Excitonic Transitions and Off-resonant Optical Limiting in CdS Quantum Dots Stabilized in a Synthetic Glue Matrix

Stable films containing CdS quantum dots of mean size 3.4 nm embedded in a solid host matrix are prepared using a room temperature chemical route of synthesis. CdS/synthetic glue nanocomposites are characterized using high resolution transmission electron microscopy, infrared spectroscopy, differential scanning calorimetry and thermogravimetric analysis. Significant blue shift from the bulk absorption edge is observed in optical absorption as well as photoacoustic spectra indicating strong quantum confinement. The exciton transitions are better resolved in photoacoustic spectroscopy compared to optical absorption spectroscopy. We assign the first four bands observed in photoacoustic spectroscopy to 1se–1sh, 1pe–1ph, 1de–1dhand 2pe–2phtransitions using a non interacting particle model. Nonlinear absorption studies are done using z-scan technique with nanosecond pulses in the off resonant regime. The origin of optical limiting is predominantly two photon absorption mechanism

Greenhouse gas mitigation potential of the world’s grazing lands: Modeling soil carbon and nitrogen fluxes of mitigation practices

This study provides estimates of the net GHG mitigation potential of a selected range of management practices in the world’s native and cultivated grazing lands. The Century and Daycent models are used to calculate the changes in soil carbon stocks, soil N2O emissions, and forage removals by ruminants associated with these practices. GLEAM is used in combination with these models to establish grazing area boundaries and to parameterize links between forage consumption, animal production and animal GHG emissions. This study provides an alternative to the usual approach of extrapolating from a small number of field studies and by modeling the linkage between soil, forage and animals it sheds new light on the net mitigation potential of C sequestration practices in the world’s grazing lands. Three different mitigation practices are assessed in this study, namely, improved grazing management, legume sowing and N fertilization. We estimate that optimization of grazing pressure could sequester 148 Tg CO2 yr−1. The soil C sequestration potential of 203 Tg CO2 yr−1 for legume sowing was higher than for improved grazing management, despite being applied over a much smaller total area. However, N2O emissions from legumes were estimated to offset 28% of its global C sequestration benefits, in CO2 equivalent terms. Conversely, N2O emissions from N fertilization exceeded soil C sequestration, in all regions. Our estimated potential for increasing C stocks though in grazing lands is lower than earlier worldwide estimates (Smith et al., 2007 and Lal, 2004), mainly due to the much smaller grazing land area over which we estimate mitigation practices to be effective. A big concern is the high risk of the practices, particularly legumes, increasing soil-based GHGs if applied outside of this relatively small effective area. More work is needed to develop indicators, based on biophysical and management characteristics of grazing lands, to identify amenable areas before these practices can be considered ready for large scale implementation. The additional ruminant GHG emissions associated with higher forage output are likely to substantially reduce the mitigation potential of these practices, but could contribute to more GHG-efficient livestock production

The depth distribution of organic carbon in the soils of eastern Australia

Subsurface soil organic carbon (SOC) is a large but still poorly understood component of the global carbon cycle. We investigated the depth distribution of SOC in eastern Australia, testing the hypotheses that SOC content near the surface is linked with water availability, whereas the distribution of SOC with depth is linked with land use, site factors and temperature. To do this, we measured SOC concentration to 1 m at 100 sites across eastern Australia, and fitted three parameter exponential depletion models to the results. Three machine learning algorithms were used to identify predictors important to the model parameters. Multiple regression models were then created based upon the machine learning results using bootstrapped stepwise regressions and the relative importance of the selected variables was assessed using proportional marginal variance decomposition. Surface SOC concentration was influenced predominantly by climate variables, of which seasonal rainfall was by far the most important. At depth, SOC storage was most influenced by site factors (mainly bulk density and soil type), and both land use and climate contributed similar amounts to model explained variance. The depth distribution of SOC was most influenced by land use, which accounted for ~60% of model explained variance, with site and climate factors being approximately equally important. These results support our hypotheses regarding the drivers of SOC depth distribution in eastern Australia and can be used to identify regions with the potential for additional subsurface soil carbon storage