interpret-uv-vis-spectrum
About
This skill analyzes UV-Vis spectra to identify chromophores, classify electronic transitions, and predict absorption maxima using Woodward-Fieser rules. It also performs quantitative analysis via the Beer-Lambert law. Use it for interpreting spectroscopic data from conjugated systems and determining concentrations.
Quick Install
Claude Code
Recommendednpx skills add pjt222/agent-almanac -a claude-code/plugin add https://github.com/pjt222/agent-almanacgit clone https://github.com/pjt222/agent-almanac.git ~/.claude/skills/interpret-uv-vis-spectrumCopy and paste this command in Claude Code to install this skill
Documentation
Interpret UV-Vis Spectrum
Analyze ultraviolet-visible absorption spectra to identify chromophores, classify electronic transitions, predict absorption maxima for conjugated systems, and apply the Beer-Lambert law for quantitative determination.
When to Use
- Identifying chromophores and the extent of conjugation in an organic compound
- Confirming the presence of aromatic rings, conjugated dienes, or enones
- Performing quantitative analysis (determining concentration from absorbance)
- Monitoring reaction kinetics by tracking absorbance changes over time
- Characterizing metal-ligand complexes via d-d and charge-transfer transitions
- Assessing solvent effects on electronic transitions (solvatochromism)
Inputs
- Required: UV-Vis spectrum data (wavelength in nm vs. absorbance or molar absorptivity)
- Required: Solvent used for measurement
- Optional: Concentration and path length (for Beer-Lambert calculations)
- Optional: Molar absorptivity (epsilon) values at lambda-max
- Optional: Spectra in multiple solvents (for solvatochromism analysis)
- Optional: Structural information from other spectroscopic methods
Procedure
Step 1: Verify Instrument Parameters and Spectrum Quality
Ensure the data is reliable before interpreting absorption bands:
- Wavelength range: Confirm the spectrum covers the relevant range. Standard UV-Vis spans 190--800 nm. Solvents impose low-wavelength cutoffs:
| Solvent | UV Cutoff (nm) | Notes |
|---|---|---|
| Water | 190 | Excellent UV transparency |
| Hexane | 195 | Non-polar, minimal solvent effects |
| Methanol | 205 | Protic, may cause blue shifts |
| Acetonitrile | 190 | Good general-purpose UV solvent |
| Dichloromethane | 230 | Absorbs below 230 nm |
| Chloroform | 245 | Absorbs below 245 nm |
| Acetone | 330 | Absorbs strongly, poor UV solvent |
- Absorbance range: Reliable measurements require absorbance between 0.1 and 1.0. Below 0.1, noise dominates; above 1.0, stray light causes non-linear response. Flag any lambda-max values outside this range.
- Baseline and blank: Verify that a solvent blank was subtracted. Residual solvent absorption or cuvette artifacts appear as a rising baseline at short wavelengths.
- Slit width: Narrow slit widths give better resolution but lower signal-to-noise. If fine structure is expected (vibrational progression on electronic bands), confirm the slit width is appropriate (1--2 nm).
Got: Instrument parameters documented, solvent cutoff respected, absorbance values within the linear range, and baseline confirmed clean.
If fail: If absorbance exceeds 1.0 at lambda-max, the sample must be diluted and remeasured. If the solvent absorbs in the region of interest, recommend re-acquisition in a more transparent solvent.
Step 2: Identify Lambda-Max and Band Characteristics
Locate and characterize all absorption bands:
- Locate lambda-max values: Identify each absorption maximum (lambda-max) and record its wavelength (nm) and absorbance (or molar absorptivity epsilon if known).
- Measure band shape: Note whether each band is broad and featureless (typical of solution-phase electronic transitions) or shows vibrational fine structure (typical of rigid chromophores like polycyclic aromatics).
- Record shoulders: Absorption shoulders indicate overlapping transitions. Note their approximate wavelength and intensity.
- Classify by molar absorptivity:
| epsilon (L mol-1 cm-1) | Transition Type | Example |
|---|---|---|
| < 100 | Forbidden (n -> pi*) | Ketone ~280 nm |
| 100--10,000 | Weakly allowed | Aromatic 250--270 nm |
| 10,000--100,000 | Fully allowed (pi -> pi*) | Conjugated diene ~220 nm |
| > 100,000 | Charge transfer | Metal complexes, dyes |
Got: All absorption maxima and shoulders tabulated with wavelength, absorbance/epsilon, and qualitative band shape.
If fail: If the spectrum shows no distinct maxima (monotonic rise), the compound may lack a chromophore in the measured range, or the concentration may be too low. Increase concentration or extend the wavelength range.
Step 3: Classify Electronic Transitions
Assign each absorption band to a specific electronic transition type:
- sigma -> sigma transitions* (< 200 nm): Observed only in vacuum UV. Relevant for saturated hydrocarbons and C-C/C-H bonds. Not measured in standard UV-Vis.
- n -> sigma transitions* (150--250 nm): Lone pair to sigma antibonding. Observed for heteroatoms (O, N, S, halogens). Saturated amines absorb near 190--200 nm; alcohols/ethers near 175--185 nm.
- pi -> pi transitions* (200--500 nm): Bonding pi to antibonding pi*. These are the strongest absorptions for organic compounds. Intensity and wavelength increase with extended conjugation.
- n -> pi transitions* (250--400 nm): Lone pair to pi antibonding. Formally forbidden (low epsilon, 10--100). Characteristic of C=O (270--280 nm for simple ketones), N=O, and C=S groups.
- Charge-transfer transitions: Electron transfer between donor and acceptor groups, or between metal and ligand. Typically very intense (epsilon > 10,000) and broad. Found in metal complexes and donor-acceptor organic molecules.
- d-d transitions (for transition metal complexes): Weak, broad bands in the visible region arising from crystal field or ligand field splitting.
Got: Each absorption band assigned to a transition type with supporting rationale (position, intensity, solvent sensitivity).
If fail: If a band cannot be assigned to a standard transition type, consider charge-transfer character or the possibility of impurity absorption. Multiple overlapping transitions may require deconvolution.
Step 4: Apply Woodward-Fieser Rules for Conjugated Systems
Predict lambda-max for conjugated dienes and enones and compare with observed values:
- Conjugated dienes (Woodward rules):
| Component | Increment (nm) |
|---|---|
| Base value (heteroannular diene) | 214 |
| Base value (homoannular diene) | 253 |
| Each additional conjugated C=C | +30 |
| Each exocyclic C=C | +5 |
| Each alkyl substituent on C=C | +5 |
| -OAcyl substituent | +0 |
| -OR substituent | +6 |
| -SR substituent | +30 |
| -Cl, -Br substituent | +5 |
| -NR2 substituent | +5 |
- Alpha-beta unsaturated carbonyls (Woodward-Fieser rules):
| Component | Increment (nm) |
|---|---|
| Base value (alpha-beta unsat. ketone, 6-ring or acyclic) | 215 |
| Base value (alpha-beta unsat. aldehyde) | 208 |
| Each additional conjugated C=C | +30 |
| Each exocyclic C=C | +5 |
| Homoannular diene component | +39 |
| Alpha substituent (alkyl) | +10 |
| Beta substituent (alkyl) | +12 |
| Gamma and higher substituent (alkyl) | +18 |
| -OH (alpha) | +35 |
| -OH (beta) | +30 |
| -OAc (alpha, beta, gamma) | +6 |
| -OR (alpha) | +35 |
| -OR (beta) | +30 |
| -Cl (alpha) | +15 |
| -Cl (beta) | +12 |
| -Br (beta) | +25 |
| -NR2 (beta) | +95 |
- Calculate predicted lambda-max: Sum the base value and all applicable increments.
- Compare with observed: Agreement within +/- 5 nm supports the proposed chromophore. Deviations > 10 nm suggest an incorrect structural assignment or strong solvent/steric effects.
Got: Predicted lambda-max calculated and compared with observed value, supporting or refuting the proposed chromophore structure.
If fail: If the predicted and observed values disagree significantly, re-examine the assumed chromophore structure. Common errors: miscounting substituents, overlooking an exocyclic double bond, or applying the wrong base value (homoannular vs. heteroannular).
Step 5: Apply Beer-Lambert Law for Quantitative Analysis
Use absorbance data for concentration determination or molar absorptivity characterization:
- Beer-Lambert equation: A = epsilon * b * c, where A = absorbance (dimensionless), epsilon = molar absorptivity (L mol-1 cm-1), b = path length (cm), c = concentration (mol L-1).
- Determine molar absorptivity: If concentration and path length are known, calculate epsilon from the measured absorbance at lambda-max.
- Determine concentration: If epsilon is known (from literature or a calibration curve), calculate the concentration from the measured absorbance.
- Check linearity: Beer-Lambert law is valid only in the linear range (A = 0.1--1.0). At higher absorbances, deviations occur due to stray light, molecular interactions, and instrumental limitations.
- Assess solvent effects: Compare spectra in polar vs. non-polar solvents:
- Bathochromic (red) shift: lambda-max moves to longer wavelength. pi -> pi* transitions red-shift in more polar solvents; n -> pi* transitions red-shift in less polar solvents.
- Hypsochromic (blue) shift: lambda-max moves to shorter wavelength. n -> pi* transitions blue-shift in more polar/protic solvents (hydrogen bonding stabilizes the lone pair ground state).
- Hyperchromic/hypochromic effects: Increase or decrease in epsilon without wavelength change.
Got: Quantitative results calculated with appropriate significant figures, linearity verified, and solvent effects documented if spectra in multiple solvents are available.
If fail: If Beer-Lambert linearity fails, check for sample degradation, aggregation at high concentration, or fluorescence interference. Dilute the sample and remeasure to confirm.
Validation
- Solvent cutoff respected and absorbance within the linear range (0.1--1.0)
- All lambda-max values and shoulders tabulated with wavelength, absorbance, and epsilon
- Each absorption band assigned to an electronic transition type
- Woodward-Fieser calculation performed where applicable and compared with observed lambda-max
- Beer-Lambert law applied correctly with verified linearity
- Solvent effects characterized if multi-solvent data is available
- Chromophore assignment consistent with molecular structure from other spectroscopic methods
Pitfalls
- Measuring above A = 1.0: High absorbance values are unreliable due to stray light effects. Always dilute and remeasure if lambda-max absorbance exceeds 1.0.
- Ignoring the solvent cutoff: Attempting to interpret absorptions below the solvent cutoff wavelength produces artifacts, not real sample data.
- Confusing transition types by intensity alone: A weak band near 280 nm could be an n -> pi* transition of a carbonyl or a forbidden pi -> pi* of an aromatic. Context and solvent effects are needed to distinguish them.
- Misapplying Woodward-Fieser rules: These empirical rules apply only to conjugated dienes and alpha-beta unsaturated carbonyls. They cannot be used for aromatic systems, isolated chromophores, or metal complexes.
- Neglecting impurity absorption: Even small amounts of a strongly absorbing impurity can dominate the spectrum. If lambda-max does not match expectations, consider impurity contributions.
- Assuming one band = one transition: Broad UV-Vis bands often contain multiple overlapping transitions. Band deconvolution may be necessary for accurate assignment.
Related Skills
interpret-nmr-spectrum-- determine molecular connectivity to support chromophore identificationinterpret-ir-spectrum-- identify functional groups that contribute to the chromophoreinterpret-mass-spectrum-- establish molecular formula and detect conjugation via fragmentationinterpret-raman-spectrum-- complementary vibrational data for symmetric chromophoresplan-spectroscopic-analysis-- select and sequence spectroscopic techniques before data acquisition
GitHub Repository
Related Skills
llamaguard
OtherLlamaGuard is Meta's 7-8B parameter model for moderating LLM inputs and outputs across six safety categories like violence and hate speech. It offers 94-95% accuracy and can be deployed using vLLM, Hugging Face, or Amazon SageMaker. Use this skill to easily integrate content filtering and safety guardrails into your AI applications.
cost-optimization
OtherThis Claude Skill helps developers optimize cloud costs through resource rightsizing, tagging strategies, and spending analysis. It provides a framework for reducing cloud expenses and implementing cost governance across AWS, Azure, and GCP. Use it when you need to analyze infrastructure costs, right-size resources, or meet budget constraints.
quantizing-models-bitsandbytes
OtherThis skill quantizes LLMs to 8-bit or 4-bit precision using bitsandbytes, achieving 50-75% memory reduction with minimal accuracy loss. It's ideal for running larger models on limited GPU memory or accelerating inference, supporting formats like INT8, NF4, and FP4. The skill integrates with HuggingFace Transformers and enables QLoRA training and 8-bit optimizers.
dispatching-parallel-agents
OtherThis Claude Skill dispatches multiple agents to investigate and fix 3+ independent problems concurrently. It is designed for scenarios involving unrelated failures that can be resolved without shared state or dependencies. The core capability is parallel problem-solving, assigning one agent per independent problem domain to maximize efficiency.
