PHD
  1. Inorganic chemistry  1.1. Coordination chemistry  1.1.1. Ligand field theory  1.1.2. Crystal field theory  1.1.3. Molecular Orbital Theory for Coordination Compounds  1.1.4. Stability of Coordination Compounds  1.1.5. Electronic spectra of coordination compounds  1.1.6. Isomerism in Coordination Compounds  1.1.7. Kinetics and mechanism of coordination reactions  1.2. Organometallic Chemistry  1.2.1. Metal Carbonyls  1.2.2. Metal alkyl and aryl  1.2.3. Fischer and Schrock carbonaceans  1.2.4. Oxidative Addition and Reductive Elimination  1.2.5. Metal Hydrides  1.2.6. Catalysis by organometallic compounds  1.2.7. Metallocenes and Sandwich Complexes  1.2.8. CH activation  1.3. Solid state chemistry  1.3.1. Crystal structures and lattices  1.3.2. Band theory and electrical properties  1.3.3. Defects in the crystal  1.3.4. Synthesis of solid state materials  1.3.5. Magnetic and optical properties of solids  1.3.6. Superconductivity  1.3.7. Solid state ionics  1.4. Bio-inorganic chemistry  1.4.1. Metalloproteins and enzymes  1.4.2. Metal ion transport and storage  1.4.3. The role of metals in medicine  1.4.4. Bioinspired catalysis  1.4.5. Metal–DNA interactions  1.4.6. Metal Complexes in Medical Science  1.5. Lanthanides and Actinides  1.5.1. Electronic configuration and oxidation states in lanthanides and actinides  1.5.2. Spectral and Magnetic Properties in Lanthanides and Actinides  1.5.3. Separation and Extraction of Lanthanides and Actinides  1.5.4. Coordination chemistry of f-block elements  1.6. Main Group Chemistry  1.6.1. Chemistry of boron compounds  1.6.2. Chemistry of Silicon and Germanium Compounds  1.6.3. Chemistry of Phosphorus and Sulfur Compounds  1.6.4. Organosilicon chemistry  1.6.5. Noble Gas Chemistry
  2. Organic chemistry  2.1. Reaction mechanism  2.1.1. Nucleophilic substitution reactions  2.1.2. Electrophilic addition and substitution reactions  2.1.3. Elimination reactions  2.1.4. Rearrangement reactions  2.1.5. Radical reactions  2.1.6. Pericyclic Reactions  2.1.7. Photochemical reactions  2.2. Stereoscopic  2.2.1. Chirality and optical activity  2.2.2. Structural analysis  2.2.3. Stereoelectronic effects  2.2.4. Asymmetric synthesis  2.2.5. Dynamic Stereochemistry  2.3. Organometallic Chemistry in Organic Synthesis  2.3.1. Organolithium and organomagnesium reagents  2.3.2. Transition metal catalyzed coupling reactions  2.3.3. Palladium-catalyzed reactions  2.3.4. Olefin Metathesis  2.3.5. Gold and silver catalysis  2.4. Natural products chemistry  2.4.1. Alkaloids and terpenoids  2.4.2. Steroids and Flavonoids  2.4.3. Total synthesis of natural products  2.4.4. Biosynthesis of natural products  2.5. Supramolecular Chemistry  2.5.1. Host-Guest Chemistry  2.5.2. Self-assembly and molecular recognition  2.5.3. Supramolecular Catalysis  2.5.4. Molecular Machines
  3. Physical Chemistry  3.1. Thermodynamics  3.1.1. Laws of Thermodynamics  3.1.2. Chemical Potential and Equilibrium  3.1.3. Statistical thermodynamics  3.1.4. Non-equilibrium thermodynamics  3.2. Quantum Chemistry  3.2.1. Schrödinger equation and its applications  3.2.2. Molecular orbital theory  3.2.3. Density functional theory  3.2.4. Post-Hartree–Fock methods  3.3. Chemical kinetics  3.3.1. Reaction rate theory  3.3.2. Mechanism and rate laws  3.3.3. Catalysis and enzyme kinetics  3.3.4. Reaction dynamics  3.4. Spectroscopy and molecular structure  3.4.1. Infrared Spectroscopy  3.4.2. UV-visible spectroscopy  3.4.3. Nuclear Magnetic Resonance Spectroscopy  3.4.4. Mass Spectrometry  3.4.5. Raman Spectroscopy  3.5. Surface and Colloid Chemistry  3.5.1. Absorption and Catalysis  3.5.2. Surfactants and micelles  3.5.3. Colloidal Stability  3.5.4. Nanomaterials and Interfaces
  4. Analytical chemistry  4.1. Chromatography  4.1.1. Gas Chromatography  4.1.2. High Performance Liquid Chromatography  4.1.3. Thin Layer Chromatography  4.1.4. Ion Chromatography  4.2. Electroanalytical techniques  4.2.1. Voltammetry and Polarography  4.2.2. Conductometry and potentiometry  4.2.3. Colometry  4.3. Spectroscopic Methods  4.3.1. Atomic absorption spectroscopy  4.3.2. Fluorescence Spectroscopy  4.3.3. X-ray diffraction  4.3.4. Electron paramagnetic resonance spectroscopy  4.4. Chemometrics  4.4.1. Data processing and statistical methods  4.4.2. Multidisciplinary analysis  4.4.3. Machine Learning in Analytical Chemistry
  5. Theoretical and Computational Chemistry  5.1. Molecular dynamics simulation  5.2. Quantum chemistry methods  5.3. Computational drug design  5.4. Machine Learning in Chemistry  5.5. Ab initio molecular dynamics
  6. Biophysics and Medicinal Chemistry  6.1. Drug discovery and design  6.2. Enzyme kinetics and inhibition  6.3. Chemical biology approach  6.4. Protein-ligand interactions  6.5. Bioorthogonal chemistry
  7. Materials chemistry  7.1. Polymer chemistry  7.1.1. Polymerization mechanism  7.1.2. Functional Polymers  7.1.3. Biodegradable Polymer  7.1.4. Conducting and semiconducting polymers  7.2. Nano Chemistry  7.2.1. Nanoparticles and nanostructures  7.2.2. Carbon-based nanomaterials  7.2.3. Quantum dots  7.3. Energy and Environmental Chemistry  7.3.1. Battery and Fuel Cell Chemistry  7.3.2. Green chemistry and sustainable materials  7.3.3. Photocatalysis

PHD → Analytical chemistry ↓


Chemometrics


Chemometrics is a field that involves the use of mathematical and statistical methods to design experiments and analyze chemical data. In analytical chemistry, chemometrics plays a vital role by providing tools and techniques to interpret complex data and extract meaningful information. This integration of statistical analysis with chemistry is essential for improving the quality and efficiency of chemical analysis.

Introduction to chemometrics

At its core, chemometrics involves the use of data-driven techniques to interpret analytical data. It relies heavily on statistical methods and quantitative analysis to correlate measurable data with underlying chemical parameters. The main goal is to transform raw data into actionable insights, which can then guide decision-making processes in various chemical-related fields.

Historical background

The term "chemometrics" was first used in the 1970s by Svante Wold and Bruce Kowalski. The need for chemometrics arose from the explosive growth of analytical techniques that generated enormous amounts of data. Early analytical instruments lacked the sophistication to comprehensively process and interpret such data. Therefore, chemometrics was born to provide a set of techniques for handling and understanding data from chemical experiments.

Applications of chemometrics

Chemometrics is useful in many fields, including pharmaceutical development, environmental analysis, food chemistry, and the petrochemical industry. Here are some detailed examples of how chemometrics is used in different fields:

1. Pharmaceutical development: In pharmaceuticals, chemometrics can be used to optimize the formulation of drugs by analyzing patterns and relationships in experimental data. This helps to improve the efficacy and safety of pharmaceuticals.

2. Environmental chemistry: For environmental monitoring, chemometrics helps in modelling and predicting the spread of pollution. It can process large datasets from sensors and give accurate predictions on pollution levels.

Environmental Data

3. Food chemistry: Chemometrics aids in quality control by evaluating the composition and authenticity of food products. It helps to differentiate between genuinely organic and adulterated foods.

Key concepts in chemometrics

Several key concepts form the backbone of chemometrics. These concepts are important to understand for anyone wanting to delve deeper into this field:

1. Multivariate analysis: Most chemical problems involve multiple variables. Multivariate analysis is a statistical approach that considers multiple input variables simultaneously to understand patterns and correlations. Common methods include principal component analysis (PCA) and partial least squares (PLS).

An example of multivariate analysis could be PCA, which reduces the dimensionality of data to identify the most important components. Here is a simple example of how PCA is applied:

        Data Matrix:
X1 | X2 | X3
----|----|----
  2 |  5 |  6
  3 |  8 |  9
  4 |  4 |  3 
Eigenvalues of the covariance matrix: [λ1, λ2, λ3]
Principal Components: PC1 = λ1 * X1 + λ2 * X2 + λ3 * X3
Main ingredient 1

2. Calibration methods: Calibration involves relating known values of a property to a measurable response. In chemometrics, this often involves linear regression methods to calibrate instruments for accurate measurements.

        Example: Calibration curve for UV spectrometry.
Absorbance (A) = ε * c * l
Where:
    A = measured absorbance,
    ε = molar absorptivity,
    c = concentration of the solution,
    l = path length of the cuvette.

Data preprocessing in chemometrics

Before applying any chemometric analysis, the raw data requires preprocessing. Preprocessing improves the quality of the data and helps remove any noise or irrelevant information. Common preprocessing techniques include normalization, centering, and scaling of the data.

        Example: Normalization of spectroscopic data
Raw Data: [102, 98, 105, 110]
Normalized Data: [0.2857, 0.2746, 0.2946, 0.3085]

In this example, normalization helps bring the data to a common scale, which is important for effective analysis. This can be especially important when comparing datasets from different sources or situations.

Conclusion

In conclusion, chemometrics is an integral part of modern analytical chemistry, providing a thorough and comprehensive way to interpret complex data. Its scope extends far beyond traditional methods, allowing chemists and researchers to make informed decisions, optimize processes, and find new solutions to complex chemical problems. By using statistical and mathematical tools, chemometrics bridges the gap between data and actionable insights, thus enhancing the field of chemometrics substantially.


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Chemometrics

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