A review of nanomaterials and their applications in oil & petroleum industries
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
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.
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) | 0.002 | 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 it