interpret-uv-vis-spectrum
Über
Diese Fähigkeit analysiert UV-Vis-Spektren, um Chromophore zu identifizieren und elektronische Übergänge zu klassifizieren, wobei die Woodward-Fieser-Regeln für konjugierte Systeme angewendet werden. Sie führt auch quantitative Konzentrationsanalysen mit dem Lambert-Beer-Gesetz durch. Nutzen Sie sie, um strukturelle Merkmale wie aromatische Ringe zu bestätigen oder Reaktionskinetik über Absorbanzänderungen zu verfolgen.
Schnellinstallation
Claude Code
Empfohlennpx 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-spectrumKopieren Sie diesen Befehl und fügen Sie ihn in Claude Code ein, um diese Fähigkeit zu installieren
Dokumentation
Interpret UV-Vis Spectrum
Analyze UV-Vis absorption → id chromophores, classify electronic transitions, predict λ-max conjugated sys, apply Beer-Lambert for quant.
Use When
- ID chromophores + extent of conjugation in organic compound
- Confirm aromatic rings, conjugated dienes, enones
- Quant analysis (conc from absorbance)
- Monitor rxn kinetics via abs changes over time
- Characterize metal-ligand complexes (d-d + charge-transfer)
- Solvent effects on electronic transitions (solvatochromism)
In
- Req: UV-Vis data (λ nm vs abs / molar absorptivity)
- Req: Solvent
- Opt: Conc + path length (for Beer-Lambert)
- Opt: ε at λ-max
- Opt: Spectra in multi-solvents (solvatochromism)
- Opt: Structural info from other spectra
Do
Step 1: Verify Instrument Params + Quality
Ensure reliable data before interpret:
- λ range: Confirm relevant range. Standard UV-Vis 190-800 nm. Solvent 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 A = 0.1-1.0. <0.1 → noise; >1.0 → stray light non-linear. Flag λ-max outside.
- Baseline + blank: Verify solvent blank subtracted. Residual solvent abs / cuvette artifacts → rising baseline at short λ.
- Slit width: Narrow → better res, lower S/N. Fine structure expected (vibrational progression) → confirm slit appropriate (typ 1-2 nm).
→ Instrument params documented, solvent cutoff respected, abs in linear range, baseline clean.
If err: A > 1.0 at λ-max → dilute + remeasure. Solvent absorbs in region → re-acquire in more transparent solvent.
Step 2: Locate λ-Max + Band Characteristics
Locate + characterize all abs bands:
- Locate λ-max: Per abs max → record λ (nm) + abs (or ε if known).
- Band shape: Broad featureless (typical soln-phase) or vibrational fine structure (rigid chromophores, polycyclic aromatics).
- Shoulders: Overlapping transitions → note approx λ + int.
- Classify by ε:
| 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 |
→ All abs maxima + shoulders tabulated w/ λ, abs/ε, qualitative shape.
If err: No distinct maxima (monotonic rise) → compound lacks chromophore in range, or conc too low. Increase conc / extend range.
Step 3: Classify Electronic Transitions
Assign each band → transition type:
- σ → σ* (<200 nm): Vacuum UV only. Saturated HCs + C-C/C-H. Not typically measured standard.
- n → σ* (150-250 nm): Lone pair → σ antibonding. Heteroatoms (O, N, S, halogens). Saturated amines ~190-200; alcohols/ethers ~175-185.
- π → π* (200-500 nm): Bonding π → antibonding π*. Strongest abs for organics. Int + λ increase w/ extended conjugation.
- n → π* (250-400 nm): Lone pair → π antibonding. Formally forbidden (low ε, 10-100). Characteristic C=O (270-280 simple ketones), N=O, C=S.
- Charge-transfer: e- transfer donor↔acceptor, or metal↔ligand. Very intense (ε > 10,000) + broad. Metal complexes + donor-acceptor organics.
- d-d (transition metal complexes): Weak broad in visible → crystal/ligand field splitting.
→ Each band assigned → transition type w/ rationale (pos, int, solvent sensitivity).
If err: Band unassignable → consider charge-transfer character / impurity abs. Multiple overlapping → deconvolution.
Step 4: Woodward-Fieser Rules for Conjugated Sys
Predict λ-max for conjugated dienes + enones, compare observed:
- Conjugated dienes (Woodward):
| 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 |
- α-β unsaturated carbonyls (Woodward-Fieser):
| 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 |
- Calc predicted λ-max: Sum base + all applicable increments.
- Compare observed: ±5 nm → supports proposed chromophore. Deviations > 10 nm → incorrect assignment / strong solvent+steric effects.
→ Predicted λ-max calc + compared observed → supports/refutes proposed chromophore.
If err: Disagreement → re-examine chromophore. Common errs: miscount substituents, overlook exocyclic double bond, wrong base val (homoannular vs heteroannular).
Step 5: Beer-Lambert for Quant
Absorbance → conc / ε characterization:
- Equation: A = ε * b * c, A = abs (dimensionless), ε = molar absorptivity (L mol-1 cm-1), b = path length (cm), c = conc (mol L-1).
- Determine ε: Conc + b known → calc ε from A at λ-max.
- Determine conc: ε known (lit / calibration) → calc c from A.
- Linearity: Valid in linear range (A = 0.1-1.0). Higher → deviations (stray light, mol interactions, instrumental).
- Solvent effects: Compare polar vs non-polar:
- Bathochromic (red) shift: λ-max → longer λ. π→π* red-shifts in more polar; n→π* in less polar.
- Hypsochromic (blue) shift: λ-max → shorter λ. n→π* blue-shifts in more polar/protic (H-bonding stabilizes lone pair ground state).
- Hyperchromic/hypochromic: Increase / decrease ε w/o λ change.
→ Quant results calc w/ appropriate sig figs, linearity verified, solvent effects documented if multi-solvent avail.
If err: Linearity fails → check sample degradation, aggregation at high conc, fluorescence interference. Dilute + remeasure to confirm.
Check
- Solvent cutoff respected + abs in linear range (0.1-1.0)
- All λ-max + shoulders tabulated w/ λ, abs, ε
- Each band → electronic transition type
- Woodward-Fieser calc where applicable + compared observed
- Beer-Lambert applied correctly w/ verified linearity
- Solvent effects characterized if multi-solvent
- Chromophore consistent w/ structure from other spectra
Traps
- Measure > A=1.0: Unreliable due to stray light. Always dilute + remeasure if λ-max abs > 1.0.
- Ignore solvent cutoff: Interpret abs below cutoff → artifacts, not real.
- Confuse transition types by intensity: Weak band ~280 could be n→π* carbonyl / forbidden π→π* aromatic. Context + solvent effects distinguish.
- Misapply Woodward-Fieser: Empirical rules → conjugated dienes + α-β unsat carbonyls only. Not for aromatic sys, isolated chromophores, metal complexes.
- Neglect impurity abs: Small amount of strongly-absorbing impurity → dominate spectrum. λ-max mismatch expectations → consider impurity.
- Assume 1 band = 1 transition: Broad bands often multi overlapping transitions. Deconvolution may be needed.
→
interpret-nmr-spectrum— mol connectivity → support chromophore IDinterpret-ir-spectrum— func groups contributing to chromophoreinterpret-mass-spectrum— formula + detect conjugation via fraginterpret-raman-spectrum— complementary vibrational → symmetric chromophoresplan-spectroscopic-analysis— select + sequence techniques pre-acquisition
GitHub Repository
Verwandte Skills
llamaguard
AndereLlamaGuard ist Metas 7-8B-Parameter-Modell zur Moderation von LLM-Eingaben und -Ausgaben in sechs Sicherheitskategorien wie Gewalt und Hassrede. Es bietet eine Genauigkeit von 94-95 % und kann mit vLLM, Hugging Face oder Amazon SageMaker eingesetzt werden. Nutzen Sie diese Skill, um Inhaltsfilterung und Sicherheitsguardrails einfach in Ihre KI-Anwendungen zu integrieren.
cost-optimization
AndereDiese Claude Skill unterstützt Entwickler bei der Optimierung von Cloud-Kosten durch Ressourcen-Dimensionierung, Tagging-Strategien und Ausgabenanalysen. Sie bietet einen Rahmen zur Senkung von Cloud-Ausgaben und zur Implementierung von Kosten-Governance für AWS, Azure und GCP. Nutzen Sie sie, wenn Sie Infrastrukturkosten analysieren, Ressourcen richtig dimensionieren oder Budgetvorgaben einhalten müssen.
quantizing-models-bitsandbytes
AndereDiese Fähigkeit quantisiert LLMs auf 8-Bit- oder 4-Bit-Präzision mittels bitsandbytes und erreicht dabei eine Speicherreduzierung von 50–75 % bei minimalem Genauigkeitsverlust. Sie ist ideal für den Betrieb größerer Modelle mit begrenztem GPU-Speicher oder zur Beschleunigung von Inferenzvorgängen und unterstützt Formate wie INT8, NF4 und FP4. Die Fähigkeit integriert sich in HuggingFace Transformers und ermöglicht QLoRA-Training sowie 8-Bit-Optimierer.
dispatching-parallel-agents
AndereDiese Claude-Fähigkeit verteilt mehrere Agenten, um drei oder mehr unabhängige Probleme gleichzeitig zu untersuchen und zu beheben. Sie ist für Szenarien konzipiert, die unabhängige Fehler umfassen, die ohne gemeinsamen Zustand oder Abhängigkeiten gelöst werden können. Die Kernfähigkeit ist die parallele Problemlösung, bei der pro unabhängigem Problembereich ein Agent zugewiesen wird, um die Effizienz zu maximieren.
