A review of nanomaterials and their applications in oil & petroleum industries
Bibliographic record
Abstract
Abstract The swiftly growing global economies remain the root cause of the soaring demand for oil and gas to satisfy their excessive energy demands, thus making the oil and gas sector one of the most important industrial sectors. Though renewable energy technologies are the more sustainable option, technological advances are required to make them more accessible to the common people. Therefore, due to the limitation of renewable energy technologies, oil and gas continue to be a more viable alternative. Extensive research is being conducted on the applications of nanotechnology to make the upstream, midstream, and downstream processes efficient in the oil and gas sector. Nanomaterials make the activities in processing and transportation more economical, efficient, and environment-friendly than their conventional counterparts. In this review, we have highlighted the need for nanomaterials in oil and gas, for example, in crude oil exploration, including drilling and EOR, separation techniques, refining, transportation, and other related activities. Further, this review summarizes novel nanomaterials developed and used in the activities mentioned above, and at the end, we have briefly described the synthesis mechanism of these nanomaterials. Finally, we emphasize the current challenges and future work prospects in this area of study.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".