Surface‐Anisotropic Janus Silicon Quantum Dots via Masking on 2D Silicon Nanosheets
Bibliographic record
Abstract
Surface-anisotropic nanoparticles represent a new class of materials that shows potential in a variety of applications, including self-assembly, microelectronics, and biology. Here, the first synthesis of surface-anisotropic silicon quantum dots (SiQDs), obtained through masking on 2D silicon nanosheets, is presented. SiQDs are deposited on the 2D substrate, thereby exposing only one side of the QDs, which is functionalized through well-established hydrosilylation procedures. The UV-sensitive masking substrate is removed through UV-irradiation, which simultaneously initiates the hydrosilylation of a second substrate, thereby introducing a second functional group to the other side of the now free-standing SiQDs. This renders surface-anisotropic SiQDs that have two different functional groups on either side of the particle. This method can be used to introduce a variety of functional groups including hydrophilic and hydrophobic substrates, while the unique optoelectronic properties of the SiQDs remain unaffected. The anisotropic morphology of the QDs is confirmed through the aggregation behavior of amphiphilic Janus SiQDs at the interface of water and hexane. Additionally, anisotropic SiQDs are used to produce the first controlled (sub)monolayer of SiQDs on a gold wafer.
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 | 0.000 |
| 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".