Nanofabrication by thermal plasma jets: From nanoparticles to low-dimensional nanomaterials
Bibliographic record
Abstract
Current fabrication of nanomaterials is facing the following two challenges: high selectivity toward specific chemical compositions or morphologies and their scalable production. This usually requires new extreme fabrication conditions beyond the conventional approaches. Thermal plasma jets are flows of partially ionized gases where gas and electron temperatures reach their equilibrium state around 10 000 K, and thus provide high fluxes of energy and chemically active species including electrons and ions with their strong spatial gradients. Such extreme environments can trigger reactions that are not thermodynamically favorable or require high activation barriers, leading to the production of materials with exotic chemical compositions or structures. Since their first operation in 1960, thermal plasma jets were soon recognized as a unique and effective medium for material transformation such as melting, vaporization, and pyrolysis and recently have also found their important applications in nanomaterial fabrication. In this Perspective, we briefly review the latest progress in the thermal plasma jet-assisted fabrication of nanomaterials from nanoparticles to low-dimensional nanostructures. A special focus is made on the advantages of the thermal plasma jet technology in nanostructure fabrication, discussing plasma properties responsible for the nanomaterial growth with high throughput, high purity, anisotropy, desired compositions, or narrow size distributions. This Perspective closes with an outlook of challenges and opportunities for further advancement in this emerging field.
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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".