Detailed Molecular Composition of Wood Pyrolysis Bio-Oils Revealed by HPLC-FT-ICR MS
Bibliographic record
Abstract
The highly complex nature of wood pyrolysis bio-oils, which contain thousands of distinct molecular species with varying ionization efficiencies, poses a significant challenge for characterization by direct-infusion high-resolution mass spectrometry. This study presents a novel method combining high-performance liquid chromatography (HPLC) with 21 Tesla Fourier transform ion cyclotron resonance mass spectrometry (21T FT-ICR MS) for detailed molecular characterization of bio-oils within the scope of negative-ion ESI. The HPLC method is optimized to separate polyfunctional oxygen-containing molecules using a polymeric stationary phase with dimethylaminopropyl functionalities, and a methanol–water eluent with dimethylamine. The acidic compounds in bio-oils equilibrate between the DEA-containing mobile phase and the stationary phase, facilitating efficient gradient elution of oxygen-rich species. Coupling online HPLC with 21T FT-ICR MS revealed ∼3,000 additional monoisotopic O x molecular formulas compared to direct-infusion FT-ICR MS. Newly detected compounds exhibited higher H/C ratios and a wider range of oxygen content, characteristic of low-molecular-weight carbohydrates and species with a composition that resembles biomass. The method enabled the detection of carbohydrate-like species (O/C ≈ 1, H/C ≈ 2) and highly aromatic compounds (H/C < 0.6, O/C < 0.3) that were undetectable via direct infusion. Early eluting, methanol-soluble species showed higher H/C ratios (∼1.5 to 2.0) and oxygen content consistent with lignin oligomers, while later-eluting compounds exhibited increased aromaticity, with compositions typical of condensed aromatic species. Advanced data processing using a Python-based, PyC2MC, software package further revealed compositional trends aligned with the solubility of bio-oils. Despite the overlap between LC–MS and direct infusion MS, single ion chromatograms revealed distinct elution patterns for identical molecular formulas, providing insights into potential isomeric diversity that are not accessible through direct infusion analyses. These findings demonstrate the enhanced molecular-level characterization achieved by HPLC-FT-ICR MS, providing key insights into the intricate composition of bio-oils and their potential for energy applications. The proposed approach provides a unique perspective on isomeric diversity and the distribution of functional groups, laying the groundwork for understanding the molecular basis of reactivity and upgrading potential in bio-oils. As the developed method targets the separation and characterization of polyfunctional oxygen-containing species, it can also be applied to dissolved/natural organic matter, photo-oxidation products, and emerging contaminants, e.g., water-soluble species leaching from materials like asphalt and petroleum-based road sealants.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".