Variation Of Irradiation Research Articles

Modelling and numerical simulations of eco-friendly double absorber solar cell “Spiro-OmeTAD/CIGS/MASnI3/CdS/ZnO” and its PV-module

A broad study for design of higher efficiency modules based on low-cost, non-toxic Au/Spiro-OmeTAD/CIGS/MASnI3/CdS/ZnO/FTO tandem configuration solar cells has been conducted using solar cell capacitance simulator package “SCAPS-1D”. For the cause of highly efficient device performance, the selection of suitable layer materials such as “ZnO” as electron transport layer “ETL”, “CIGS” and “MASnI3→CH3NH3SnI3” as absorber, “Spiro-OmeTAD” as hole transport layer “HTL”, and CdS as active buffer layer material have been developed. The structural layout of the recommended composition was enhanced by variation in thickness of the specified layers, different back contact layers and temperature. The power conversion efficiency “PCE” was found to be 36.38%, along with other electrical parameters such as short-circuit current density “Jsc” of 48.28 mA/cm2, open-circuit voltage “Voc” of 1.38 V, and fill factor of 54.24% for optimized layer thickness of 0.084 μm for “HTL”, 0.051 μm for ETL”, 0.028 μm for buffer layer, 2.10 μm and 1.51 μm for CIGS and CH3NH3SnI3 absorber layers respectively. The overall device performance in perspective of charge carrier generation and recombination was investigated in depth. The improvement of diffusion length has increased the charge carrier's generation around 3.69 μm and the recombination rate was found at a lower value of 2.2 μm which overall enhance the device performance. At standard conditions, the formation of supplementary electron-hole pairs by varying the absorber layers thickness shows the maximum extent for Jt, FF% and PCE% during optimized investigation. A module of dimensions (2.2 × 1.1) m consisting of 72 cells in series was configured in PVsyst software to get the panel performance. Input parameters obtained through solar capacitance simulator were incorporated to evaluate module performance and output power by variation in solar irradiation and temperature. Yearly performance ratio for home location has been explored and reported.

Adaptive Coordination of Relays in AC Microgrid Considering Operational and Topological Changes

Conventional protection schemes are vulnerable to varying changes in a microgrid with different types of distributed generators (DGs), which alter the fault current levels, leading to miscoordination of directional over-current relays (DOCRs). Due to limited storage in numeric relays, having one setting for each network scenario in the relays is infeasible. Also, schemes with one setting considering grid-connected mode (GCM) and islanded mode (IM) of operation with multiple scenarios may be infeasible for a microgrid with inverter-interfaced DGs due to low short-circuit level in IM. Thus, this article proposes an adaptive trip characteristics-based relay coordination scheme for a microgrid with different DGs, where two trip characteristics are assigned to each DOCR, one each for GCM and IM. Also, a novel framework for selecting the suitable trip characteristic is proposed. Hardware-in-loop simulations using Numerical Relay Development Environment for a meshed CIGRE microgrid, modified IEEE 34-bus distribution system, and comparison with previous works validate the proposed method's performance for various scenarios, such as the outage of a DG or line, variations in solar irradiation and wind speed, and network reconfiguration for different fault types during GCM and IM.

Renewable Energy-Based DC Microgrid with Hybrid Energy Management System Supporting Electric Vehicle Charging System

Growing Electric vehicle (EV) ownership leads to an increase in charging stations, which raises load demand and causes grid outages during peak hours. Microgrids can significantly resolve these issues in the electrical distribution system by implementing an effective energy management approach. The suggested hybrid optimization approach aims to provide constant power regardless of the generation discrepancy and should prevent the early deterioration of the storage devices. This study suggests using a dynamic control system based on the Fuzzy-Sparrow Search Algorithm (SSA) to provide a reliable power balance for microgrid (MG) operation. The proposed DC microgrid integrating renewable energy sources (RES) and battery storage system (BSS) as sources are designed and evaluated, and the findings are further validated using MATLAB Simulink simulation. In comparing the hybrid SSA strategy with the most widely used Particle Swarm Optimization (PSO)-based power management, it was observed that the hybrid SSA approach was superior in terms of convergence speed and stability. The effectiveness of the given energy management system is evaluated using two distinct modes, the variation of solar irradiation and the variation of battery state of charge, ensuring the microgrid’s cost-effective operation. The enhanced response characteristics indicate that the Fuzzy-SSA can optimise power management of the DC microgrid, making better use of energy resources. These results show the relevance of algorithm configuration for cost-effective power management in DC microgrids, as it saves approximately 7.776% in electricity expenses over a year compared to PSO.

Solar power forecasting beneath diverse weather conditions using GD and LM-artificial neural networks

Large-scale solar energy production is still a great deal of obstruction due to the unpredictability of solar power. The intermittent, chaotic, and random quality of solar energy supply has to be dealt with by some comprehensive solar forecasting technologies. Despite forecasting for the long-term, it becomes much more essential to predict short-term forecasts in minutes or even seconds prior. Because key factors such as sudden movement of the clouds, instantaneous deviation of temperature in ambiance, the increased proportion of relative humidity and uncertainty in the wind velocities, haziness, and rains cause the undesired up and down ramping rates, thereby affecting the solar power generation to a greater extent. This paper aims to acknowledge the extended stellar forecasting algorithm using artificial neural network common sensical aspect. Three layered systems have been suggested, consisting of an input layer, hidden layer, and output layer feed-forward in conjunction with back propagation. A prior 5-min te output forecast fed to the input layer to reduce the error has been introduced to have a more precise forecast. Weather remains the most vital input for the ANN type of modeling. The forecasting errors might enhance considerably, thereby affecting the solar power supply relatively due to the variations in the solar irradiations and temperature on any forecasting day. Prior approximation of stellar radiations exhibits a small amount of qualm depending upon climatic conditions such as temperature, shading conditions, soiling effects, relative humidity, etc. All these environmental factors incorporate uncertainty regarding the prediction of the output parameter. In such a case, the approximation of PV output could be much more suitable than direct solar radiation. This paper uses Gradient Descent (GD) and Levenberg Maquarndt Artificial Neural Network (LM-ANN) techniques to apply to data obtained and recorded milliseconds from a 100 W solar panel. The essential purpose of this paper is to establish a time perspective with the greatest deal for the output forecast of small solar power utilities. It has been observed that 5 ms to 12 h time perspective gives the best short- to medium-term prediction for April. A case study has been done in the Peer Panjal region. The data collected for four months with various parameters have been applied randomly as input data using GD and LM type of artificial neural network compared to actual solar energy data. The proposed ANN based algorithm has been used for unswerving petite term forecasting. The model output has been presented in root mean square error and mean absolute percentage error. The results exhibit a improved concurrence between the forecasted and real models. The forecasting of solar energy and load variations assists in fulfilling the cost-effective aspects.

Optimal Design and Performance Investigation of Artificial Neural Network Controller for Solar- and Battery-Connected Unified Power Quality Conditioner

Nowadays, integration of renewable sources into the local distribution system and the nonlinear behavior of advanced power electronic equipment have made a large impact on the power quality (PQ). The unified power quality conditioner (UPQC) is a multifunctional FACTS device, which is a combination of both shunt active filter and series active filters via a common DC link. Presently, the artificial intelligence is playing a vital role in the development of the intelligent control methods. Traditional training methods of artificial neural network (ANN) like back propagation and Levenberg-Marquardt may get stuck in local optimal solution which leads to the invention of ANN trained optimally by metaheuristic algorithms. This paper develops a firefly algorithm-trained ANN (FF-ANNC) controller for the shunt active filter and proportional integral controller (PI-C) for the series active filter of the UPQC integrated with the solar energy system and battery energy storage via boost converter (B-C) and buck boost converters (B-B-C). The main aim of the proposed FF-ANNC is to reduce the mean square error (MSE) thereby achieving the constant DC link capacitor voltage (DLCV) during load and irradiation variations, reduction of imperfections in current waveforms, improvement in power factor (PF), and mitigation of sag, swell, disturbances, and unbalances in the grid voltage. The working of developed FF-ANNC was tested on five test studies with different types of loads and source voltage balancing/unbalancing conditions. However, to demonstrate supremacy of the suggested FF-ANNC, a comparative study with the training methods like genetic algorithm (GA) and ant colony optimization (AC-O) and also with other methods that exist in literature like PI-C, fuzzy logic controller (FL-C), and artificial neuro fuzzy interface system (ANFI-S) was conducted. The proposed method reduces the total harmonic distortion to 2.39%, 2.32%, 2.27%, 2.45%, and 2.66% which are lower than the existing methods that are available in literature. The FF-ANNC shows an excellent performance in reducing voltage fluctuations and total harmonic distortion (THD) successfully and thereby improving PF.

Optimizing the morphology of titania nanorods for enhanced solar seawater splitting

Nanorod-TiO2 electrodes were obtained by a hydrothermal method in the presence of different concentrations of sodium chloride. The addition of NaCl during the synthesis promoted the formation of thinner, well-crystallized nanorods growing along the [001] crystallographic direction, while still, the most intense reflection is related to (101). The optimal electrode demonstrated applied bias photon to current efficiency (ABPE) of 0.24% in solar seawater splitting, which is among the highest reported efficiencies for the pristine TiO2 nanorods. Noteworthy, the ABPE of the obtained electrodes stayed intact during variation of the solar irradiation in the range of 0.2–1 Sun. It was also demonstrated that the efficiency of nanorod-TiO2 electrodes is higher for seawater splitting (0.5 M NaCl) than for water photoelectrolysis in the presence of 0.5 M Na2SO4. This phenomenon is the result of chloride evolution reaction taking place in addition to water oxidation. A gradual decrease in efficiency resulting from the low mobility of holes was observed for all electrodes. This conclusion was confirmed by experiments with a hole-scavenger (improved performance of the cell), as well as surface photovoltage measurements and electrochemical impedance spectroscopy.

On-Grid PV Performance in Various Irradiation Conditions, Types, and Load Power

There are several types of photovoltaic system configurations, one of which is the on-grid PV system. This system is simple compared to other systems. Because there are two different energy sources that can supply the load either together or separately, an analysis of how the irradiation affects the electrical parameters on the load side or the grid itself is required. The goal of this research is to examine the performance of on-grid PV under various irradiation variation, type, and load power to power factor, grid frequency, and load conditions. To measure the performance of on-grid PV, parameter calculations are carried out in the form of PV efficiency, final yield, reference yield, and performance ratio, and the results of measurements of power factor, grid frequency, and load are observed due to variations in irradiation, type, and load power. The results show that the performance of on-grid PV is good; low irradiation can result in a decrease in the grid power factor, while the grid and load frequencies are in normal conditions for various variations of irradiation, type, and load power.

Performance Analysis of Artificial Intelligence Controller for PV and Battery Connected UPQC

Nowadays, integration of the non-conventional energy sources like wind, tidal, solar etc into the grid is suggested in order to minimize the losses in the distribution network and to meet the demand. The arrival of the power electronics equipments to control the nonlinear loads has made an impact on the power quality. The unified power quality conditioner (UPQC) is a FACTS device with the back to back converters, coupled together with a DC-Link capacitor. This paper suggests an intelligent hybrid controller for the solar Photo-voltaic system and Battery storage system integrated UPQC. The proposed controller adapts both the qualities of artificial neural network and Integral sliding-mode controller. The synchronization of phases is created by self tuning filter (STF) in association with unit vector generation method (STF-UVGM) for the superior performance of UPQC during the unbalanced/ distorted supply voltages conditions. Therefore, the necessity of Phase-locked-loop, Low pass filters and High pass filters are eliminated. However, STF is used for separating the Harmonic and Fundamental components. In addition, STF-UVGM was used for generation of synchronization phases of series and shunt filters. The prime objectives of the suggested artificial neural network integral sliding mode hybrid controller (ANNISMHC) are fast action to retain the DC-Link voltage to the constant value during load/ irradiation variations, diminish the harmonics in the current waveforms, power-factor enhancement, maximum mitigation of sag, swell and disturbances in supply voltage, and compensation for the unbalanced supply voltages. The working of suggested ANNISMHC was investigated on five test cases for several combinations of loads, and balanced/unbalanced supply voltages. However, to demonstrate supremacy of the suggested ANNISMHC comparative study with the different controllers those are available in literature and also with the standard controllers like PIC, SMC, and FLC. The ANNISMHC shows an extra-ordinary performance in diminishing THD thereby improving PF and reducing voltage distortions.

Enhancing power quality in solar-grid integrated systems with ANFIS-based control using split source inverters

Power generation and renewable energy consumption benefit greatly from integrated solar grid systems. However, the intermittent nature of solar electricity and potential voltage swings might raise power quality concerns when solar energy is integrated with the grid. The benefits of the split-source inverter architecture include higher efficiency, lower harmonic distortion, and the ability of power flow in both directions. The ANFIS controller improves the control parameters of the single-stage split-source inverter, eliminating power quality issues including voltage fluctuations, harmonics, and reactive power variations. These difficulties can negatively affect the overall quality of power. ANFIS-based control of the solar-grid integrated system guarantees optimal power conversion, precise voltage management, and higher power quality by intelligently altering the control settings. ANFIS solar electricity and potential voltage swings might raise power quality concerns when solar energy is integrated with the grid. Using an Adaptive Neuro-Fuzzy Inference System (ANFIS)- based control with split source inverters, this research provides a unique method to improve power quality in solar-grid integrated systems. The benefits of the split-source inverter architecture include higher efficiency, lower harmonic distortion, and the ability for power to flow in both directions. The ANFIS controller improves the control parameters of the single-stage split-source inverter, eliminating power quality issues including voltage fluctuations, harmonics, and reactive power variations. These difficulties can negatively impact the power's overall quality. ANFIS-based control of the solar-grid integrated system dynamically adjusts the voltage and current waveforms in response to variations in solar irradiation and grid circumstances. The simulation results show that the harmonic distortion is lowered, voltage stability is enhanced, and reactive power is managed more effectively.

Optimal Control of BESS for Improved Grid Integration of the Hybrid DFIG/PV System Using Dual-Layer Adaptive Control

In view of the stochastic nature of wind and solar energy, maintaining quality power in grid-connected hybrid energy system is crucial for efficient energy utilization, especially in the presence of non-linear loads. This paper deals with the control and performance assessment of grid-integrated hybrid energy system that comprises of doubly fed induction generator (DFIG) and photovoltaic (PV) system including battery energy storage (BES) as a stand-by supply to meet utility and load demand. In order to maintain power balance and address various power quality issues encountered with hybrid renewable integration along the source and the grid/load end, a dual-layer variable-step size adaptive control is implemented for the grid-interfaced converter of the proposed system. The variable step-size based dual-layer least mean square (LMS) adaptive control functions by extracting weights of various control signals (stator and load current) where the step-size parameter adjusts according to the conditions that limit the maladjustment error and increase the rate of convergence. The DC-link voltage control realized by bidirectional BES control reduces the steady-state error and fluctuations in the voltage. An artificial bee colony (ABC) optimization is adopted for optimal gain tuning of the DC-link voltage controller as a result of which the battery performance and grid-side converter performance is enhanced with the increased power exchange to the grid. The system model is developed in MATLAB/Simulink and controller performance is evaluated for diverse operating conditions including steady-state, unbalanced non-linear load conditions, wind-speed and solar irradiation variation and specified grid power modes.

Analysis of control strategies for smoothing of solar PV fluctuations with storage devices

The irradiation variations caused by cloud changes can cause rapid power fluctuations in large photovoltaic (PV) plants. The increased PV power share of the grid adversely affects the quality of power and the reliability of the power supply. Energy storage systems (ESSs)are often used to mitigate power fluctuations in the grid through various control algorithms. These algorithms create an ESS power reference that opposes the variations of the PV and reduces them to an acceptable value. Despite their everyday use, there have been few performance comparisons among the various methods, especially battery status, and the battery can operate in several systems with various smoothing methods. The paper aims to analyze the ramp-rate and step-rate control methods for smoothing solar PV fluctuations based on the irradiation profiles in a DC microgrid (MG) environment. The battery in the system operates as a continuous energy application, and a supercapacitor (SC) is used to remove transients in the battery system. The combined battery-SC coordinated system improves the performance of the DC microgrid system. The battery cycle analysis and depth are conducted using the well-established Coulomb counting technique. MATLAB/Simulink validates the simulation results, and the OP-5700 HIL test bench conducts real-time results.

A comprehensive review on various cooling techniques to decrease an operating temperature of solar photovoltaic panels

• The operating temperature of the photovoltaic panel plays a vital role during the energy conversion process • Technology development and application prospects for PV panel cooling are reviewed. • Active, Passive, and Hybrid cooling are the main technologies. • PCM with the nanoparticles usage makes the cooling system more effective. At present solar power is considered to be one of the most predominant non-conventional and renewable resources when compared with other resources. It does not produce toxic pollutants like conventional fuels while converting them into electric power. Photovoltaic (PV) power generation will be varying continuously according to climatic conditions. The variation in irradiation, wind speed, temperature, and soling affects the power produced by the panel. The operating temperature of the photovoltaic panel plays a vital role during the energy conversion process. PV panel excessive surface operating temperatures and high ambient temperature results in overheating of panels, which in turn significantly decreases the lifetime, efficiencies, and increased payback period of the system. To address these challenges, different cooling approaches in restricting the panel to nominal operating temperature. In light of the above, a comprehensive review is presented on the different cooling techniques resulting in enhancing the performance of solar panels. Air-based, water-based cooling systems, phase change material (PCM), and hybrid cooling by using PCM, nanomaterials, and nanofluids have been researched to ensure reduced panel degradation. The effectiveness of each cooling approach and performance enhancement of the PV panel will also be reported. This review outlines that water-based cooling is better than air-based cooling unless recovered heat is particularly utilized for domestic applications. However, cooling techniques involving the combination of PCM with nanoparticles usage, make the cooling system more effective when compared with water-based systems. The usage of different PCMs and their properties are presented in this review.

Photocontrol of pattern formation and hysteresis loops in polymer gels with host-guest interactions.

Patterns formed under external stresses are often critical for ensuring the functionality of soft materials. We focus on dynamic control of pattern formation and restructuring in hydrogels with host-guest interactions. We extend the three-dimensional gel Lattice Spring Model to capture the dynamics of photo-responsive hydrogels with pendant azobenzene moieties immersed in the α-cyclodextrin (α-CD) solution. While trans-azobenzene moieties are accommodated by the α-CD cavities forming inclusion complexes resulting in hydrogel swelling, exposure to UV irradiation induces trans-to-cis photoisomerization leading to decomplexation and matrix deswelling. We demonstrate swelling-induced patterns in confined samples upon adding the α-CD solution in the dark. Further, we show that spatiotemporal variations in UV irradiation effectively control patterns formed and hysteresis loops. We introduce soft confinements via illuminating specific regions of unconstrained samples with UV light. Our results indicate that features of patterns and hysteresis in systems' response to external conditions can be regulated via well-defined illumination patterns.

LMS Adaptive FIR Filter-Predictive Controller Based On D-Q Control Theory for Grid-Connected Solar PV System

A Least Mean Square (LMS) adaptive Finite Impulse Response (FIR) filter-predictive controller is presented for the control of a grid-connected solar PV energy system. The objective of the work is to reduce total harmonic distortion (THD) in the grid and load current. It follows the systematic development and analysis of the implementation of an adaptive LMS control based on the direct-quadrature control theory. The analysis is done for the proposed adaptive control technique for grid-connected solar photovoltaic systems under the variation of solar irradiation and load. In this proposed work, the reference current is estimated by developing the LMS control technique. The proposed system is modeled in d-q frame form and simulated on the MATLAB Simulink platform. Developing an LMS control algorithm using the p-q theory reduces computational hard work. The reduction in load current harmonics is improved. The simulation results are used to discuss the convergence rate, harmonics reduction, and advantages of the adaptive LMS algorithm d-q control theory.

Experimental and CFD investigation of a helical coil heat exchanger coupled with a parabolic trough solar collector for heating a batch reactor: An exergy approach

An experimental and numerical study was conducted to evaluate the performances of an innovative thermal system composed of a batch reactor surrounded by a helical coil heat exchanger (HX), coupled with a parabolic trough solar collector. It was equipped with K-type thermocouples connected to a laptop to instantaneously record the heat transfer fluid temperatures at the inlet and outlet of the HX. Temperature and pressure of water were also measured inside the reactor. It was found that all temperature profiles follow similar trends as the Direct Normal Irradiation (DNI) variation, and the thermal efficiency does not exceed 20% because of significant thermal losses. The exergy analysis was applied to the mathematically modeled HX and simulated using COMSOL Multiphysics software. Temperatures from the CFD simulation and those measured during experiment tests are in good agreement. The Bejan number varies rapidly from 0.6 to 1 because of the strong dominance of heat irreversibilities. The total entropy generation increases with temperature and DNI until reaching its maximum value, then decreases in the cooling phase. A new heat exchanger design was proposed and simulated to reduce thermal losses. Consequently, the destroyed exergy was considerably reduced, and the efficiency was enhanced by almost 16%.

Beta-borophene under the circularly polarized radiation: polaritonic and polaronic dynamic band structure

We study the effect of circularly polarized electromagnetic radiation on the optical polarons in a monolayer \({\beta}\)-borophene. We focus our attention on the off-resonant regime of large driving frequencies, which allows us to set up an effectively time-independent Hamiltonian describing the radiation field up to the second perturbational order. The contributions of optical surface phonons are incorporated according to the Lee–Low–Pines theory, via a sequence of appropriate unitary transformations. A central object in our investigation is the electronic band gap, which is shown to be widely controllable via the variation of light irradiation and substrate properties. We also show that the polaron self-energy contribution gives more contribution than the combination of the polaronic and polaritonic effects.

BODY WEIGHT KINETICS OF F1 GENERATION IRRADIATED In-utero

To investigate radiation risks associated with a low dose and low dose rates, pregnant Swiss albino mice were exposed to 0.20 Gy, 0.40Gy and 0.80 Gy of gamma rays from a Cobalt-60 source at two different dose rates (0.0584 Gy/min and 0.00091 Gy/min) on 18-day post conception. Irradiated as well as control animals were allowed to reach term and to deliver normally. Post irradiation variations in the body weight of mice were studied in the F1 generation at 0 days and 1,2,3,4,5,6 and 12 weeks of postpartum age. Bodyweight at both and at different postnatal ages was found to be influenced by the exposure dose as well as by the dose rate. The higher the dose, the greater is the weight loss. Moreover, at different postnatal age’s body weight was less in the High dose rate (HDR) subgroup than in the Low dose rate (LDR) subgroup. This holds true for all the exposed dose groups. In all the dose groups ratio of LDR to HDR, values for the bodyweight revealed the following relation: 0.20 Gy < 0.40Gy < 0.80 Gy.

Holocene relative water level and storminess variation recorded in the coastal peat bogs of the Vistula Lagoon, southern Baltic Sea

The history of regional sea-level changes is critically important for building global environmental reconstruction models and identifying major driving forces. The coastal peatlands located on the terraces of the Vistula Lagoon (Gulf of Gdańsk, southern Baltic Sea) are one of the most valuable, and not yet fully explored, archives of the regional history of sea-level and storminess changes during the middle and late Holocene. The comprehensive analysis of peat and gyttja allowed establishing radiocarbon calibrated time series of stable carbon isotope composition (δ13C), pollen and diatom data. The results indicate that the sea-level increased from ∼20 to 8 m below present sea level between 9000 and 7000 yr b2k at an average rate of ∼6 mm/year. Around 5000 yr b2k, the sea-level was ∼2.5 m lower than at present, which indicates a decrease in the rate of sea-level rise to ∼2.75 mm/year between 7000 and 5000 yr b2k. During the last 5000 years, the sea-level had risen at ∼0.5 mm/year to the present position. Changes in brackish diatoms, Pediastrum sp. concentrations, and maxima of δ13C suggest ten periods of increased rates of sea-level rise and storminess between 7000 and 1500 yr b2k. Increased sea-levels and stormy surges on the peat surface occurred mainly during cold periods or the periods of transitions. These observations are in good agreement with climate fluctuations in the Northern Hemisphere related to the variations in total regional solar irradiation.

Heat Storage and Release Performance of Cascade Phase Change Units for Solar Heating in a Severe Cold Region of China

The heat storage and release performance of cascade phase change units are investigated numerically for users in Inner Mongolia’s severe cold region. Three schemes of phase change material combinations are thoroughly tested. We obtained a better material combination scheme S3 (palmitic acid + polyethylene glycol), which has higher heat storage capacity per unit mass, higher average heat flux, and better unit synchronisation performance, so that it is more suitable for solar heating and cascade heat storage units in cold regions of Inner Mongolia. This study takes into account the irradiation variation of typical days during the winter heating season. The results show that the palmitic acid and polyethylene glycol combination scheme has the highest total heat storage per unit mass. This scheme also performs well in the synchronisation of two-stage storage units. When compared to the other two schemes, the average heat flux is increased by 25.5% for the first stage unit and 16.8% for the second stage unit.

Bioinspired Dual-Driven Binary Heterogeneous Nanofluidic Ionic Diodes.

Recently, bioinspired 2D material-based nanofluidic systems with unique properties and advantages have been receiving considerable research interest and getting rapid development. However, it remains a huge challenge to integrate adaptive responsiveness to external stimuli and asymmetric ion transport characteristics into the 2D nanofluidic systems. Herein, we report a dual-driven switchable asymmetric ionic transport phenomenon through a graphene oxide-based heterogeneous 2D nanofluidic membrane. Taking advantage of the formation of a charge heterojunction induced by the variation of pH or UV irradiation, a maximum ionic current rectification (ICR) ratio of ca. 56 for pH or 140 for light was achieved. Such smart nanofluidic devices with pH and light dual-responsiveness and asymmetric ion transport behaviors provide a universal strategy for potential applications in chemical sensing, water treatment, and energy conversion and establish a promising platform for exploring advanced quantum ionics biodevices with ultrafast signal transmission, nanochannel-structured bioreactors with high efficiency, etc.