Representative Sampling – Another Cinderella of Particle Size Analysis

Rawle, Alan F.

doi:10.1016/j.proeng.2015.01.306

Cited by 12 publications

(6 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…A representative, well‐mixed, and dry 5‐g soil material (<2 mm) sample was collected from each of the 54 selected standard materials according to the best fundamental sampling error calculation proposed by Rawle (2015). The 5‐g material sample was placed in a 470‐mL container with 100 mL of a 5% sodium hexametaphosphate solution, vigorously mixed, and left to soak overnight to disperse the soil particles.…”

Section: Methodsmentioning

confidence: 99%

Making Soil Particle Size Analysis by Laser Diffraction Compatible with Standard Soil Texture Determination Methods

Faé

Montes

Bazilevskaya

et al. 2019

Soil Science Soc of Amer J

View full text Add to dashboard Cite

The standard sieving, pipette, and hydrometer methods for soil particle size analysis (PSA) have three main drawbacks: (i) the procedures are tedious, (ii) the procedures are time consuming, (iii) and the results are protocol dependent. Laser diffraction PSA delivers rapid results using standardized procedures, but so far it has been difficult to reconcile results with those from standard sedimentation methods. The objective of this study was to develop a protocol that would permit direct usage of laser diffraction PSA and render results compatible with current methods. The protocol was developed using 54 standard soil samples from different textural classes. Regression of the laser diffraction PSA against the hydrometer/pipette method yielded R2 values of 0.92/0.9, 0.92/0.94, and 0.99/0.99 and RMSE values of 0.04/0.05, 0.07/0.06 and 0.05/0.03 for clay, silt, and sand, respectively. These statistics are comparable to those obtained by regressing results of the hydrometer against the sieve and pipette methods. A key factor in securing accurate and precise results was limiting the particle size range of the samples by wet sieving the sand fraction. This created representative samples and stable soil dispersed suspensions, allowing accurate estimations of particle size distribution for clay and silt fractions without empirical transformations. Results obtained with the proposed protocol matched those of standard sedimentation analyses for a wide range of soils, encouraging further adoption of laser diffraction for soil PSA. Core Ideas Laser diffraction particle size analysis can produce results compatible with standard pipette and hydrometer methods. A key step is to wet‐sieve the sand fraction after suspending the soil sample in the dispersant solution. The proposed protocol is faster, uses smaller samples, and provides more detail than standard sedimentation methods.

show abstract

Section: Methodsmentioning

confidence: 99%

Making Soil Particle Size Analysis by Laser Diffraction Compatible with Standard Soil Texture Determination Methods

Faé

Montes

Bazilevskaya

et al. 2019

Soil Science Soc of Amer J

View full text Add to dashboard Cite

show abstract

“…All methods that use samples from sediment cores are limited by the availability of sample, due to the small core diameters, and pressures from other sampling requirements. The consequence of this is that the minimum sample volume/mass required significantly increases with the maximum grain size that is to be captured accurately (Kowalenko & Babuin, 2013; Rawle, 2015). In addition to sample availability, the LPSA method is also limited by the analytical method.…”

Section: Methods Of Determining Ird Fluxmentioning

confidence: 99%

A Comparison of Methods for Identifying and Quantifying Ice Rafted Debris on the Antarctic Margin

McKay

Albot

Dunbar

et al. 2022

Paleoceanog and Paleoclimatol

View full text Add to dashboard Cite

Quantification of ice rafted debris (IRD) abundances in deep‐sea records is often used as a key proxy for identifying links between ice sheet instability and the oceanic overturning circulation. There currently exist multiple methods to determine IRD content in deep‐sea sediment cores. The preference for a given method is often determined by the accessibility to core material and destructive nature of some methods. While many studies have discussed the caveats between linking IRD to ice sheet dynamics, the uncertainties relating to the methodological approaches are often not considered in the interpretation or comparisons between different IRD datasets, particularly in the Antarctic. To address this, we compare three independent methodologies of obtaining IRD abundances and also discuss how different approaches will affect determinations of mass accumulation rates (MARs). The three methodologies we examine include: counting clasts >2 mm in x‐radiographs; the sieved weight percentage of the medium‐to‐coarse sand fraction (250 μm–2 mm); and volumetric estimates of the >125 μm sand fraction using laser diffraction particle size analysis. The x‐radiograph and sieve methods produced comparable results, while the laser particle size analysis, although showing comparable long‐term signals at most locations, in general has lower correlation to the other two methods and therefore a higher potential to obtain a noisy signal. We discuss the caveats associated with all methods, and emphasize that a combination of multiple methods should ideally be employed when assessing if a core is suitable for IRD study, and if sand or gravel fractions are accurately reflecting contents of IRD.

show abstract

“…The number of coloured grains in each increment is an unbiased estimate of the mean value of the lot. Implicit in the notion that sampling results "constitute a range of appropriate and statistically valid answers" [17] is the understanding that more samples provide a better estimate of the mean. Each repetition of the experiment produced 32 samples to be 'assayed' by counting the discrete grains of blue kernels, steel balls, lead balls, tungsten carbide bits.…”

Section: The Sampling Experimentsmentioning

confidence: 99%

The Grouping and Segregation Error in the Rice Experiment

Minnitt

2022

Minerals

View full text Add to dashboard Cite

The grouping and segregation error, one of the seven sampling errors defined by Pierre Gy, is related to the combined effects of gravity and characteristics of the target analyte such as particle size, density, shape, and moisture content of the particulate materials being sampled. Kinetic energy acting on particulate materials that are moved, flow, transported, or stockpiled causes the spatial distribution of fragments relative to one another to change. The grouping and segregation error is identified, quantified, and measured in relative sampling variance terms by comparing the sampling variability due to fractional shovelling (scooping) with that using a Jones riffle splitter. The relative sampling variance of low concentrations, approximately 0.01%, of steel balls, lead balls, and flakes of tungsten carbide in the host substrate indicates that, in this specific sampling space, the grouping and segregation error is primarily a function of particle density. Conclusions from the experiments are that components of the grouping and segregation error, namely the grouping factor and segregation factor, can be identified, measured, and mitigated. Whereas the grouping and segregation error has historically been considered to be less than the fundamental sampling error, these experiments suggest that it can be up to four times the fundamental sampling error depending on the density of the segregated materials.

show abstract

Representative Sampling – Another Cinderella of Particle Size Analysis

Cited by 12 publications

References 3 publications

Making Soil Particle Size Analysis by Laser Diffraction Compatible with Standard Soil Texture Determination Methods

Making Soil Particle Size Analysis by Laser Diffraction Compatible with Standard Soil Texture Determination Methods

A Comparison of Methods for Identifying and Quantifying Ice Rafted Debris on the Antarctic Margin

The Grouping and Segregation Error in the Rice Experiment

Contact Info

Product

Resources

About