A valley-spin qubit in a carbon nanotube

Although electron spins in III-V semiconductor quantum dots have shown great promise as qubits, a major challenge is the unavoidable hyperfine decoherence in these materials. In group IV semiconductors, the dominant nuclear species are spinless, allowing for qubit coherence times that have been extended up to seconds in diamond and silicon. Carbon nanotubes are a particularly attractive host material, because the spin-orbit interaction with the valley degree of freedom allows for electrical manipulation of the qubit. In this work, we realise such a qubit in a nanotube double quantum dot. The qubit is encoded in two valley-spin states, with coherent manipulation via electrically driven spin resonance (EDSR) mediated by a bend in the nanotube. Readout is performed by measuring the current in Pauli blockade. Arbitrary qubit rotations are demonstrated, and the coherence time is measured via Hahn echo. Although the measured decoherence time is only 65 ns in our current device, this work offers the possibility of creating a qubit for which hyperfine interaction can be virtually eliminated

Controlling the quantum dynamics of a mesoscopic spin bath in diamond

Understanding and mitigating decoherence is a key challenge for quantum science and technology. The main source of decoherence for solid-state spin systems is the uncontrolled spin bath environment. Here, we demonstrate quantum control of a mesoscopic spin bath in diamond at room temperature that is composed of electron spins of substitutional nitrogen impurities. The resulting spin bath dynamics are probed using a single nitrogen-vacancy (NV) centre electron spin as a magnetic field sensor. We exploit the spin bath control to dynamically suppress dephasing of the NV spin by the spin bath. Furthermore, by combining spin bath control with dynamical decoupling, we directly measure the coherence and temporal correlations of different groups of bath spins. These results uncover a new arena for fundamental studies on decoherence and enable novel avenues for spin-based magnetometry and quantum information processing

Double quantum dot with integrated charge sensor based on Ge/Si heterostructure nanowires

Coupled electron spins in semiconductor double quantum dots hold promise as the basis for solid-state qubits. To date, most experiments have used III-V materials, in which coherence is limited by hyperfine interactions. Ge/Si heterostructure nanowires seem ideally suited to overcome this limitation: the predominance of spin-zero nuclei suppresses the hyperfine interaction and chemical synthesis creates a clean and defect-free system with highly controllable properties. Here we present a top gate-defined double quantum dot based on Ge/Si heterostructure nanowires with fully tunable coupling between the dots and to the leads. We also demonstrate a novel approach to charge sensing in a one-dimensional nanostructure by capacitively coupling the double dot to a single dot on an adjacent nanowire. The double quantum dot and integrated charge sensor serve as an essential building block required to form a solid-state spin qubit free of nuclear spin.Comment: Related work at http://marcuslab.harvard.edu and http://cmliris.harvard.ed

Optoelectronics with electrically tunable PN diodes in a monolayer dichalcogenide

One of the most fundamental devices for electronics and optoelectronics is the PN junction, which provides the functional element of diodes, bipolar transistors, photodetectors, LEDs, and solar cells, among many other devices. In conventional PN junctions, the adjacent p- and n-type regions of a semiconductor are formed by chemical doping. Materials with ambipolar conductance, however, allow for PN junctions to be configured and modified by electrostatic gating. This electrical control enables a single device to have multiple functionalities. Here we report ambipolar monolayer WSe2 devices in which two local gates are used to define a PN junction exclusively within the sheet of WSe2. With these electrically tunable PN junctions, we demonstrate both PN and NP diodes with ideality factors better than 2. Under excitation with light, the diodes show photodetection responsivity of 210 mA/W and photovoltaic power generation with a peak external quantum efficiency of 0.2%, promising numbers for a nearly transparent monolayer sheet in a lateral device geometry. Finally, we demonstrate a light-emitting diode based on monolayer WSe2. These devices provide a fundamental building block for ubiquitous, ultra-thin, flexible, and nearly transparent optoelectronic and electronic applications based on ambipolar dichalcogenide materials.Comment: 14 pages, 4 figure

A quantum spin transducer based on nano electro-mechancial resonator arrays

Implementation of quantum information processing faces the contradicting requirements of combining excellent isolation to avoid decoherence with the ability to control coherent interactions in a many-body quantum system. For example, spin degrees of freedom of electrons and nuclei provide a good quantum memory due to their weak magnetic interactions with the environment. However, for the same reason it is difficult to achieve controlled entanglement of spins over distances larger than tens of nanometers. Here we propose a universal realization of a quantum data bus for electronic spin qubits where spins are coupled to the motion of magnetized mechanical resonators via magnetic field gradients. Provided that the mechanical system is charged, the magnetic moments associated with spin qubits can be effectively amplified to enable a coherent spin-spin coupling over long distances via Coulomb forces. Our approach is applicable to a wide class of electronic spin qubits which can be localized near the magnetized tips and can be used for the implementation of hybrid quantum computing architectures

Icodextrin as salvage therapy in peritoneal dialysis patients with refractory fluid overload

BACKGROUND: Icodextrin is a high molecular weight, starch-derived glucose polymer, which is capable of inducing sustained ultrafiltration over prolonged (12–16 hour) peritoneal dialysis (PD) dwells. The aim of this study was to evaluate the ability of icodextrin to alleviate refractory, symptomatic fluid overload and prolong technique survival in PD patients. METHODS: A prospective, open-label, pre-test/post-test study was conducted in 17 PD patients (8 females/9 males, mean age 56.8 ± 2.9 years) who were on the verge of being transferred to haemodialysis because of symptomatic fluid retention that was refractory to fluid restriction, loop diuretic therapy, hypertonic glucose exchanges and dwell time optimisation. One icodextrin exchange (2.5 L 7.5%, 12-hour dwell) was substituted for a long-dwell glucose exchange each day. RESULTS: Icodextrin significantly increased peritoneal ultrafiltration (885 ± 210 ml to 1454 ± 215 ml, p < 0.05) and reduced mean arterial pressure (106 ± 4 to 96 ± 4 mmHg, p < 0.05), but did not affect weight, plasma albumin concentration, haemoglobin levels or dialysate:plasma creatinine ratio. Diabetic patients (n = 12) also experienced improved glycaemic control (haemoglobin Alc decreased from 8.9 ± 0.7% to 7.9 ± 0.7%, p < 0.05). Overall PD technique survival was prolonged by a mean of 11.6 months (95% CI 6.0–17.3 months). On multivariate Cox proportional hazards analysis, extension of technique survival by icodextrin was only significantly predicted by baseline net daily peritoneal ultrafiltration (adjusted HR 2.52, 95% CI 1.13–5.62, p < 0.05). CONCLUSIONS: Icodextrin significantly improved peritoneal ultrafiltration and extended technique survival in PD patients with symptomatic fluid overload, especially those who had substantially impaired peritoneal ultrafiltration

Scale‐free brain dynamics under physical and psychological distress: Pre‐treatment effects in women diagnosed with breast cancer

Stressful life events are related to negative outcomes, including physical and psychological manifestations of distress, and behavioral deficits. Patients diagnosed with breast cancer report impaired attention and working memory prior to adjuvant therapy, which may be induced by distress. In this article, we examine whether brain dynamics show systematic changes due to the distress associated with cancer diagnosis. We hypothesized that impaired working memory is associated with suppression of “long‐memory” neuronal dynamics; we tested this by measuring scale‐free (“fractal”) brain dynamics, quantified by the Hurst exponent (H). Fractal scaling refers to signals that do not occur at a specific time‐scale, possessing a spectral power curve P(f)∝f−β; they are “long‐memory” processes, with significant autocorrelations. In a BOLD functional magnetic resonance imaging study, we scanned three groups during a working memory task: women scheduled to receive chemotherapy or radiotherapy and aged‐matched controls. Surprisingly, patients' BOLD signal exhibited greater H with increasing intensity of anticipated treatment. However, an analysis of H and functional connectivity against self‐reported measures of psychological distress (Worry, Anxiety, Depression) and physical distress (Fatigue, Sleep problems) revealed significant interactions. The modulation of (Worry, Anxiety) versus (Fatigue, Sleep Problems, Depression) showed the strongest effect, where higher worry and lower fatigue was related to reduced H in regions involved in visuospatial search, attention, and memory processing. This is also linked to decreased functional connectivity in these brain regions. Our results indicate that the distress associated with cancer diagnosis alters BOLD scaling, and H is a sensitive measure of the interaction between psychological versus physical distress. Hum Brain Mapp 36:1077–1092, 2015. © 2014 Wiley Periodicals, Inc.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/110706/1/hbm22687-sup-0001-suppinfo01.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/110706/2/hbm22687.pd

Four-electron deoxygenative reductive coupling of carbon monoxide at a single metal site

Carbon dioxide is the ultimate source of the fossil fuels that are both central to modern life and problematic: their use increases atmospheric levels of greenhouse gases, and their availability is geopolitically constrained. Using carbon dioxide as a feedstock to produce synthetic fuels might, in principle, alleviate these concerns. Although many homogeneous and heterogeneous catalysts convert carbon dioxide to carbon monoxide, further deoxygenative coupling of carbon monoxide to generate useful multicarbon products is challenging. Molybdenum and vanadium nitrogenases are capable of converting carbon monoxide into hydrocarbons under mild conditions, using discrete electron and proton sources. Electrocatalytic reduction of carbon monoxide on copper catalysts also uses a combination of electrons and protons, while the industrial Fischer–Tropsch process uses dihydrogen as a combined source of electrons and electrophiles for carbon monoxide coupling at high temperatures and pressures6. However, these enzymatic and heterogeneous systems are difficult to probe mechanistically. Molecular catalysts have been studied extensively to investigate the elementary steps by which carbon monoxide is deoxygenated and coupled, but a single metal site that can efficiently induce the required scission of carbon–oxygen bonds and generate carbon–carbon bonds has not yet been documented. Here we describe a molybdenum compound, supported by a terphenyl–diphosphine ligand, that activates and cleaves the strong carbon–oxygen bond of carbon monoxide, enacts carbon–carbon coupling, and spontaneously dissociates the resulting fragment. This complex four-electron transformation is enabled by the terphenyl–diphosphine ligand, which acts as an electron reservoir and exhibits the coordinative flexibility needed to stabilize the different intermediates involved in the overall reaction sequence. We anticipate that these design elements might help in the development of efficient catalysts for converting carbon monoxide to chemical fuels, and should prove useful in the broader context of performing complex multi-electron transformations at a single metal site

Become the best coach you can be: the role of coach training and coaching experience in workplace coaching quality and quality control

This paper explores whether coach training or coaching experience leads to better coaching quality and quality control. In two large studies, both coaches (N1 = 2267) and personnel managers who book coaches for their company (N2 = 754) answered questions about coaching quality and quality control. The results show that more coach training leads to not only a better self-perceived coaching quality (Study 1) but also a better other-perceived coaching-quality (Study 2); moreover, more coach training positively affects quality control. It is remarkable that coaching experience showed no significant relation regarding other-perceived coaching quality and quality control. Study 2 further revealed that references lead to more recommendations but not to a better coaching quality or quality control. Thus, coach training is an essential factor when selecting organizational coaches. Further research is needed to understand the impact of different approaches to coach trainings on coaching outcomes

What Can Causal Networks Tell Us about Metabolic Pathways?

Graphical models describe the linear correlation structure of data and have been used to establish causal relationships among phenotypes in genetic mapping populations. Data are typically collected at a single point in time. Biological processes on the other hand are often non-linear and display time varying dynamics. The extent to which graphical models can recapitulate the architecture of an underlying biological processes is not well understood. We consider metabolic networks with known stoichiometry to address the fundamental question: “What can causal networks tell us about metabolic pathways?”. Using data from an Arabidopsis BaySha population and simulated data from dynamic models of pathway motifs, we assess our ability to reconstruct metabolic pathways using graphical models. Our results highlight the necessity of non-genetic residual biological variation for reliable inference. Recovery of the ordering within a pathway is possible, but should not be expected. Causal inference is sensitive to subtle patterns in the correlation structure that may be driven by a variety of factors, which may not emphasize the substrate-product relationship. We illustrate the effects of metabolic pathway architecture, epistasis and stochastic variation on correlation structure and graphical model-derived networks. We conclude that graphical models should be interpreted cautiously, especially if the implied causal relationships are to be used in the design of intervention strategies