Spin orbit coupling at the level of a single electron

We utilize electron counting techniques to distinguish a spin conserving fast tunneling process and a slower process involving spin flips in AlGaAs/GaAs-based double quantum dots. By studying the dependence of the rates on the interdot tunnel coupling of the two dots, we find that as many as 4% of the tunneling events occur with a spin flip related to spin-orbit coupling in GaAs. Our measurement has a fidelity of 99 % in terms of resolving whether a tunneling event occurred with a spin flip or not

Quantum Noise Measurement of a Carbon Nanotube Quantum Dot in the Kondo Regime

The current emission noise of a carbon nanotube quantum dot in the Kondo regime is measured at frequencies $\nu$ of the order or higher than the frequency associated with the Kondo effect $k_B T_K/h$, with $T_K$ the Kondo temperature. The carbon nanotube is coupled via an on-chip resonant circuit to a quantum noise detector, a superconductor-insulator-superconductor junction. We find for $h \nu \approx k_B T_K$ a Kondo effect related singularity at a voltage bias $eV \approx h \nu$, and a strong reduction of this singularity for $h \nu \approx 3 k_B T_K$, in good agreement with theory. Our experiment constitutes a new original tool for the investigation of the non-equilibrium dynamics of many-body phenomena in nanoscale devices.Comment: 6 pages, 4 figure

Variation in carbon footprint of milk due to management differences between Swedish dairy farms

To identify mitigation options to reduce greenhouse gas (GHG) emissions from milk production (i.e. the carbon footprint (CF) of milk), this study examined the variation in GHG emissions among dairy farms using data from previous CF studies on Swedish milk. Variation between farms in these production data, which were found to have a strong influence on milk CF were obtained from existing databases of e.g. 1051 dairy farms in Sweden in 2005. Monte Carlo analysis was used to analyse the impact of variations in seven important parameters on milk CF concerning milk yield (energy corrected milk (ECM) produced and delivered), feed dry matter intake (DMI), enteric methane emissions, N content in feed DMI, N-fertiliser rate and diesel used on farm. The largest between farm variation among the analysed production data were N-fertiliser rate (kg/ha) and diesel used (l/ha) on farm (coefficient of variation (CV) 31-38%). For the parameters concerning milk yield and feed DMI the CV was approx. 11 and 8%, respectively. The smallest variation in production data was found for N content in feed DMI. According to the Monte Carlo analysis, these variations in production data led to a variation in milk CF of between 0.94 and 1.33 kg CO2 equivalents (CO2e) per kg ECM, with an average value of 1.13 kg/CO2e kg ECM. We consider that this variation of ±17% that was found based on the used farm data would be even greater if all Swedish dairy farms were included, as the sample of farms in this study was not totally unbiased. The variation identified in milk CF indicates that a potential exists to reduce GHG emissions from milk production on both national and farm level through changes in management. As milk yield and feed DMI are two of the most influential parameters for milk CF, feed conversion efficiency (i.e. units ECM produced per unit DMI) can be used as a rough key performance indicator for predicting CF reductions. However, it must be borne in mind that feeds have different CF due to where and how they are produced