Hydrothermal carbonization of glucose: experimental data and kinetic modeling
Bibliographic record
Abstract
This study investigated the kinetic modeling of hydrothermal carbonization (HTC) of glucose using extensive data on hydrochar yield over time at 160–250 °C and glucose concentrations of 0.1, 0.16 and 0.25 g·mL-¹. More than 250 experimental data points were analyzed and fitted to different kinetic models, confirming the Hill-type sigmoidal model as the most suitable for describing kinetic behavior and determining the formation rate constant. The Arrhenius model confirmed the temperature dependence of the rate constants and revealed two activation energies (E1 below 200 °C and E1 above 200 °C). For example, at a glucose concentration of 0.1 g.mL-1, the activation energies were E1=42.23 kJ.mol-1 and E2=91.54 kJ.mol-1; at a concentration of 0.16 g.mL-1, E1=43.69 kJ.mol-1 and E2=77.11 kJ.mol-1; and at a concentration of 0.25 g.mL-1, E1=48.98 kJ.mol-1 and E2=55.19 kJ.mol-1. The results indicate that the activation energy remains relatively stable below 200 °C, ranging from 42 to 48 kJ·mol−1 with an average of 45 kJ·mol−1. However, above 200 °C, the activation energy decreases markedly, dropping from approximately 92 to 55 kJ·mol−1 as the glucose concentration increases. The study highlights the complex, temperature-dependent kinetics of hydrochar production from glucose. It offers insights into the fundamental processes and establishes a basis for future kinetic modeling.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".