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Deep cryogenic treatment (DCT) has been known since the 1930s to improve hardness, fatigue resistance, and wear resistance of steel. While the effect of DCT on wear properties has been well documented, there is no consensus regarding the causal mechanisms, nor a widely accepted quantitative description of them. DCT transforms retained austenite into martensite and triggers the precipitation of fine carbides, among other things. We observed that DCT had a negligible effect on Young's modulus and the yield limit of high carbon spring steel. The observed microstructural changes (presence of specific dendritic inhomogeneities typical for inclusions of austenitic phase in non-treated specimens and homogeneous microstructure of treated ones) can serve for qualitative purposes only. However, we found that DCT led to a decrease in steel electrical resistivity which can be explained by noticeable differences between the resistivities of the martensitic and austenitic phases. We propose a micromechanical model for electrical resistivity which allows monitoring of the content of retained austenite and postulate that it can be used for other materials as well. We also observed increased resistivity after mechanical loading of the specimens, correlating with increased dislocation density caused by loading. This quantity can be used to assess the average dislocation density.

Emission abatement cyclone performance is improved by increasing collection effectiveness or decreasing energy consumption. The object of this study was to quantify the pressure drop and fine particulate (PM2.5) collection of 1D3D cyclones (H = 4Dc, h = 1Dc) at inlet velocities from 8 to 18 m s− 1 (Stk = 0.7–1.5) using heterogeneous particulate as a test material at inlet concentrations from 3 to 75 g m− 3. Cyclone exhaust was passed through filters. Laser diffraction particle size distribution analysis was used to estimate PM2.5 emissions. Response surface models showed a strong correlation between cyclone pressure loss (Euler number) and inlet velocity and predicted a 46% reduction in pressure loss for a 25% reduction in inlet velocity (Stokes number). The model for PM2.5 emissions was less definitive and, surprisingly, predicted a 31% decrease in PM2.5 emissions when operating 25% below the design inlet velocity. Operating below the design inlet velocity (at a lower Stokes number) to reduce pressure losses (Euler number) would reduce both the financial and the environmental cost of procuring electricity. The unexpected co-benefit suggested by these trials was that emission abatement may improve at the same time, though other empirical trials have shown emissions to be independent of inlet velocity and Stokes number.

Growers and field scouts need assistance in surveying cotton (Gossypium hirsutum L.) fields subjected to thermal defoliation to reap the benefits provided by this nonchemical defoliation method. A study was conducted to evaluate broadband spectral data subjected to unsupervised classification for surveying cotton plots subjected to thermal defoliation. Ground-based reflectance measurements of thermally treated and non-treated cotton canopies were collected at two study Sites (Site 1 and Site 2) with a handheld hyperspectral spectroradiometer. The hyperspectral data were merged into eight broad spectral bands: coastal blue (400–450 nm), blue (450–510 nm), green (510–580 nm), yellow (585–625 nm), red (630– 690 nm), red-edge (705–745 nm), near-infrared (770–895 nm), and panchromatic (450–800 nm). Also, a broadband normalized difference vegetation index (NDVI) was created with the red (630–690 nm) and near-infrared bands (770–895 nm). For each study Site, two datasets were analyzed: (1) two-class case (thermally treated cotton observations and non-treated cotton observations) and (2) five-class case (thermally treated cotton observations and non-treated cotton observations and three additional classes created with the weighted average of the thermally treated cotton observations and non-treated cotton observations). The clustering algorithm referred to as CLUES (CLUstEring based on local Shrinking) was employed to automatically group the data into clusters without the user selecting the number of clusters. Cluster validation was determined with the average silhouette width; also accuracy was assessed with contingency matrixes. Clustering analysis worked well in dividing the data into appropriate groups, with the best cluster structure occurring for the NDVI. User’s and producer’s accuracies for the NDVI were greater than 86%, indicating an excellent classification. Findings support future endeavors to assess airborne and satellite-borne systems equipped with sensors sensitive to the wavelengths deemed useful in this study and unsupervised classification techniques that automatically determines the numbers of clusters to evaluate thermal defoliation of cotton fields. Published by Elsevier B.V.

A high electric field, radio-frequency ion mobility spectrometry (RF-IMS) analyzer was used as a small detector in gas chromatographic separations of mixtures of volatile organic compounds including alcohols, aldehydes, esters, ethers, pheromones, and other chemical attractants for insects. The detector was equipped with a 2 mCi 63Ni ion source and the drift region for ion characterization was 5 mm wide, 15 mm long and 0.5 mm high. The rate of scanning for the compensation voltages was 60 V s(-1) and permitted four to six scans to be obtained across a capillary chromatographic elution profile for each component. The RF-IMS scans were characteristic of a compound and provided a second dimension of chemical identity to chromatographic retention adding specificity in instances of co-elution. Limits of detection were 1.6-55 x 10(-11) g with an average detection limit for all chemicals of 9.4 x 10(-11) g. Response to mass was linear from 2-50 x 10(-10) g with an average sensitivity of 4 pA ng(-1). Separations of pheromones and chemical attractants for insects illustrated the distinct patterns obtained from gas chromatography with RF-IMS scans in real time and suggest an analytical utility of the RF-IMS as a small, advanced detector for on-site gas chromatographs.

The international standard procedure for testing solar cookers and reporting performance was proposed at the Third World Conference on Solar Cooking (Avinashilingam University, Coimbatore, India, 6–10 January, 1997) and revised by the committee over the following months. The standard sets limits for environmental conditions, specifies test procedures and calls for performance to be reported in terms of cooking power (W). While this value, like the fuel economy rating of an automobile, is not a guarantee of performance, it does provide a useful tool for comparison. The entire standard is presented in this paper. It was evaluated both by using it to analyze data previously collected and by using it to test one of the solar cookers in the historical data set. The test standard cooking power curve clearly distinguishes between solar cookers of differing design. Estimates of solar cooker performance for different locations and dates are fairly consistent when the test standard is employed. The criteria of being easy to use and presenting data predictive of thermal performance are also satisfied.

This paper presents a model for prediction of the cooking power of a solar cooker based on three controlled parameters (solar intercept area, overall heat loss coefficient, and absorber plate thermal conductivity) and three uncontrolled variables (insolation, temperature difference, and load distribution). The model basis is a fundamental energy balance equation. Coefficients for each term in the model were determined by regression analysis of experimental data. The model was validated for commercially available solar cookers of both the box and concentrating types. The valid range of model application includes most of the feasible design space for family-sized solar cookers. The model can be used to estimate the cooking capacity of existing box type and concentrating type solar cookers. It can also be used to find the combinations of intercept area and heat loss coefficient required to cook a given quantity of food in a given climate.