Sampling and Sample Preparation

Module 3: Sampling and Sample Preparation

• Covers statistics of sampling
• Covers dissolving the sample
• Sample preparation also included

Sample Preparation

• A chemical analysis requires a meaningful sample.
• The concentration of nitrate in lake sediment decreases by two orders of magnitude within the first 3 mm below the surface.
• Selection of a core sample can dramatically affect results. For example, a 1m deep analysis vs skimming the top 2mm

Sampling

• Representative samples are collected for analysis.
• The process selects a portion of material to represent a larger body.
• Sampling reduces the bulk material size.
• Maintains important information from the bulk material

Real Samples

• Sample preparation removes interfering substances that interfere with the analysis of the desired analyte
• Converts the analyte into a form suitable for analysis

Key Definitions

• A lot is the total material from which samples are taken.
• A Bulk sample (or gross sample) is taken from the lot for analysis or archiving.
• Sampling is the process of selecting a representative bulk sample from the lot.
• Sample preparation are the steps to get to a homogenous laboratory sample from the bulk sample
• sample preparation eliminates interfering species or concentrates the analyte.
• The bulk sample must be representative of the lot.
• Choice of bulk sample is critical to producing a valid analysis.
• The smaller, homogeneous laboratory sample must have the same composition as the bulk sample.
• A laboratory sample is obtained by grinding the solid bulk sample to a fine powder, mixing thoroughly, and storing one bottle of powder.
• Aliquots are small portions of laboratory samples used for individual analyses.
• Sampling is the process of selecting a representative bulk sample from the lot.
• Sample preparation is the process to convert a sample into a form suitable for analysis

Forms of Lots

• Lots can be solid, liquid or gas
• Lots may be homogenous or heterogeneous
• Can be static or dynamic
• Can be Inorganic, Organic or Biological

Types of Sampling

• Lots and batches are sampled using a variety of techniques
• Increments are taken forming a gross sample
• Composite sampling can then be undertaken
• Subsamples are secondary samples
• Further processed to produced a laboratory sample
• Which then produced a test sample

Random Sampling

• Samples are collected randomly from the target population, with any portion of the bulk having the same probability.
• This reduces bias if not eliminated

Judgmental / Selective Sampling

• Uses available information about the target population to select samples.
• Follows a sampling plan to select materials with specific characteristics.
• This tends to be more biased
• Fewer samples are often required
• This is most useful when limiting the number of independent variables

Systematic Sampling.

• The target population is sampled at regular intervals in space or time.
• Increments are taken at intervals defined by the sampling plan
• Displays intermediate bias as compared to random and selective sampling

Stratified Sampling

• Target populations are subdivided into distinct units (strata) with random sampling done in each.
• The composition of each stratum is more homogeneous, leading to lower sampling variance (uncertainty).

Representative Sampling Techniques

• Simple random sampling
• Selective sampling
• Stratified sampling
• Systematic sampling

Sampling Operations

• Grab sampling involves taking a single sample from the target bulk.
• Composite sampling combines several grab samples to make a single sample.
• In-situ sampling is done within the population without removing the sample.

Sampling Operations Examples

• Grab sampling technique is most appropriate for sampling canned sardines coming out through a conveyor belt
• In-situ sampling is most appropriate for analysis of Lunar soil in the lunar environment
• Soil in your hacienda requires composite sampling technique

Sample Storage

• Select samples carefully but store carefully
• Composition can alter after collection due to chemical changes, for example, reactions with air
• Reactions with the sample container may also occur
• Glass is a notorious ion exchanger and alters concentrations of trace ions in solution.
• Plastic or Teflon collection bottles are often employed.
• Even these materials can absorb trace levels of analytes.
• A 0.2-micro M HgCl2 solution lost 40–95% of its concentration in polyethylene bottles in 4 hours.
• A 2-M Ag solution in a Teflon bottle lost 2% of its concentration in a day and 28% in a month.
• Plastic containers must be washed before use.
• Manganese in blood serum samples increased by a factor of seven when stored in unwashed polyethylene containers before analysis.
• Mercury levels were 1.6 pM in Lake Michigan which are two orders of magnitude below concentrations observed in earlier studies
• Handling techniques for the analysis of lead in rivers investigated variations in; sample collection, sample containers, protection during transportation, filtration, preservatives, and preconcentration procedures
• Each step that deviated from best practice doubled the apparent concentration of lead in stream water.
• Clean rooms with filtered air supplies are essential in trace analysis.
• Even with the best precautions, the precision of trace analysis becomes poorer as the concentration of analyte decreases.

Lab Notes

• Describe how a sample was collected, stored, and handled in a lab notes

Statistics of Sampling

• For random errors, the overall variance, s2o, is the sum of the variance of the analytical procedure, s2a and the variance of the sampling operation, s2s
• **

Adding Variance

• s2o = s2a + s2s
• Little is achieved by reducing the smaller component
• If ss is 10% and sa is 5%, then √[(.1)^2 + (.05)^2]=0.11
• If reducing sa to 1% only improves by √[(.1)^2 + (.01)^2]=0.10

Sources of Variance

• Understanding the source of uncertainty when selecting a sample for analysis requires considering a random mixture with the probability of drawing A as p and drawing B as q
• **

Particles drawn at random

• Drawing n particles are random allows the calculation of the expected number of particles of type A as np
• The standard deviation of many drawings is known as the binomial distribution
• Standard deviation in sampling operation, Sn = √(npq)

Drawing Particles

• A mixture contains 1% KCl particles and 99% KNO3 particles.
• If 104 particles are taken, the expected number of KCl particles is 100 and the standard deviation of that will be approximately 9.9 or around ~10%.
• If 105 particles are taken, what is the relative standard deviation of each measurement?

Sample Quantities

• How much sample corresponds to 104 particles if particles are 1-mm diameter spheres?
• The volume of a 1-mm-diameter sphere is calculated by (4/3)π(0.5 mm)3 = 0.524 μL.

Sample densities

• The average density of the mixture is calculated using; (0.01)(1.984) + (0.99)(2.109) = 2.108 g/mL, for a mixture where KCl is 1.984 g/mL and KNO3 is 2.109 g/mL
• The mass of the mixture containing 104 particles will be calculated as (104)(0.524 10-4 mL)(2.108 g/mL) = 11.0 g

chloride standards

• If taking 11.0-g test portions from a bigger laboratory sample, the predicted sampling standard deviation for chloride is 9.9% but the sampling standard deviation for nitrate will be only 0.1%.
• How can prepare 1-mm diameter particles with the use of a 16 Mesh sieve with screen openings of 1.18mm squares with sides of length 1.18mm
• **

Standard Test Sieves

• Standard test sieves particles that pass through will be screened through with a 20 mesh sieve of 0.85 mm square openings, and material not passed is kept as your particles

• This will produce diameters in the range of 0.85 and 1.18mm which is referred to 16/20 mesh

• What happens if much finer particles are used instead?

• Consider if using particles of 80/120 mesh

mass of particles

• The mass containing 104 particles is reduced from 11.8 to
• Sample uncertainty for chloride reduced if larger samples are used
• There is no advantage to reducing the analytical uncertainty if the sampling uncertainty is high, and vice versa.
• When sampling uncertainty arises from the random nature of drawing particles from solids and other mixtures
• Solids must be ground to very fine dimensions
• large quantities used to ensure small sampling variance
• samples contaminated invariably from grinding
• Liquids and the mixture's particles are molecules, and can contain 1022 particles/mL
• volume is needed of homogenous liquid sample to to reduce sample error

Nickel Ore

• Nickel ore is crushed into smaller sized particles
• Nickel in ore is relatively resistant to fracture
• Larger particles have composition similar to smaller particles
• the entire ore must be ground to a find powder in order to obtain a representative sample

Solid and liquid samples

• A well-mixed powder with KCI and KNO3 becomes a heterogenous mix where there is random variances
• **

Tissue

• Tissue will typically be homogenized
• Homogenized materials are now a suspension of particles in water
• The number for radio active averaged 237g's

Sampling

• Equation used: mR2 = Ks
• where mR2 constant for large sampling and poor for smallest samples
• What is the cause of sampling issues in the figure?
  • At these levels, a 0.7g sample should produce a 7% sample deviation which is strictly a sampling standard
  • sampling plus variations of analysis will have an impact

Sample Mass increase

• if the sample must increase to reduce sample standard deviation
• If the equation is followed, then it will work
• A single 0.7 g sample gives a sample standard that will lead to being 7% off Analysing a number of samples will allow improvement We have to get 0.7 g sampled analysed A number of variables must also factor in.