ANALYTICAL INSTRUMENTS LECTURE SERIES

In this course, students will be introduced to the foundational principles of analytical chemistry and gain hands-on experience with a variety of instrumental techniques critical to the field of geochemistry. The course will cover the theoretical framework necessary to understand the chemical properties and behaviours of environmental and geological materials. Students will explore the full process of geochemical sampling, including sample preparation, data acquisition, and subsequent data analysis. Emphasis will be placed on the use of advanced instrumentation for chemical analysis, particularly X-ray fluorescence (XRF), scanning electron microscopy (SEM), electron probe microanalysis (EPMA), and inductively coupled plasma mass spectrometry (ICP-MS).

Through a combination of lectures and laboratory work, students will not only learn about the underlying principles behind these analytical techniques but also practice applying them in real-world scenarios. The laboratory component of the course is designed to be highly interactive, with a focus on working with environmental and geological samples, ensuring that students develop practical skills in operating the instrumentation and interpreting complex data sets.

The course structure includes 6 hours of lecture per week, where theoretical concepts will be discussed, and laboratory work, where students will engage in hands-on exercises. Over the span of two weeks, this intensive format will prepare students to confidently approach both routine and advanced geochemical analyses in academic or professional settings.

Program for 2025:

2025-09-29 Monday 14:10-17:00: XRF – SEM – ICP MS theory

2025-09-30 Tuesday 09:10-10:00: Sample preparation XRF/SEM/LA-ICP-MS theory

2025-10-03 Friday 12:10-13:00: Handling datasets

2025-10-06 Monday 14:10-17:00: Data reduction and geochronology practical session

2025-10-08 Wednesday 11:10-12:00: Dealing with analytical data practical session

X-RAY FLUORESCENCE:

X-ray fluorescence (XRF) is an analytical technique used to determine the elemental composition of materials. It is widely applied in various fields such as geology, material science, environmental analysis, and even in industrial applications for quality control.

SCANNING ELECTRON MICROSCOPES AND MICROPROBES:

A Scanning Electron Microscope (SEM) is a powerful and versatile imaging tool used to observe the surface structure and composition of a wide range of materials at very high magnifications, often on the order of nanometers. SEM is widely used in fields such as materials science, biology, geology, and nanotechnology because of its ability to produce highly detailed, three-dimensional images of sample surfaces.

An Electron Probe Microanalyser (EPMA) is a specialised type of electron microscope used for quantitative chemical analysis of solid materials at a microscopic scale. It is particularly useful in geology, materials science, metallurgy, and other fields where precise elemental composition and microstructural information are needed. EPMA combines the imaging capabilities of a scanning electron microscope (SEM) with highly accurate, quantitative elemental analysis through X-ray spectroscopy.

MASS SPECTROMETRY:

Mass Spectrometry (MS) is an analytical technique used to measure the mass-to-charge ratio (m/z) of ions. It is a powerful tool for identifying the composition of a sample by detecting and quantifying different molecules or atoms based on their mass. MS is widely used in chemistry, biology, environmental science, and many other fields to analyse complex mixtures, determine molecular structures, and measure isotopic abundances.

HANDLING ANALYTICAL DATA:

Handling analytical data involves a series of steps designed to ensure accurate data collection, processing, analysis, storage, and interpretation. Whether the data comes from techniques like mass spectrometry, chromatography, spectroscopy, or any other analytical instrumentation, managing the data properly is critical for deriving meaningful results and ensuring the reliability and reproducibility of findings.

PRACTICAL EXERCISE I

Working with XRF Data Here we are going to explore of how to process and interpret data from X-ray fluorescence (XRF) analyses. The focus is on practical exercises aimed at transforming raw elemental data into meaningful geochemical interpretations, particularly for applications in mineral chemistry and whole-rock analysis.

PRACTICAL EXERCISE II

Data reduction for LA-ICP-MS using Saturn software involves converting raw laser ablation data into meaningful geochemical and isotopic information through a streamlined and user-friendly workflow. Saturn automates critical steps such as background subtraction, isotope ratio calculation, drift correction, and fractionation adjustments. It offers advanced features like customizable correction models (linear, exponential, or polynomial) to handle diverse fractionation behaviours, ensuring precise U-Pb dating, trace element analysis, and isotope ratio determinations. The software also integrates visualisation tools, enabling real-time data quality assessment and facilitating the identification of patterns or anomalies. With its emphasis on flexibility and accuracy, Saturn is a powerful tool for researchers working in geochronology, provenance studies, and other fields leveraging LA-ICP-MS data.

Sampling Protocols

Sampling ProtocolsDownload

1. Introduction

Sampling forms the foundation of any geochemical or mineralogical investigation. The accuracy and reliability of analytical results depend critically on how well the sample represents the system being studied. Poor sampling design or contamination during collection can lead to misleading interpretations, regardless of the sophistication of the analytical techniques that follow. It is therefore essential to implement correct and stringent sampling protocols to obtain meaningful results in ore characterisation and geochemical studies.

2. Objectives

In this session, we aim to:

  • Understand the importance of correct and stringent sampling protocols.
  • Become familiarised with analytical techniques used for ore characterisation:
    • Routine versus specialised techniques.
    • Techniques for mineralogical versus chemical characterisation.
    • In situ versus bulk (whole-rock) characterisation techniques.

3. Designing a Sampling Campaign

The design of a sampling campaign is strongly informed by the research question or problem being addressed. Each sampling strategy should ensure representativeness, minimise contamination, and align with the analytical objectives.

Key factors to consider:

  • Type of Analysis
    The intended analysis dictates the sampling approach. This includes:
    • Chemical composition (major, trace, or isotope geochemistry)
    • Mineralogy and texture
    • Physical properties, such as hardness, grain size, or density
  • Sample Locus and Representative Mass
    Samples can be collected from a variety of contexts, including:
    • Core, chip, trench, or bulk samples
    • Soil or regolith
    • Run-of-mine (ROM) feed, conveyor belt material, or final product
      The representative mass should reflect the natural heterogeneity of the material. A sample that is too small risks bias, while one that is too large may be impractical to process.
  • Analytical Format: Disaggregated vs In situ
    • Disaggregated (grain mounts, powders): Used when homogenisation is required for bulk chemical analysis.
    • In situ analyses: Preserve textural and mineralogical context (e.g. LA-ICP-MS, SIMS, EPMA).
  • Budget and Equipment Availability
    The design of the sampling programme must take into account available resources. Field logistics, sample transport, and laboratory capabilities can all influence the final sampling strategy.

4. Importance of Stringent Sampling Protocols

  • Representativeness: Ensures that samples genuinely reflect the variability of the system.
  • Reproducibility: Allows different analysts to obtain consistent results from comparable materials.
  • Contamination Control: Minimises the introduction of external materials that could distort analytical outcomes.
  • Traceability: Proper labelling, documentation, and chain-of-custody are essential for maintaining data integrity.

5. Linking Sampling to Analytical Techniques

Sampling must be designed in harmony with the analytical methods to be employed:

  • Routine vs Specialised Techniques:
    Routine assays (e.g. XRF, ICP-OES) require homogenised bulk samples, whereas specialised techniques (e.g. LA-ICP-MS, SIMS, MC-ICP-MS) can target individual minerals or isotopic domains.
  • Mineralogical vs Chemical Characterisation:
    Mineralogical studies (XRD, SEM-EDS) benefit from preserved textures, whereas chemical characterisation often demands thorough homogenisation.
  • In situ vs Bulk Characterisation:
    In situ methods preserve geological context and are ideal for understanding processes, zoning, or micro-scale variations.
    Bulk analyses average the whole-rock composition and are more suitable for identifying large-scale geochemical trends.

6. Summary

A successful analytical workflow begins with a well-designed sampling protocol. The effectiveness of any ore characterisation effort depends on:

  • Clearly defined objectives,
  • Appropriate sample type and quantity,
  • Methodical and contamination-free collection, and
  • Alignment of the sampling strategy with the intended analytical techniques.

7. Quality Control (QC) and Quality Assurance (QA)

1. Introduction

Quality Control (QC) and Quality Assurance (QA) are essential components of any analytical programme. While sampling protocols ensure representativeness and reliability at the field stage, QA/QC practices safeguard data integrity from sampling through to analysis and interpretation. Together, they provide confidence that results are both accurate and reproducible.

2. Distinction Between QA and QC

  • Quality Assurance (QA)
    QA refers to the systematic framework of policies, procedures, and documentation that governs how sampling and analysis are conducted. It focuses on preventing errors by defining standards and ensuring that all processes follow recognised guidelines.
  • Quality Control (QC)
    QC involves the practical checks and measurements used to detect and correct errors during analysis. It is implemented throughout laboratory and field operations to verify that results remain within acceptable limits.

In short, QA is proactive (preventing problems), while QC is reactive (detecting and correcting them).

3. QA in Sampling and Analysis

Quality Assurance encompasses:

  • Standard Operating Procedures (SOPs): Clearly defined sampling, handling, and analytical methods that ensure consistency between operators and laboratories.
  • Training and Competency: Personnel should be trained and regularly assessed to maintain high standards of performance.
  • Documentation and Traceability: All steps—from sample collection to data reporting—must be fully documented to enable auditability.
  • Instrument Calibration and Maintenance: Regular calibration against certified standards guarantees analytical reliability.
  • Inter-laboratory Comparisons: Participation in proficiency testing or round-robin exercises ensures that results are comparable across laboratories.

4. QC in Sampling and Analysis

Quality Control activities are applied at each analytical stage to verify data reliability. These typically include:

  • Field Duplicates: Parallel samples collected to test the reproducibility of sampling.
  • Blanks: Used to monitor contamination during preparation and analysis.
  • Certified Reference Materials (CRMs): Samples of known composition analysed alongside unknowns to assess accuracy.
  • Internal Standards: Added to correct for instrumental drift or matrix effects.
  • Replicates and Repeats: Reanalysis of samples to test analytical precision.
  • Control Charts: Statistical tools (e.g. Shewhart charts) used to monitor analytical performance over time.

5. Importance of QA/QC in Geochemical Studies

  • Data Integrity: Ensures that results are accurate, precise, and defensible.
  • Reproducibility: Facilitates comparison between different studies and laboratories.
  • Decision Confidence: In exploration or process control, reliable data underpin critical decisions about resource evaluation, mining strategies, and environmental compliance.
  • Regulatory Compliance: Adherence to international QA/QC standards (e.g. ISO/IEC 17025) supports the credibility of analytical results in both academic and industrial contexts.