Application of 1D and 2D MFR reactor technology for the isolation of insecticidal and anti-microbial properties from pyrolysis bio-oils
Bibliographic record
Abstract
Valuable chemicals can be separated from agricultural residues by chemical or thermochemical processes. The application of pyrolysis has already been demonstrated as an efficient means to produce a liquid with a high concentration of desired product. The objective of this study was to apply an insect and microorganism bioassay-guided approach to separate and isolate pesticidal compounds from bio-oil produced through biomass pyrolysis. Tobacco leaf (Nicotianata bacum), tomato plant (Solanum lycopersicum), and spent coffee (Coffea arabica) grounds were pyrolyzed at 10°C/min from ambient to 565°C using the mechanically fluidized reactor (MFR). With one-dimensional (1D) MFR pyrolysis, the composition of the product vapors varied as the reactor temperature was raised allowing for the selection of the temperature range that corresponds to vapors with a high concentration of pesticidal properties. Further product separation was performed in a fractional condensation train, or 2D MFR pyrolysis, thus allowing for the separation of vapor components according to their condensation temperature. The 300–400°C tobacco and tomato bio-oil cuts from the 1D MFR showed the highest insecticidal and anti-microbial activity compared to the other bio-oil cuts. The 300–350 and 350–400°C bio-oil cuts produced by 2D MFR had the highest insecticidal activity when the bio-oil was collected from the 210°C condenser. The tobacco and tomato bio-oil had similar insecticidal activity (LC50 of 2.1 and 2.2 mg/mL) when the bio-oil was collected in the 210°C condenser from the 300–350°C reactor temperature gases. The 2D MFR does concentrate the pesticidal products compared to the 1D MFR and thus can reduce the need for further separation steps such as solvent extraction.
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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 | 0.000 |
| 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".