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Sampling Methods: Sample Types

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Sampling materials are classified into three main types: solid, liquid, and gas.
Solid samples include a variety of substances, such as sediments from water bodies, soil, metals, and biological tissues. Two standard methods for extracting sediments from water bodies are grab sampling and piston coring. Grab sampling involves using a device to collect a discrete sediment sample from the bottom of a water body with minimal disturbance. Grab samples do not always represent the entire area due to...

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A Microfluidic Chip for ICPMS Sample Introduction

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Published on: March 5, 2015

Sampling reliability, spatial resolution, spatial precision, and extraction efficiency in droplet-based liquid

Vilmos Kertesz¹, Gary J Van Berkel

¹Organic and Biological Mass Spectrometry Group, Chemical Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, 37831-6131, USA.

Rapid Communications in Mass Spectrometry : RCM

|May 28, 2014

Summary

This study optimized droplet-based liquid extraction for surface sampling with HPLC/MS. Key findings include maintaining probe-to-surface distance for reliable junctions and improving extraction efficiency through increased cycles or time.

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Fluorescence detection methods for microfluidic droplet platforms

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Published on: December 10, 2011

Area of Science:

Analytical Chemistry
Surface Science

Background:

Droplet-based liquid extraction coupled with HPLC/MS enables spatially resolved surface sampling.
Current limitations include the need for improved sampling reliability, spatial resolution, precision, and extraction efficiency.

Purpose of the Study:

To develop and optimize a droplet-based liquid extraction platform for surface sampling.
To investigate the impact of various parameters on sampling reliability, spatial resolution, precision, and extraction efficiency.

Main Methods:

Coupling a commercial autosampler for droplet-based liquid extraction with an HPLC/MS system.
Evaluating liquid junction formation (reliability, location, size) by adjusting solvent composition, probe distance, and droplet volume.
Assessing analyte extraction efficiency by varying extraction time and cycles.

Main Results:

Reliable liquid junction formation was achieved by maintaining a probe-to-surface distance of ≤0.4 mm.
Optimal conditions for small junction diameter (1.6 mm) involved a 1 μL volume and 90% aqueous methanol or acetonitrile.
Good sampling precision was obtained with ≥50% methanol or acetonitrile, and extraction efficiency increased with longer extraction times or more cycles.

Conclusions:

A robust platform using a commercial autosampler and HPLC/MS was developed for surface sampling optimization.
The study successfully identified key parameters influencing the reliability, resolution, precision, and efficiency of droplet-based liquid junction surface sampling.