insights into design and optimization of nanotechnology-based solar energy technology for minimizing global energy crisis and developing eco-friendly energy sources sustainable and simultaneously.

Review of the structure and performance of through-holed anodes and cathodes prepared with a picosecond pulsed laser for lithium-ion batteries

To move the performance of lithium-ion batteries into the next stage,the modification of the structure of cells is the only choice except for the development of materials exhibiting higher performance.In this review paper,the employment of through-holing structures of anodes and cathodes prepared with a picosecond pulsed laser has been proposed.The laser system and the structure for improving the battery performance were introduced.The performance of laminated cells constructed with through-holed anodes and cathodes was reviewed from the viewpoints of the improvement of high-rate performance and energy density,removal of unbalanced capacities on both sides of the current collector,even greater high-rate performance by hybridizing cathode materials and removal of irreversible capacity.In conclusion,the points that should be examined and the problem for the through-holed structure to be in practical use are summarized.

Challenges and opportunities for battery health estimation:Bridging laboratory research and real-world applications

Addressing climate change demands a significant shift away from fossil fuels,with sectors like electricity and transportation relying heavily on renewable energy.Integral to this transition are energy storage systems,notably lithium-ion batteries.Over time,these batteries degrade,affecting their efficiency and posing safety risks.Monitoring and predicting battery aging is essential,especially estimating its state of health(SOH).Various SOH estimation methods exist,from traditional model-based approaches to machine learning approaches.

Internet of robotic things for mobile robots:Concepts,technologies,challenges,applications,and future directions

Nowadays,Multi Robotic System(MRS)consisting of different robot shapes,sizes and capabilities has received significant attention from researchers and are being deployed in a variety of real-world applications.From sensors and actuators improved by communication technologies to powerful computing systems utilizing advanced Artificial Intelligence(AI)algorithms have rapidly driven the development of MRS,so the Internet of Things(IoT)in MRS has become a new topic,namely the Internet of Robotic Things(IoRT).This paper summarizes a comprehensive survey of state-of-the-art technologies for mobile robots,including general architecture,benefits,challenges,practical applications,and future research directions.In addition,remarkable research of i)multirobot navigation,ii)network architecture,routing protocols and communications,and iii)coordination among robots as well as data analysis via external computing(cloud,fog,edge,edge-cloud)are merged with the IoRT architecture according to their applicability.Moreover,security is a long-term challenge for IoRT because of various attack vectors,security flaws,and vulnerabilities.Security threats,attacks,and existing solutions based on IoRT architectures are also under scrutiny.Moreover,the identification of environmental situations that are crucial for all types of IoRT applications,such as the detection of objects,human,and obstacles,is also critically reviewed.Finally,future research directions are given by analyzing the challenges of IoRT in mobile robots.

Life-cycle assessment of batteries for peak demand reduction

At present,a life-cycle assessment of energy storage systems(ESSs)is not widely available in the literature.Such an assessment is increasingly vital nowadays as ESS is recognized as one of the important equipment in power systems to reduce peak demands for deferring or avoiding augmentation in the network and power generation.As the battery cost is still very high at present,a comprehensive assessment is necessary to determine the optimum ESS capacity so that the maximum financial gain is achievable at the end of the batteries’lifespan.Therefore,an effective life-cycle assessment is proposed in this paper to show how the optimum ESS capacity can be determined such that the maximum net financial gain is achievable at the end of the batteries’lifespan when ESS is used to perform peak demand reductions for the customer or utility companies.The findings reveal the positive financial viability of ESS on the power grid,otherwise the projection of the financial viability is often seemingly poor due to the high battery cost with a short battery lifespan.An improved battery degradation model is used in this assessment,which can simulate the battery degradation accurately in a situation whereby the charging current,discharging current,and temperature of the batteries are intermittent on a site during peak demand reductions.This assessment is crucial to determine the maximum financial benefits brought by ESS.

Robust Control of Manned Submersible Vehicle With Nonlinear MPC and Disturbance Observer

Dear Editor,This letter focuses on the trajectory tracking of 7000 m JIAOLONG manned submersible vehicle(MSV)with disturbances.The robust controller is realized by a composite control law,where an analytical nonlinear model predictive control(MPC)component is proposed to meet the requirements on tracking performance.

A Special Issue:“Optimization and Key Equipments of Integrated Energy System”for Global Energy Interconnection

Energy is a critical basis for the survival and progress of humanity.Traditional energy systems,which are planned,designed,and operated in isolation,have artificially disrupted the interconnections among various energy forms.This limitation has reduced the reliability and flexibility of system operations,rendering them unsuitable for societal advancement.Integrated energy systems(IESs)dismantle the technical,market,and managerial barriers inherent in traditional systems.

How AI-enabled SDN technologies improve the security and functionality of industrial IoT network:Architectures,enabling technologies,and opportunities

The ongoing expansion of the Industrial Internet of Things(IIoT)is enabling the possibility of effective Industry 4.0,where massive sensing devices in heterogeneous environments are connected through dedicated communication protocols.This brings forth new methods and models to fuse the information yielded by the various industrial plant elements and generates emerging security challenges that we have to face,providing ad-hoc functions for scheduling and guaranteeing the network operations.Recently,the large development of SoftwareDefined Networking(SDN)and Artificial Intelligence(AI)technologies have made feasible the design and control of scalable and secure IIoT networks.This paper studies how AI and SDN technologies combined can be leveraged towards improving the security and functionality of these IIoT networks.After surveying the state-of-the-art research efforts in the subject,the paper introduces a candidate architecture for AI-enabled Software-Defined IIoT Network(AI-SDIN)that divides the traditional industrial networks into three functional layers.And with this aim in mind,key technologies(Blockchain-based Data Sharing,Intelligent Wireless Data Sensing,Edge Intelligence,Time-Sensitive Networks,Integrating SDN&TSN,Distributed AI)and improve applications based on AISDIN are also discussed.Further,the paper also highlights new opportunities and potential research challenges in control and automation of IIoT networks.

Development of a roll-to-roll high-speed laser micro processing machine for preparing through-holed anodes and cathodes of lithium-ion batteries

Aiming to improve the battery performance of lithium-ion batteries(LIBs),modification of the cathodes and anodes of LIBs using laser beams to prepare through-holes,non-through-holes or ditches arranged in grid and line patterns has been proposed by many researchers and engineers.In this study,a laser processing system attached to rollers,which realizes this modification without large changes in the present mass-production system,was developed.The laser system apparatus comprises roll-to-roll equipment and laser equipment.The roll-to-roll equipment mainly consists of a hollow cylinder with openings on its circumferential surface.Cathode and anode electrodes for LIBs are wound around the cylinder in the longitudinal direction of the electrodes.A pulsed beam reflected from the central axis of the cylinder can continuously open a large number of through-holes in the thin electrodes.Through-holes were formed at a rate of 100000 holes per second on lithium iron phosphate cathodes and graphite anodes with this system.The through-holed cathodes and anodes prepared with this system exhibited higher C-rate performance than nontreated cathodes and anodes.

Enhancing performance of inverted quantum-dot light-emitting diodes based on a solution-processed hole transport layer via ligand treatment

The performance of inverted quantum-dot light-emitting diodes(QLEDs)based on solution-processed hole transport layers(HTLs)has been limited by the solvent-induced damage to the quantum dot(QD)layer during the spin-coating of the HTL.The lack of compatibility between the HTL’s solvent and the QD layer results in an uneven surface,which negatively impacts the overall device performance.In this work,we develop a novel method to solve this problem by modifying the QD film with 1,8-diaminooctane to improve the resistance of the QD layer for the HTL’s solvent.The uniform QD layer leads the inverted red QLED device to achieve a low turn-on voltage of 1.8 V,a high maximum luminance of 105500 cd/m2,and a remarkable maximum external quantum efficiency of 13.34%.This approach releases the considerable potential of HTL materials selection and offers a promising avenue for the development of high-performance inverted QLEDs.

Overview of the Global Electricity System in Oman Considering Energy Demand Model Forecast

Lately,in modern smart power grids,energy demand for accurate forecast of electricity is gaining attention,with increased interest of research.This is due to the fact that a good energy demand forecast would lead to proper responses for electricity demand.In addition,proper energy demand forecast would ensure efficient planning of the electricity industry and is critical in the scheduling of the power grid capacity and management of the entire power network.As most power systems are been deregulated and with the rapid introduction and development of smart-metering technologies in Oman,new opportunities may arise considering the efficiency and reliability of the power system;like price-based demand response programs.These programs could either be a large scale for household,commercial or industrial users.However,excellent demand forecasting models are crucial for the deployment of these smart metering in the power grid based on good knowledge of the electricity market structure.Consequently,in this paper,an overview of the Oman regulatory regime,financial mechanism,price control,and distribution system security standard were presented.More so,the energy demand forecast in Oman was analysed,using the econometric model to forecasts its energy peak demand.The energy econometric analysis in this study describes the relationship between the growth of historical electricity consumption and macro-economic parameters(by region,and by tariff),considering a case study of Mazoon Electricity Distribution Company(MZEC),which is one of the major power distribution companies in Oman,for effective energy demand in the power grid.

Prediction of Attention and Short-Term Memory Loss by EEG Workload Estimation

Mental workload plays a vital role in cognitive impairment. The impairment refers to a person’s difficulty in remembering, receiving new information, learning new things, concentrating, or making decisions that seriously affect everyday life. In this paper, the simultaneous capacity (SIMKAP) experiment-based EEG workload analysis was presented using 45 subjects for multitasking mental workload estimation with subject wise attention loss calculation as well as short term memory loss measurement. Using an open access preprocessed EEG dataset, Discrete wavelet transforms (DWT) was utilized for feature extraction and Minimum redundancy and maximum relevancy (MRMR) technique was used to select most relevance features. Wavelet decomposition technique was also used for decomposing EEG signals into five sub bands. Fourteen statistical features were calculated from each sub band signal to form a 5 × 14 window size. The Neural Network (Narrow) classification algorithm was used to classify dataset for low and high workload conditions and comparison was made using some other machine learning models. The results show the classifier’s accuracy of 86.7%, precision of 84.4%, F1 score of 86.33%, and recall of 88.37% that crosses the state-of-the art methodologies in the literature. This prediction is expected to greatly facilitate the improved way in memory and attention loss impairments assessment.

Identification and Visualization of Spatial and Temporal Trends in Textile Industry

The research volume increases at the study rate,causing massive text corpora.Due to these enormous text corpora,we are drowning in data and starving for information.Therefore,recent research employed different text mining approaches to extract information from this text corpus.These proposed approaches extract meaningful and precise phrases that effectively describe the text’s information.These extracted phrases are commonly termed keyphrases.Further,these key phrases are employed to determine the different fields of study trends.Moreover,these key phrases can also be used to determine the spatiotemporal trends in the various research fields.In this research,the progress of a research field can be better revealed through spatiotemporal bibliographic trend analysis.Therefore,an effective spatiotemporal trend extraction mechanism is required to disclose textile research trends of particular regions during a specific period.This study collected a diversified dataset of textile research from 2011–2019 and different countries to determine the research trend.This data was collected from various open access journals.Further,this research determined the spatiotemporal trends using quality phrasemining.This research also focused on finding the research collaboration of different countries in a particular research subject.The research collaborations of other countries’researchers show the impact on import and export of those countries.The visualization approach is also incorporated to understand the results better.

Electrical properties and reliability of HfO2 gate-dielectric MOS capacitors with trichloroethylene surface pretreatment

Trichloroethylene （TCE） pretreatment of Si surface prior to HfO2 deposition is employed to fabricate HfO2 gatedielectric MOS capacitors. Influence of this processing procedure on interlayer growth, HfO2/Si interface properties, gate-oxide leakage and device reliability is investigated. Among the surface pretreatments in NH3, NO, N2O and TCE ambients, the TCE pretreatment gives the least interlayer growths the lowest interface-state density, the smallest gate leakage and the highest reliability. All these improvements should be ascribed to the passivation effects of Cl2 and HC1 on the structural defects in the interlayer and at the interface, and also their gettering effects on the ion contamination in the gate dielectric.

Investigation of the Relevance Between Electronic Courseware Implementation and Students’ Learning Styles

Currently many methods of implementation are available if we want the courseware to be used in e-learning interactivly with media rich.This paper focuses the attention to the relevance between various implementations in presentation adopted in the courseware and students' learning styles,in order to consider what kind of implementation or description is preferable to what kind of students or order to support their learning.We carried out the canonical correlation analysis for this purpose and investigated this relevance on the basis of the experiments.Main results of the experiment are given with detailed discussion.

Design and Simulation of an Audio Signal Alerting and Automatic Control System

A large part of our daily lives is spent with audio information. Massive obstacles are frequently presented by the colossal amounts of acoustic information and the incredibly quick processing times. This results in the need for applications and methodologies that are capable of automatically analyzing these contents. These technologies can be applied in automatic contentanalysis and emergency response systems. Breaks in manual communication usually occur in emergencies leading to accidents and equipment damage. The audio signal does a good job by sending a signal underground, which warrants action from an emergency management team at the surface. This paper, therefore, seeks to design and simulate an audio signal alerting and automatic control system using Unity Pro XL to substitute manual communication of emergencies and manual control of equipment. Sound data were trained using the neural network technique of machine learning. The metrics used are Fast Fourier transform magnitude, zero crossing rate, root mean square, and percentage error. Sounds were detected with an error of approximately 17%;thus, the system can detect sounds with an accuracy of 83%. With more data training, the system can detect sounds with minimal or no error. The paper, therefore, has critical policy implications about communication, safety, and health for underground mine.