Enhanced dark fermentative biohydrogen production from marine macroalgae Padina tetrastromatica by different pretreatment processes
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.
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 | 0.000 |
| 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 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".