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The electrochemical CO2reduction reaction (CO2RR) is a promising strategy to alleviate excessive CO2levels in the atmosphere and produce value-added feedstocks and fuels. However, the synthesis of high-efficiency and robust electrocatalysts remains a great challenge. This work reports the green preparation of surface-oxygen-rich carbon-nanorod-supported bismuth nanoparticles (SOR Bi@C NPs) for an efficient CO2RR toward formate. The resultant SOR Bi@C NPs catalyst displays a Faradaic efficiency of more than 91% for formate generation over a wide potential range of 440 mV. Ex situXPS and XANES and in situRaman spectroscopy demonstrate that the Bi-O/Bi (110) structure in the pristine SOR Bi@C NPs can remain stable during the CO2RR process. DFT calculations reveal that the Bi-O/Bi (110) structure can facilitate the formation of the *OCHO intermediate. This work provides an approach to the development of high-efficiency Bi-based catalysts for the CO2RR and offers a unique insight into the exploration of advanced electrocatalysts.

An aqueous electrolytic MnO2–Zn battery with eye-catching Mn2+/MnO2cathode chemistry has been attracting immense interest for next-generation energy storage devices due to its irreplaceable advantages. However, the limited MnO2conductivity restricts its long service life at high areal capacities. Here, we report a high-performance electrolytic MnO2–Zn battery via a bromine redox mediator, to enhance its electrochemical performance. The MnO2/Br2–Zn battery displays a high discharge voltage of 1.98 V with a capacity of ∼5.8 mAh cm–2. It also shows an excellent rate performance of 20 C with a long-term stability of over 600 cycles. Furthermore, the scaled-up MnO2/Br2–Zn battery with a capacity of ∼950 mAh exhibits a stable 100 cycles with a practical cell energy density of ∼32.4 Wh kg–1and an attractively low energy cost of below 15 US$ kWh–1. The design approach can be generalized to other electrodes and battery systems, thus opening up new possibilities for large-scale energy storage.

The development of efficient, stable, and low-cost bifunctional catalysts for the hydrogen evolution/oxidation reaction (HER/HOR) is critical to promote the application of hydrogen gas batteries in large scale energy storage systems. Here we demonstrate a non-noble metal high-entropy alloy grown on Cu foam (NNM-HEA@CF) as a self-supported catalytic electrode for nickel-hydrogen gas (Ni-H2) batteries. Experimental and theoretical calculation results reveal that the NNM-HEA catalyst greatly facilitates the HER/HOR catalytic process through the optimized electronic structures of the active sites. The assembled Ni-H2battery with NNM-HEA@CF as the anode shows excellent rate capability and exceptional cycling performance of over 1800 h without capacity decay at an areal capacity of 15 mAh cm–2. Furthermore, a scaled-up Ni-H2battery fabricated with an extended capacity of 0.45 Ah exhibits a high cell-level energy density of ∼109.3 Wh kg–1. Moreover, its estimated cost reaches as low as ∼107.8 $ kWh–1based on all key components of electrodes, separator and electrolyte, which is reduced by more than 6 times compared to that of the commercial Pt/C-based Ni-H2battery. This work provides an approach to develop high-efficiency non-noble metal-based bifunctional catalysts for hydrogen batteries in large-scale energy storage applications.

ABSTRACTThe present study computes multidimensional poverty and compares it with unidimensional estimates of poverty for the district of Mandi Bahuddin in the Punjab province of Pakistan for the years 2010 and 2014. By employing the Foster, Greer and Thorbecke (FGT) and Alkire-Foster Methods, the incidence, severity and depth of unidimensional and multidimensionality poverty was estimated. We found an increase in absolute and relative poverty levels in 2014 as compared to 2010. The multidimensional poverty in terms of household assets has also increased over time. However, the relative proportion of educational and health poverty towards MPI remains higher, thereby calling for a holistic approach to identify multidimensional poverty in the social sector. A comprehensive policy dossier needs to be framed for designing effective poverty alleviation and social welfare programmes in the Punjab, Pakistan.

Daily and annual maximum wind speed quantiles can be estimated using extreme value theory for any metrological site of interest. These estimates are of vast importance for modelling and predicting maximum wind speed. This paper develops an efficient modelling paradigm of extreme winds by analyzing daily and annual maximum wind speed data via frequentist and Bayesian methodologies. For this purpose, the generalized extreme value (GEV) model is used for yearly maxima, and the generalized Pareto distribution (GPD) is used for daily exceedance over a high threshold. In frequentist inference, the parameters of both models are estimated using the maximum likelihood and linear moments method. In contrast, the Bayesian Markov Chain Monte Carlo procedure with the Metropolis-Hasting algorithm is used. In addition, the informative priors for both models are constructed empirically using historical records of wind speed data from five other weather stations of Pakistan and one belonging to India. The results show that the Bayesian modelling provides apparent benefits in terms of improved accuracy in the estimation of the parameters as well as return levels of both distributions. Furthermore, the Bayesian analysis expresses that posterior inference might be affected by the choice of priors used to formulate the informative priors. Overall, based on assessment measures, the GPD fitted through Bayesian informative priors provides an efficient estimation strategy in terms of precision than other frameworks when uncertainty in parameters and return levels are taken into account. Our methodology can be implemented easily to other regions by considering the prior information from the border area stations of other countries (e.g., China, Afghanistan, India, and Iran). Moreover, the return level estimates of the GPD based on informative Bayesian priors are very beneficial in policymaking and wind energy generation engineering for the Thatta region of the country.

Aqueous rechargeable zinc metal batteries display high theoretical capacity along with economical effectiveness, environmental benignity and high safety. However, dendritic growth and chemical corrosion at the Zn anodes limit their widespread applications. Here, we construct a Zn/Bi electrode via in-situ growth of a Bi-based energizer upon Zn metal surface using a replacement reaction. Experimental and theoretical calculations reveal that the Bi-based energizer composed of metallic Bi and ZnBi alloy contributes to Zn plating/stripping due to strong adsorption energy and fast ion transport rates. The resultant Zn/Bi electrode not only circumvents Zn dendrite growth but also improves Zn anode anti-corrosion performance. Specifically, the corrosion current of the Zn/Bi electrode is reduced by 90% compared to bare Zn. Impressively, an ultra-low overpotential of 12 ​mV and stable cycling for 4000 ​h are achieved in a Zn/Bi symmetric cell. A Zn–Cu/Bi asymmetric cell displays a cycle life of 1000 cycles, with an average Coulombic efficiency as high as 99.6%. In addition, an assembled Zn/Bi-activated carbon hybrid capacitor exhibits a stable life of more than 50,000 cycles, an energy density of 64 ​Wh kg−1, and a power density of 7 ​kW ​kg−1. The capacity retention rate of a Zn/Bi–MnO2full cell is improved by over 150% compared to a Zn–MnO2cell without the Bi-based energizer. Our findings open a new arena for the industrialization of Zn metal batteries for large-scale energy storage applications.

Demand for bandwidth hungry applications and mobile services is pushing an unquenchable need for wireless capacity. Existing radio frequency networks are characterized by shared medium, inadequate spectrum, and restricted user capacity. Solid state lighting is modernizing indoor as well as outdoor illumination. The capability of quick switching of light emitting diode makes them superior to other lighting sources enabling simultaneous use as a communication and illumination device. Visible light communication (VLC) using light emitting diodes is an attractive approach for many networking scenarios and is considered as a complementary technology for future heterogeneous wireless networks. This article presents an architectural perspective on the applications and data rate improvement strategies. A wide range of VLC applications have been investigated, comparison has been drawn with radio frequency, and generalized network architecture is also proposed. Numerous applications of VLC are data rate intensive, so data rate improvement approaches have also been studied. At the end, a lucid conclusion is drawn about the applicability, acceptability, and utilization of VLC and co‐VLC–based systems. Visible light communication (VLC) is a revolutionary technology which has the potential not only to disentangle RF spectrum crunch issue but also fulfil the requirements of future bandwidth hungry applications. In next generation networks, VLC will be working in conjunction with the existing RF technologies to enrich our lives by enabling ubiquitous, high‐speed, and secure communication. The future of wireless communication is GREEN!

Visible light positioning techniques employing received signal strength (RSS)–based fingerprints are becoming popular and ubiquitous. However, RSS is more susceptible to signal degradation and environmental changes resulting in location inaccuracies. To minimize these limitations, clustering in conjunction with linear regression is applied to RSS database made up of light intensity variations of light emitting diodes. Optimum cluster size is determined and trained clusters are exploited for location assessment by curtailing the difference between the database readings and cluster centroids. Regression is then applied on the clustered data, which partitions it further and helps refining the results. Simulation results of the proposed algorithm dictate a significant improvement in location estimation accuracy of up to 40 cm in an indoor environment with the dimensions of 5 m × 5 m × 4 m and exhibit superior performance than many state‐of‐the‐art RSS‐based methods. A novel visible light based indoor localization system has been developed utilizing received signal strength (RSS) and machine learning approaches. Results show that signal degradations and other environmental changes resulting location inaccuracies in RSS can be minimized using machine learning techniques.