Non-enzymatic electronic detection of glucose using aminophenylboronic acid functionalized reduced graphene oxide

We report the non-enzymatic electronic detection of glucose using field effect transistor (FET) devices made of aminophenylboronic acid (APBA) functionalized reduced graphene oxide (RGO). Detection of glucose molecules was carried out over a wide dynamic range of concentration varying from 100 pM to 100 mM with a detection limit of similar to 2 nM using both covalently and non-covalently functionalized APBA-RGO complex. The normalized change in electrical conductance data shows that the FET devices made of non-covalently functionalized APBA-RGO complex (nc-APBA-RGO) exhibited a linear response to glucose aqueous solution of concentrations varying from 1 nM to 10 mM and showed 4 times enhanced sensitivity over the devices made of covalently functionalized APBA-RGO complex (c-APBA-RGO). Specificity of APBA-RGO complex to glucose is confirmed from the observation of negligible change in electrical conductance after exposure to 0.1 mM of lactose and other interfering factors. (C) 2015 Elsevier B.V. All rights reserved.

On the detection of human influence in extreme precipitation over India

Climate change is expected to influence extreme precipitation which in turn might affect risks of pluvial flooding. Recent studies on extreme rainfall over India vary in their definition of extremes, scales of analyses and conclusions about nature of changes in such extremes. Fingerprint-based detection and attribution (D&A) offer a formal way of investigating the presence of anthropogenic signals in hydroclimatic observations. There have been recent efforts to quantify human effects in the components of the hydrologic cycle at large scales, including precipitation extremes. This study conducts a D&A analysis on precipitation extremes over India, considering both univariate and multivariate fingerprints, using a standardized probability-based index (SPI) from annual maximum one-day (RX1D) and five-day accumulated (RX5D) rainfall. The pattern-correlation based fingerprint method is used for the D&A analysis. Transformation of annual extreme values to SPI and subsequent interpolation to coarser grids are carried out to facilitate comparison between observations and model simulations. Our results show that in spite of employing these methods to address scale and physical processes mismatch between observed and model simulated extremes, attributing changes in regional extreme precipitation to anthropogenic climate change is difficult. At very high (95%) confidence, no signals are detected for RX1D, while for the RX5D and multivariate cases only the anthropogenic (ANT) signal is detected, though the fingerprints are in general found to be noisy. The findings indicate that model simulations may underestimate regional climate system responses to increasing human forcings for extremes, and though anthropogenic factors may have a role to play in causing changes in extreme precipitation, their detection is difficult at regional scales and not statistically significant. (C) 2015 Elsevier B.V. All rights reserved.