Applications of nanotechnology in oil and gas industry: Progress and perspective
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 Nanotechnology has been successfully implemented in many applications, such as nanoelectronics, nanobiomedicine, and nanodevices. However, this technology has rarely been applied to the oil and gas industry, especially in upstream exploration and production. The oil and gas industry needs to improve oil recovery and exploit unconventional resources. The cost of research and oil production is under immense pressure, and it is becoming more difficult to justify such investment when the crude oil price is weak and depressed. There is a widespread belief that nanotechnology may be exploited to develop novel nanomaterials with enhanced performance to combat these technological barriers. Increasing funding resources from governmental and global oil industry have been allocated to exploration, drilling, production, refining, and wastewater treatment. For example, nanosensors allow for precise measurement of reservoir conditions. Nanofluids prepared using functional nanomaterials may exhibit better performance in oil production processes, and nanocatalysts have improved the efficiency in oil refining and petrochemical processes. Nanomembranes enhance oil, water and gas separation, oil and gas purification, and the removal of impurities from wastewater. Functional nanomaterials can play an important role in the production of smart, reliable, and more durable equipment. In this review paper, we summarize the research progress and prospective applications of nanotechnology and nanomaterials in the oil and gas industry.
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 | 0.000 | 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