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Scanning thermal microscopy (SThM) enables thermal conductivity (λ) measurements with a lateral resolution down to a few tens of nanometers. The present work investigates ways to improve SThM images recorded with resistive probes. Probes based on resistance thermometry act both as a thermometer and as a Joule heated nanoscale heat source. The influence of amplitude and frequency of the applied heating voltage on the SThM image quality was systematically studied. To connect the investigated heating parameters to the temperature change at the apex of the SThM probe, electrical–thermal finite element simulations were performed. Image quality was assessed according to three criteria. The first criterion was the thermal contrast (thermal resolution) between materials of different λ's. To convert measured SThM signals (in mV) into thermal resolution (in Wm−1 K−1), reference measurements were performed by time-domain thermoreflectance, and an implicit calibration method was employed. The second criterion was the distortion of the thermal image by topography. To illustrate the image distortion, the standard deviation of the thermal trace-minus-retrace profile was taken, which could be reduced nearly ten times by changing the heating parameters of the used SThM setup. The third criterion was the spatial resolution of the thermal images. To assess the spatial resolution, gradients in the thermal signal at interfaces between materials were extracted from profiles through thermal images. [ABSTRACT FROM AUTHOR]

Users are demanding that soil surveys contain statistical information about soil and map unit properties. Numerous sampling and statistical methodologies are available, but statistical methods should be appropriate for both sampling methods and data distributions. Rigorous investigations of soil property distributions are rare, and statistical methodologies have received little attention for soil survey applications. Two hypotheses were tested: (i) frequency distributions of soil properties within map units are non-normally distributed, and (ii) variability estimates will depend on the statistical assumptions and methods. Twelve transects were randomly selected in a Boone County, Missouri soil survey map unit and 12 soil attributes were investigated from a population of 120 soil cores. Three methods were used to estimate variances: (i) simple random sampling, (ii) cluster sampling, and (ii) two-stage random sampling. Frequency distributions were significantly skewed for 11 of the soil properties. The median is an appropriate estimator of central tendency for these properties. The random sampling approach provided the smallest variances, but is conceptually questionable. Two-stage sampling is conceptually most correct, but requires dubious assumptions about population size. Nonparametric confidence intervals for variable medians were appropriate, but are wide for most properties. None of the applied assumptions and techniques are universally applicable. Statistical assumptions and methodologies should be tested across a spectrum of depositional, weathering, and land-use conditions to determine the most suitable approaches for particular user needs and soil conditions.

Open-volume defects caused by sawing wafers from gallium arsenide ingots using a diamond saw cutter were studied utilizing Doppler broadening spectroscopy amd positron lifetime measurements. It was observed that the advance of the saw blade affected the depth and concentration of the defects. The positron lifetime of the dominant defect was also measured. It was therefore concluded that the sawing procedure creates vacancy aggregates composed of at least two vacancies.

We are concerned with the documentation of taxonomic variability within soil survey map units, and how that variability is expressed statistically. Twelve randomly selected transects were used to sample soil properties and classify pedons within a fluvial map unit of the Boone County, Missouri, soil survey. Map unit taxonomic variability was high, with 28 taxonomic family classes. Twenty-five percent of the sampled soils were in the named taxonomic class of the map unit, but most were similar inclusions. Interpretive purity of the sample was 83%. Variances about this sample proportion were calculated using three methods: (i) simple random sampling, (ii) cluster sampling, and (iii) two-stage random sampling. The simple random sampling formula provided the smallest variance, compared with the cluster formula (55% larger) or the two-stage formula (72% larger). However, the interpretation of transect sampling as simple random sampling is questioned. The two-stage random sampling method seemed most conceptually correct, but is computationally intensive and requires questionable assumptions about the population size. Confidence intervals based on small sample sizes drawn from binomial populations are highly suspect, and perhaps should be referred to as 'approximate bounds'. It is important to carefully consider the assumptions and effects of any statistical technique chosen to analyze map unit composition.