Low Efficiency Research Articles

In-situ capture defects through molecule grafting assisted in coal-based hard carbon anode for sodium-ion batteries

Sodium-ion batteries is not essential but also potential to support energy storage system due to the low cost. It's worth noting that low price and high carbon production helps coal extraordinary promise as hard carbon precursor. However, low initial coulombic efficiency (ICE) and practical Na+ storage specific capacity barrier their application owing to lots of defects. Herein, a grafting method is proposed to decrease defects, introducing glucose with simple one step carbonization process. Utilizing the reaction between glucose and coal reduces the defects, which generates a high ICE with less irreversible sodium ion consumption. Meanwhile the expanded carbon layer spaces enable the fast sodium ions transfer, leading to an optimized sodium storage performance. The grafted glucose obviously decreases the defects during the carbonization process and contributes extra sodium storage capacity. The optimal CG0.1 exhibits higher reversible capacity of 293 mAh∙g−1 and ICE of 84 % than pristine coal pyrolytic carbon (250 mAh∙g−1 and 79 %). Besides, it also delivers outstanding capacity retention with 95.3 % and 95.8 % after 1000 cycles at 0.3 A∙g−1 (240 mAh∙g−1) and at 1 A∙g−1 (207 mAh∙g−1)

Smart traffic forecasting: leveraging adaptive machine learning and big data analytics for traffic flow prediction

The issue of road traffic congestion has become increasingly apparent in modern times. With the rise of urbanization, technological advancements, and an increase in the number of vehicles on the road, almost all major cities are experiencing poor traffic environments and low road efficiency. To address this problem, researchers have turned to diverse data resources and focused on predicting traffic flow, a crucial issue in Intelligent Transportation Systems (ITS) that can help alleviate congestion. By analyzing data from correlated roads and vehicles, such as speed, density, and flow rate, it is possible to anticipate traffic congestion and patterns. This paper presents an adaptive traffic system that utilizes supervised machine learning and big data analytics to predict traffic flow. The system monitors and extracts relevant traffic flow data, analyzes and processes the data, and stores it to enhance the model's accuracy and effectiveness. A simulation was conducted by the authors to showcase the proposed solution. The outcomes of the study carry substantial implications for transportation systems, offering valuable insights for enhancing traffic flow management.

Research Progress of Chitosan-based Multifunctional Nanoparticles in Cancer Targeted Therapy.

Conventional tumor therapeutic modalities, such as radiotherapy, chemotherapy, and surgery, involve low tumor inhibition efficiency, non-targeted drug delivery, and side effects. The development of novel and practical nano-drug delivery systems (DDSs) for targeted tumor therapy has become particularly important. Among various bioactive nanoparticles, chitosan is considered a suitable candidate for drug delivery due to its non-toxicity, good biocompatibility, and biodegradability. The amino and hydroxyl groups of chitosan endow it with the diverse function of chemical modification, thereby improving its physical and biological properties to meet the requirements of advanced biomedical applications. Therefore, it is necessary to review the property and applications of chitosan-based materials in biomedicine. In this review, the characteristics of chitosan related to its applications are first introduced, and then the preparation and modification of chitosan-based nanoparticles, including the function tailoring of chitosan-modified nanoparticles, are demonstrated and discussed. Finally, the opportunities and challenges of chitosan-based nanomaterials in this emerging field are proposed from the perspective of the rational and systematic design for the biomedicine field.

Enhancing power conversion efficiency in five-level multilevel inverters using reduced switch topology

This paper presents extensive research on improving the power conversion efficiency of five-level multilevel inverters (MLIs) by utilizing a reduced switch topology. MLIs have received an abundance of focus because of their ability to generate high-quality output waveforms and have better harmonic outcomes than traditional two-level inverters. The high number of switches in MLIs, on the other hand, can result in increased power losses and lower overall efficiency. In this paper, a novel reduced switch topology for five-level MLIs, which is having five switches is proposed with the aim of minimizing power losses while preserving superior performance due to lesser number of switches. To achieve efficient power conversion, the proposed topology employs advanced pulse width modulation control strategies and optimized switching patterns. The simulation results show that the minimized switch topology improves the power conversion efficiency of the five-level MLI, resulting in lower losses and better overall system performance. The total harmonic distortion (THD) value of the output current has been reduced to 7.12% and the efficiency has been achieved to 96.92%. The findings of this investigation help to advance MLI technology, allowing for more efficient and reliable power conversion in a variety of applications such as renewable energy systems, electric vehicles, and industrial drives.

Particle swarm optimization based sliding mode control for maximum power point tracking in solar PV systems

One of the most significant renewable energies is photovoltaic (PV) energy, however it has a low efficiency due to its variable maximum power point that depends on weather conditions. In order to guarantee the system's best performance, intelligent algorithms can effectively track this point in real-time utilizing the maximum power point tracking (MPPT) method. Consequently, it is crucial to maximize the use of the solar energy that has been captured as well as the PV system's generated electricity. Variations in solar irradiance affects the amount of electric energy obtained from solar arrays. For efficient extraction of electricity from solar PV systems, MPPT algorithms are required. Sliding mode control (SMC) can be used in the control of nonlinear systems. However, the effectiveness of SMC can be improved by the choice of the sliding coefficients. In this paper, optimal search using particle swarm optimization (PSO) is used in the design of the sliding manifold. Results obtained via simulations showed that MPPT tracking efficiencies obtained for the PSO based SMC and the conventional SMC are 99.65% and 96.79% respectively. That means, PSO based SMC is 2.86% better than conventional SMC.

A fast analysis algorithm for structural vibration modal sensitivity

Vibration modal sensitivity analysis has a wide range of applications in structural optimization design, model correction, fault detection, and reliability analysis. The existing methods for calculating modal sensitivity mainly include modal superposition method, Nelson’s method, and some improved algorithms derived from them. However, the existing algorithms have the drawback of low computational efficiency when applied to modal sensitivity analysis of the large-scale structures. In view of this, the objective of this work is to develop a fast modal sensitivity analysis method that can more efficiently calculate the modal sensitivity of the large structures with thousands of degrees of freedom. This new sensitivity analysis technique integrates Sherman-Morrison-Woodbury formula, subspace iteration and forward-difference to achieve the goal of fast calculation. The innovations of the proposed method mainly include the following aspects. Firstly, the Sherman-Morrison-Woodbury formula is used to quickly calculate the perturbed flexibility matrix due to the minor modification in the structure. Then, the perturbed flexibility matrix is introduced into the subspace iteration method to calculate the perturbed modal parameters. Finally, the forward-difference algorithm is used to calculate the modal sensitivity. Two numerical examples are used to verify the superiority of the proposed modal sensitivity algorithm. Taking the 1952-bar structure as an example, the calculation time of the proposed algorithm is only 8.3 % and 18.2 % of that of the existing approximate modal superposition method and the improved Nelson’s method, respectively. In terms of calculation accuracy, the results obtained by the proposed method are exactly the same as the exact solution when the perturbation scale is set to 10−5. It is found that the proposed method significantly reduces computation time compared to the existing methods on the premise that the calculation accuracy is basically unchanged.

Microwave-Assisted “One-Pot” Acidolysis and Extraction for the Rapid Determination of Mancozeb in Fruit and Vegetable Samples

Mancozeb is an extensively consumed fungicide, which often leaves high residue levels on agricultural products. The conventional method for detecting mancozeb involves a time-consuming process using gas chromatography (GC) after a 2-hour water-bath acidolysis, resulting in low efficiency and recovery rates. This study developed a rapid method for detecting mancozeb in fruits and vegetables using microwave-assisted acidolysis and extraction coupled with GC analysis. Mancozeb underwent “one-pot” acidolysis to generate CS2 gas and was subsequently extracted from samples using microwave treatment, requiring only 50 seconds of pretreatment time. The average recoveries of mancozeb ranged from 81% to 112%. The limit of detection and limit of quantification were 0.003 and 0.01 mg kg−1, respectively. The scanning electron microscope imaging showed that strong cell crumpling after microwave treatment improved the acidolysis rate significantly, where the acidolysis rate was 91.8% for mancozeb. In addition, this method is rapid, simple, and precise for detecting residues of mancozeb and other dithiocarbamate fungicides.

Measurement and Spatio-Temporal Characteristics of High-Quality Development Efficiency in Metropolitan Areas: A Case Study of the Changchun Metropolitan Area

The concept of high-quality development (HQD) is characterized by its emphasis on efficiency, equity, and environmental sustainability. In the context of China’s new urbanization development, the metropolitan area plays a crucial role in facilitating and sustaining HQD. This study focuses on the Changchun Metropolitan Area (CCMA) as a case study to measure the efficiency of high-quality development (HQDE) at the county level using the super-efficiency SBM model and spatial autocorrelation model. Additionally, we examine the spatio-temporal distribution characteristics of HQDE in terms of economy, innovation, coordination, greenness, openness, and sharing (EICGOS). The main findings are as follows: (1) The HQDE of the CCMA ranges from 0.7 to 0.8 with an initial rapid increase followed by a gradual decline; however, there are notable variations among different counties. (2) Regarding spatial structure within the metropolitan area, highest efficiency is observed in the half-hour living circle followed by the 2-h accessibility circle while lowest efficiency is found in the 1-h commuting circle. Over time, there is a declining trend in efficiency within core leading areas. (3) In terms of dimensions, CCMA demonstrates the highest level of economic development efficiency (EDE), whereas green development efficiency (GDE) exhibits lower levels compared to other dimensions. Furthermore, development efficiencies across all dimensions show a decline over time. (4) Spatially distributed patterns reveal significant agglomeration areas for HQDE within the CCMA region. High-high agglomeration areas are predominantly concentrated in the central region of Changchun and southern region of Liaoyuan while low-low agglomeration areas primarily exist in northwest Songyuan and specific counties within Changchun. To attain HQD of the CCMA, it is advisable to bolster the economic scale of the central city, mitigate developmental disparities between counties and cities, and expedite green transformations in old industrial cities. These findings offer a valuable point of reference for optimizing resource allocation at the metropolitan level and devising strategies to foster regional HQD.

Modulating the Electrolyte Microenvironment in Electrical Double Layer for Boosting Electrocatalytic Nitrate Reduction to Ammonia.

Electrochemical nitrate reduction reaction (NO3RR) is a promising approach to achieve remediation of nitrate-polluted wastewater and sustainable production of ammonia. However, it is still restricted by the low activity, selectivity and Faraday efficiency for ammonia synthesis. Herein, we propose an effective strategy to modulate the electrolyte microenvironment in electrical double layer (EDL) by mediating alkali metal cations in the electrolyte to enhance the NO3RR performance. Taking bulk Cu as a model catalyst, the experimental study reveals that the NO3--to-NH3 performance in different electrolytes follows the trend Li+ < Cs+ < Na+ < K+. Theoretical studies illustrate that the proton transport rate in NO3RR and the activity of the rate-determining step (NO3- to NO2-) increase in the order Li+ < Cs+ < Na+ < K+. The cation effects are also general for two typical nanostructured catalysts including copper/cuprous oxide and nickel phosphides, achieving near-100% Faradaic efficiency and over 99% conversion of NO3- to NH3. Furthermore, we demonstrate that NO3- can be converted to high-purity NH4Cl by copper/cuprous oxide catalyst in K+-containing electrolyte.

Dynamic changes in dissolved organic matter during transport of landfill leachate in porous medium.

The dynamic changes in dissolved organic matter (DOM) during the transport of landfill leachate (LL) in porous medium should be explored, considering the high levels of DOM in the LL of municipal solid waste. Column experiments were carried out at 25°C at a Darcy's flux of 0.29cm/h for 2722h to compare the transport of Cl-, ultraviolet absorbance at 254nm (UV254), chemical oxygen demand (COD), and dissolved organic carbon (DOC) in the simulated porous medium by using the CXTFIT2.1 code. Results showed that the convection-dispersion equation (CDE) could describe Cl- transport well. The high levels of λ and D could be highly correlated with the physicochemical properties of the porous medium. The transport of the studied DOM with evident aromatic character could be described appropriately by the CDE model with the first-order reaction assumption, considering the similar variation trends of UV254, COD, and DOC in the effluent during experiments. Specifically, the values of retardation factor (R) were in the following order: DOC > UV254 > COD, whereas the low values of the first-order decay coefficient (k1) for DOC and COD were still higher than that for UV254. High contents of humic-like substances in the DOM with complex toxic components resulted in the natural low removal efficiencies of COD, DOC, and UV254 (≤ 23%), which could be confirmed by the variations of fluorescence index (FI) and humification index (HIX) in the effluent. The results should be helpful in evaluating the environmental risk induced by the LL leakage in a landfill site.

Carbon Dioxide Capture and Storage (CCS) in Saline Aquifers versus Depleted Gas Fields

Saline aquifers have been used for CO2 storage as a dedicated greenhouse gas mitigation strategy since 1996. Depleted gas fields are now being planned for large-scale CCS projects. Although basalt host reservoirs are also going to be used, saline aquifers and depleted gas fields will make up most of the global geological repositories for CO2. At present, depleted gas fields and saline aquifers seem to be treated as if they are a single entity, but they have distinct differences that are examined here. Depleted gas fields have far more pre-existing information about the reservoir, top-seal caprock, internal architecture of the site, and about fluid flow properties than saline aquifers due to the long history of hydrocarbon project development and fluid production. The fluid pressure evolution paths for saline aquifers and depleted gas fields are distinctly different because, unlike saline aquifers, depleted gas fields are likely to be below hydrostatic pressure before CO2 injection commences. Depressurised depleted gas fields may require an initial injection of gas-phase CO2 instead of dense-phase CO2 typical of saline aquifers, but the greater pressure difference may allow higher initial injection rates in depleted gas fields than saline aquifers. Depressurised depleted gas fields may lead to CO2-injection-related stress paths that are distinct from saline aquifers depending on the geomechanical properties of the reservoir. CO2 trapping in saline aquifers will be dominated by buoyancy processes with residual CO2 and dissolved CO2 developing over time whereas depleted gas fields will be dominated by a sinking body of CO2 that forms a cushion below the remaining methane. Saline aquifers tend to have a relatively limited ability to fill pores with CO2 (i.e., low storage efficiency factors between 2 and 20%) as the injected CO2 is controlled by buoyancy and viscosity differences with the saline brine. In contrast, depleted gas fields may have storage efficiency factors up to 80% as the reservoir will contain sub-hydrostatic pressure methane that is easy to displace. Saline aquifers have a greater risk of halite-scale and minor dissolution of reservoir minerals than depleted gas fields as the former contain vastly more of the aqueous medium needed for such processes compared to the latter. Depleted gas fields have some different leakage risks than saline aquifers mostly related to the different fluid pressure histories, depressurisation-related alteration of geomechanical properties, and the greater number of wells typical of depleted gas fields than saline aquifers. Depleted gas fields and saline aquifers also have some different monitoring opportunities. The high-density, electrically conductive brine replaced by CO2 in saline aquifers permits seismic and resistivity imaging, but these forms of imaging are less feasible in depleted gas fields. Monitoring boreholes are less likely to be used in saline aquifers than depleted gas fields as the latter typically have numerous pre-existing exploration and production well penetrations. The significance of this analysis is that saline aquifers and depleted gas fields must be treated differently although the ultimate objective is the same: to permanently store CO2 to mitigate greenhouse gas emissions and minimise global heating.

An Intrusion Detection Model Based on Extra Trees Algorithm with Dimensionality Reduction and Oversampling

With the advancement of the university information process, more and more application systems are running on the campus network, and the information system becomes larger and more complex. With the rapid growth of network users and the popularization and deepening of computer knowledge, the campus network has been transformed from an experimental network for education and scientific research into an operational network that attaches equal importance to education, scientific research, and service. As the most important transmission carrier of digital information, how to ensure its security has become an urgent issue for colleges and universities. Therefore, this paper uses advanced intrusion detection technology to design the corresponding model to solve the security problem of the campus network. When traditional machine learning algorithms train network intrusion data sets, they are prone to problems such as too many feature dimensions, overfitting, and imbalance of data sets, which lead to lower accuracy and low time efficiency of intrusion detection algorithms. In order to solve the above problems, this paper proposes an intrusion detection model based on Extra Trees, which uses linear discriminant analysis to reduce the dimension of data, then uses oversampling to reduce the influence of imbalance of sample categories of the network intrusion dataset, and finally uses Extra Trees algorithm to train the model. The experimental results show that after LDA reduction and oversampling, using the Extra Trees classification model can improve the overall recognition performance of imbalanced data sets under multi-classification and satisfy the network intrusion detection.

Nanoengineered Cobalt Electrocatalyst for Alkaline Oxygen Evolution Reaction

The alkaline oxygen evolution reaction (OER) remains a bottleneck in green hydrogen production owing to its slow reaction kinetics and low catalytic efficiencies of earth abundant electrocatalysts in the alkaline OER reaction. This study investigates the OER performance of hierarchically porous cobalt electrocatalysts synthesized using the dynamic hydrogen bubble templating (DHBT) method. Characterization studies revealed that electrocatalysts synthesized under optimized conditions using the DHBT method consisted of cobalt nanosheets, and hierarchical porosity with macropores distributed in a honeycomb network and mesopores distributed between cobalt nanosheets. Moreover, X-ray photoelectron spectroscopy studies revealed the presence of Co(OH)2 as the predominant surface cobalt species while Raman studies revealed the presence of the cubic Co3O4 phase in the synthesized electrocatalysts. The best performing electrocatalyst required only 360 mV of overpotential to initiate a current density of 10 mA cm−2, exhibited a Tafel slope of 37 mV dec−1, and stable OER activity over 24 h. The DHBT method offers a facile, low cost and rapid synthesis approach for preparation for highly efficient cobalt electrocatalysts.

Optimization of mixed cargo allocation and stowage with heterogeneous vehicles

In order to solve the problem of low efficiency and high loading costs in business logistics and distribution operations, this paper classifies non-standard cargoes based on their spatial and temporal characteristics. Based on this, a dual-layer planning model for multi-vehicle cargoes loading and transportation is constructed to jointly optimize the loading process. This paper designs a space-inspired heuristic algorithm to solve the model and verifies the feasibility of the model. The results show that compared to a single vehicle type, the joint optimization of multi-vehicle combinations for loading and transportation has stronger adaptability and can better accommodate changes in cargo volume. Compared to the single vehicle type, not only did the loading rate increase by 18%, but also effectively reduced the transportation cost by 3%. Different proportions of spatial and temporal characteristic cargoes have different effects on the loading and transportation. Adjusting the proportion of cargoes that can be placed on both upper and lower layers to 50% can better utilize transportation resources such as vehicles. Increasing the proportion of urgent cargoes can improve the economic efficiency of loading and transportation.

Detection of partial discharge patterns in hybrid high voltage power transmission lines based on parallel recognition method

Due to their specially designed structures, the partial discharge detection of hybrid high-voltage power transmission lines (HHVPTL) composed of overhead lines and power cables has made it difficult to monitor the conditions of power transmission lines. A parallel recognition method for partial discharge patterns of HHVPTLs is proposed by implementing wavelet analysis and improved backpropagation neural network (BPNN) to address the shortcomings of low efficiency, poor accuracy, and inability to parallel analysis of current partial discharge (PD) detection algorithms for HHVPTLs. Firstly, considering the non-smoothness of the partial discharge of the HHVPTLs, the wavelet packet decomposition algorithm is implemented to decompose the PD of the HHVPTL and resolve the relevant signal indicators to form the attribute vectors. Then, BPNN is implemented as a classification model. A beetle optimization (DBO) algorithm based on orthogonal contrastive learning improvement is introduced to optimize the BPNN parameters since BPNN has a slow convergence problem and fails easily into a local optimum. The proposed IDBO-BPNN is employed as the model that recognizes and analyzes the parallel partial discharge patterns of HHVPTLs. Finally, the suggested model is implemented to investigate the local discharge data of an HHVPTL in the Kaggle Featured Prediction Competition and is compared with other algorithms. The experimental results indicate that the proposed model can more accurately identify whether PDs occur in an HHVPTL and detect phases where PDs occur, with higher overall accuracy and efficiency. An excellent practical performance is achieved. The proposed model can achieve the recognition accuracy of 95.5%, which is 5.3333% higher than that of the DBO-BPNN and far more than other recognition algorithms.

Multi-Criteria Optimization of a Laboratory Top-Lit Updraft Gasifier in Order to Reduce Greenhouse Gases and Particulate Matter Emissions

Air pollution from combustion processes is harming human health and the environment. To mitigate this, one needs to adopt cleaner energy production methods, in particular, to optimize combustion systems in order to minimize pollutants and increase efficiency. Flue gas analysis and particulate matter (PM) monitoring, starting from the prototype phase, is crucial to minimize and regulate pollutant emissions. This article analyses the emissions of pollutants and particulate matter from a combustion test gasifier working on the Top-Lit Updraft (TLUD) principle in order to optimize functionality and reduce exhaust emissions. Three experiments were performed in which the primary (gasification) air flow rate (GA) was kept constant at 25 L/min, and the secondary (combustion) air flow rate (CA) was adjusted to obtain a CA/GA ratio of 2 (50 L/min), 3 (75 L/min), and 4 (100 L/min) respectively. Based on a multi-criterial analysis, the optimal CA/GA ratio for TLUD combustion is 3, offering a well-rounded performance in output temperatures, PM and greenhouse gases (GHG) emissions, and efficiency, while the CA/GA ratio of 4 has good PM and GHG emissions performance but lower efficiency, and the CA/GA ratio of 2 is the least favorable due to its poor performance in output temperatures, PM and GHG emissions.

Hollow g-C3N4@Ag3PO4 Core-Shell Nanoreactor Loaded with Au Nanoparticles: Boosting Photothermal Catalysis in Confined Space.

Low solar energy utilization efficiency and serious charge recombination remain major challenges for photocatalytic systems. Herein, a hollow core-shell Au/g-C3N4@Ag3PO4 photothermal nanoreactor is successfully prepared by a two-step deposition method. Benefit from efficient spectral utilization and fast charge separation induced by the unique hollow core-shell heterostructure, the H2 evolution rate of Au/g-C3N4@Ag3PO4 is 16.9 times that of the pristine g-C3N4, and the degradation efficiency of tetracycline is increased by 88.1%. The enhanced catalytic performance can be attributed to the ordered charge movement on the hollow core-shell structure and a local high-temperature environment, which effectively accelerates the carrier separation and chemical reaction kinetics. This work highlights the important role of the space confinement effect in photothermal catalysis and provides a promising strategy for the development of the next generation of highly efficient photothermal catalysts.

Research on Application of Improved Quantum Optimization Algorithm in Path Planning

For the building emergency evacuation path planning problem, existing algorithms suffer from low convergence efficiency and the problem of getting trapped in local optima. The Bloch Spherical Quantum Genetic Algorithm (BQGA) based on the least-squares principle for single-robot path planning and Bloch Spherical Quantum Bee Colony Algorithm (QABC) for multi-robots path planning are studied. Firstly, the characteristics of three-dimensional path planning are analyzed, and a linear decreasing inertia weighting approach is used to balance the global search ability of chromosomes and accelerate the search performance of the algorithm. Then, the application algorithm can generate a clear motion trajectory in the raster map. Thirdly, the least squares approach is used to fit the results, thus obtaining a progressive path. Finally, multi-robots path planning approaches based on QABC are discussed, respectively. The experimental results show that BQGA and QABC do not need to have a priori knowledge of the map, and they have strong reliability and practicality and can effectively avoid local optimum. In terms of convergence speed, BQGA improved by 3.39% and 2.41%, respectively, while QABC improved by 13.31% and 17.87%, respectively. They are more effective in sparse paths.

FOXP3 splice variant expression in males and females in healthy populations and in kidney transplant recipients

The forkhead box P3 (FOXP3) transcript is essential for tolerance of alloantigens. Here, we describe the expression of FOXP3 mRNA variants in healthy females and males, and in kidney transplant recipients (KTR). We measured FOXP3 in peripheral blood mononuclear cells from healthy kidney donors (N = 101), and in blood from KTRs (N = 248) before and after transplantation. FOXP3 was measured with quantitative polymerase chain reaction, and differentiated between pre-mature mRNA FOXP3, Total mature FOXP3, FOXP3 in which exon two is spliced, and full length FOXP3. We found similar levels of FOXP3 in healthy female and male kidney donors. We confirmed this result in a publicly available cohort (N = 33) of healthy individuals (GSE97475). Homogenously, female and male KTR FOXP3 levels were similar pre-transplantation, one day post-transplantation and 29 days post-transplantation. This may suggest that kidney transplantation and related immunosuppressive treatments do not influence FOXP3 expression differently in females and males. Finally, fold difference analysis revealed that KTRs express lower levels of mature FOXP3 and higher levels of pre-mature FOXP3 mRNA pre-transplant compared to healthy individuals. This finding may suggest higher pre-mRNA synthesis, lower pre-mRNA degradation, lower spliceosome efficiency or higher degradation of mature FOXP3 mRNA in kidney transplant candidates.

Simulation and optimization of 30.17% high performance N-type TCO-free inverted perovskite solar cell using inorganic transport materials

Perovskite solar cells (PSCs) have gained much attention in recent years because of their improved energy conversion efficiency, simple fabrication process, low processing temperature, flexibility, light weight, and low cost of constituent materials when compared with their counterpart silicon based solar cells. Besides, stability and toxicity of PSCs and low power conversion efficiency have been an obstacle towards commercialization of PSCs which has attracted intense research attention. In this research paper, a Glass/Cu2O/CH3NH3SnI3/ZnO/Al inverted device structure which is made of cheap inorganic materials, n-type transparent conducting oxide (TCO)-free, stable, photoexcited toxic-free perovskite have been carefully designed, simulated and optimized using a one-dimensional solar cell capacitance simulator (SCAPS-1D) software. The effects of layers’ thickness, perovskite’s doping concentration and back contact electrodes have been investigated, and the optimized structure produced an open circuit voltage (Voc) of 1.0867 V, short circuit current density (JSC) of 33.4942 mA/cm2, fill factor (FF) of 82.88% and power conversion efficiency (PCE) of 30.17%. This paper presents a model that is first of its kind where the highest PCE performance and eco-friendly n-type TCO-free inverted CH3NH3SnI3 based perovskite solar cell is achieved using all-inorganic transport materials.