Why Do Results from the Same Sieve Set and Same Sample Sometimes Differ? Causes and Remedies

Analytical sieving is one of the most widely used particle size analysis techniques in laboratories and industrial quality control. In principle, using the same sieve set on the same material sample should yield identical results. However, practitioners frequently observe small but significant variations in the particle size distribution data when tests are repeated.

These variations are not necessarily due to sieve defects, but rather a combination of sample-related, operator-related, and instrument-related factors. Understanding these sources of error and adopting scientific remedies ensures reliable, reproducible, and compliant results in line with ASTM E11, ISO 3310-1, and ISO 2591-1 standards.

1. Sample-Related Factors

1.1 Non-Homogeneous Samples

Even when taken from the same bulk material, individual test portions may not be perfectly identical. Segregation of fines and coarse particles occurs naturally during handling and transport.

• Remedy: Use sample splitters (riffle splitter or rotary sample divider) to ensure representative test portions. Always remix bulk samples before sub-sampling.

1.2 Moisture Content Variations

Moisture causes fine particles to agglomerate, altering their effective particle size. Two aliquots of the same sample may behave differently if one portion has absorbed more atmospheric moisture.

• Remedy: Control laboratory temperature and humidity, and pre-dry moisture-sensitive materials before sieving.

1.3 Agglomeration and Cohesion

Powders with electrostatic charges or cohesive forces can form aggregates that pass or block mesh apertures inconsistently.

• Remedy: Use dispersing agents, ultrasonic sieving, or controlled tapping to break agglomerates.

2. Operator-Related Factors

2.1 Inconsistent Sieving Time and Technique

Different operators may vary the sieving time, shaking intensity, or tapping technique. Even mechanical shakers, if not standardized, can produce slightly different energy inputs.

• Remedy: Follow a standard operating procedure (SOP) defining exact sieving time, shaker amplitude/frequency, and sample mass. Automate sieving where possible.

2.2 Human Error in Weighing and Recording

Errors in weighing the test sample, collecting fractions, or recording results can introduce discrepancies.

• Remedy: Calibrate balances regularly and train operators. Use digital data logging to minimize transcription errors.

3. Instrument and Sieve-Related Factors

3.1 Mesh Blockage or Blinding

Particles can wedge into sieve apertures during one run but may dislodge in another, changing the mass retained on a sieve.

• Remedy: Clean sieves carefully between tests using soft brushing, ultrasonic cleaning, or air blasting (depending on mesh size).

3.2 Mechanical Wear of Sieves

Even high-quality stainless steel monolithic sieves (ASTM E11 / ISO 3310-1) can undergo gradual aperture enlargement or wire fatigue over time, altering separation efficiency.

• Remedy: Perform periodic calibration and inspection of sieves, and replace those outside tolerance.

3.3 Variation in Sieving Energy

Manual shaking, different mechanical shakers, or variations in clamping pressure can change how particles stratify through the mesh stack.

• Remedy: Use a standardized sieve shaker with controlled amplitude, frequency, and duration to minimize variability.

3.4 Electrostatic Charge

In fine powders, static charges can cause particles to cling to mesh or frame, reducing passage. This may vary between test runs depending on ambient humidity.

• Remedy: Use anti-static devices, ionized air, or controlled humidity environments.

4. Environmental Factors

4.1 Temperature and Humidity

High humidity can cause hygroscopic powders to agglomerate; very dry conditions increase electrostatic charging.

• Remedy: Maintain laboratory conditions within 20–25 °C and 45–55% relative humidity, as recommended for precision testing.

4.2 Vibration and External Disturbances

Unwanted vibrations from nearby equipment may alter particle migration during sieving.

• Remedy: Isolate the sieve shaker on a stable, vibration-free platform.

5. Remedies in Practice: Ensuring Repeatable Results

To achieve reproducibility when using the same sieve set on the same sample, laboratories should implement:

1. Standardized Test Procedure: Define mass of test portion, sieving duration, amplitude, and endpoint criteria (e.g., <0.1% weight change after 1 min).

2. Representative Sampling: Use mechanical splitters to avoid bias from sample segregation.

3. Controlled Environment: Maintain constant temperature and humidity; minimize electrostatic effects.

4. Sieve Integrity Assurance: Use Versatile’s stainless steel frame and stainless steel mesh monolithic sieves (ASTM E11 / ISO 3310-1 compliant) for superior dimensional stability and durability. Inspect and recalibrate regularly.

5. Operator Training: Train staff to follow SOPs rigorously and reduce human variation.

6. Automation: Where possible, use automated sieve shakers with programmable parameters to eliminate operator variability.

Conclusion

Variability in sieve analysis results, even with the same sieve set and same material, arises from a combination of sample heterogeneity, operator technique, sieve condition, and environmental influences. These variations can be minimized — but not entirely eliminated — by adhering to standardized methods, ensuring representative sampling, maintaining controlled test conditions, and using high-quality sieves.

Versatile’s stainless steel monolithic sieves, compliant with ASTM E11 and ISO 3310-1, provide laboratories with maximum reliability by eliminating weak points found in conventional soldered or glued designs. When combined with strict SOPs and environmental control, they ensure high repeatability and reproducibility, the hallmarks of credible particle size analysis.

1. Causes of Variation and Their Scientific Basis

1.1 Sampling Error

Even from the same bulk sample, test portions differ in particle representation. This introduces sampling error, governed by Gy’s Sampling Theory:

σs2=C⋅d3M\sigma^2_{s} = \frac{C \cdot d^3}{M}σs2​=MC⋅d3​

Where:

• σs2\sigma^2_{s}σs2​ = variance due to sampling,

• CCC = material constant (depends on density, shape, and heterogeneity),

• ddd = particle diameter,

• MMM = sample mass.

Smaller samples and coarser materials increase variance.

Remedy: Use rotary sample dividers to improve representativity and increase test sample mass.

1.2 Moisture & Cohesion Effects

Moisture increases inter-particle cohesion, causing agglomeration. Effective particle diameter becomes:

deff=dp⋅f(ϕ,W)d_{eff} = d_p \cdot f(\phi, W)deff​=dp​⋅f(ϕ,W)

Where:

• dpd_pdp​ = true particle diameter,

• ϕ\phiϕ = cohesion factor (0 < ϕ\phiϕ ≤ 1),

• WWW = water content.

Agglomerates behave as larger particles, altering mass retained per sieve.

Remedy: Pre-dry samples to constant weight at controlled temperature (e.g., 105 °C for sand).

1.3 Operator Variability

Sieving time and energy affect stratification. According to kinetic sieving models:

P(t)=1−e−ktP(t) = 1 - e^{-kt}P(t)=1−e−kt

Where:

• P(t)P(t)P(t) = fraction of undersize passing at time ttt,

• kkk = sieving rate constant (depends on shaker amplitude and frequency).

Two operators running different times will obtain different retained fractions.

Remedy: Use standardized sieving time (e.g., 10 minutes) and automated shakers.

1.4 Mesh Blockage & Blinding

If nbn_bnb​ apertures are blocked in a sieve of total ntn_tnt​ apertures, the effective open area reduces to:

Aeff=A0(1−nbnt)A_{eff} = A_0 \left(1 - \frac{n_b}{n_t}\right)Aeff​=A0​(1−nt​nb​​)

Where A0A_0A0​ = original open area.Blocked sieves reduce probability of passage, yielding higher-than-true retention.

Remedy: Ultrasonic cleaning for fine sieves; brushing and rinsing for coarse meshes.

1.5 Electrostatic Effects

Electrostatic adhesion adds an effective “force barrier” against particle passage:

Fadh≥FgF_{adh} \geq F_gFadh​≥Fg​

Where:

• FadhF_{adh}Fadh​ = electrostatic adhesion force,

• FgF_gFg​ = gravitational + vibrational force on the particle.

If adhesion exceeds driving force, fines remain trapped.

Remedy: Use ionized air neutralizers or maintain controlled humidity (45–55%).

1.6 Sieve Wear & Tolerance Drift

With repeated use, mesh apertures enlarge slightly. Aperture size deviation (Δd\Delta dΔd) shifts the cumulative distribution function (CDF) of the particle size curve:

F(d)=11+e−(d−d50)σF(d) = \frac{1}{1 + e^{-\frac{(d-d_{50})}{\sigma}}}F(d)=1+e−σ(d−d50​)​1​

An enlarged aperture shifts d50d_{50}d50​ lower, falsely reporting finer PSD.

Remedy: Regular verification against reference sieves or calibration beads.

2. Remedies in Practice

• Representative Sampling: Use mechanical dividers; avoid scoop sampling.

• Moisture Control: Dry hygroscopic materials before sieving.

• SOP Enforcement: Standardize mass, time, and shaker energy.

• Clean Sieves Thoroughly: Ultrasonic baths for ≤100 µm sieves.

• Environmental Control: Maintain constant humidity and temperature.

• Calibration: Inspect sieves regularly; replace when tolerance exceeds ASTM E11/ISO 3310-1 limits.

• High-Quality Sieves: Use Versatile’s stainless steel monolithic sieves (ASTM E11 / ISO 3310-1 compliant) for dimensional stability, corrosion resistance, and long service life.

3. Conclusion

Variations in sieve analysis arise from statistical sampling error, particle cohesion, operator technique, sieve blockage, electrostatics, and mechanical wear. These factors can be mathematically modeled, showing their effect on reproducibility.

By implementing rigorous sampling protocols, standardized testing procedures, and calibrated equipment, laboratories can reduce variability. The use of Versatile stainless steel monolithic sieves ensures stable aperture geometry, compliance with international standards, and enhanced reproducibility — making them an essential tool for credible, scientific particle size analysis.

Causes of Variation in Sieve Analysis: Scientific Effects and Remedies

Cause	Scientific / Mathematical Effect	Remedy
Sampling Error	Sampling variance from Gy’s theory: σs2=C⋅d3M\sigma^2_s = \frac{C \cdot d^3}{M}σs2​=MC⋅d3​. Smaller sample mass or larger particle size increases error.	Use rotary/ riffle sample dividers; increase test portion size.
Moisture & Cohesion	Agglomeration increases effective particle size: deff=dp⋅f(ϕ,W)d_{eff} = d_p \cdot f(\phi, W)deff​=dp​⋅f(ϕ,W) leading to false coarse fraction.	Pre-dry samples to constant weight; control lab humidity.
Operator Variability	Inconsistent sieving time/energy: P(t)=1−e−ktP(t) = 1 - e^{-kt}P(t)=1−e−kt causes variable fraction passing.	Standardize sieving time & amplitude; use automated sieve shakers.
Mesh Blockage (Blinding)	Reduction in open area: Aeff=A0(1−nbnt)A_{eff} = A_0 \left(1 - \frac{n_b}{n_t}\right)Aeff​=A0​(1−nt​nb​​). Blocked mesh retains excess fines.	Ultrasonic cleaning for fine sieves; soft brushing for coarse sieves.
Electrostatic Effects	Adhesion force FadhF_{adh}Fadh​ > gravitational/vibrational force FgF_gFg​ → particles cling to mesh.	Use anti-static ionizers; maintain humidity (45–55%).
Sieve Wear / Aperture Drift	Aperture enlargement shifts PSD curve: F(d)=11+e−(d−d50)/σF(d) = \frac{1}{1+e^{-(d-d_{50})/\sigma}}F(d)=1+e−(d−d50​)/σ1​ → underestimation of coarse fraction.	Regular calibration; replace sieves outside ASTM E11/ISO 3310-1 limits.
Environmental Disturbance	Vibrations or uncontrolled humidity cause inconsistent particle stratification.	Isolate sieve shakers; maintain stable temperature and humidity.