Sustainable green synthesis of silver nanoparticles by using Rangoon creeper leaves extract and their spectral analysis and anti‐bacterial studies

The plant‐based biological molecules possess exceptionally controlled assembling properties to make them suitable in the synthesis of metal nanoparticles. In the present study, an efficient simple one‐pot method was employed for the synthesis of silver nanoparticles (SNPs) from the Rangoon creeper (RC) aqueous leaf extract. Biomolecules present in the leaf extract play a significant role as reducing agent as well as capping agent in the formation of RC‐SNPs. The formation of RC‐SNPs was confirmed by using several analytical techniques such as Fourier‐transform infrared spectroscopy and ultraviolet–visible spectrophotometer studies. The presence of a sharp surface plasmon resonance peak at 449 nm showed the formation of RC‐SNPs. X‐ray diffraction analysis showed the crystalline nature of the RC‐SNPs with a face‐centred cubic structure. Elemental analysis of RC‐SNPs was done by using energy‐dispersive X‐ray spectroscopy and X‐ray photoelectron spectroscopy. The morphology of RC‐SNPs was examined by transmission electron microscopy (TEM) in the nano range 12 nm, and thermogravimetric‐differential thermal analysis demonstrated the mechanical strength of RC‐SNPs at various temperatures. The authors’ newly synthesised RC‐SNPs exhibited significant anti‐bacterial activity against Staphylococcus aureus and Escherichia coli. Inspec keywords: silver, nanoparticles, X‐ray photoelectron spectra, antibacterial activity, ultraviolet spectra, microorganisms, X‐ray chemical analysis, differential thermal analysis, X‐ray diffraction, transmission electron microscopy, visible spectra, nanofabrication, surface plasmon resonance, Fourier transform infrared spectra, mechanical strengthOther keywords: silver nanoparticles, ultraviolet–visible spectrophotometry, antibacterial activity, sustainable green synthesis, plant‐based biological molecules, assembling properties, reducing agent, capping agent, Fourier‐transform infrared spectroscopy, surface plasmon resonance, Rangoon creeper aqueous leaf extract, X‐ray diffraction, face‐centred cubic structure, elemental analysis, energy‐dispersive X‐ray spectroscopy, X‐ray photoelectron spectroscopy, transmission electron microscopy, TEM, thermogravimetric‐differential thermal analysis, mechanical strength, Staphylococcus aureus, Escherichia coli, Ag     

Electron-Affinity Substituent in 2,6-Dicarbonitrile Diphenyl-1λ5-Phosphinine Towards High-Quality Organic Lasing and Electroluminescence under High Current Injection

Rationally manipulating the functional substituents plays a crucial role in tuning the luminescence and lasing properties of organic gain media. Herein, a cyanophenyl-moiety, which exhibits relatively weaker electron affinity, is connected to 2,6-dicarbonitrile diphenyl-1λ⁵-phosphinine (DCNP) via para-linking. Resultantly, the appreciated locally-excited characteristics ensuring a large oscillator strength and high radiative rate can be reserved in DCNP-4-(4-cyanophenyl) (DCNP-pCN). Interestingly, the weak charge-transfer state from the relative donor (D)/acceptor (A) interplay enables small singlet-triplet splitting (ΔE_ST ≈ 0.45 eV). Thus the triplets generated on DCNP-pCN can be efficiently scavenged by 4,4'-bis[(N-carbazole)styryl]biphenyl (BSBCz), which is used as the host with a lower-lying triplet energy level for DCNP-pCN. Moreover, benefitting from the mediation between the conjugated length extension and weak D/A interplay, the emission spectrum cannot be largely shifted, which can effectively suppress the overlap between the lasing emission of DCNP-pCN and the excited-state absorption of BSBCz, thereby avoiding detrimental singlet-triplet annihilation. Thus, high-quality distributed feedback lasings with ≈2.0 μJ cm⁻² thresholds are achieved, and the organic light-emitting diodes exhibit external quantum efficiency exceeding 2.0% without efficiency rolloff under high current injection, indicating the potential for electrical-pumping organic lasings.     

Downscaling Monsoon Rainfall over River Godavari Basin under Different Climate-Change Scenarios

Evaluating the impact of climate change at river basin level has become essential for proper management of the water resources. In the present study, Godavari River basin in India is taken as study area to project the monthly monsoon precipitation using statistical downscaling. The downscaling method used is a regression based downscaling termed as fuzzy clustering with multiple regression. Among the atmospheric variables simulated by global circulation/climate model (GCM) mean sea level pressure, specific humidity and 500 hPa geopotential height are used as predictors. 1^o × 1^o gridded rainfall data over Godavari river basin are collected from India Meteorological Department (IMD). A statistical relationship is established between the predictors and predictand (monsoon rainfall) to project the monsoon rainfall for the future using the Canadian Earth System Model (CanESM2) over IMD grid points under the Representative Concentration Pathways 2.6, 4.5 and 8.5 (RCP 2.6, 4.5, 8.5) scenarios of Fifth Coupled Model Inter-Comparison Project (CMIP 5). Downscaling procedure is applied to all 25 IMD grid points over the basin to find out the spatial distribution of monsoon rainfall for the future scenarios. For 2.6 and 4.5 scenarios results show an increasing trend. For scenario 8.5 rainfall showed a mixed trend with rainfall decreasing in the first thirty years of prediction and then increasing gradually over the next sixty years.     

Size selective excitonic transition energies in strongly confined CdSe quantum dots

We report on the synthesis of CdSe nanocrystal quantum dots (QDs) of different radii (R). Size dependent optical properties like increase in the confinement energy with decreasing radius for different excitonic transitions are studied. Different excitonic transitions are calculated from the second derivative of UV-vis absorption spectra of as synthesized CdSe QDs. The transitions are assigned to specific states by calculating the transition energies using effective mass approximation. A close matching of the transition energies with the experiment suggesting that the second derivative of the absorption spectra could provide a direct knowledge of the electronic transition for the direct band gap semiconductor quantum dots.     

Spatio-Temporal Variation of Water Availability in a River Basin under CORDEX Simulated Future Projections

In the present study, spatio-temporal variability of hydrological components under climate change is analysed over Wainganga River basin, India. In order to address the climate change projection, hydrological modelling is carried out using a macro scale, semi-distributed three (3)-Layer Variable Infiltration Capacity (VIC-3 L) model. The high-resolution (0.5^o?×?0.5^o) meteorological variables are divided into multiple periods to calibrate and validate the VIC-3 L model. The future projections (2020–2094) of the water balance components are achieved using the high resolution hydrological variables from the COordinated Regional Downscaling EXperiment (CORDEX) dataset under Representative Concentration Pathway (RCP) 4.5 and 8.5 scenarios. The uncertainty associated with the multi-model projections are evaluated using Reliability Ensemble Averaging (REA) and the bias correction is accomplished with non-parametric quantile mapping. A probabilistic based areal drought index is also computed for different scenarios using Standardized Precipitation Evapotranspiration Index (SPEI). From the results, it is observed that amount of rainfall, evapotranspiration, and runoff has increased over the basin with no change in the spatial pattern. However, temporal variability is noticed with an increasing trend for rainfall and runoff in the non-monsoon season than the monsoon. Streamflow is expected to increase significantly, especially for medium to low flows (those occurring between 0.2 and 0.9 probability of exceedance in a Flow Duration Curve). In addition, the area under the drought condition has decreased under the projected climate scenarios.     

Modelling Impacts of Climate Change on a River Basin: Analysis of Uncertainty Using REA & Possibilistic Approach

In the context of climate change, the uncertainty associated with Global Climate Models (GCM) and scenarios needs to be assessed for effective management practices and decision-making. The present study focuses on modelling the GCM and scenario uncertainty using Reliability Ensemble Averaging (REA) and possibility theory in projecting streamflows over Wainganga river basin. A macro scale, semi-distributed, grid-based hydrological model is used to project the streamflows from 2020 to 2094. The observed meteorological data are collected from the India Meteorological Department (IMD) and the streamflow data is obtained from Central Water Commission (CWC) Hyderabad. In REA, meteorological data are weighted based on the performance and convergence criteria (GCM uncertainty). Whereas in possibility theory, based on the projection of different GCMs and scenarios during recent past (2006–2015) possibility values are assigned. Based on the possibility values most probable experiment and weighted mean possible CDF for the future periods are obtained. The result shows that there is no significant difference in the outcomes is observed between REA and possibility theory. The uncertainty associated with GCM is more significant than the scenario uncertainty. An increasing trend in the low and medium flows is predicted in annual and monsoon period. However, flows during the non-monsoon season are projected to increase significantly. Moreover, it is observed that streamflow generation not only depends on the change in precipitation but also depends on the previous state of physical characteristics of the region.     

Non-Stationary Rainfall Intensity-Duration-Frequency Relationship: a Comparison between Annual Maximum and Partial Duration Series

The rainfall Intensity-Duration-Frequency (IDF) relationship is the primary input for storm water management and other engineering design applications across the world and it is developed by fitting an appropriate theoretical probability distribution to annual maximum (AM) series or partial duration series (PDS) of rainfall. The existing IDF relationship developing methods consider the extreme rainfall series as a stationary series. There exist few studies that compared AM and PDS datasets for developing rainfall IDF relationship in a stationary condition. However, during the last few decades, the intensity and frequency of extreme rainfall events are increasing due to global climate change and creating a non-stationary component in the extreme rainfall series. Therefore, the rainfall IDF relationship developed with the stationary assumption is no longer tenable in a changing climate. Hence, it is inevitable to develop non-stationary rainfall IDF relationship and to understand the differences in non-stationary rainfall IDF relationships derived using AM and PDS datasets. Consequently, the objectives of this study are: (1) to develop non-stationary rainfall IDF relationships using both AM and PDS datasets; (2) to compare them in terms of return level estimation. In particular, the non-linear trend in different durations’ PDS and AM datasets of Hyderabad city (India) rainfall is modeled using Multi-objective Genetic Algorithm (MGA) generated Time based covariate. In this study, the PDS datasets are modeled by the Generalized Pareto Distribution (GPD) while the AM datasets are modeled by the Generalized Extreme Value Distribution (GEVD). The time-varying component is introduced in the scale parameter of the GPD and the location parameter of the GEVD by linking the MGA generated covariate. In addition, the complexity of each non-stationary model is identified using the corrected Akaike Information Criteria (AICc) and the statistical significance of trend parameter in the non-stationary models is estimated using the Likelihood Ratio (LR) test. Upon detecting significant superiority of non-stationary models, the return levels of extreme rainfall event for 2-, 5-, 10- and 25-year return periods are calculated using non-stationary models. From the results, it is observed that the non-stationary return levels estimated with PDS datasets are higher than those estimated with AM datasets for short durations and smaller return periods while the non-stationary return levels estimated with AM datasets are higher than those estimated with PDS datasets for long durations and higher return periods.     

Technical Communication: Two-Phase Stochastic Dynamic Programming Model for Optimal Operation of Irrigation Reservoir

A two-phase stochastic dynamic programming model is developed for optimal operation of irrigation reservoirs under a multicrop environment. Under a multicrop environment, the crops compete for the available water whenever the water available is less than the irrigation demands. The performance of the reservoir depends on how the deficit is allocated among the competing crops. The proposed model integrates reservoir release decisions with water allocation decisions. The water requirements of crops vary from period to period and are determined from the soil moisture balance equation taking into consideration the contribution of soil moisture and rainfall for the water requirements of the crops. The model is demonstrated over an existing reservoir and the performance of the reservoir under the operating policy derived using the model is evaluated through simulation.     

Comparison of stochastic and fuzzy dynamic programming models for the operation of a multipurpose reservoir

Reservoir management is a highly complex problem, which includes the uncertainty in the inflows as well as in the objectives. Stochastic dynamic programming (SDP) has been widely used to develop the optimal operating policies of multipurpose reservoirs. In the recent years, fuzzy optimization has been used to incorporate the uncertainty caused due to the imprecise nature of objectives. In the present study, a comparison is made between a conventional SDP model and fuzzy dynamic programming models for optimal operation of a multipurpose reservoir. The two models are developed for Hirakud Reservoir on River Mahanadi in the State of Orissa in India. The performance of the reservoir is evaluated under the operating policies derived from these models through simulation. The results indicate that the fuzzy dynamic programming model performs better in achieving the flood control objective and thus in the overall performan1ce of reservoir.     

Operation of a hydropower system considering environmental flow requirements: A case study in La Nga river basin,Vietnam

A reservoir operation simulation study is carried out to analyze the impact of alternative scenarios of a hydropower system operation on energy production and natural flow regime in the La Nga river basin in Vietnam. The current operation policy causes severe hydrologic alteration in the natural flow regime represented by 32 parameters of the Range of Variability Approach (RVA). In the cases of the hydropower system operating at its full generation capacity and under a specified RVA target range at a downstream location, the power production can be increased by eight and four percent while reducing the overall degree of hydrologic alterations by 24 and 27 percent respectively compared to the existing rule-curve based operation. Results indicate that the system operation can be improved with increased power production while maintaining the environmental flow requirements for downstream ecosystems.