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Considering the need of applicability of green chemistry in research, a series of heterogeneous catalysts, viz., sulfated iron oxide, zirconia supported tungstophosphoric acid and sulfated zirconia have been synthesized by a solvent-free method. The prepared catalysts were used in the esterification of nonanoic acid with methanol and were compared with ion exchange resins for the assessment of their catalytic performance. Sulfated iron oxide was found to be best with an acid conversion of 83%, which is quite comparable with Amberlyst 15 and Dowex50Wx2. The high catalyst loading, cost, low thermal stability, and long reaction time make ion exchange resins uneconomical to use over other alternatives that result in same efficiency with low cost. Sulfated iron oxide was further optimized for its preparation conditions for high catalytic performance in the esterification reaction. The catalysts were characterized for their crystallinity, surface morphology, composition, weight loss, and structure by X-ray diffraction, scanning electron microscopy, energy-dispersive X-ray spectroscopy, thermogravimetric analysis, and Fourier transform infrared spectroscopy. The evaluated catalysts were compared on the basis of their preparation time, catalytic performance, catalyst loading, reaction time, and overall cost.

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Web spectrum monitoring systems based on crowdsourcing have recently gained popularity. These systems are however limited to applications of interest for governamental organizations or telecom providers, and only provide aggregated information about spectrum statistics. The result is that there is a lack of interest for layman users to participate, which limits its widespread deployment. We present Electrosense+ which addresses this challenge and creates a generalpurpose and open platform for spectrum monitoring using low-cost, embedded, and softwaredefined spectrum IoT sensors. Electrosense+ allows users to remotely decode specific parts of the radio spectrum. It builds on the centralized architecture of its predecessor, Electrosense, for controlling and monitoring the spectrum IoT sensors, but implements a real-time and peer-to-peer communication system for scalable spectrum data decoding. We propose different mechanisms to incentivize the participation of users for deploying new sensors and keep them operational in the Electrosense network. As a reward for the user, we propose an incentive accounting system based on virtual tokens to encourage the participants to host IoT sensors. We present the new Electrosense+ system architecture and evaluate its performance at decoding various wireless signals, including FM radio, AM radio, ADS-B, AIS, LTE, and ACARS.

The present study demonstrates a spatially distributed application of a field-scale annual soil loss model, the modified-MMF (MMMF), to a large watershed using hydrological routing techniques, remote sensing data and geospatial technologies. In this study, the MMMF model is implemented after incorporating the corrections suggested in recent literature along with appropriate modifications of the model to suit the agro-climatological conditions prevailing in most parts of India. Sensitivity analysis carried out through an Average Linear Sensitivity approach indicates that the model outputs are highly sensitive to soil moisture (MS), bulk density (BD), effective hydraulic depth (EHD), ground cover (GC) and settling velocity for clay (VS c ). During calibration and validation, the performance evaluation statistics are mostly in the range of very good to satisfactory for both runoff and soil loss at the watershed outlet. Even spatial validation of the results of intermediate processes in the water phase and the sediment phase, although qualitative, seems to be reasonable and rational. Furthermore, the soil erosion severity analysis for different land-uses existing in the watershed indicates that about 90% of the watershed area, especially that occupied by agricultural lands, is vulnerable to the longterm effects of soil erosion. HIGHLIGHTS OF THE STUDY• Implements the MMMF model, a field scale soil erosion model to a large watershed.• Provides a framework for implementation of the MMMF model at watershed scale.• Improves the MMMF model to suit Indian climatic conditions. • Point based quantitative tests show acceptable performance of the MMMF Model.