The effect of switchgrass loadings on feedstock solubilization and biofuel production by Clostridium thermocellum
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
Efficient deconstruction and bioconversion of solids at high mass loadings is necessary to produce industrially relevant titers of biofuels from lignocellulosic biomass. To date, only a few studies have investigated the effect of solids loadings on microorganisms of interest for consolidated bioprocessing. Here, the effects that various switchgrass loadings have on Clostridium thermocellum solubilization and bioconversion are investigated. Clostridium thermocellum was grown for 10 days on 10, 25, or 50 g/L switchgrass or Avicel at equivalent glucan loadings. Avicel was completely consumed at all loadings, but total cellulose solubilization decreased from 63 to 37% as switchgrass loadings increased from 10 to 50 g/L. Washed, spent switchgrass could be additionally hydrolyzed and fermented in second-round fermentations suggesting that access to fermentable substrates was not the limiting factor at higher feedstock loadings. Results from fermentations on Avicel or cellobiose using culture medium supplemented with 50% spent fermentation broth demonstrated that compounds present in the supernatants from the 25 or 50 g/L switchgrass loadings were the most inhibitory to continued fermentation. Recalcitrance alone cannot fully account for differences in solubilization and end-product formation between switchgrass and Avicel at increased substrate loadings. Experiments aimed at separating metabolic inhibition from inhibition of hydrolysis suggest that C. thermocellum’s hydrolytic machinery is more vulnerable to inhibition from switchgrass-derived compounds than its fermentative metabolism.
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 | 0.000 | 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 | 0.001 | 0.001 |
| 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 it