Nanocrystal Plasma Polymerization: From Colloidal Nanocrystals to Inorganic Architectures
Bibliographic record
Abstract
Nanocrystal superstructures are increasingly becoming a subject of intense study. Such materials could constitute a new class of nanocomposites of designed structure, of homogeneous composition, and with unique properties. New phenomena are observed in these materials because of the interaction at such diminutive length scales. A common problem in the development of devices relying on colloidal nanocrystal assemblies is that the individual nanocrystal building blocks require organic molecules to control their size. These ligands are responsible for the colloidal stability of the individual nanocrystal building blocks and are thus necessary for their solution processibility. Because of the ligands' incompatibility with many solid state applications, it is important to develop post-processing techniques that mildly remove them from these nanocomposites, while maintaining the size-dependent properties of the building blocks. This Account highlights a new strategy, nanocrystal plasma polymerization (NPP), for processing colloidal nanocrystal assemblies. This technique exposes the nanocomposite to a mild air plasma and allows for the removal of the nanocrystals' capping ligands while preserving their size-dependent and material properties. As a result, the process yields a nearly all-inorganic flexible solid-state material with unprecedented characteristics. We describe early experiments, in which NPP was used to create arbitrarily complex 1D, 2D, and 3D inorganic free-standing architectures entirely composed of nanocrystals, as well as future directions and challenges. We expect this platform will be useful for the design of new materials and will be a valuable new addition to the nanoscientist's toolbox.
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 | 0.000 | 0.000 |
| 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".