Bibliographic record
Abstract
In this study fs pulsed laser of wavelength 800 nm was used to reduce two sets of aqueous graphene oxide solution namely low (0.5 mg/mL) and high concentration (6.2 mg/mL). It was found that after fs laser treatment the surface roughness of the graphene oxide flakes was reduced by 95.6% and 66% for the high and low GO concentration films respectively. It was also found that the resistivity of high concentration (6.2 mg/ml) rGO films was enhanced by 4 orders of magnitude as a result of the laser treatment which could raise the electron mobility of the rGO films close to that of pristine graphene films. The C-O atomic ratio for the high concentration graphene oxide samples was found to increase by 3 times after 6 hrs of laser treatment. \nA novel technique of graphene oxide sol-gel (GOSG) fabrication was introduced where GOSG was obtained by placing the laser focal point at the solution/air interface. By manipulating the exposure parameters such as the ablated volume, laser energy and focal length, the GOSG was obtained at various times in the range of as 8 min to 7 hrs. The chemical analysis (XPS) revealed the reduction of the COOH molecules as a result of the exposure process, which is due to removing the OH- from the GO sheets and vaporization of the water content within the solution. Surface morphology analysis using AFM and SEM showed that all GOSG thin films except the 7 hrs one depicted a high surface roughness and porous surface area with randomly stacked graphene oxide layers. However, a smooth stacking layered structure and the smallest average surface roughness was observed in the 7 hrs GOSG, which was pertained the behavior of pure graphene as proved by the FTIR analysis.
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) | 0.003 | 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".