High Surface Area Microporous Activated Carbon from Corn Fiber Using Graphene Oxide-Assisted Hydrothermal Carbonization
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide This study investigated a novel process that explored the use of graphene oxide (GO) as a catalyst in the hydrothermal carbonization (HTC) process of low-value, high moisture-containing corn fiber (CF) to analyze the morphology, surface area, and porosity characteristics of activated carbon (AC) derived from GO-assisted hydrochar. The SEM results showed significant alteration to the hydrochar morphology revealing carbon spheres with flakes or platelet-like structures when GO was added to the process, which led to increased carbonization and promoted the hydrochar surface area. The surface areas of the ACs produced from the hydrochars were further increased, and a well-developed porous structure was produced with significant micropore volume. The highest surface area of 2549.1 m 2 /g obtained for the AC derived from the hydrochar with the highest GO ratio. Despite the absence of a strong trend between the GO ratio and AC surface area, the SEM analysis and pore size results revealed that the ACs derived from the GO-assisted hydrochars had more intact structures and smaller micropores with interconnected pore channels which would be very favorable for hydrogen storage capacity. The nitrogen content in the ACs was also found to be comparable or higher than carbons from other studies using nitrogen doping steps and was detected in surface functional groups through FT-IR and XPS analysis. Overall, the developed process provides valuable insight into the influence of GO for tailoring porous carbon materials to enhance surface area and pore structure in low-cost and effective bio-based adsorbents, offering opportunities for emerging applications while promoting circular economy principles.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".