Enhanced dark fermentative biohydrogen production from marine macroalgae Padina tetrastromatica by different pretreatment processes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Marine macroalgae are promising substrates for biofuel production. Pretreating macroalgae with chemicals could remove microbial inhibitors and enhance the accessibility of the microorganisms involved in the process to the substrates leading to increased product yield. In the present study, Padina tetrastromatica a seaweed species was subjected to different chemical pretreatment in order to remove phenolic content and to enhance biohydrogen production. Different mineral acids (i.e., HCl, H2SO4, and HNO3) and bases (NaOH and KOH) were applied for effective pretreatment of the seaweed. Dilute sulphuric acid treatment of seaweed resulted in the highest cumulative biohydrogen production of 78 ± 2.9 mL/0.05 g VS and reduced phenolic content to 1.6 ±0.072 mg gallic acid equivalent (GAE)/g. Optimization of three variables for pretreatment (i.e., substrate concentration, acid concentration, and reaction time) was examined by Response Surface Methodology. After the optimization of the pretreatment conditions, phenolic content was decreased to 0.06 mg GAE/g. and enhanced biohydrogen production was observed. Structural changes due to pretreatment was studied by FTIR and XRD analyses. The results clearly indicated that the dilute sulphuric acid pretreatment was effective in removing phenolic content and enhancing biohydrogen production.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it