Biomass-to-biocrude on a chip via hydrothermal liquefaction of algae
Bibliographic record
Abstract
Hydrothermal liquefaction uses high temperatures and pressures to break organic compounds into smaller fractions, and is considered the most promising method to convert wet microalgae feedstock to biofuel. Although, hydrothermal liquefaction of microalgae has received much attention, the specific roles of temperature, pressure, heating rate and reaction time remain unclear. We present a microfluidic screening platform to precisely control and observe reaction conditions at high temperature and pressure. In situ observation using fluorescence enables direct, real-time monitoring of this process. A strong shift in the fluorescence signature from the algal slurry at 675 nm (chlorophyll peak) to a post-HTL stream at 510 nm is observed for reaction temperatures at 260 °C, 280 °C, 300 °C and 320 °C (P = 12 MPa), and occurs over a timescale on the order of 10 min. Biocrude formation and separation from the aqueous phase into immiscible droplets is directly observed and occurs over the same timescale. The higher heating values for the sample are observed to increase over shorter timescales on the order of minutes. After only 1 minute at 300 °C, the higher heating value increases from an initial value of 21.97 MJ kg(-1) to 33.63 MJ kg(-1). The microfluidic platform provides unprecedented control and insight into this otherwise opaque process, with resolution that will guide the design of large scale reactors and processes.
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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 | 0.001 |
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 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".