Showing papers by "Jadavpur University published in 2015"

Green synthesis of zinc oxide nanoparticles using Hibiscus subdariffa leaf extract: effect of temperature on synthesis, anti-bacterial activity and anti-diabetic activity

Abstract: Zinc oxide (ZnO) nanoparticles (NPs) have been synthesized using Hibiscus subdariffa leaf extract. Temperature dependent synthesis and particle growth have been studied. Formation of NPs was confirmed by UV-visible (UV-VIS) spectroscopy, Fourier transform infrared (FTIR) spectroscopy and X-ray diffraction (XRD). Electron microscopy has been used to study the morphology and size distribution of the synthesized particles. The synthesized ZnO nanoparticles as potential anti-bacterial agents have been studied on Escherichia coli and Staphylococcus aureus. Another study has indicated that small sized ZnO NPs, stabilized by plant metabolites had better anti-diabetic effect on streptozotocin (STZ) induced diabetic mice than that of large sized ZnO particles. It has also been observed by enzyme linked immunosorbent assay (ELISA) and real time polymerase chain reaction (RT-PCR) that ZnO can induce the function of Th1, Th2 cells and expressions of insulin receptors and other genes of the pancreas associated with diabetes.

Understanding the pathway of antibacterial activity of copper oxide nanoparticles

Abstract: This work investigates the role of oxidation state in the antibacterial activity of copper oxide nanoparticles (NPs). The findings add strong support to a contact killing mechanism of copper oxides (CuO and Cu2O) through which bacteria initially suffer severe damage to the cell envelope. Then further damage ensues by an independent pathway of each copper oxide nanoparticle. Formation of copper(I)–peptide complex from cuprous oxide (Cu2O) and free radical generation from cupric oxide (CuO) were identified as key sources of toxicity towards E.coli. Cu2O rapidly inactivated Fumarase A, an iron sulphur cluster enzyme suggesting the cuprous state of copper binding to the proteins. This inactivation was not noticed in CuO. The percentage of biocidal/bacteriostatic activity is closely related to the oxidation state of the copper oxides. In the case of E.coli, Cu2O nanoparticles showed more efficient antibacterial activity and higher affinity to the bacterial cells. CuO nanoparticles produced significant ROS in terms of super oxides while Cu2O did not. The diminishing defective emission peaks of Cu2O after incubation with microbes strongly suggest the formation of protein complexes. This work is carried out to enable better understanding of the mechanistic pathways of copper oxide nanoparticles.

Alteration of Zeta potential and membrane permeability in bacteria: a study with cationic agents.

Abstract: In the present study, we have tried to establish the correlation between changes in Zeta potential with that of cell surface permeability using bacteria (Escherichia coli and Staphylococcus aureus). An effort has been made to establish Zeta potential as a possible marker for the assessment of membrane damage, with a scope for predicting alteration of cell viability. Cationic agents like, cetyl trimethyl ammonium bromide and polymyxin B were used for inducing alteration of Zeta potential, and the changes occurring in the membrane permeability were studied. In addition, assessment of poly-dispersity index (PDI), cell viability along with confocal microscopic analysis were performed. Based on our results, it can be suggested that alteration of Zeta potential may be correlated to the enhancement of membrane permeability and PDI, and it was observed that beyond a critical point, it leads to cell death (both Gram-positive and Gram-negative bacteria). The present findings can not only be used for studying membrane active molecules but also for understanding the surface potential versus permeability relationship.

Self-powered flexible Fe-doped RGO/PVDF nanocomposite: an excellent material for a piezoelectric energy harvester

Abstract: In this work, we report the superior piezoelectric energy harvester ability of a non-electrically poled Fe-doped reduced graphene oxide (Fe-RGO)/poly(vinylidene fluoride) (PVDF) nanocomposite film prepared through a simple solution casting technique that favors the nucleation and stabilization of ≈99% relative proportion of polar γ-phase. The piezoelectric energy harvester was made with non-electrically poled Fe-RGO/PVDF nanocomposite film that gives an open circuit output voltage and short circuit current up to 5.1 V and 0.254 μA by repetitive human finger imparting. The improvement of the output performance is influenced by the generation of the electroactive polar γ-phase in the PVDF, due to the electrostatic interactions among the –CH2–/–CF2– dipoles of PVDF and the delocalized π-electrons and remaining oxygen functionalities of Fe-doped RGO via ion-dipole and/or hydrogen bonding interactions. Fourier transform infrared spectroscopy (FT-IR) confirmed the nucleation of the polar γ-phase of PVDF by electrostatic interactions and Raman spectroscopy also supported the molecular interactions between the dipoles of PVDF and the Fe-doped RGO nanosheets. In addition, the nanocomposite shows a higher electrical energy density of ≈0.84 J cm−3 at an electric field of 537 kV cm−1, which indicates that it is appropriate for energy storage capabilities. Moreover, the surface of the prepared nanocomposite film is electrically conducting and shows an electrical conductivity of ≈3.30 × 10−3 S cm−1 at 2 wt% loading of Fe-RGO.

Optic Disc and Optic Cup Segmentation Methodologies for Glaucoma Image Detection: A Survey

Abstract: Glaucoma is the second leading cause of loss of vision in the world. Examining the head of optic nerve (cup-to-disc ratio) is very important for diagnosing glaucoma and for patient monitoring after diagnosis. Images of optic disc and optic cup are acquired by fundus camera as well as Optical Coherence Tomography. The optic disc and optic cup segmentation techniques are used to isolate the relevant parts of the retinal image and to calculate the cup-to-disc ratio. The main objective of this paper is to review segmentation methodologies and techniques for the disc and cup boundaries which are utilized to calculate the disc and cup geometrical parameters automatically and accurately to help the professionals in the glaucoma to have a wide view and more details about the optic nerve head structure using retinal fundus images. We provide a brief description of each technique, highlighting its classification and performance metrics. The current and future research directions are summarized and discussed.

Role of Fermentation in Improving Nutritional Quality of Soybean Meal - A Review.

Abstract: Soybean meal (SBM), a commonly used protein source for animal feed, contains anti-nutritional factors such as trypsin inhibitor, phytate, oligosaccharides among others, which limit its utilization. Microbial fermentation using bacteria or fungi has the capability to improve nutritional value of SBM by altering the native composition. Both submerged and solid state fermentation processes can be used for this purpose. Bacterial and fungal fermentations result in degradation of various anti-nutritional factors, an increase in amount of small-sized peptides and improved content of both essential and non-essential amino acids. However, the resulting fermented products vary in levels of nutritional components as the two species used for fermentation differ in their metabolic activities. Compared to SBM, feeding non-ruminants with fermented SBM has several beneficial effects including increased average daily gain, improved growth performance, better protein digestibility, decreased immunological reactivity and undesirable morphological changes like absence of granulated pinocytotic vacuoles.

Reduced graphene oxide-polyaniline composites—synthesis, characterization and optimization for thermoelectric applications

Abstract: Reduced graphene oxide (rGO) can improve the thermoelectric properties of polyaniline (PANI) by varying its concentration in composites of rGO nanosheets and PANI. The figure of merit (ZT) of rGO–PANI composites is increased with an increasing percentage of rGO (up to 50%), which is 7.5 times higher as compared to pure PANI. High resolution transmission electron microscopy (HRTEM), field emission scanning electron microscopy (FESEM) and X-ray diffraction (XRD) analyses show a uniform growth of PANI over the surface of rGO as a template, leading to a more ordered structure with high crystallinity during polymerization. Compared to pure PANI, both the electrical conductivity and thermoelectric power of the rGO–PANI composite is higher due to the increased carrier mobility as confirmed by a Hall effect measurement. Fourier transform infrared spectroscopy (FTIR), ultra-violet visible range spectroscopy (UV-Vis) and Raman spectroscopy analyses reveal that strong π–π interactions assisted the uniform distribution of PANI on the rGO nanosheets. Other strong interactions include electrostatic forces and hydrogen bonding between rGO and PANI, which provide a route for constructing highly ordered chain structures with improved thermoelectric performance of PANI. There is no significant change in the thermal conductivity of the rGO–PANI composite as compared to pure PANI, which improves the thermoelectric performance of composite.

Gene selection from microarray gene expression data for classification of cancer subgroups employing PSO and adaptive K-nearest neighborhood technique

Abstract: A PSO-adaptive KNN based gene selection method is proposed to select useful genes.A heuristic for selecting the optimal values of K efficiently is also proposed.The proposed technique is applied on SRBCT, ALL_AML and MLL microarray datasets.The usefulness of the identified genes is reconfirmed using SVM classifier.The method finds 6, 3 and 4 genes for SRBCT, ALL_AML, and MLL with high accuracy. These days, microarray gene expression data are playing an essential role in cancer classifications. However, due to the availability of small number of effective samples compared to the large number of genes in microarray data, many computational methods have failed to identify a small subset of important genes. Therefore, it is a challenging task to identify small number of disease-specific significant genes related for precise diagnosis of cancer sub classes. In this paper, particle swarm optimization (PSO) method along with adaptive K-nearest neighborhood (KNN) based gene selection technique are proposed to distinguish a small subset of useful genes that are sufficient for the desired classification purpose. A proper value of K would help to form the appropriate numbers of neighborhood to be explored and hence to classify the dataset accurately. Thus, a heuristic for selecting the optimal values of K efficiently, guided by the classification accuracy is also proposed. This proposed technique of finding minimum possible meaningful set of genes is applied on three benchmark microarray datasets, namely the small round blue cell tumor (SRBCT) data, the acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML) data and the mixed-lineage leukemia (MLL) data. Results demonstrate the usefulness of the proposed method in terms of classification accuracy on blind test samples, number of informative genes and computing time. Further, the usefulness and universal characteristics of the identified genes are reconfirmed by using different classifiers, such as support vector machine (SVM).

Conditional spatial fuzzy C-means clustering algorithm for segmentation of MRI images

Abstract: A conditional spatial fuzzy C-means (csFCM) clustering algorithm to improve the robustness of the conventional FCM algorithm is presented.The method incorporates conditional affects and spatial information into the membership functions.The algorithm resolves the problem of sensitivity to noise and intensity inhomogeneity in magnetic resonance imaging (MRI) data.The experimental results on four volumes of simulated and one volume of real-patient MRI brain images, each one having 51 images, support efficiency of the csFCM algorithm.The csFCM algorithm has superior performance in terms of qualitative and quantitative studies on the image segmentation results than the k-means, FCM and some other recently proposed FCM-based algorithms. The fuzzy C-means (FCM) algorithm has got significant importance due to its unsupervised form of learning and more tolerant to variations and noise as compared to other methods in medical image segmentation. In this paper, we propose a conditional spatial fuzzy C-means (csFCM) clustering algorithm to improve the robustness of the conventional FCM algorithm. This is achieved through the incorporation of conditioning effects imposed by an auxiliary (conditional) variable corresponding to each pixel, which describes a level of involvement of the pixel in the constructed clusters, and spatial information into the membership functions. The problem of sensitivity to noise and intensity inhomogeneity in magnetic resonance imaging (MRI) data is effectively reduced by incorporating local and global spatial information into a weighted membership function. The experimental results on four volumes of simulated and one volume of real-patient MRI brain images, each one having 51 images, show that the csFCM algorithm has superior performance in terms of qualitative and quantitative studies such as, cluster validity functions, segmentation accuracy, tissue segmentation accuracy and receiver operating characteristic (ROC) curve on the image segmentation results than the k-means, FCM and some other recently proposed FCM-based algorithms.

Determination of Johnson cook material and failure model constants and numerical modelling of Charpy impact test of armour steel

Abstract: The behaviour of typical armour steel material under large strains, high strain rates and elevated temperatures needs to be investigated to analyse and reliably predict its response to various types of dynamic loading like impact. An empirical constitutive relation developed by Johnson and Cook (J–C) is widely used to capture strain rate sensitivity of the metals. A failure model proposed by Johnson and Cook is used to model the damage evolution and predict failure in many engineering materials. In this work, model constants of J–C constitutive relation and damage parameters of J–C failure model for a typical armour steel material have been determined experimentally from four types of uniaxial tensile test. Some modifications in the J–C damage model have been suggested and Finite Element simulation of three different tensile tests on armour steel specimens under dynamic strain rate (10 −1 s −1 ), high triaxiality and elevated temperature respectively has been done in ABAQUS platform using the modified J–C failure model as user material sub-routine. The simulation results are validated by the experimental data. Thereafter, a moderately high strain rate event viz. Charpy impact test on armour steel specimen has been simulated using J–C material and failure models with the same material parameters. Reasonable agreement between the simulation and experimental results has been achieved.

Nonintrusive Load Monitoring: A Temporal Multilabel Classification Approach

Abstract: The article tackles the issues related to the identification of electrical appliances inside residential buildings. Each appliance can be identified from the aggregate power readings at the meter panel. The possibility of applying a temporal multilabel classification approach in the domain of nonintrusive load monitoring is explored (nonevent-based method). A novel set of metafeatures is proposed. The method is tested on sampling rates based on the capabilities of current smart meters. The proposed approach is validated over a dataset of energy readings at residences for a period of a year for 100 houses containing different sets of appliances (water heater, washing machines, etc.). This method is applicable for the demand side management of households in the current limitation of smart meters; from the inhabitants or from the grid operator's point of view.

DNA-Assisted β-phase Nucleation and Alignment of Molecular Dipoles in PVDF Film: A Realization of Self-Poled Bioinspired Flexible Polymer Nanogenerator for Portable Electronic Devices.

Abstract: A flexible nanogenerator (NG) is fabricated with a poly(vinylidene fluoride) (PVDF) film, where deoxyribonucleic acid (DNA) is the agent for the electroactive β-phase nucleation. Denatured DNA is co-operating to align the molecular -CH2/-CF2 dipoles of PVDF causing piezoelectricity without electrical poling. The NG is capable of harvesting energy from a variety of easily accessible mechanical stress such as human touch, machine vibration, football juggling, and walking. The NG exhibits high piezoelectric energy conversion efficiency facilitating the instant turn-on of several green or blue light-emitting diodes. The generated energy can be used to charge capacitors providing a wide scope for the design of self-powered portable devices.

Inducing Niching Behavior in Differential Evolution Through Local Information Sharing

Abstract: In practical situations, it is very often desirable to detect multiple optimally sustainable solutions of an optimization problem. The population-based evolutionary multimodal optimization algorithms can be very helpful in such cases. They detect and maintain multiple optimal solutions during the run by incorporating specialized niching operations to aid the parallel localized convergence of population members around different basins of attraction. This paper presents an improved information-sharing mechanism among the individuals of an evolutionary algorithm for inducing efficient niching behavior. The mechanism can be integrated with stochastic real-parameter optimizers relying on differential perturbation of the individuals (candidate solutions) based on the population distribution. Various real-coded genetic algorithms (GAs), particle swarm optimization (PSO), and differential evolution (DE) fit the example of such algorithms. The main problem arising from differential perturbation is the unequal attraction toward the different basins of attraction that is detrimental to the objective of parallel convergence to multiple basins of attraction. We present our study through DE algorithm owing to its highly random nature of mutation and show how population diversity is preserved by modifying the basic perturbation (mutation) scheme through the use of random individuals selected probabilistically. By integrating the proposed technique with DE framework, we present three improved versions of well-known DE-based niching methods. Through an extensive experimental analysis, a statistically significant improvement in the overall performance has been observed upon integrating of our technique with the DE-based niching methods.

The influence of hydrogen bonding on the dielectric constant and the piezoelectric energy harvesting performance of hydrated metal salt mediated PVDF films

Abstract: Polyvinylidene fluoride (PVDF) films are filled with various mass fractions (wt%) of hydrated metal salt (MgCl2·6H2O) (Mg-salt) to fabricate high performance piezoelectric energy harvesters (PEHs). They deliver up to 4 V of open circuit voltage by simply repeated human finger imparting (under a pressure of ∼4.45 kPa) and also generate sufficient power to turn on at least ten commercial blue light emitting diodes (LEDs) instantly. The enhanced piezo-response is attributed to the combined effect of the change in the inherent dipole moment of the electroactive phase containing PVDF itself and H-bonding arising between the Mg-salt filler and PVDF via electrostatic interactions. Furthermore, it also successfully charged the capacitors, signifying practical applicability as a piezoelectric based energy harvester power source. UV-visible optical absorption spectral analysis revealed the possibility to estimate a change in the optical band gap value at different concentrations of Mg-salt filler added PVDF films that possess a useful methodology where the Mg-salt can be used as an optical probe. In addition dielectric properties have been studied to understand the role of molecular kinetic and interfacial polarization occurs in H-bond PVDF films at different applied frequencies at room temperature.

Mobility Models, Traces and Impact of Mobility on Opportunistic Routing Algorithms: A Survey

Abstract: Mobile Opportunistic Network (MON) is characterized by intermittent connectivity where communication largely depends on the mobility pattern of the participating nodes. In MON, a node can take the custody of a packet for a long time and carry it until a new forwarding path has been established, unlike mobile adhoc network (MANET), where a node must drop the packet otherwise. Therefore, routing in MON depends on the repeated make-and-break of communication links, which again depends on the mobility of the nodes as they encounter and drift away from each other. MONs can simply be formed by humans carrying hand-held devices (like Personal Digital Assistant [PDAs] or cell phones) or on-board devices installed in vehicles. Therefore, with mobility playing a major role in the performance of MON, researchers have repeatedly tried to understand the nature of mobility with respect to humans, vehicles, and wild animals. To study the nature of mobility, researchers have collected mobility traces, proposed mobility models, and analyzed the performance of MON with respect to various mobility parameters. This article provides a detailed survey of different mobility models which have been proposed to date and how mobility largely determines the performance of opportunistic routing. We divide the article into four major sections. First, we provide a detailed survey of all the synthetic mobility models which have been developed to date. Second, we study the various mobility traces which have been collected and analyzed. Third, we study how mobility parameters affect the performance of MON. Finally, we highlight on some of the research areas and open challenges which yet remain unsolved.

A Survey of Multiobjective Evolutionary Clustering

Abstract: Data clustering is a popular unsupervised data mining tool that is used for partitioning a given dataset into homogeneous groups based on some similarity/dissimilarity metric. Traditional clustering algorithms often make prior assumptions about the cluster structure and adopt a corresponding suitable objective function that is optimized either through classical techniques or metaheuristic approaches. These algorithms are known to perform poorly when the cluster assumptions do not hold in the data. Multiobjective clustering, in which multiple objective functions are simultaneously optimized, has emerged as an attractive and robust alternative in such situations. In particular, application of multiobjective evolutionary algorithms for clustering has become popular in the past decade because of their population-based nature. Here, we provide a comprehensive and critical survey of the multitude of multiobjective evolutionary clustering techniques existing in the literature. The techniques are classified according to the encoding strategies adopted, objective functions, evolutionary operators, strategy for maintaining nondominated solutions, and the method of selection of the final solution. The pros and cons of the different approaches are mentioned. Finally, we have discussed some real-life applications of multiobjective clustering in the domains of image segmentation, bioinformatics, web mining, and so forth.

Photocatalytic activity of biogenic silver nanoparticles synthesized using yeast ( Saccharomyces cerevisiae ) extract

Abstract: Synthesis of metallic and semiconductor nanoparticles through physical and chemical route is quiet common but biological synthesis procedures are gaining momentum due to their simplicity, cost-effectivity and eco-friendliness. Here, we report green synthesis of silver nanoparticles from aqueous solution of silver salts using yeast (Saccharomyces cerevisiae) extract. The nanoparticles formation was gradually investigated by UV–Vis spectrometer. X-ray diffraction analysis was done to identify different phases of biosynthesized Ag nanoparticles. Transmission electron microscopy was performed to study the particle size and morphology of silver nanoparticles. Fourier transform infrared spectroscopy of the nanoparticles was performed to study the role of biomolecules capped on the surface of Ag nanoparticles during interaction. Photocatalytic activity of these biosynthesized nanoparticles was studied using an organic dye, methylene blue under solar irradiation and these nanoparticles showed efficacy in degrading the dye within a few hours of exposure.

Fractional-order load-frequency control of interconnected power systems using chaotic multi-objective optimization

Abstract: Multi-objective optimization-based fractional-order PID controller is designed.NSGA-II algorithm is augmented with chaotic Logistic and Henon map.Load disturbance rejection and controller effort are minimized as two conflicting objectives.FOPID controller outperforms the PID controller in suppressing frequency deviation.Better trade-off is obtained for load-frequency control of power systems with FOPID. Fractional-order proportional-integral-derivative (FOPID) controllers are designed for load-frequency control (LFC) of two interconnected power systems. Conflicting time-domain design objectives are considered in a multi-objective optimization (MOO)-based design framework to design the gains and the fractional differ-integral orders of the FOPID controllers in the two areas. Here, we explore the effect of augmenting two different chaotic maps along with the uniform random number generator (RNG) in the popular MOO algorithm-the Non-dominated Sorting Genetic Algorithm-II (NSGA-II). Different measures of quality for MOO, e.g. hypervolume indicator, moment of inertia-based diversity metric, total Pareto spread, spacing metric, are adopted to select the best set of controller parameters from multiple runs of all the NSGA-II variants (i.e. nominal and chaotic versions). The chaotic versions of the NSGA-II algorithm are compared with the standard NSGA-II in terms of solution quality and computational time. In addition, the Pareto optimal fronts showing the trade-off between the two conflicting time domain design objectives are compared to show the advantage of using the FOPID controller over that with simple PID controller. The nature of fast/slow and high/low noise amplification effects of the FOPID structure or the four quadrant operation in the two inter-connected areas of the power system is also explored. A fuzzy logic-based method has been adopted next to select the best compromise solution from the best Pareto fronts corresponding to each MOO comparison criteria. The time-domain system responses are shown for the fuzzy best compromise solutions under nominal operating conditions. Comparative analysis on the merits and de-merits of each controller structure is reported then. A robustness analysis is also done for the PID and the FOPID controllers.

Self-Poled Transparent and Flexible UV Light-Emitting Cerium Complex–PVDF Composite: A High-Performance Nanogenerator

Abstract: Cerium(III)-N,N-dimethylformamide-bisulfate [Ce(DMF)(HSO4)3] complex is doped into poly(vinylidene fluoride) (PVDF) to induce a higher yield (99%) of the electroactive phases (β- and γ-phases) of PVDF. A remarkable enhancement of the output voltage (∼32 V) of a nanogenerator (NG) based on a nonelectrically poled cerium(III) complex containing PVDF composite film is achieved by simple repeated human finger imparting, whereas neat PVDF does not show this kind of behavior. This high electrical output resembles the generation of self-poled electroactive β-phase in PVDF due to the electrostatic interactions between the fluoride of PVDF and the surface-active positive charge cloud of the cerium complex via H-bonding and/or bipolar interaction among the opposite poles of cerium complex and PVDF, respectively. The capacitor charging capability of the flexible NG promises its applicability as piezoelectric-based energy harvester. The cerium(III) complex doped PVDF composite film exhibit an intense photoluminescenc...

Lead-free ZnSnO3/MWCNTs-based self-poled flexible hybrid nanogenerator for piezoelectric power generation.

Abstract: A high-performance flexible piezoelectric hybrid nanogenerator (HNG) based on lead-free perovskite zinc stannate (ZnSnO3) nanocubes and polydimethylsiloxane (PDMS) composite with multiwall carbon nanotubes (MWCNTs) as supplement filling material is demonstrated. Even without any electrical poling treatment, the HNG possesses an open-circuit voltage of 40 V and a short-circuit current of 0.4 μA, respectively, under repeated human finger impact. It has been demonstrated that the output volume power density of 10.8 μW cm(-3) from a HNG can drive several colour light emitting diodes (LEDs) and a charge capacitor that powers up a calculator, indicating an effective means of energy harvesting power source with high energy conversion efficiency (∼1.17%) for portable electronic devices.

Low temperature solution processed ZnO/CuO heterojunction photocatalyst for visible light induced photo-degradation of organic pollutants

Abstract: The possibility of integrating manifold functionalities, coupled with various associated noble interface phenomena in the hierarchical nanoforms, either comprised of geometrical intricacies or achieved via the rational coupling of several components, has made them immensely pertinent from both research and technological aspects. Here, an oxide based nanostructure hybrid has been realized by integrating low bandgap copper oxide nanosheet with high bandgap one dimensional zinc oxide nanowires on a flexible carbon cloth as well as on a flat substrate. These bandgap modulated hybrid nanostructures are generated for the efficient absorption of visible light, targeting their possible use in waste water management. Our work presents a novel ambient condition protocol for morphological tuning in the nanoscale or their organization in a hierarchical structure. Environmental remediation through catalytic activity under the visible light irradiation of the synthesized samples was inspected using both anionic and cationic dyes (methyl orange and Rhodamine B, respectively) as the model contaminants, where the optimized heterostructure exhibits significantly better performance than the mono component oxides. Such enhanced performance could be explained by the formation of favorable staggered gap multiple p–n junctions at ZnO/CuO interface, which in turn retards the photogenerated electron–hole pair recombination within the heterostructure. The signature of successful p–n junction formation at ZnO nanorod/CuO nanosheet interface has been identified via current–voltage measurements with a conducting tip AFM in contact mode. The creative designing of novel heterojunctions adopting this protocol will pave the way for the utilization of the entire visible light range: thus, offering potential in solar energy conversion devices.