Interlaboratory Study of Sample Homogeneity Impact on CHNS, Water, and ICP Analysis of Biomass Liquefaction Oils
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Five biomass liquefaction oils (BLOs) comprising four different lignocellulosic fast pyrolysis bio-oils (FPBO) and one algae-derived hydrothermal liquefaction (HTL) biocrude were subject to an interlaboratory study (ILS). Participating laboratories were asked to analyze all BLOs after gentle sample mixing and again but with vigorous mixing. Blind duplicates were included to improve the statistical power. Requested analysis included CHN, water, trace nitrogen, and sulfur (using solvent-miscible solvents) and ICP by an independently developed method. Blind duplicates applied in this study indicated that representative sampling was achieved to the extent required for bulk composition analysis. More vigorous sample mixing did not yield benefits for precision or reproducibility for composition analysis; however, the importance of consistent and precise sample mixing is underscored. Results of CHN and water showed performance similar to those of previous studies. However, adapted trace nitrogen methods consistently report 25–50% lower nitrogen content compared to ASTM D5291 and highlight the need for reconciliation. Poor performance of ICP across eight participant laboratories further underscores the need for standardization. ICP sample preparation by dissolution is generally ineffective for the recovery of more recalcitrant multivalent analytes. Digestion is recommended as the sample preparation of choice for ICP analysis of BLOs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.055 | 0.061 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".