Whether calibrating equipment or inspecting products on the factory floor, metrology requires many complicated statistical calculations to achieve a full understanding and evaluation of measurement uncertainty and quality. In order to assist its workforce in performing these calculations in a consistent and rigorous way, the Primary Standards Lab at Sandia National Laboratories (SNL) has developed a free and open-source software package for computing various metrology calculations from uncertainty propagation to risk analysis. In addition to propagating uncertainty through a measurement model using the well-known Guide to Expression of Uncertainty in Measurement or Monte Carlo approaches, evaluating the individual Type A and Type B uncertainty components that go into the measurement model often requires other statistical methods such as analysis of variance or determining uncertainty in a fitted curve. Once the uncertainty in a measurement has been calculated, it is usually evaluated from a risk perspective to ensure the measurement is suitable for making a particular conformance decision. Finally, SNL’s software can perform all these calculations in a single application via an easy-to-use graphical interface, where the different functions are integrated so the results of one calculation can be used as inputs to another calculation.
The Primary Standards Lab employs guardbanding methods to reduce risk of false acceptance in calibration when test uncertainty ratios are low. Similarly, production agencies guardband their requirements to reduce false accept rates in product acceptance. The root-sum-square guardbanding method is recommended by PSL, but many other guardbanding methods have been proposed in literature or implemented in commercial software. This report analyzes the false accept and reject rates resulting from the most common guardbanding methods. It is shown that the root-sum-square method and the Dobbert Managed Guardband strategy are similar and both are suitable for calibration and product acceptance work in the NSE.
Nickel-doped α-MnO2 nanowires (Ni-α-MnO2) were prepared with 3.4% or 4.9% Ni using a hydrothermal method. A comparison of the electrocatalytic data for the oxygen reduction reaction (ORR) in alkaline electrolyte versus that obtained with α-MnO2 or Cu-α-MnO2 is provided. In general, Ni-α-MnO2 (e.g., Ni-4.9%) had higher n values (n = 3.6), faster kinetics (k = 0.015 cm s-1), and lower charge transfer resistance (RCT = 2264 Ω at half-wave) values than MnO2 (n = 3.0, k = 0.006 cm s-1, RCT = 6104 Ω at half-wave) or Cu-α-MnO2 (Cu-2.9%, n = 3.5, k = 0.015 cm s-1, RCT = 3412 Ω at half-wave), and the overall activity for Ni-α-MnO2 trended with increasing Ni content, i.e., Ni-4.9% > Ni-3.4%. As observed for Cu-α-MnO2, the increase in ORR activity correlates with the amount of Mn3+ at the surface of the Ni-α-MnO2 nanowire. Examining the activity for both Ni-α-MnO2 and Cu-α-MnO2 materials indicates that the Mn3+ at the surface of the electrocatalysts dictates the activity trends within the overall series. Single nanowire resistance measurements conducted on 47 nanowire devices (15 of α-MnO2, 16 of Cu-α-MnO2-2.9%, and 16 of Ni-α-MnO2-4.9%) demonstrated that Cu-doping leads to a slightly lower resistance value than Ni-doping, although both were considerably improved relative to the undoped α-MnO2. The data also suggest that the ORR charge transfer resistance value, as determined by electrochemical impedance spectroscopy, is a better indicator of the cation-doping effect on ORR catalysis than the electrical resistance of the nanowire. (Figure Presented).
Nanowire transistors are generally formed by metal contacts to a uniformly doped nanowire. The transistor can be modeled as a series combination of resistances from both the channel and the contacts. In this study, a simple model is proposed consisting of a resistive channel in series with two Schottky metal-semiconductor contacts modeled using the WKB approximation. This model captures several phenomena commonly observed in nanowire transistor measurements, including the mobility as a function of gate potential, mobility reduction with respect to bulk mobility, and non-linearities in output characteristics. For example, the maximum measured mobility as a function of gate voltage in a nanowire transistor can be predicted based on the semiconductor bulk mobility in addition to barrier height and other properties of the contact. The model is then extended to nanowires with axial p-n junctions having an inde- pendent gate over each wire segment by splitting the channel resistance into a series component for each doping segment. Finally, the contact-channel model is applied to low-frequency noise analysis in nanowire devices, where the noise can be generated in both the channel and the contacts. Because contacts play a major, yet often neglected, role in nanowire transistor operation, they must be accounted for in order to extract meaningful parameters from I-V and noise measurements.