Plan Spectroscopic Analysis
技能 已验证 活跃Plan a comprehensive spectroscopic analysis campaign by defining the analytical question, assessing sample characteristics, selecting appropriate techniques using a decision matrix, planning sample preparation for each technique, sequencing analyses from non-destructive to destructive, and defining success criteria with a cross-validation strategy.
To systematically plan and optimize spectroscopic analysis campaigns, ensuring the most appropriate techniques are chosen, sample is used efficiently, and clear success criteria are defined.
功能
- Defines analytical question and success criteria
- Assesses sample characteristics (state, quantity, stability, hazards)
- Selects techniques using a decision matrix
- Plans sample preparation for various spectroscopic methods
- Sequences analyses from non-destructive to destructive
- Defines cross-validation strategies and contingency plans
使用场景
- Starting an investigation of an unknown compound
- Optimizing the analysis sequence for resource-limited scenarios
- Planning sample preparation before acquiring instrument time
- Ensuring cross-validation between complementary techniques
非目标
- Interpreting raw spectroscopic data (e.g., NMR, IR, MS)
- Operating or calibrating spectroscopic instruments
- Performing actual sample preparation or analysis
安装
/plugin install agent-almanac@pjt222-agent-almanac质量评分
已验证类似扩展
Interpret UV Vis Spectrum
98Systematically interpret ultraviolet-visible absorption spectra to identify chromophores, classify electronic transitions, apply Woodward-Fieser rules for conjugated systems, and perform quantitative analysis using the Beer-Lambert law.
Interpret Mass Spectrum
95Systematically interpret mass spectra to determine molecular formula, identify fragmentation pathways, and propose molecular structures. Covers ionization method assessment, molecular ion identification, isotope pattern analysis, common fragmentation losses, and purity evaluation.
Interpret IR Spectrum
95Systematically interpret infrared spectra to identify functional groups present in a sample. Covers diagnostic region analysis (4000-1500 cm-1), fingerprint region assessment (1500-400 cm-1), hydrogen bonding effects, and compilation of a functional group inventory with confidence levels.
Interpret Nmr Spectrum
93Systematically interpret nuclear magnetic resonance spectra (1H, 13C, DEPT, and 2D experiments) to elucidate molecular structure. Covers chemical shift assignment, coupling pattern analysis, integration, and correlation of multi-dimensional data into coherent structural proposals.
Molfeat
99Molecular featurization for ML (100+ featurizers). ECFP, MACCS, descriptors, pretrained models (ChemBERTa), convert SMILES to features, for QSAR and molecular ML.
Deepchem
99Molecular ML with diverse featurizers and pre-built datasets. Use for property prediction (ADMET, toxicity) with traditional ML or GNNs when you want extensive featurization options and MoleculeNet benchmarks. Best for quick experiments with pre-trained models, diverse molecular representations. For graph-first PyTorch workflows use torchdrug; for benchmark datasets use pytdc.