Synthesis of low dimensional nanomaterials by pulsed laser ablation in liquid
Bibliographic record
Abstract
With the increasing application of functional nanomaterials in numerous fields, considerable effort has been devoted to exploring simple and efficient methods for their synthesis. Pulsed laser ablation in liquid (PLAL) is one such novel technique for producing colloidal nanomaterials. It is simple to setup, easy to operate, and can be carried out at room temperature and under atmosphere. This method employs a pulsed laser beam to ablate bulk targets or powders within different liquids, thereby creating colloidal nanomaterials. As a result, it holds significant promise for scalable processing. However, most prior research on PLAL has focused on the synthesis of larger spherical nanoparticles, even though low-dimensional nanomaterials, including zero-dimensional quantum dots, one-dimensional nanowires and nanotubes, and two-dimensional nanosheets and nanobelts, find more usage in various applications, such as optoelectronic devices, catalysis, and biomedicine. In the PLAL process, the high-intensity laser pulses not only fragment the illuminated solids to produce nanomaterials but also interact with liquid molecules, generating multiple reactive ions for chemical reactions. Consequently, various low-dimensional nanomaterials can also be generated. This study provides a comprehensive review of low-dimensional nanomaterials synthesized via PLAL, including their formation mechanisms and applications.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".