In this work, TiO2 nanowires (TNWs) were synthesized using a plasma‐assisted method, and then Pt clusters were impregnated onto these TNWs. The nanowires supported Pt when combined with various tungsten‐based co‐catalysts, which improved the catalytic performance. The direct conversion of cellulose to C2‐C3 alcohols using tungsten‐based co‐catalysts was enhanced, even at low temperatures. X‐ray photoelectron spectroscopy (XPS) and Raman analysis showed oxygen vacancy (Ov) enrichment on the surface of Pt/TiO2 in the presence of tungsten co‐catalysts, which improved their catalytic activity. The role of metallic platinum (Pto) was also investigated and was found to have a linear relationship with their activity as follows: H2WO4 > (NH4)6H2W12O40 · xH2O > H3PW12O40. Maximum yields of 32.33% and 51.52% of ethanol and propane‐2‐ol at optimum temperatures of 220 and 250 °C, respectively, were obtained with H2WO4. A catalytic reaction performed using tandem catalytic system gave a high ethanol yield of 25.56%, which is low in comparison with an integrated catalytic system. Cellulose conversion was also quantified here by probe electrospray ionization (pESI) coupled with Fourier transform mass spectroscopy (FTMS), which was never reported earlier for these reactions. This method provides evidence of negligible fragmentation of high molecular‐weight compounds, and the maximum cellulose conversion reported here is 100%. A reaction pathway is proposed, based on the experimental results, which elaborates the activation and cleavage of specific C‐C and C‐O bonds.

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Map Reduce is a widely adopted implementation in many fields like that of scientific analysis for data processing, processing data on web as well as areas like high performance computing.Computing systems with heavy data handling requirements should provide an effective scheduling method so that utilization is enhanced.The major problems encountered in scheduling MapReduce jobs are mostly caused by locality and overhead of synchronization.Various other factors like fairness constraints and distribution of workload have been discussed further in the paper and are the highlight of the paper.The paper describes the Hadoop and working of MapReduce in brief.Our paper compares different scheduling methods for handling the mentioned issues in MapReduce and they are compared on the basis of their strength, weakness and features.Through this paper, we aim to consider three different factors along with introducing a small modification to enhance the scheduling by using tabular approach.The purpose is to provide researchers further with a direction in which they can proceed and come up with a more generic algorithm for task scheduling in Hadoop MapReduce.