Analytical instrumentation course

Lesson 1 • Sources and Classification of Errors

Distinguishes systematic, random, and gross errors and their origins. Learners learn to identify dominant error sources before selecting mitigation strategies.

Lesson 2 • Units, Standards, and Traceability

Covers SI units, reference standards, and metrological traceability chains. Ensures learners anchor all measurements to internationally recognised benchmarks.

Lesson 3 • Measurement Science Fundamentals

Establishes core vocabulary: measurand, signal, noise, and transduction. Connects physical phenomena to instrument design logic used throughout the course.

Lesson 4 • Performance Characteristics of Instruments

Defines static and dynamic performance metrics used to evaluate any instrument. Learners apply these metrics to compare instrument suitability for specific tasks.

Lesson 1 • Analog-to-Digital Conversion

Explains sampling theory, quantisation, and ADC architectures. Learners select appropriate ADC resolution and sampling rate for given measurement tasks.

Lesson 2 • Digital Filtering and Signal Analysis

Applies FIR and IIR filters and Fourier analysis to digitised signals. Learners extract meaningful analytical information from noisy datasets.

Lesson 3 • Data Acquisition System Design

Integrates sensors, conditioning, ADC, and software into a complete DAQ chain. Learners configure a multichannel acquisition system for a defined analytical task.

Lesson 4 • Analog Signal Conditioning

Covers amplification, attenuation, and impedance matching for raw sensor outputs. Prepares learners to optimise signal quality before digitisation.

Lesson 1 • Calibration Curve Construction

Applies linear and nonlinear regression to calibration data. Learners assess curve fit quality and identify concentration ranges of reliable response.

Lesson 2 • Method Validation Essentials

Validates analytical methods using selectivity, linearity, LOD, LOQ, and recovery metrics. Learners document validation results to meet regulatory and quality requirements.

Lesson 3 • Measurement Uncertainty Fundamentals

Introduces the GUM framework for uncertainty evaluation using Type A and Type B methods. Learners quantify individual uncertainty components for a given measurement.

Lesson 4 • Calibration Principles and Methods

Covers single-point, multipoint, and bracketing calibration strategies. Learners select the appropriate method based on instrument linearity and required accuracy.

Lesson 1 • Mass Spectrometry Fundamentals

Explains ionisation methods, mass analysers, and detector types for molecular identification. Learners interpret mass spectra and identify molecular ions and fragmentation patterns.

Lesson 2 • Atomic Spectroscopy Techniques

Covers flame AAS, graphite furnace AAS, ICP-OES, and ICP-MS for elemental analysis. Learners select the appropriate technique based on detection limit and matrix requirements.

Lesson 3 • Infrared and Raman Spectroscopy

Explains molecular vibration modes and FTIR instrument design for qualitative identification. Learners interpret IR spectra and compare FTIR with Raman for complementary information.

Lesson 4 • Electromagnetic Radiation and Matter Interaction

Establishes the physical basis of absorption, emission, and scattering phenomena. Provides the theoretical foundation for all spectroscopic techniques in this chapter.

Lesson 1 • Chromatographic Method Development

Applies systematic scouting and optimisation workflows to develop validated chromatographic methods. Learners document method parameters and perform robustness testing.

Lesson 2 • Hyphenated Chromatographic Techniques

Integrates GC-MS and LC-MS for simultaneous separation and identification. Learners configure hyphenated systems and interpret combined chromatographic-spectral data.

Lesson 3 • Chromatographic Theory and Terminology

Defines retention, selectivity, efficiency, and resolution using the van Deemter equation. Provides the theoretical basis for method optimisation in subsequent sections.

Lesson 4 • High-Performance Liquid Chromatography

Explains reversed-phase, normal-phase, and ion-exchange HPLC modes and mobile phase optimisation. Learners develop isocratic and gradient methods for complex mixtures.

Lesson 1 • Conductometry and Coulometry

Covers conductometric titrations, Karl Fischer coulometry, and electrogravimetry. Learners apply these techniques to water content and purity determinations.

Lesson 2 • Electrochemical Measurement Fundamentals

Introduces electrode potential, Nernst equation, and electrochemical cell design. Establishes the thermodynamic and kinetic basis for all electroanalytical techniques.

Lesson 3 • Potentiometry and Ion-Selective Electrodes

Covers pH measurement, ion-selective electrode theory, and direct potentiometry. Learners calibrate ISEs and apply standard addition for complex matrix analysis.

Lesson 4 • Electrochemical Sensor Design

Introduces modified electrodes, biosensors, and miniaturised electrochemical platforms. Learners evaluate sensor performance metrics and identify application domains.

Lesson 1 • Thermogravimetric Analysis Applications

Applies TGA to decomposition, moisture content, and filler quantification in real samples. Learners design TGA experiments and interpret derivative thermogravimetry curves.

Lesson 2 • Differential Scanning Calorimetry Applications

Uses DSC to measure melting, crystallisation, glass transition, and reaction enthalpy. Learners quantify thermal transitions and assess material purity from DSC data.

Lesson 3 • X-Ray Diffraction for Phase Analysis

Explains Bragg's law, powder XRD, and phase identification using reference databases. Learners perform qualitative and quantitative phase analysis of crystalline materials.

Lesson 4 • Thermal Analysis Principles

Introduces thermogravimetry, differential scanning calorimetry, and thermal mechanical analysis. Learners connect thermal events to material composition and phase behaviour.

Lesson 1 • Reporting and Interpretation of Results

Structures analytical reports with uncertainty, traceability, and decision rules. Learners communicate findings to technical and non-technical stakeholders effectively.

Lesson 2 • Sample Preparation Strategies

Covers digestion, extraction, and cleanup techniques that precede instrumental analysis. Learners match sample preparation to matrix type and target analyte properties.

Lesson 3 • Instrument Troubleshooting and Maintenance

Applies systematic fault diagnosis to common instrument failures across all technique classes. Learners perform preventive maintenance and document corrective actions.

Lesson 4 • Chemometrics for Analytical Data

Applies PCA, PLS, and cluster analysis to multivariate analytical datasets. Learners reduce data dimensionality and build predictive calibration models.