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A distinguishing feature of spin accumulation in ferromagnet–semiconductor devices is its precession in a magnetic field. This is the basis for detection techniques such as the Hanle effect, but these approaches become ineffective as the spin lifetime in the semiconductor decreases. For this reason, no electrical Hanle measurement has been demonstrated in GaAs at room temperature. We show here that by forcing the magnetization in the ferromagnet to precess at resonance instead of relying only on the Larmor precession of the spin accumulation in the semiconductor, an electrically generated spin accumulation can be detected up to 300 K. The injection bias and temperature dependence of the measured spin signal agree with those obtained using traditional methods. We further show that this approach enables a measurement of short spin lifetimes (<100 ps), a regime that is not accessible in semiconductors using traditional Hanle techniques.

Many envisioned applications of semiconductor nanocrystals (NCs), such as thermoelectric generators and transparent conductors, require metallic (nonactivated) charge transport across an NC network. Although encouraging signs of metallic or near-metallic transport have been reported, a thorough demonstration of nonzero conductivity, σ, in the 0 K limit has been elusive. Here, we examine the temperature dependence of σ of ZnO NC networks. Attaining both higher σ and lower temperature than in previous studies of ZnO NCs (T as low as 50 mK), we observe a clear transition from the variable-range hopping regime to the metallic regime. The critical point of the transition is distinctly marked by an unusual power law close to σ ∝ T1/5. We analyze the critical conductivity data within a quantum critical scaling framework and estimate the metal-insulator transition (MIT) criterion in terms of the free electron density, n, and interparticle contact radius, ρ.

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Objectives: This study investigated whether emergency department (ED) variables could be used in mathematical models to predict a future surge in ED volume based on recent levels of use of physician capacity. The models may be used to guide decisions related to on-call staffing in non-crisis-related surges of patient volume.Methods: A retrospective analysis was conducted using information spanning July 2009 through June 2010 from a large urban teaching hospital with a Level I trauma center. A comparison of significance was used to assess the impact of multiple patient-specific variables on the state of the ED. Physician capacity was modeled based on historical physician treatment capacity and productivity. Binary logistic regression analysis was used to determine the probability that the available physician capacity would be sufficient to treat all patients forecasted to arrive in the next time period. The prediction horizons used were 15 minutes, 30 minutes, 1 hour, 2 hours, 4 hours, 8 hours, and 12 hours. Five consecutive months of patient data from July 2010 through November 2010, similar to the data used to generate the models, was used to validate the models. Positive predictive values, Type I and Type II errors, and real-time accuracy in predicting noncrisis surge events were used to evaluate the forecast accuracy of the models. Results:The ratio of new patients requiring treatment over total physician capacity (termed the care utilization ratio [CUR]) was deemed a robust predictor of the state of the ED (with a CUR greater than 1 indicating that the physician capacity would not be sufficient to treat all patients forecasted to arrive). Prediction intervals of 30 minutes, 8 hours, and 12 hours performed best of all models analyzed, with deviances of 1.000, 0.951, and 0.864, respectively. A 95% significance was used to validate the models against the July 2010 through November 2010 data set. Positive predictive values ranged from 0.738 to 0.872, true positives ranged from 74% to 94%, and true negatives ranged from 70% to 90% depending on the threshold used to determine the state of the ED with the 30-minute prediction model. Conclusions:The CUR is a new and robust indicator of an ED system's performance. The study was able to model the tradeoff of longer time to response versus shorter but more accurate predictions, by investigating different prediction intervals. Current practice would have been improved by using the proposed models and would have identified the surge in patient volume earlier on noncrisis days.