Nanotechnology Prospects in the Petroleum Industry
Bibliographic record
Abstract
Fulfilling worldwide energy demand in the 21st century is the most challenging problem. Current technologies simply cannot meet these demands. Therefore, we need new discoveries in underlying core science and engineering to find answers to this problem. Nanotechnology offers the prospect of opening an entirely new frontier for energy exploration, the ability to stretch the limits of our current energy sources and to go beyond these sources into new and uncharted territories. Nanotechnology is poised to impact dramatically on all sectors of the energy industry. There is great potential in nanotechnology research to revolutionize the petroleum industry and for the industry to capitalize on nanotechnology's immense benefits. Nanotechnology may someday boost the average global recovery factor of oil and gas. Research in nanotechnology in the petroleum industry is advancing rapidly and an explosion in the application of nanotechnology in this area is to be expected in the next 5 years. This article will specifically address the need for nanotechnology in the petroleum industry that can aid in the development of cheaper, more efficient, and environmentally appealing energy supplies. It also covers the promises of alternative energy sources using nanotechnology.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".